EPA/600/R-00/081
September, 2000
Revision A
Exposure Analysis Modeling System
(EXAMS): User Manual and
System Documentation
By
Lawrence A. Burns, Ph.D.
Ecologist, Ecosystems Research Division
U.S. Environmental Protection Agency
960 College Station Road
Athens, Georgia 30605-2700
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
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Notice
The U.S. Environmental Protection Agency through its Office of Research and Development funded and
managed the research described here under GPRA Goal 4, Preventing Pollution and Reducing Risk in
Communities, Homes, Workplaces and Ecosystems, Objective 4.3, Safe Handling and Use of Commercial
Chemicals and Microorganisms, Subobjective 4.3.4, Human Health and Ecosystems, Task 6519, Advanced
Pesticide Risk Assessment Technology. It has been subjected to the Agency's peer and administrative review
and approved for publication as an EPA document. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
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Foreword
Environmental protection efforts are increasingly directed to ward preventing adverse health and ecological
effects associated with specific chemical compounds of natural or human origin. As part of the Ecosystems
Research Division's research on the occurrence, movement, transformation, impact, and control of
environmental contaminants, the Ecosystems Assessment Branch studies complexes of environmental
processes that control the transport, transformation, degradation, fate, and impact of pollutants or other
materials in soil and water and develops models for assessing the risks associated with exposures to chemical
contaminants.
Concern about environmental exposure to synthetic organic chemicals has increased the need for
techniques to predict the behavior of chemicals entering the environment as a result of the manufacture, use,
and disposal of commercial products. The Exposure Analysis Modeling System (EXAMS), which has been
undergoing continual development, evaluation, and revision at this Division since 1978, provides a convenient
tool to aid in judging the environmental consequences should a specific chemical contaminant enter a natural
aquatic system. Because EXAMS requires no chemical monitoring data, it can be used for new chemicals not
yet introduced into commerce as well as for those whose pattern and volume of use are known. EXAMS and
other exposure assessment models should contribute significantly to efforts to anticipate potential problems
associated with environmental pollutants.
Rosemarie C. Russo
Director
Ecosystems Research Division
Athens, Georgia
111
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Abstract
The Exposure Analysis Modeling System, first published in 1982 (EPA-600/3-82-023), provides interactive
computer software for formulating aquatic ecosystem models and rapidly evaluating the fate, transport, and
exposure concentrations of synthetic organic chemicals - pesticides, industrial materials, and leachates from
disposal sites. EXAMS contains an integrated Database Management System (DBMS) specifically designed for
storage and management of project databases required by the software. User interaction is provided by a full-
featured Command Line Interface (CLl), context-sensitive help menus, an on-line data dictionary and CLI users'
guide, and plotting capabilities for review of output data. EXAMS provides 20 output tables that document the
input datasets and provide integrated results summaries for aid in ecological risk assessments.
EXAMS' core is a set of process modules that link fundamental chemical properties to the limnological
parameters that control the kinetics of fate and transport in aquatic systems. The chemical properties are
measurable by conventional laboratory methods; most are required under various regulatory authority. When
run under the EPA'S GEMS or pcGEMS systems, EXAMS accepts direct output from QSAR software. EXAMS
limnological data are composed of elements historically of interest to aquatic scientists world-wide, so
generation of suitable environmental datasets can generally be accomplished with minimal project-specific field
investigations.
EXAMS provides facilities for long-term (steady-state) analysis of chronic chemical discharges, initial-value
approaches for study of short-term chemical releases, and full kinetic simulations that allow for monthly
variation in mean climatological parameters and alteration of chemical loadings on daily time scales. EXAMS
has been written in generalized (N-dimensional) form in its implementation of algorithms for representing
spatial detail and chemical degradation pathways. This DOS implementation allows for study of five
simultaneous chemical compounds and 100 environmental segments; other configurations can be created
through special arrangement with the author. EXAMS provides analyses of
Exposure: the expected (96-hour acute, 21-day and long-term chronic) environmental concentrations of
synthetic chemicals and their transformation products,
Fate: the spatial distribution of chemicals in the aquatic ecosystem, and the relative importance of each
transformation and transport process (important in establishing the acceptable uncertainty in chemical
laboratory data), and
Persistence: the time required for natural purification of the ecosystem (via export and degradation
processes) once chemical releases end.
EXAMS includes file-transfer interfaces to the PRZM3 terrestrial model and the FGETS and BASS bio-
accumulation models; it is a complete implementation of EXAMS in Fortran 90.
This report covers a period from December 1, 1999 to September 15, 2000 and work was completed as
of September 17,2000. This version of the Manual reflects continuous maintenance and upgrade of EXAMS.
IV
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Contents
Foreword iii
Abstract iv
1.0 Introduction to the Exposure Analysis Modeling System (EXAMS) 1
1.1 Background 1
1.2 Exposure Analysis in Aquatic Systems 1
1.2.1 Exposure 1
1.2.2 Persistence 1
1.2.3 Fate 1
1.3 The EXAMS program 2
1.4 Sensitivity Analysis and Error Evaluation 2
1.5 EXAMS Process Models 3
1.5.1 lonization and Sorption 3
1.5.2 Transformation Processes 4
1.5.3 Transport Processes 5
1.5.4 Chemical Loadings 5
1.6 Ecosystems Analysis and Mathematical Systems Models 5
1.6.1 EXAMS Design Strategy 5
1.6.2 Temporal and Spatial Resolution 6
1.6.3 Assumptions 7
1.7 Further Reading 8
2.0 Fundamental Theory 10
2.1 Compartment Models and Conservation of Mass 10
2.2 Equilibrium Processes 11
2.2.1 lonization Reactions 11
2.2.2 Sediment Sorption 13
2.2.3 Biosorption 15
2.2.4 Complexation with DOC 16
2.2.5 Computation of Equilibrium Distribution Coefficients 16
2.3 Kinetic Processes 19
2.3.1 Transport 19
2.3.1.1 Hydrology and advection 21
2.3.1.2 Advected water flows 22
2.3.1.3 Advective sediment transport 24
2.3.1.4 Dispersive transport 26
2.3.1.4.1 Dispersion within the water column (27)
2.3.1.4.2 Dispersion within the bottom sediments (28)
2.3.1.4.3 Exchanges between bed sediments and overlying waters (28)
2.3.1.5 Transport of synthetic organic chemicals 31
2.3.2 Volatilization 32
2.3.2.1 Chemical Data Entry 35
2.3.2.2 Exchange Constants for Water Vapor 35
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2.3.2.3 Exchange Constants for Molecular Oxygen 36
2.3.2.4 Validation and Uncertainty Studies 36
2.3.2.4.1 Radon in small lakes in the Canadian Shield (37)
2.3.2.4.2 1,4-Dichlorobenzene in Lake Zurich (38)
2.3.3 Direct Photolysis 42
2.3.3.1 Direct photolysis in aquatic systems 42
2.3.3.2 Light attenuation in natural waters 45
2.3.3.2.1 Distribution functions (D) in natural waters (45)
2.3.3.2.2 Absorption coefficients (a) in natural waters (46)
2.3.3.3 Reaction quantum yields (; Qyield) 47
2.3.3.4 Absorption spectra (ABSORG) 48
2.3.3.5 Effects of sorption on photolysis rates 49
2.3.3.5.1 Sensitivity analysis of sorption effects on direct photolysis (50)
2.3.3.6 Near-surface solar beam and sky irradiance 52
2.3.3.7 Input data and computational mechanics - Summary 53
2.3.4 Specific Acid, Specific Base, and Neutral Hydrolysis 55
2.3.4.1 Temperature effects 57
2.3.4.2 lonization effects 58
2.3.4.3 Sorption effects 58
2.3.5 Indirect Photochemistry, Oxidation and Reduction 60
2.3.5.1 Oxidation 61
2.3.5.2 Singlet Oxygen 62
2.3.5.3 Reduction 63
2.3.6 Microbial Transformations 64
2.4 Input Pollutant Loadings 68
2.4.1 Product Chemistry 70
2.5 Data Assembly and Solution of Equations 71
2.5.1 Exposure 71
2.5.2 Fate 73
2.5.3 Persistence 73
3.0 Tutorials and Case Studies 77
3.1 Tutorial 1: Introduction to Exposure Analysis with EXAMS - Laboratory 1 77
3.2 Tutorial 1: Introduction to Exposure Analysis with EXAMS - Laboratory 2 81
3.3 Command Sequences for Tutorial 1, Lab. 1 85
3.3.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1) 85
3.3.2 Lab. 1, Exercise 2: Mode 2 Analysis of Initial Value Problems - Sixty Day RUn Time 86
3.3.3 Lab. 1, Exercise 3: Time-varying seasonal analysis using Mode 3 88
3.4 Introduction to EXAMS - Lab. 1 Exercises with Complete EXAMS Responses 90
3.4.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1) 90
3.4.2 Lab. 1, Exercise 2: EXAMS in Mode 2 99
3.4.3 Lab. 1, Exercise 3: EXAMS in Mode 3 106
3.5 Tutorial 2: Chemical & Environmental Data Entry 113
3.5.1 Exercise 4: p-DCB in Lake Zurich 113
3.5.2 Exercise 4 (Lake Zurich) with Complete EXAMS Responses 116
4.0 EXAMS Command Language Interface (CLI) User's Guide 125
4.1 Conventions Used in this Chapter 125
4.2 Overview 125
4.3 Entering Commands 125
4.4 Command Prompting 126
4.5 EXAMS Messages 127
4.6 The HELP Command 127
4.7 Command Procedures 127
VI
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4.8 Wild Card Characters 127
4.9 Truncating Command Names and Keywords 128
4.10 Summary Description of EXAMS' System Commands 128
5.0 System Command Descriptions 129
AUDIT 129
CATALOG 131
CHANGE 133
CONTINUE 135
DESCRIBE 139
DO 141
ERASE 144
EXIT 146
HELP 147
LIST 149
NAME 152
PLOT 153
PRINT 158
QUIT 159
READ 160
RECALL 162
RUN 164
SET 165
SHOW 167
STORE 170
WRITE 172
ZERO 173
6.0 EXAMS Data Dictionary 175
Appendices 191
Appendix A
Partitioning to Natural Organic Colloids 191
Appendix B
EXAMS data entry template for chemical molar absorption speclra (ABSOR) 195
Appendix C
Implementing the microcomputer runtime EXAMS 196
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1.0 Introduction to the Exposure Analysis Modeling System (EXAMS)
1.1 Background
Industrial production of agricultural chemicals, plastics, and
Pharmaceuticals has increased steadily over the past five
decades. More recently, growth of the chemical industry has
been accompanied by increasing concern over the effects of
synthetic chemicals on the environment. The suspicion has
arisen that, in some cases, the benefits gained by using a
chemical may not offset the cost of incidental damage to man's
natural life-support system - the biosphere. The toxicity of a
chemical does not of itself indicate that the environmental risks
associated with its use are unacceptable, however, as it is the
dose that makes the poison. A rational evaluation of the risk
posed by the use and disposal of synthetic chemicals must begin
from a knowledge of the persistence and mobility of chemicals
in the environment, which in turn establish the conditions of
exposure leading to absorption of lexicological dose.
The Exposure Analysis Modeling System (EXAMS), developed
at the U.S. Environmental Protection Agency's research
laboratory in Athens, Georgia, is an interactive computer
program intended to give decision-makers in industry and
government access to aresponsive, general, and controllable tool
for readily deriving and evaluating the behavior of synthetic
chemicals in the environment. The research and development
effort has focused on the creation of the interactive command
language and user aids that are the core of EXAMS, and on the
genesis of reliable EXAMS mathematical models. EXAMS was
designed primarily for the rapid screening and identification of
synthetic organic chemicals likely to adversely impact aquatic
systems. This report is intended to acquaint potential users with
the underlying theory, capabilities, and use of the system.
1.2 Exposure Analysis in Aquatic Systems
EXAMS was conceived as an aid to those who must execute
hazard evaluations solely from laboratory descriptions of the
chemistry of a newly synthesized toxic compound. EXAMS
estimates exposure, fate, and persistence following release of an
organic chemical into an aquatic ecosystem. Each of these terms
was given a formal operational definition during the initial
design of the system.
1.2.1 Exposure
When a pollutant is released into an aquatic ecosystem, it is
entrained in the transport field of the system and begins to
spread to locations beyond the original point of release. During
the course of these movements, chemical and biological
processes transform the parent compound into daughter
products. In the face of continuing emissions, the receiving
system evolves toward a "steady-state" condition. At steady
state, the pollutant concentrations are in a dynamic equilibrium
in which the loadings are balanced by the transport and
transformation processes. Residual concentrations can be
compared to those posing a danger to living organisms. The
comparison is one indication of the risk entailed by the presence
of a chemical in natural systems or in drinking-water supplies.
These "expected environmental concentrations" (EECs), or
exposure levels, in receiving water bodies are one component of
a hazard evaluation.
1.2.2 Persistence
Toxicological and ecological "effects" studies are of two kinds:
investigations of short-term "acute" exposures, as opposed to
longer-term "chronic" experiments. Acute studies are often used
to determine the concentration of a chemical resulting in 50%
mortality of a test population over a period of hours. Chronic
studies examine sub-lethal effects on populations exposed to
lower concentrations over extended periods. Thus, for example,
an EEC that is 10 times less than the acute level does not affirm
that aquatic ecosystems will not be affected, because the
probability of a "chronic" impact increases with exposure
duration. A computed EEC thus must be supplemented with an
estimate of "persistence" in the environment. (A compound
immune to all transformation processes is by definition
"persistent" in a global sense, but even in this case transport
processes will eventually reduce the pollutantto negligible levels
should the input loadings cease.) The notion of "persistence" can
be given an explicit definition in the context of a particular
contaminated ecosystem: should the pollutant loadings cease,
what time span would be required for dissipation of most of the
residual contamination? (For example, given the half-life of a
chemical in a "first-order" system, the time required to reduce
the chemical concentration to any specified fraction of its initial
value can be easily computed.) With this information in hand,
the appropriate duration and pollutant levels for chronic studies
can be more readily decided. More detailed dynamic simulation
studies can elicit the probable magnitude and duration of acute
events as well.
1.2.3 Fate
The toxicologist also needs to know which populations in the
system are "at risk." Populations at risk can be deduced to some
extent from the distribution or "fate" of the compound, that is,
by an estimate of EECs in different habitats of single ecosystems.
EXAMS reports a separate EEC for each compartment, and thus
each local population, used to define the system.
The concept of the "fate" of a chemical in an aquatic system has
an additional, equally significant meaning. Each transport or
transformation process accounts for only part of the total
behavior of the pollutant. The relative importance of each
process can be determined from the percentage of the total
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system loadings consumed by the process. The relative
importance of the transformations indicate which process is
dominant in the system, and thus in greatest need of accuracy
and precision in its kinetic parameters. Overall dominance by
transport processes may imply a contamination of downstream
systems, loss of significant amounts of the pollutant to the
atmosphere, or pollution of ground-water aquifers.
1.3 The EXAMS program
The need to predict chemical exposures from limited data has
stimulated a variety of recent advances in environmental
modeling. These advances fall into three general categories:
• Process models giving a quantitative, often theoretical, basis
for predicting the rate of transport and transformation
processes as a function of environmental variables.
• Procedures for estimating the chemical parameters required
by process models. Examples include linear free energy
relationships, and correlations summarizing large bodies of
experimental chemical data.
• Systems models that combine unit process models with
descriptions of the environmental forces determining the
strength and speed of these processes in real ecosystems.
The vocabulary used to describe environmental models includes
many terms, most of which reflect the underlying intentions of
the modelers. Models may be predictive, stochastic, empirical,
mechanistic, theoretical, deterministic, explanatory, conceptual,
causal, descriptive, etc. The EXAMS program is a deterministic,
predictive systems model, based on a core of mechanistic
process equations derived from fundamental theoretical
concepts. The EXAMS computer code also includes descriptive
empirical correlations that ease the user's burden of parameter
calculations, and an interactive command language that
facilitates the application of the system to specific problems.
EXAMS "predicts" in a somewhat limited sense of the term. Many
of the predictive water-quality models currently in use include
site-specific parameters that can only be found via field
calibrations. After "validation" of the model by comparison of
its calibrated outputs with additional field measurements, these
models are often used to explore the merits of alternative
management plans. EXAMS, however, deals with an entirely
different class of problem. Because newly synthesized chemicals
must be evaluated, little or no field data may exist. Furthermore,
EECs at any particular site are of little direct interest. In this case,
the goal, at least in principle, is to predict EECs for a wide range
of ecosystems under a variety of geographic, morphometric, and
ecological conditions. EXAMS includes no direct calibration
parameters, and its input environmental data can be developed
from a variety of sources. For example, input data can be
synthesized from an analysis of the outputs of hydrodynamic
models, from prior field investigations conducted without
reference to toxic chemicals, or from the appropriate
limnological literature. The EECs generated by EXAMS are thus
"evaluative" (Lassiter etal. 1979) predictions designed to reflect
typical or average conditions. EXAMS' environmental database
can be used to describe specific locales, or as a generalized
description of the properties of aquatic systems in broad
geographic regions.
EXAMS relies on mechanistic, rather than empirical, constructs
for its core process equations wherever possible. Mechanistic
(physically determinate) models are more robust predictors than
are purely empirical models, which cannot safely be extended
beyond the range of prior observations. EXAMS contains a few
empirical correlations among chemical parameters, but these are
not invoked unless the user approves. For example, the partition
coefficient of the compound on the sediment phases of the
system, as a function of the organic carbon content of its
sediments, can be estimated from the compound's octanol-water
partition coefficient. A direct load of the partition coefficient
(KOC, see the EXAMS Data Dictionary beginning on page 175)
overrides the empirical default estimate, however. (Because
EXAMS is an interactive program in which the user has direct
access to the input database, much of this documentation has
been written using the computer variables (e.g., KOC above) as
identifiers and as quantities in the process equations. Although
this approach poses some difficulties for the casual reader, it
allows the potential user of the program to see the connections
between program variables and the underlying process theory.
The EXAMS data dictionary in this document (beginning on page
175) includes an alphabetical listing and definitions of EXAMS'
input variables.)
EXAMS is a deterministic, rather than a stochastic, model in the
sense that a given set of inputs will always produce the same
output. Uncontrolled variation is present both in ecosystems and
in chemical laboratories, and experimental results from either
milieu are often reported as mean values and their associated
variances. Probabilistic modeling techniques (e.g., Monte Carlo
simulations) can account, in principle, for this variation and
attach an error bound or confidence interval to each important
output variable. Monte Carlo simulation is, however, very
time-consuming(i.e., expensive), and the statistical distributions
of chemical and environmental parameters are not often known
in the requisite detail. The objective of this kind of modeling, in
the case of hazard evaluations, would in any case be to estimate
the effect of parameter errors on the overall conclusions to be
drawn from the model. This goal can be met less expensively
and more efficiently by some form of sensitivity analysis.
1.4 Sensitivity Analysis and Error Evaluation
EXAMS does not provide a formal sensitivity analysis among its
options: the number of sub-simulations needed to fully account
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for interactions among chemical and environmental variables is
prohibitively large (Behrens 1979). When, for example, the
second-order rate constant for alkaline hydrolysis of a compound
is described to EXAMS via an Arrhenius function, the rate
constant computed for each compartment in the ecosystem
depends on at least six parameters. These include the frequency
factor and activation energy of the reaction, the partition
coefficient of the compound (KOC), the organic carbon content
of the sedimentphase, the temperature, and the concentration of
hydroxide ion. The overall rate estimate is thus as dependent
upon the accuracy of the system definition as it is upon the skill
of the laboratory chemist; in this example, the rate could vary six
orders of magnitude as a function of differences among
ecosystems. In order to fully map the parameter interactions
affecting a process, all combinations of parameter changes
would have to be simulated. Even this (simplified) example
would require 63 simulations (2n-1, where n is the number (6) of
parameters) merely to determine sensitivities of a single
component process in a single ecosystem compartment.
Sensitivity analysis remains an attractive technique for
answering a crucial question that arises during hazard
evaluation. This question can be simply stated: "Are the
chemical data accurate enough, and precise enough, to support
an analysis of the risk entailed by releases of the chemical into
the environment?" Like many simple questions, this question
does not have a simple, definitive answer. It can be broken
down, however, into a series of explicit, more tractable questions
whose answers sum to a reasonably complete evaluation of the
significance that should be attached to a reported error bound or
confidence interval on any input datum. Using the output tables
and command language utilities provided by EXAMS, these
questions can be posed, and answered, in the following order.
• Which geographic areas, and which ecosystems, develop the
largest chemical residuals? EXAMS allows a user to load the
data for any environment contained in his files, specify a
loading, and run a simulation, through a simple series of
one-line English commands.
• Which process is dominant in the most sensitive
ecosystem(s)? The dominant process, i.e., the process most
responsible for the decomposition of the compound in the
system, is the process requiring the greatest accuracy and
precision in its chemical parameters. EXAMS produces two
output tables that indicate the relative importance of each
process. The first is a "kinetic profile" (or frequency
scaling), which gives a compartment-by-compartment listing
with all processes reduced to equivalent (hour"1) terms. The
second is a tabulation of the overall steady-state fate of the
compound, giving a listing of the percentage of the load
consumed by each of the transport and transformation
processes at steady state.
Given the dominant process, the input data affecting this process
can be varied over the reported error bounds, and a simulation
can be executed for each value of the parameters. The effect of
parameter errors on the EECs and persistence of the compound
can then be documented by compiling the results of these
simulations.
This sequence of operations is, in effect, a sensitivity analysis,
but the extent of the analysis is controlled and directed by the
user. In some cases, for example, one process will always
account for most of the decomposition of the compound. When
the database for this dominant process is inadequate, the obvious
answer to the original question is that the data do not yet support
a risk analysis. Conversely, if the dominant process is well
defined, and the error limits do not substantially affect the
estimates of exposure and persistence, the data may be judged to
be adequate for the exposure analysis portion of a hazard
evaluation.
1.5 EXAMS Process Models
In EXAMS, the loadings, transport, and transformations of a
compound are combined into differential equations by using the
mass conservation law as an accounting principle. This law
accounts for all the compound entering and leaving a system as
the algebraic sum of (1) external loadings, (2) transport
processes exporting the compound out of the system, and (3)
transformation processes within the system that degrade the
compound to its daughter products. The fundamental equations
of the model describe the rate of change in chemical
concentrations as a balance between increases due to loadings,
and decreases due to the transport and transformation processes
removing the chemical from the system.
The set of unit process models used to compute the kinetics of
a compound is the central core of EXAMS. These unit models are
all "second-order" or "system-independenfmodels: each process
equation includes a direct statement of the interactions between
the chemistry of a compound and the environmental forces that
shape its behavior in aquatic systems. Thus, each realization of
the process equations implemented by the user in a specific
EXAMS simulation is tailored to the unique characteristics of that
ecosystem. Most of the process equations are based on standard
theoretical constructs or accepted empirical relationships. For
example, 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. Detailed explanations of the process models
incorporated in EXAMS, and of the mechanics of the
computations, are presented in Chapter 2.
1.5.1 lonization and Sorption
lonization of organic acids and bases, complexation with
dissolved organic carbon (DOC), and sorption of the compound
with sediments and biota, are treated as thermodynamic
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properties or (local) equilibria that alter the operation of 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 28 molecular
species of a chemical. These include the parent uncharged
molecule, and singly, doubly, or triply charged cations and
anions, each of which can occur in adissolved, sediment-sorbed,
DOC-complexed, or biosorbed form. The program computes the
fraction of the total concentration of compound that is present in
each of the 28 molecular structures (the "distribution
coefficients," a).
These (a) values enterthe kinetic equations as multipliers on the
rate constants. In this way, the program accounts for differences
in reactivity that depend on the molecular form of the chemical,
as a function of the spatial distribution of environmental
parameters controlling molecular speciation. For example, the
lability of a particular molecule to hydrolytic decompositionmay
depend on whether it is dissolved or is sorbed with the sediment
phase of the system. EXAMS makes no intrinsic assumptions
about the relative transformation reactivities of the 28 molecular
species, with the single exception that biosorbed species are
unavailable to inorganic reactions. These assumptions are
controlled through the structure of the input data describing the
species-specific chemistry of the compound.
1.5.2 Transformation Processes
EXAMS computes the kinetics of transformations attributable 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 a pair of values defining the rate constant as a
function of environmental temperatures. For example, the input
data for alkaline hydrolysis of the compound consists of two
computer variables: KBH, and EBH. When EBH is zero, the
program interprets KBH as the second-order rate constant. When
EBH is non-zero, EBH is interpreted as the activation energy of
the reaction, and KBH is re-interpreted as the pre-exponential
(frequency) factor in an Arrhenius equation giving the
second-order rate constant as a function of the environmental
temperature (TCEL) in each system compartment. (KBH and EBH
are both actually matrices with 21 elements; each element of the
matrix corresponds to one of the 21 possible molecular species
of the compound, i.e., the 7 ionic species occurring in dissolved,
DOC-complexed, or sediment-sorbed form—as noted above,
biosorbed forms do not participate in extra-cellular reactions.)
EXAMS includes two algorithms for computing the rate of
photolytic transformation of a synthetic organic chemical. These
algorithms accommodate the two more common kinds of
laboratory data and chemical parameters used to describe
photolysis reactions. The simpler algorithm requires only an
average pseudo-first-order rate constant (KDP) applicable to
near-surface waters under cloudless conditions at a specified
reference latitude (RFLAT). To control reactivity assumptions,
KDP is coupled to nominal (normally unit-valued) reaction
quantum yields (QYlELD) for each molecular species of the
compound. This approach makes possible a first approximation
of photochemical reactivity, but neglects the very important
effects of changes in the spectral quality of sunlight with
increasing depth in a body of water. The more complex
photochemical algorithm computes photolysis rates directly from
the absorption spectra (molar extinction coefficients) of the
compound and its ions, measured values of the reaction quantum
yields, and the environmental concentrations of competing light
absorbers (chlorophylls, suspended sediments, DOC, and water
itself). When using a KDP, please be aware that data from
laboratory photoreactors usually are obtained at intensities as
much as one thousand times larger than that of normal sunlight.
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 are entered as a set of
pseudo-first-order rate coefficients (or Arrhenius functions) for
reaction of the 28 (potential) molecular species with the water
molecule.
EXAMS computes 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 actively degrading the compound (Paris et
al. 1982). This approach is of demonstrated validity for at least
some biolysis processes, and provides the user with a minimal
semi-empirical means of distinguishing between eutrophic and
oligotrophic ecosystems. The second-order rate constants
(KBACW for the water column, KBACS 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 Q10 of a
biotransformation (parameters QTBAW and QTBAS, respectively),
KBAC is interpreted as the rate constant at 25 degrees Celsius,
and the biolysis rate in each sector of the ecosystem is adjusted
for the local temperature (TCEL).
Oxidation reactions are computed from the chemical input data
and the total environmental concentrations of reactive oxidizing
species (alkylperoxy and alkoxyl radicals, etc.), corrected for
ultra-violet light extinction in the water column. The chemical
data can again be entered either as simple second-order rate
constants or as Arrhenius functions. Oxidations due to singlet
oxygen are computed from chemical reactivity data and singlet
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oxygen concentrations; singlet oxygen is estimated as a function
of the concentration of DOC, oxygen tension, and light intensity.
Reduction is included in the program as a simple second-order
reaction process driven by the user entries for concentrations of
reductants in the system. As with biolysis, this provides the user
with a minimal empirical means of assembling a simulation
model that includes specific knowledge of the reductants
important to a particular chemical safety evaluation.
1.5.3 Transport Processes
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
(JFRAD, etc.) that specify the location and strength of both
advective and dispersive transportpathways. Advection of water
through the system is then computed from the water balance,
using hydrologic data (rainfall, evaporation rates, stream flows,
groundwater seepages, etc.) supplied to EXAMS as part of the
definition of each environment.
Dispersive interchanges within the system, and across system
boundaries, are computed from the usual geochemical
specificationof the characteristic length (CHARL), cross-sectional
area (XSTUR), and dispersion coefficient (DSP) for each active
exchange pathway. EXAMS can compute transport of synthetic
chemicals via whole-sediment bed loads, suspended sediment
wash-loads, exchanges with fixed-volume sediment beds,
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
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.
1.5.4 Chemical Loadings
External loadings of a toxicant can enter the ecosystem via point
sources (STRLD), non-point sources (NPSLD), dry fallout or aerial
drift (DRFLD), atmospheric wash-out (PCPLD), and ground-water
seepage (SEELD) entering the system. Any type of load can be
entered for any system compartment, but the program will not
implement a loading that is inconsistent with the system
definition. For example, the program will automatically cancel
arainfall loading (PCPLD) entered for the hypolimnion orbenthic
sediments of a lake ecosystem. When this type of corrective
action is executed, the change is reported to the user via an error
message.
1.6 Ecosystems Analysis and Mathematical
Systems Models
The EXAMS program was constructed from a systems analysis
perspective. Systems analysis begins by defining a system's
goals, inputs, environment, resources, and the nature of the
system's components and their interconnections. The system
goals describe the outputs produced by the system as a result of
operating on its input stream. The system environment comprises
those factors affecting system outputs over which the system has
little or no control. These factors are often called "forcing
functions" or "external driving variables." Examples for an
aquatic ecosystem include runoff and sediment erosion from its
watershed, insolation, and rainfall. Systemresources are defined
as those factors affecting performance over which the system
exercises some control. Resources of an aquatic ecosystem
include, for example, the pH throughout the system, light
intensity in the water column, and dissolved oxygen concentra-
tions. The levels of these internal driving variables are
determined, at least in part, by the state of the system itself. In
other words, these factors are not necessarily single-valued
functions of the system environment. Each of the components or
"state variables" of a system can be described in terms of its
local input/output behaviors and its causal connections with
other elements of the system. The systems approach lends itself
to the formulation of mathematical systems models, which are
simply tools for encoding knowledge of transport and
transformation processes and deriving the implications of this
knowledge in a logical and repeatable way.
A systems model, when built around relevant state variables
(measurable properties of system components) and causal
processmodels, provides a method for extrapolating future states
of systems from knowledge gained in the past. In order for such
a model to be generally useful, however, most of its parameters
must possess an intrinsic interest transcending their role in any
particular computer program. For this reason, EXAMS was
designed to use chemical descriptors (Arrhenius functions, pKa,
vapor pressure, etc.) and water quality variables (pH,
chlorophyll, biomass, etc.) that are independently measured for
many chemicals and ecosystems.
1.6.1 EXAMS Design Strategy
The conceptual view adopted for EXAMS begins by defining
aquatic ecosystems as a series of distinct subsystems,
interconnected by physical transport processes that move
synthetic chemicals into, through, and out of the system. These
subsystems include the epilimnion and hypolimnion of lakes,
littoral zones, benthic sediments, etc. The basic architecture of
a computer model also depends, however, on its intended uses.
EXAMS was designed for use by toxicologists and deci-
sion-makers who must evaluate the risk posed by use of a new
chemical, based on a forecast from the model. The EXAMS
program is itself part of a "hazard evaluation system," and the
structure of the program was necessarily strongly influenced by
the niche perceived for it in this "system."
Many intermediate technical issues arise during the development
of a systems model. Usually these issues can be resolved in
several ways; the modeling "style" or design strategy used to
build the model guides the choices taken among the available
-------
alternatives. The strategy used to formulate EXAMS begins from
a primary focus on the needs of the intended user and, other
things being equal, resolves most technical issues in favor of the
more efficient computation. For example, all transport and
transformation processes are driven by internal resource factors
(pH, temperature, water movements, sediment deposition and
scour, etc.) in the system, and each deserves separate treatment
in the model as an individual state variable or function of several
state variables. The strategy of model development used for
EXAMS suggests, however, that the only state variable of any
transcendent interest to the user is the concentration of the
chemical itself in the system compartments. EXAMS thus treats all
environmental state variables as coefficients describing the state
of the ecosystem, and only computes the implications of that
state, as residual concentrations of chemicals in the system.
Although this approach vastly simplifies the mathematical
model, with corresponding gains in efficiency and speed, the
system definition is now somewhat improper. System resources
(factors affecting performance that are subject to feedback
control) have been redefined as part of the system environment.
In fact, the "system" represented by the model is no longer an
aquatic ecosystem, but merely a chemical pollutant. Possible
failure modes of the model are immediately apparent. For
example, introduction of a chemical subject to alkaline
hydrolysis and toxic to plant life into a productive lake would
retard primary productivity. The decrease in primary productivity
would lead to a decrease in the pH of the system and,
consequently, a decrease in the rate of hydrolysis and an increase
in the residual concentration of the toxicant. This sequence of
events would repeatitself indefinitely, and constitutes apositive
feedback loop that could in reality badly damage an ecosystem.
Given the chemical buffering and functional redundancy present
in most real ecosystems, this example is inherently improbable,
or at least self-limiting. More importantly, given the initial EEC,
the environmental toxicologist could anticipate the potential
hazard.
There is a more telling advantage, moreover, to the use of
environmental descriptors in preference to dynamic
environmental state variables. Predictive ecosystem models that
include all the factors of potential importance to the kinetics of
toxic pollutants are only now being developed, and will require
validation before any extensive use. Furthermore, although
extremely fine-resolution (temporal and spatial) models are often
considered an ultimate ideal, their utility as components of a fate
model for synthetic chemicals remains suspect. Ecosystems are
driven by meteorological events, and are themselves subject to
internal stochastic processes. Detailed weather forecasts are
limited to about nine days, because at the end of this period all
possible states of the system are equally probable. Detailed
ecosystem forecasts are subject to similar constraints (Platt et al.
1977). For these reasons, EXAMS was designed primarily to
forecastthe prevailing climate of chemical exposures, rather than
to give detailed local forecasts of EECs in specific locations.
1.6.2 Temporal and Spatial Resolution
When a synthetic organic chemical is released into an aquatic
ecosystem, the entire array of transport and transformation
processes begins at once to act on the chemical. The most
efficient way to accommodate this parallel action of the
processes is to combine them into a mathematical description of
their total effect on the rate of change of chemical concentration
in the system. Systems that include transport processes lead to
partial differential equations, which usually must be solved by
numerical integration. The numerical techniques in one way or
another break up the system, which is continuously varying in
space and time, into a set of discrete elements. Spatial discrete
elements are often referred to as "grid points" or "nodes", or, as
in EXAMS, as "compartments." Continuous time is often
represented by fixing the system driving functions for a short
interval, integrating over the interval, and then "updating" the
forcing functions before evaluating the next time-step. At any
given moment, the behavior of the chemical is a complicated
function of both present and past inputs of the compound and
states of the system.
EXAMS is oriented toward efficient screening of a multitude of
newly invented industrial chemicals and pesticides. Ideally, a full
evaluation of the possible risks posed by manufacture and use of
a new chemical would begin from a detailed time-series
describing the expected releases of the compound into aquatic
systems over the entire projected history of its manufacture.
Given an equivalentry detailed time-series for environmental
variables, machine integration would yield a detailed picture of
EECs in the receiving water body over the entire period of
concern. The great cost of this approach, however, militates
against its use as a screening tool. Fine resolution evaluation of
synthetic chemicals can probably be used only for compounds
that are singularly deleterious and of exceptional economic
significance.
The simplest situation is that in which the chemical loadings to
systems are known only as single estimates pertaining over
indefinite periods. This situation is the more likely for the vast
majority of new chemicals, and was chosen for development of
EXAMS. It has an additional advantage. The ultimate fate and
exposure of chemicals often encompasses many decades, making
detailed time traces of EECS feasible only for short-term
evaluations. In EXAMS, the environment is represented via
long-term average values of the forcing functions that control the
behavior of chemicals. By combining the chemistry of the
compound with average properties of the ecosystem, EXAMS
reduces the screening problem to manageable proportions. These
simplified "first-order" equations are solved algebraically in
EXAMS'S steady-state Mode 1 to give the ultimate (i.e.,
steady-state) EECS that will eventually result from the input
loadings. In addition, EXAMS provides a capability to study initial
-------
value problems ("pulse loads" in Mode 2), and seasonal
dynamics in which environmental driving forces are updated on
a monthly basis (Mode 3). Mode 3 is particularly valuable for
coupling to the output of the PRZM model, which can provide a
lengthy time-series of contamination events due to runoff and
erosion of sediments from agricultural lands.
Transport of a chemical from a loading point into the bulk of the
system takes place by advected flows and by turbulent
dispersion. The simultaneous transformations presently result in
a continuously varying distribution of the compound over the
physical space of the system. This continuous distribution of the
compound can be described viapartial differential equations. In
solving the equations, the physical space of the system must be
broken down into discrete elements. EXAMS is a compartmental
or "box" model. The physical space of the system is broken
down into a series of physically homogeneous elements
(compartments) connected by advective and dispersive fluxes.
Each compartment is a particular volume element of the system,
containing water, sediments, biota, dissolved and sorbed
chemicals, etc. Loadings and exports are represented as mass
fluxes across the boundaries of the volume elements; reactive
properties are treated as point processes within each
compartment.
In characterizing aquatic systems for use with EXAMS, particular
attention must be given the grid-size of the spatial net used to
represent the system. In effect, the compartments must not be so
large that internal gradients have a major effect on the estimated
transformation rate of the compound. In other words, the
compartments are assumed to be "well-mixed," that is, the
reaction processes are not slowed by delays in transporting the
compound from less reactive to more reactive zones in the
volume element. Physical boundaries that can be used to delimit
system compartments include the air-water interface, the
thermocline, the benthic interface, and perhaps the depth of
bioturbation of sediments. Some processes, however, are driven
by environmental factors that occur as gradients in the system,
or are most active at interfaces. For example, irradiance is
distributed exponentially throughout the water column, and
volatilization occurs only at the air-water interface. The rate of
these transformations may be overestimated in, for example,
quiescent lakes in which the rate of supply of chemical to a
reactive zone via vertical turbulence controls the overall rate of
transformation, unless a relatively fine-scale segmentation is
used to describe the system. Because compartment models of
strongly advected water masses (rivers) introduce some
numerical dispersion into the calculations, a relatively fine-scale
segmentation is often advisable for highly resolved evaluations
of fluvial systems. In many cases the error induced by highly
reactive compounds will be of little moment to the probable fate
of the chemical in that system, however. For example, it makes
little difference whether the photolytic half-life of a chemical is
4 or 40 minutes; in either case it will not long survive exposure
to sunlight.
1.6.3 Assumptions
EXAMS has been designed to evaluate the consequences of
longer-term, primarily time-averaged chemical loadings that
ultimately result in trace-level contamination of aquatic systems.
EXAMS generates a steady-state, average flow field (long-term or
monthly) for the ecosystem. The program thus cannot fully
evaluate the transient, concentrated EECs that arise, for example,
from chemical 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, an
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 program.
• A useful evaluation can be executed independently of the
chemical's actual effects on the system. In other words, the
chemical is assumed not to 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; the compound is assumed not
to itself absorb a significant fraction of the light entering the
system; bacterial populations do not significantly increase
(or decline) in response to the presence of the chemical.
• 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 the low concentrations of typical of environmental
contaminants; 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 in which any
dissolved species exceeds 50% of its aqueous solubility (see
Chapter 2.2.2 for additional detail). This restraint
incidentally allows the program to ignore precipitation of
the compound from solution and precludes inputs of solid
particles of the chemical. Although solid precipitates have
occasionally been treated as a separate, non-reactive phase
in continuous equilibrium with dissolved forms, the efficacy
of this formulation has never been adequately evaluated,
and the effect of saturated concentrations on the linearity of
sorption isotherms would introduce several problematic
complexities to the simulations.
• Sorption is treated as a thermodynamic or constitutive
property of each segment of 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, where their
release to the water column is controlled by their
availability to 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.
1.7 Further Reading
Baughman, G. L., and L. A. Burns. 1980. Transport and
transformation of chemicals: a perspective, pp. 1-17 In: O.
Hutzinger (Ed.). The Handbook of Environmental
Chemistry, vol.2, part A. Springer-Verlag, Berlin, Federal
Rep ubl ic o f G ermany.
Burns, L. A. 1989. Method 209--Exposure Analysis Modeling
System (EXAMS-Version 2.92). pp. 108-115 In: OECD
Environment Monographs No. 27: Compendium of
Environmental Exposure Assessment Methods for
Chemicals. Environment Directorate, Organization for
Economic Co-Operation and Development, Paris, France.
Burns, L. A. 1986. Validation methods for chemical exposure
and hazard assessmentmodels. pp. 148-172In:Gesellschaft
fur Strahlen- und Umwelt forschung mbH Miinchen,
Proj ektgruppe "Umwelt gefahrdungsp otentiale von Chemik-
alien" (Eds.) Environmental Modelling for Priority Setting
among Existing Chemicals. Ecomed, Munch en-Landsberg-
/Lech, Federal Republic of Germany.
Burns, L. A. 1985. Models for predicting the fate of synthetic
chemicals in aquatic systems, pp. 176-190 In: T.P. Boyle
(Ed.) Validation and Predictability of Laboratory Methods
for Assessing the Fate and Effects of Contaminants in
Aquatic Ecosystems. ASTM STP 865, American Society for
Testing and Materials, Philadelphia, Pennsylvania.
Burns, L. A. 1983a. Fate of chemicals in aquatic systems:
process models and computer codes, pp. 25-40 In: R.L.
Swann and A. Eschenroeder (Eds.) Fate of Chemicals in the
Environment: Compartmental and Multimedia Models for
Predictions. Symposium Series 225, American Chemical
Society, Washington, D.C.
Burns, L. A. 1983b. Validation of exposure models: the role of
conceptual verification, sensitivity analysis, and alternative
hypotheses, pp. 255-281 In: W.E. Bishop, R.D. Cardwell,
and B.B. Heidolph (Eds.) Aquatic Toxicology and Hazard
Assessment. ASTM STP 802, American Society for Testing
and Materials, Philadelphia,, Pennsylvania.
Burns, L. A. 1982. Identification and evaluation of fundamental
transport and transformation process models, pp. 101-126
In: K.L. Dickson, A.W. Maki, and J. Cairns, Jr. (Eds.).
Modeling the Fate of Chemicals in the Aquatic
Environment. Ann Arbor Science Publ., Ann Arbor,
Michigan.
Burns, L. A., and G. L. Baughman. 1985. Fate modeling, pp.
558-584 In: G.M. Rand and S.R. Petrocelli (Eds.)
Fundamentals of Aquatic Toxicology: Methods and
Applications. Hemisphere Publ. Co., New York, NY.
Games, L. M. 1982. Field validation of Exposure Analysis
Modeling System (EXAMS) in a flowing stream, pp. 325-346
In: K. L. Dickson, A. W. Maki, and J. Cairns, Jr. (Eds.)
Modeling the Fate of Chemicals in the Aquatic
Environment. Ann Arbor Science Publ., Ann Arbor,
Michigan.
Games, L. M. 1983. Practical applications and comparisons of
environmental exposure assessmentmodels. pp. 282-299 In:
W. E. Bishop, R. D. Cardwell, and B. B. Heidolph (Eds.)
Aquatic Toxicology and Hazard Assessment, ASTM STP 802.
American Society for Testing and Materials, Philadelphia,
Pennsylvania.
Kolset, K., B. F Aschjem, N. Christopherson, A. Heiberg, and
B. Vigerust. 1988. Evaluation of some chemical fate and
transport models. A case study on the pollution of the
Norrsundet Bay (Sweden), pp. 372-386In: G. Angelettiand
A. Bj0rseth (Eds.) Organic Micropollutants in the Aquatic
Environment (Proceedings of the Fifth European
Symposium, held in Rome, Italy October 20-22, 1987).
Kluwer Academic Publishers, Dordrecht.
Lassiter, R. R. 1982. Testing models of the fate of chemicals in
aquatic environments, pp. 287-301 In: K. L. Dickson, A. W.
Maki, and J. Cairns, Jr. (Eds.) Modeling the Fate of
Chemicals in the Aquatic Environment. Ann Arbor Science
Publ., Ann Arbor, Michigan.
Lassiter, R. R., R. S. Parrish, and L. A. Burns. 1986.
Decomposition by planktonic and attached microorganisms
improves chemical fate models. Environmental Toxicology
and Chemistry 5:29-39.
Mulkey, L. A., R. B. Ambrose, and T. O. Barnwell. 1986.
Aquatic fate and transport modeling techniques for
predicting environmental exposure to organic pesticides and
other toxicants—a comparative study. In: Urban Runoff
Pollution. Springer-Verlag, New York.
Paris, D. F., W. C. Steen, and L. A. Burns. 1982. Microbial
transformation kinetics of organic compounds, pp. 73-81 In:
O. Hutzinger (Ed.). The Handbook of Environmental
Chemistry, v.2, pt. B. Springer-Verlag, Berlin, Germany.
Plane, J. M. C., R. G. Zika, R. G. Zepp, and L.A. Burns. 1987.
Photochemical modeling applied to natural waters, pp. 250-
267 In: R. G. Zika and W. J. Cooper (Eds.) Photochemistry
of Environmental Aquatic Systems. ACS Symposium Series
327, American Chemical Society, Washington, D.C.
Pollard, J. E., and S. C. Hern. 1985. A field test of the EXAMS
model in the Monongahela River. Environmental
Toxicology and Chemistry 4:362-369.
Platt, T., K. L. Denman, and A.D. Jassby. 1977. Modeling the
productivity of phytoplankton. pp. 807-856 In: E.D.
Goldberg, I. N. McCave, J. J. O'Brian, and J. H. Steele,
-------
Eds. Marine Modeling: The Sea, Vol. 6. Wiley-
Interscience: New York.
Reinert, K. EL, P. M. Rocchio, and J. H. Rodgers, Jr. 1987.
Parameterization of predictive fate models: a case study.
Environmental Toxicology and Chemistry 6:99-104.
Reinert, K.H., and J. H. Rodgers, Jr. 1986. Validation trial of
predictive fate models using an aquatic herbicide
(Endothall). Environmental Toxicology and Chemistry
5:449-461.
Ritter, A.M., J.L. Shaw, W.M. Williams, andK.Z. Travis. 2000.
Characterizing aquatic ecological risks from pesticides
using a diquat bromide case study. I. Probabilistic exposure
estimates. Environmental Toxicology and Chemistry
19:749-759.
Sanders, P. F., and J. N. Seiber. 1984. Organophosphorus
pesticide volatilization: Model soil pits and evaporation
ponds, pp. 279-295 In: R. F. Kreuger and J. N. Seiber (Eds.)
Treatment and Disposal of Pesticide Wastes. ACS
Symposium Series 259, American Chemical Society,
Washington, B.C.
Schnoor, J. L., C. Sato, D. McKetchnie, and D. Sahoo. 1987.
Processes, Coefficients, and Models for Simulating Toxic
Organics and Heavy Metals in Surface Waters. EPA/600/3-
87/015, U.S. EPA, Athens, Georgia.
Sato, C., and J. L. Schnoor. 1991. Applications of three
completely mixed compartment mo dels to the long-term fate
of dieldrin in a reservoir. Water Research 25:621-631.
Schramm, K.-W., M. Hirsch, R. Twele, andO. Hutzinger. 1988.
Measured and modeled fate of Disperse Yellow 42 in an
outdoor pond. Chemosphere 17:587-595.
Slimak, M. W., and C. Delos. 1982. Predictive fate models: their
role in the U.S. Environmental Protection Agency's water
program, pp. 59-71 In: K. L. Dickson, A. W. Maki, and J.
Cairns, Jr. (Eds.) Modeling the Fate of Chemicals in the
Aquatic Environment. Ann Arbor Science Publ., Ann
Arbor, Michigan.
Staples, C.A., K. L. Dickson, F. Y. Saleh, and J. H. Rodgers, Jr.
1983. A microcosm study of Lindane and Naphthalene for
model validation, pp. 26-41 In: W. E. Bishop, R. D.
Cardwell, and B.B. Heidolph (Eds.) Aquatic Toxicology
and Hazard Assessment: Sixth Symposium, ASTMSTP 802,
American Society for Testing and Materials, Philadelphia,
Pennsylvania.
Wolfe, N. L., L. A. Burns, and W. C. Steen. 1980. 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.
Wolfe, N. L., R. G. Zepp, P. Schlotzhauer, and M. Sink 1982.
Transformation pathways of hexachlorocylcopentadiene in
the aquatic environment. Chemosphere 11:91-101.
References for Chapter 1.
Behrens, J. C. 1979. An exemplified semi-analytical approach to
the transient sensitivity of non-linear systems. Applied
Mathematical Modelling 3:105-115.
Lassiter, R. R., G. L. Baughman, and L. A. Burns. 1979. Fate of
toxic organic substances in the aquatic environment. Pages
219-246 in S. E. Jorgensen, editor. State-of-the-Art in
Ecological Modelling. Proceedings of the Conference on
Ecological Modelling, Copenhagen, Denmark 28 August -
2 September 1978. International Society for Ecological
Modelling, Copenhagen.
Paris, D. F., W. C. Steen, and L. A. Burns. 1982. Microbial
transformation kinetics of organic compounds. Pages 73-81
in O. Hutzinger, editor. The Handbook of Environmental
Chemistry, Volume 2/PartB. Springer-Verlag, Berlin.
Platt, T., K. L. Denman, and A. D. Jassby. 1977. Modeling the
productivity of phytoplankton. Pages 807-856 in E. D.
Goldberg, I. N. McCave, J. J. O'Brien, and J. H. Steele,
editors. Marine Modeling. Wiley-Interscience, New York.
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2.0 Fundamental Theory
Many excellent books, review articles, and journal reports have
been written on the subject of the fate and transport of chemicals
in the environment. This report is not intended as an exhaustive
summary of the ideas and factual information available from the
literature. EXAMS is a distillation of that literature, encoded in a
computerprogram. This chapter (2) summarizes the fundamental
ideas that were used to construct the EXAMS program. The
references cited represent only the key findings or papers that
directly influenced the EXAMS code. Additional detail and
background information can be found in the works listed in the
bibliography for this report.
2.1 Compartment Models and Conservation of
Mass
EXAMS' environmental data are contained in a file (the
"canonical environments file") that includes a series of concise
descriptions of the aquatic systems of interest to a user. (The
term "canonical" simply means that the data in the file includes
only those quantities bearing directly on the fate and transport of
synthetic organic chemicals.) Each water-body is represented
via a set of N compartments or distinct zones in the system The
program is based on a series of mass balances, which give rise
to a single differential equation for each compartment. Working
from the transport and transformation process equations, EXAMS
compiles an equation for the net rate of change of chemical
concentration in each compartment. The resulting system of N
differential equations describes a mass balance for the entire
ecosystem. These equations have the general form of Equation
(2-1):
V
dt
Ls+Li- VK[C\
(2-1)
where:
Vis the volume of water in the compartment (liters),
[C] is the total chemical concentration as mg/liter of V,
Le is the total external loading on the compartment (mg/h),
Li is the total internal loading on the compartment (mg/h)
resulting from contaminated flows among system
compartments, and
K is an overall pseudo-first-order (/h) loss constant that
expresses the combined effect of transport and
transformation processes
concentration.
that decrease chemical
The "canonical environments file" currently supplied with the
EXAMS computer program is intended primaruily as a series of
test values to establish that the program is operating correctly.
Although the data supplied in this file are within the range of
observed values, this file is not intended for production runs of
the program. EXAMS has been designed to accept standard
water-quality parameters and system characteristics that are
commonly measured by limnologists throughout the world.
EXAMS also includes a descriptor language (parameters JFRAD,
JTURB, etc.) that simplifies the specification of system
geometry and connectedness. The procedure for defining an
EXAMS environment is illustrated in Chapter 3. The EXAMS code
has been written in a general (N-compartment) form; the
program can be modified and recompiled to handle up to 999
compartments. The program is available in 10-, 50-, and 100-
compartment versions.
The chemical data base supplied with the program includes 11
compounds investigated by Smith and coworkers (1978). As
with EXAMS' nominal environmental data base, these data should
not be regarded as immutably fixed. In many instances the data
of Smith et al. (1978) were augmented in order to illustrate
EXAMS' data entry capabilities, and the assumptions used to fill
gaps in the chemical data base are open to revision as additional
experimental data become available.
References for Chapter 2.1.
Smith, J. H., W. R. Mabey, N. Bohonos, B. R. Holt, S. S. Lee,
T.-W. Chou, D. C. Bomberger, and T. Mill. 1978.
Environmental Pathways of Selected Chemicals in
Freshwater Systems: Part II. Laboratory Studies. EPA-
600/7-78-074, U.S. Environmental Protection Agency,
Athens, Georgia.
10
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2.2 Equilibrium Processes
The kinetic properties of organic chemicals are often strongly
influencedbythemolecularstateofthecompound. Consider, for
example, a compound that can both ionize and sorb to suspended
sediments. In this case, the compound will be present in the
water column in ionized, unionized, and sorbed states. In inland
waters, where aerosol formation can be neglected, only the
unionized, unsorbed molecule will volatilize across the air-water
interface. An accurate evaluation of the tendency of the
compound to volatilize thus cannot be obtained until ionization
and sorption are incorporated in the estimation method.
Ionization and sorption also affect the reactivity of chemicals to
transformation processes, although the magnitude of the changes
cannot be as readily predicted.
Laboratory determinations of kinetic rate constants are often
limited to homogeneous phase (clean water) investigations.
Modeling the behavior of a compound in real systems requires
some knowledge, or some assumptions, about the effects of
sorption and ionization on a chemical's behavior in the
environment. EXAMS does notcontain "hard-wired" assumptions
that sorption either "protects" the compound, enhances its
reactivity, or has no effect. Instead, the input chemical data
include separate rate constants for each molecular form of the
compound. This data array allows a user to include unique rate
constants for ions and sorbed molecules when these are known.
When heterogeneous phase (or pH dependent) chemical data are
not available, the necessary assumptions are left to the user's
discretion.
Ionization and sorption of synthetic chemicals are treated as
thermodynamic or constitutive properties of each computational
element in EXAMS. EXAMS treats these processes as local
(within-compartment) reversible equilibria, rather than
calculating a global (system-wide) equilibriumpartitioning of the
compound among its possible molecular species. The inclusion
of partitioning to colloidal "dissolved" organic carbon precludes
any need to explicitly account for "particle concentration"
effects or desorption hysteresis (Gschwend and Wu 1985),
(Morel and Gschwend 1987).
Ionization equilibria are usually achieved within a very short
time, compared to the time-scale of transport and transformation
processes, so a (local) equilibrium assumption is usuallyjustified
in natural systems. Desorption of neutral molecules bound to
sediments, however, may be relatively slow for compounds that
are extensively bound (large partition coefficients). EXAMS'
evaluations do not explicitly include the effects of
sorption/desorption kinetics on transport or transformation
processes. The program does, however, compute the major
transport effect of slow desorption, as an implicit function of
interactions between benthic sediments and the overlying water
column.
Extensively sorbed compounds are usually predominantly
captured by the bottom sediments (benthic compartments) in an
aquatic system. Release of the compound to the water column is
then limited by the turnover rate of the bottom sediments
themselves. The turnover times of benthic sediments, except
during storm erosion, are longer than the desorptionhalf-lives of
organic chemicals. Consequently, the intrinsic kinetic limitations
imposed by residence in bottom sediments generally exceed
those attributable to desorption kinetics as such.
EXAMS allows for the existence of six ionized species, in
addition to the parent unionized molecule. Each of these can
complex with "dissolved" organic matter (EXAMS variable DOC),
and sorb with the sediment phase and the biota in an ecosystem
compartment (computational element). The program computes
the fraction of the total chemical concentration present in each
molecular structure. These distribution coefficients (a) are used
as multipliers on the user-specified transformation rate
coefficients for each decomposition process.
2.2.1 Ionization Reactions
According to the Bronsted-Lowry concept of ionization
reactions, acids and bases react with solvent (water) to form a
conjugate acid-base pair (Stumm and Morgan 1 970). The EXAMS
program regards any synthetic organic as potentially capable of
acting as an ampholyte, forming both singly, doubly, and triply
charged cations and anions in the aqueous medium. The
unionized molecule is taken as the parent compound, and is
denoted in the program documentation as "RH3." The potential
acid-base reactions are then:
(1) Basic reactions:
RH;
+
(2) Acidic reactions:
RH~
RH~
OH'
+ OH"
+ OFT
H^ O+
H- O+
(2-2)
(2-3)
(2-4)
(2-5)
(2-6)
(2-7)
This set of chemical reactions describes the simultaneous
existence of seven chemical species of the compound:
• the unionized parent molecule
• a singly charged cation
a doubly charged cation
a triply charged cation
/?//;
•2 +
11
-------
• a singly charged anion
• a doubly charged anion
• a triply charged anion
= [R3-]{H,O+y/[RH2-]{H2Oy (2-13)
, and
(Again, because EXAMS is an interactive program in which the
user has direct access to the input data base, much of this
documentation has been written using the input data variables as
identifiers and as quantities in the process equations. Although
this approach poses some difficulties for the casual reader, it
allows the potential user of the program to see the connections
between program variables and the underlying process theory.
The Data Dictionary chapter of this document (beginning on
page 175) contains an alphabetical listing and definitions of
EXAMS' input variables. Input data are named with a terminal
"G" ("Global"), and variables internal to the EXAMS program
itself are named with a terminal "L" ("Local"). For example,
"KPSG" can be specified by the user; "KPSL" is its internal
equivalent.)
Most compounds do not exhibit the full range of behaviors given
in Eq. (2-2)-(2-7). EXAMS' chemical input data stream carries,
for each chemical, a vector (SPFLG) of 7 flags that tells the
program which of the ionization reactions are appropriate for
that compound. (A "flag" in this usage means a signal used to
control execution of the program.) Setting an element of the
SPFLG vector to "1" indicates that the corresponding chemical
species in fact exists; setting an element of SPFLG to "0" (zero)
indicates the chemical species does not exist. SPFLG(!) should
usually be set to indicate the existence of the parent molecule.
When, for example, the remaining flags (SPFLG(2...7)) are all
zero, EXAMS treats the chemical as a neutral (unionizable)
organic compound. When SPFLG(2) is set (=1), and SPFLG(3...7)
are 0, the compound is taken to be an organic base forming only
a singly charged cation (RH4+).
The equilibrium constants for the ionization reactions provide a
measure of the strength of the organic acid/base relative to
water. The basicity constants corresponding to Eq. (2-2), (2-3),
and (2-4) are:
0}
0}
]{OH~
(2-8)
(2-9)
(2-10)
The acidity constants corresponding to Eq. (2-5), (2-6), and (2-7)
are:
(2-1
These ionization constants are "mixed" constants (Stumm and
Morgan 1970:82), which take pH = -log {H3O+} ({H3O+} or {H+}
is hydrogen ion activity, IUPAC convention), and the compound
is expressed in concentration units. (Salt effects on pK values,
and changes in pKw with temperature, are currently neglected in
EXAMS.)
In dilute aqueous solutions (unit water activity, (H2O}=1), the
water terms in the equilibrium constants can be neglected. In
benthic sediments, however, much of the compartment volume
is occupied by solids, and the decreased activity of water must
be considered. In EXAMS, this problem is overcome by referring
all concentrations to the aqueous phase of the compartment (note
dimensions of variables in Eq. (2-1)).
Basicity and acidity constants are entered to EXAMS as the
negative of their Briggsian logarithms, that is, as pK values. The
computer variables are PKB(l), PKB(2), and PKB(3) for the
first, second and third basicity constants (generating RH4+,
RH52+, RH63+ and respectively) and PKA(l), PKA(2), and
PKA(3) for the first, second and third acidity constants
(generating RH2 , RH2 , and R3 respectively). The equilibrium
constants can be entered either as single (tempera-
ture-independent) pK values or as functions of temperature.
Temperature dependencies of the equilibrium constants are
computed from an integrated form of the Gibbs-Helmholtz
equation (Castellan 1964:215-217). Each computer input datum
(PKB, PKA) has a corresponding enthalpy variable (EPK, EPK).
When the enthalpy term (EPK) is
zero, the PK datum is taken as a
temperature-independent pK. When
the EPK term is non-zero, the PK
datum is interpreted as the constant
in the equation describing the
equilibrium constant as a function of
temperature. For example: The
Handbook of Chemistry andPhysics,
Ed. 51 p. D122 (Weast 1971), gives
the second acidity constant (Kjj) of
phenol-sulfonic acid for 0° to 50°C
(Table 1).
EXAMS could be loaded with a
typical value drawn from this data
set, for example, at 25°C, K^ =
8.85x10 10 and PKA(2) = 9.05 (also
load EPK(2) = 0.0). The pKa will
then be taken as 9.05 regardless of
the water temperature of the
compartment.
Table 1. Ka2 of Phenol-
Sulfonic Acid
T(°C) Ka2(xlO'°)
0
5
10
15
20
25
30
35
40
45
50
4.45
5.20
6.03
6.92
7.85
8.85
9.89
10.9
12.0
13.1
14.2
12
-------
Alternatively, the visible dependence of the acidity constant on
temperature could be described to EXAMS via PKA(2) and a
non-zero value of EPK(2). Statistical regression of these data on
the model Ka = A exp(-B/RT), where R is the gas constant
(1.9872 cal/deg mol) and T is absolute temperature, yields the
parameters A = 8.2724x10 7 and (B/R) = 2.0466xl03 and
accounts for 99.7% of the variation in Ka. EXAMS requires the
logarithm of A, and B in kcal/mol, as input data. Therefore, the
dependence of Ka2 on temperature would be described to EXAMS
via
and
EPK(2) = (B/R)xRxQ.001(kcal/cal) = 4.067 kcal/mol
EXAMS will then compute alocal value of K,2 as a function of the
water temperature (TCEL) in each system compartment, using the
equation:
log(Ka)=PKA(2)-[1000xEPK(2)/{2.303xRx(TCEL+273.15)}]
2.2.2 Sediment Sorption
EXAMS computes a (local) equilibrium value for the partitioning
of each chemical species (RH3, RH4+, etc.) to sediment phases in
the system. The chemical input data includes a vector of partition
coefficients (KPSG) whose elements apply to each of the
corresponding chemical species. That is, KPSG{1) is the partition
coefficient for neutral organics or the unionized molecule (RH3),
KPSG(2) applies to the RH4+ molecule, etc.
EXAMS uses linear isotherms for all sorption computations, rather
than the non-linear Freundlich or Langmuir formulations. (A
linear isotherm is equivalent to a partition coefficient.) Linear
isotherms are usually an adequate descriptor of the capture of
neutral organics by sediments, at least up to 50% of their water
solubility (Karickhoff 1999 (pers. comm.)) residual in the
aqueous phase. For compounds whose melting point (EXAMS
input parameter MP) is greater than the ambient temperature a
crystal energy term is added (compare Eq. 12 in (Karickhoff
1984)):
23B3RT
in which X is mole fraction solubility, Ai5yis the solute entropy
of fusion, Tm is the melting point in K, Tis absolute temperature,
and R is the gas constant. Ai5y is between 12 and 15 eu (entropy
units, cal/mol K), so, for compounds that are solids at the
ambienttemperature EXAMS computes the limiting concentration
C, as
Cf = 0.5 x SOL exp
6.5(MP-'TCEL)
TCSL + Z73.15
in which SOL, MP, and TCEL are EXAMS input parameters; the
factor 6.5 results from A5/R. (For liquids, this correction is
ignored.)
EXAMS restricts its operating range to ensure that the assumption
of isotherm linearity is not violated. These restrictions are
imposed by evaluating the external loadings to ensure that the
inputs to the system are not excessive, and by a pre-output
evaluation of the computed EECs in all compartments. Loadings
via rainfall and groundwater seepage may not exceed solubility
of the neutral species, evaluated using the pH and temperature
of the receiving segment. Stream-borne and NFS loads, as with
calculated EECS, may not exceed Q in the dissolved neutral
molecule. Upon detecting errors in loadings or in final EECs,
EXAMS reports the problem and returns control to the user for
corrections.
The uptake of neutral organic chemicals by soils and aquatic
sediments apparently involves dissolution of the compound into
the organic matrix of the soil/sediment, rather than a pure
physical (surface) adsorption (see, for example, Chiou et al.
1979). Sorption of this class of chemical probably takes place by
a hydrophobic mechanism, in which the compound (or more
generally the RH3 molecule of acids and bases) is driven from
the water phase of the system by a large fugacity. This process
is conceptually similar to the extraction and recovery of an
organic pollutant from water using an organic solvent.
The partition coefficient (KPS) for a particular compound can be
normalized to the organic content of soils (Chiou et al. 1979) or,
equivalently, to the organic carbon content of aquatic sediments
(Karickhoff et al. 1979). The resulting parameter (KOC) is a
relatively stable, system-independent measure of an intrinsic
property of organic compounds. KOC can be used to compute a
local partition coefficient (KPSL) for sediment phases of an
aquatic system, as a function of the organic carbon content
(FROC, organic carbon as fraction of dry weight) of the sediment
phase of each compartment.
KOC is strongly correlated with the octanol-water partition
coefficient (KOW) (Karickhoff etal. 1979). For whole sediments,
as used in EXAMS, KOC can be estimated (within a factor of 2.5)
as 35% of KOW (Seth et al. 1999). The preferred approach is to
include at least a measured Kow and Koc measured on aquatic
sediment, as sediment Koc tends to be about twice that of soil
Koc (Chiou et al. 1998).
EXAMS computes the RH3 partition coefficient (KPSL( 1)) for each
system compartment via an hierarchical evaluation of these input
data (KPSG(l), KOCG, KOWG). KOCG is the preferred datum, and
if it is non-zero, a local (compartment-specific) KPSL(l) is
computed from KOCG and the organic carbon content (FROC) of
the sediment phase of each compartment. When KOCG is zero,
but the KOWG entry is non-zero, KPSL(l) is computed as
13
-------
0.35XKOWGXFROC. The KPSG datum is used as a system-wide
partition coefficient only when both KOCG and KOWG are zero
entries. Thus when a single KPS is preferred for a specific
simulation, both KOCG and KOWG must be omitted from the input
data (that is, set to zero) because, otherwise, the preferred
computations will override the user's intentions. Setting KOW
and KOC to zero will, however, also disable EXAMS' estimates of
KPB (see Chapter 2.2.3) and KPDOC (Chapter 2.2.4) so
bioconcentration factors and DOC complexation constants must
be entered explicitly, either as measured values or as estimates
developed using the methods described in Chapters 2.2.3 and
2.2.4.
Organic ions can exchange with the normal soil ions, to an
extent probably governed by the ion exchange capacity of a
sediment. The particle size distribution of the sediment,
however, apparently also governs the ability of the sediment to
take up organic ions (Karickhoff and Brown 1978). The
complexity of this process has hindered the development of
robust, system- independent analogs of KOC for organic ions.
EXAMS includes a series of "system- independent" measures of
ion sorption that can be invoked at the user's option. These
parameters relate the (internal) KPSL for the organic ions to the
ion exchange capacities of sediment phases in each system
compartment. The computer variables are:
h - KPSL(2) computed from CEC
'2+ - KPSL(3) computed from CEC
- KPSL(4) computed from CEC
- KPSL(5) computed from AEC
KIEC(5) for sorption of RH2' - KPSL(6) computed from AEC
KIEC(6) for sorption of R3' - KPSL(7) computed from AEC
The units of KIEC are ((mg/kg)/(mg/L))/(meq/l 00 g dry weight);
the cation (CEC) and anion (AEC) exchange capacities of system
sediments are entered as milliequivalents (meq) per 100 grams
dry weight of the sediment. When a single partition coefficient
is preferred for all sediments in the system, it must be loaded via
the appropriate element of the KPSG vector and the
corresponding element in KIEC or KIEC must be set to zero.
EXAMS uses the internal value of the partition coefficient (KPSL)
for each chemical species, however computed, to calculate the
equilibrium sediment sorption of each species on each
particulate sediment phase (P) throughout the system. The
chemical equations and equilibrium expressions are similar for
each chemical species:
KIEC(1) for sorption
KIEC(2) for sorption of Rff
KIEC(3) for sorption of
KIEC(4) for sorption of
KPSL(l) =
[Jiff 3
'); KPSL(3) =
[Kff,
(2-16)
(Tffitf +) + (F)
3+
t P+); KPSU4) =
(2-1 7)
[RH2-][P]
[RHP2-]
[RH2-][P]
(2-18)
(2-19)
. (2-20)
[RH,][P]
(2-14)
Sediment partition coefficients are usually reported as a ratio of
the concentration of sorbed chemical (mg compound/g dry
weight of sediment) to the residual aqueous phase concentration
(mg/liter), in "dimensionless" units ((mg/kg)/(mg/L)). The
sorption equilibrium constants are equivalent to conventional
partition coefficients only so long as the concentration of
particulate matter [P] is expressed as a "sediment: water ratio" p
(kg dry weight/liter of water), and the chemical concentrations
(e.g., [RH3P] and [RH3]) are referred to the aqueous phase of the
system and expressed as (mg compound/liter of water). EXAMS
strictly adheres to this convention for its internal computations.
EXAMS' output tables, however, are converted to conventional
reporting units. For example, concentrations of sediment-sorbed
chemicals are reported to the user as mg/kg dry weight of
sediment, but are carried internally as (sorbed mg)/(liter of
water).
The need for strict adherence to this convention arises from the
very large sediment/water ratios of benthic sediments. Water
column compartments can be assumed to have a water volume
essentially the same as their environmental volume (VOL). For
these compartments, EXAMS' internal variable (SEDCOL, the
sediment/water ratio (p, kg/L)) is simply computed as 10 6
(kg/mg) times the input datum (SUSED, suspended sediments in
mg/L).
Because the solid phase occupies a significant fraction of the
total environmental volume of bed sediments, p (SEDCOL) is
computed quite differently for benthic (TYPE "B")
compartments. For benthic compartments, the input datum
BULKD is the bulk density of the sediment (g/cc environmental
volume). The sediment:water ratio is computed from the bulk
density and water content (PCTWA, 100 x the fresh weight/dry
weight ratio) of the sediment, and the total environmental
volume (VOL, cubic meters) of the compartment. (The
14
-------
computation takes pw, the density of water, as 1 g/cm3.) The
equations (using the internal code variables) are:
SEDCOL = SEDMSL / WA TVOL
(2-21 )
where
TOTMAS = BUKLDXVOLX 1000.
SEDMSL = TOTMAS/(PCTWA/IOO.), and
WATVOL = (TOTMAS - SEDMSL)XPW
Here TOTMAS is the total compartment mass (kg), SEDMSL is the
mass of sediment in the compartment (kg), and WATVOL is the
volume of water contained in the compartment (liters).
The sediment/water ratio can also be computed directly from the
water content of the sediment (Eq. (2-22)):
SSDCOL =
1
-1
(2-22)
Eq. (2-21) is the more efficient computation for EXAMS,
however, because the results of the intermediate computations
(WATVOL, SEDMSL) are used elsewhere in the program.
2.2.3 Biosorption
The uptake of chemicals by living organisms is represented in
EXAMS via seven simple bioconcentration factors or biomass
partition coefficients (KPBG). KPBG is a vector of 7 elements,
each of which applies to the corresponding chemical species
(RH3, RH4+, etc.). The chemical equations and equilibrium
expressions are analogous to those for sediment sorption:
); KPBL(Y) =
h W
iB+\, KPBL(T) =
(2-23)
[RH+][B]
(2-24)
(B)
); KPBL(5) =
• (2-27)
[RH-][B]
); KPBL(6)= .L^-.i (2-28)
KPBL(T) =
.3- •
(2-29)
Bioconcentration factors (BCF, EXAMS input variable KPBG)
should be entered to EXAMS on a dry weight basis. The expected
units of KPBG are (|o,g/g(dry))/ (mg/L), or the numerically
equivalent unit (mg/kg(dry))/(mg/L). EXAMS estimates KPB from
KOW if the input data is not present. The estimation equation
(Baughman and Paris 198 1) is
KPB= 0.436 x
As with sediment sorption, EXAMS converts its input data for
compartment biomasses to an internal variable (BIOTOL, "B" in
Eq. (2-23)-(2-29)) with dimensions of kg dry weight per liter of
water in the compartment. EXAMS' input datum for water column
compartments (PLMASG in mg dry weight per liter) is simply
multiplied by 10 6 (kg/mg) to yield BIOTOL in its proper units.
For benthic compartments, the benthic biomass (BNMASG) is
entered to EXAMS with dimensions of grams (dry weight) per
square meter of bottom sediment surface. EXAMS converts this
datum to its internal variable (B (BIOTOL), kg per liter of
interstitial water) via Eq. (2-30):
jtJ?MG x BNMASG x 0.
/ g)
WATVOL
(2-30)
where AREAG is the (user-supplied) total surface area of the
benthic compartment (square meters).
The actual quantities of synthetic organics captured by the
biomass are often relatively small, compared to the amounts
sorbed with sediments or dissolved in the aqueous phase of real
systems. Thus, the biomass often plays a relatively minor role as
a transport or capture medium affecting the fate of an organic
pollutant. The role of the biomass as a food-chain vector can,
however, have great ecotoxicological significance.
(Biotransformation of chemicals is, of course, another matter
altogether (see 2.3.6).) EXAMS does not include a mobile
component of the biomass, that is, volitional movements of
fishes (etc.) among compartments are not assessed. The
plankton, however, is transported in accord with the hydrology
of the aquatic system, carrying associated chemical with it. (The
transport equations are discussed in Chapter 2.3.1.)
The KPB vector for organic compounds estimates organismal
body-burdens from simple bioconcentration factors. Note,
however, that EXAMS' computations are based on partitioning
ratios to dissolved species of the chemical. EXAMS does not
attempt to evaluate such things as the exposure of fishes to
variable chemical concentrations as they mi grate among different
zones of the ecosystem, or food-chain biomagnifications. If
desired, measured fish bioconcentration factors or estimates
15
-------
based on Kow (Meylan et al. 1999) can be entered via KPBG and
used as for preliminary evaluation of fish contamination, but
transfer of EXAMS output exposure files to the BASS model is the
preferred methodology.
2.2.4 Complication with DOC
EXAMS treats complexation with the "dissolved" (i.e., colloidal
and filtrable particulate) organic matter of natural waters via
complexation constants for each of the ionic species (Eq. (2-31)
- (2-37)) In these formulations, DOC is in kg/L, complexed
species are in mg/kg (DOC), and dissolved chemical species are
in mg/L; Kpdoc is conventionally regarded as being
"dimensionless" or as having units of (mg/kg)/(mg/L) or L/kg,
as in the discussion of sediment sorption in Chapter 2.2.2.
+ (DOC)«
KPDOCL(l) =
+ (DOC) <-
KPDOCL(2) =
> (RH-^DOC);
[RH^DOC]
[RH4DOC+]
(RHf)+ (DOC) ** (RHSDOC2+);
KPDOCL(3) =
[RHSDOC2+]
+ (DOC) ** (RH6DOC*)
[RH6DOC3+]
KPDOCL(A) =
(RH; ) + (pocj «* (RH2 DOC');
[RH2DOC~]
KPDOCL(5) =
IRH-2][DOC]
(RH2') + (DOC) ** (RHDOC2' );
[RHDOC2']
KPDOCL(6) =
[RH2'][DOC\
(£><2C) e (RDOC3');
[RDOC3' ]
KPDOCUT) =
(2-31)
(2-32)
(2-33)
(2-34)
(2-35)
(2-36)
(2-37)
Kpdoc should be measured on the material as it naturally occurs,
rather than on humic acid or fulvic acid extracts. If, as is usually
the case, a measured Kpdoc is not available, EXAMS estimates it
from Kow as Kpdoc=0.074Kow. This value was derived from
analysis of the data presented in Appendix A. This tabulation is
a compendium of literature values for Koc measurements on
natural water samples. Measurementmethods include, inter alia,
dialysis (Carter and Suffet 1982), reversed-phase separation
(Landrum et al. 1984) and solubility enhancement (Chiou et al.
1986); values developed using fluorescence quenching methods
(Gauthier et al. 1986) were excluded because this method is
subject to interferences that often lead to over-estimates of Koc
(Laor and Rebhun 1997), (Danielson et al. 1995), (Tiller and
Jones 1997). Partitioning to DOC in benthic segments is
calculated using the general Koc of the compound, as the
available literature indicates a greater affinity of benthic DOC, as
expressed via a mean Kp/Kow (0.46, Appendix A) that is well
within the uncertainty (0.14Kow-0.89Kow) of observations for
particulate organic carbon (Seth et al. 1999).
2.2.5 Computation of Equilibrium Distribution
Coefficients
EXAMS computes the distribution of the chemical among its 28
possible molecular structures (RH3, RH3P, etc.). This
distribution is expressed as a vector of concentration ratios (a).
Each element of a is the ratio of the concentration of a particular
molecular species, to the total concentration of chemical in the
system compartment (St, mg chemical per liter of water in the
compartment). These a values enter the kinetic equations as
multipliers on the user-supplied rate constants for transformation
of each molecular species.
The logic of these computations can best be seen via a sample
calculation. For example, consider the behavior of an organic
acid in pure water, subj ect to the following ionization reactions:
RH3 + H2 O <-> RH^ + H3 O + (2-38)
D ELT i C_T /~\ j •_ J3 Z-X i T T /"~V ~^~ (O *5Q\
jf\JjTo "T" jfjF •"> ^-X T—T J\JjI "T" JJ T V-X V /
In order to evaluate the effects of ionization on transformation
and transport of the chemical, EXAMS requires three
concentration ratios (CR):
(I) the fraction present as RH3 , or
CR, ,[RH3]/RT
(2) the fraction present as RH% ,or
(2-40)
(2-41)
16
-------
(3) the fraction present as RH2~ , or
r2-
(2-42)
The equilibrium constants for the chemical Eq. (2-38) and (2-39)
are:
(2-43)
(2-44)
As has already been mentioned, EXAMS computes {H3O+} as
10"pH. The equilibrium expressions, together with a concentration
condition, provide enough information for computing the desired
concentration ratios CR (Stumm and Morgan 1970:102). The
concentration condition is simply a statement of the law of
conservation of mass:
" ] (2-45)
The concentration condition (Eq. (2-45)) and the definition of
the first concentration ratio CR, (Eq. (2-40)) combine to give:
[RH3]
(2-46)
The equilibrium expressions can now be solved for[RH^]
and[RN2~] in terms of [RH3] . To wit, Eq. (2-43) can be
rearranged to give:
__ _ K.,\RH-A
—— (2-47)
and, from Eq. (2-44),
(2-48)
Substituting Eq. (2-47) into Eq. (2-48) yields:
(2-49)
Substituting Eq. (2-47) and Eq. (249) into Eq. (2-46), and
canceling [RH3] from numerator and denominator, yields the
desired result:
^al
(2-50)
The second distribution coefficient CR2 can be computed in an
analogous fashion. From the definition of CR2 in Eq (2-41), and
the concentration condition (Eq. (2-45)),
[RHj] [RHj]
2 2 - (2-51)
[Rr]
Solving Eq. (2-43) for [RH3] , and Eq. (2-44) for
yields the required expressions in [RH^] '•
*-al
(2-52)
(2-53)
Substituting Eqs. (2-52) and (2-53) into Eq. (2-50)2.33, and
canceling [ RH^ ] from numerator and denominator, then gives
an expression for CR2:
(2-54)
"al
This conventional solution for the distribution coefficient is
somewhat unsatisfying for direct implementation in a computer
program, however. Whenever the acidity constant (K^) is zero,
the machine will, nonetheless, attempt the division and make an
error. Of course, a separate segment of code could be written for
each possible subset of the 28 potential molecular species, and
the machine could be guided to the appropriate section of the
code by a logic tree. Observe, however, that Eq. (2-54) can also
be written as:
K.
•al
{H30+y
1+
"al
(2-55)
This equivalent expression can be generated by simply
substituting the second term in the denominator of Eq. (2-50)
into its own numerator. Eq. (2-55) now has the advantage that a
zero value for K^ will not cause the computer to attempt a "zero
divide," and will nonetheless yield the proper value for CR2, that
is, zero.
In the same way, substitution of the third term in the
denominator into the numerator of Eq. (2-50) gives:
17
-------
(2-56)
Eq. (2-56) is equivalent to the expression for CR3 obtained via
substitution of expressions for [RH^] and [RH%] (in terms
,-2-
of [RH ] ) into the formula for CR3 given by:
(2-57)
2--
Rearranging Eq. (2-54) gives [RH2 ] in terms of [RH ] :
(2-58)
and Eq. (2-43) gives:
^ttl
or, substituting Eq. (2-58) into Eq. (2-59),
(2-59)
(2-60)
Substituting Eqs. (2-58) and (2-60) into Eq.(2-57), and canceling
[RH ~] from numerator and denominator, then yields:
1
{//,O + } (H->O + } {H-,O+} (2-61)
—-—: —- + —-—- + 1
which is equivalent to Eq. (2-56).
This example demonstrates that an algorithm for computing the
distribution coefficients (a) of any multi-species mixture can be
constructed in the following way.
(1) Write a concentration condition for [St] that expresses the
law of conservation of mass (Eq. (2-45)).
(2) Define the distribution coefficient for the parent molecule
(RH3) as [RH3]/[RT] (Eq. (2-46)).
(3) Solve the equilibrium expressions for the full system (Eq.
(2-43) and Eq. (2-44)) for each molecular species, expressing
each species in terms of [RH3] (Eq. (2-47) and Eq. (2-49)).
(4) Cancel [RH3] from each of these expressions (Eq. (2-47) and
(2-49)), thereby obtaining the contribution of each species
(DISFCT(I)) to the denominator of Eq. (2-50).
(5) Compute a total denominator (SUMFCT) as ( 1
terms), as in Eq. (2-50) (that is,
DISFCT(l) = [RH3]/[RH3] = 1).
these
(6) Compute each distribution coefficient (a;) as
DISFCT(I)/SUMFCT, where DISFCT(l) = 1 and, in this
example,
DISFCT(2) = Kal/{H+} and
DISFCT(3) = (Kal)(Ka2)/{H+} {FT}
This computational procedure is general for all its subsets: when
any Ka = 0, the computed distribution coefficient is zero.
Furthermore, the a values always sum to 1.0, as they must in
order to conform to the law of conservation of mass.
The EXAMS algorithm for computing distribution coefficients of
the 28 (potential) molecular species of an organic chemical was
constructed on this model.
References for Chapter 2.2.
Baughman, G. L., and D. F. Paris. 1981. CRC Critical Reviews
in Microbiology 8:205.
Carter, C. W., and I. H. Suffet. 1982. Binding of DDT to
dissolved humic materials. Environmental Science and
Technology 16:735-740.
Castellan, G. W. 1964. Physical Chemistry. Addison-Wesley
Publ. Co., Reading, Massachusetts.
Chiou, C. T., R L. Malcolm, T. I. Brinton, and D. E. Kile. 1986.
Water solubility enhancement of some organic pollutants
and pesticides by dissolved humic and fulvic acids.
Environmental Science and Technology 20:502-508.
Chiou, C. T., S. E. McGroddy, and D. E. Kile. 1998. Partition
characteristics of polycyclic aromatic hydrocarbons on soils
and sediments. Environmental Science and Technology
32:264-269.
Chiou, C. T., L. J. Peters, and V. H. Freed. 1979. A physical
concept of soil-water equilibria for nonionic organic
compounds. Science 206:831-832.
Danielson, K. M., Y.-P. Chin, J. S. Buterbaugh, T. L. Gustafson,
and S. J. Traina. 1995. Solubility enhancement and
fluorescence quenching of pyrene by humic substances: The
effect of dissolved oxygen on quenching processes.
Environmental Science and Technology 29:2162-2165.
Gauthier, T. D., E. C. Shane, W. F. Guerin, W. R. Sietz, and C.
L. Grant. 1986. Fluorescence quenching method for
determining equilibrium constants for polycyclic aromatic
hydrocarbons binding to dissolved humic materials.
Environmental Science and Technology 20:162-1166.
18
-------
Gschwend, P. M., and S.-c. Wu. 1985. On the constancy of
sediment-water partition coefficients of hydrophobic
organic pollutants. Environmental Science and Technology
19:90-96.
Karickhoff, S. W. 1984. Organic pollutant sorption in aquatic
systems. Journal of Hydraulic Engineering 110:707-735.
Karickhoff, S. W., and D. S. Brown. 1978. Paraquat sorption as
a function of particle size in natural sediments. Journal of
Environmental Quality 7:246-252.
Karickhoff, S. W., D. S. Brown, andT. A. Scott. 1979. Sorption
of hydrophobic pollutants on natural sediments. Water
Research 13:241-248.
Landrum, P. F., S. R. Nihart, B. J. Eadie, and W. S. Gardner.
1984. Reverse-phase separation method for determining
pollutant binding to Aldrich humic acid and dissolved
organic carbon of natural waters. Environmental Science
and Technology 18:187-192.
Laor, Y., andM. Rebhun. 1997. Complexation-flocculation: A
new method to determine binding coefficients of organic
contaminants to dissolved humic substances. Environmental
Science and Technology 31:3558-3564.
Meylan, W. M., P. H. Howard, R. S. Boethling, D. Aronson, H.
Printup, and S. Gouchie. 1999. Improved method for
estimating bioconcentration/bioaccumulation factor from
octanol/water partition coefficient. Environmental
Toxicology and Chemistry 18:664-672.
Morel, F. M. M., and P. M. Gschwend. 1987. The role of
colloids in the partitioning of solutes in natural waters.
Pages 405-422 in W. Stumm, editor. Aquatic Surface
Chemistry — Chemical Processes at the Particle-Water
Interface. John Wiley & Sons, New York.
Seth, R., D. Mackay, and J. Muncke. 1999. Estimating the
organic carbon partition coefficient and its variability for
hydrophobic chemicals. Environmental Science and
Technology 33:2390-2394.
Stumm, W., and J. J. Morgan. 1970. Aquatic Chemistry: An
Introduction Emphasizing Chemical Equilibria in Natural
Waters. Wiley-Interscience, New York.
Tiller, C. L., andK. D. Jones. 1997. Effects of dissolved oxygen
and light exposure on determination of Koc values for PAHs
using fluorescence quenching. Environmental Science and
Technology 31:424-429.
Weast, R. C., editor. 1971. Handbookof Chemistry and Physics.
51st edition. The Chemical Rubber Co., Cleveland, Ohio.
2.3 Kinetic Processes
"Kinetic" processes, unlike rapid (local) equilibrium processes
such as ionization and sorption, occur on time scales that make
their time-dependent behavior of direct concern in a hazard
evaluation. (Very slow processes, at the further extreme of the
temporal spectrum, are usually treated by modelers as absolute
constants. For example, changes in the emission of light by the
sun (the solar "constant") are usually modeled only by
astrophysicists.) The kinetics of the transport regime in aquatic
systems, and the kinetics of the transformation processes that
degrade a chemical to innocuous forms, are the primary
constituents of EXAMS' evaluative capabilities.
2.3.1 Transport
EXAMS calculates steady-state average transport of water,
sediments, and planktonic organisms throughout the system. The
flows o f water, sediments, and plankton act as simple carriers for
the dissolved, sediment-sorbed, DOC-complexed, and biosorbed
forms of the synthetic organic chemical. These carrier flows are
ultimately reduced to coefficients that express the effects of
transport processes on the kinetics of the chemical; the vector of
concentrations of the synthetic organic chemical itself thereby
remains the only true time-dependent state variable in EXAMS.
A hydrologic sub-program was created for EXAMS in order to
minimize the labor necessary to specify, or modify, the physical
transport section of EXAMS' environmental data base. This data
base is composed primarily of readily available and easily
comprehendedparameters, such as the volume, mean depth, and
surface area of the system compartments. Any of these
parameters can be modified by the user as desired. EXAMS then
recomputes the exchanges of materials among compartments,
and the net transport of materials through the system, in effect
creating a new hydrologic model according to the user's
modifications.
The hydrologic procedure operates on three sub-sets of EXAMS'
environmental data base. The first is a description of the
volumes of water entering each zone of the system from external
sources. The second categorizes the geometry of the system, and
the properties and distribution of biomass and sediments. The
third sub-set contains structural properties of the ecosystem
itself. This last (variables JFRAD, JTURB, etc.) specifies the
direction and strength of the flow pathways interconnecting the
system compartments. The flow of water through the system
compartments is computed from a mass balance on water
entering and leaving each segment.
EXAMS' flow pathways can be specified via advective or
dispersive equations, or both. (Advection is like a freight train,
dispersion is the macro-scale analogue of diffusion, operating
like a drunken walk) The program uses conventional equations
for both processes. For compartments of constant volume, the
law of conservation of mass demands that the hydrologic inputs
19
-------
to a compartment be balanced by advected flows leaving the
compartment. The expression used for dispersive or turbulent
exchanges across compartment boundaries is, as usual, (DSP x
XSTUR / CHARL) where XSTUR is the cross-sectional area of the
exchange boundary (in square meters), CHARL is the
"characteristic length" (meters) along the flow path (that is, the
average length of the compartments or the distance between
compartment centers, measured along the exchange axis), and
DSP is the eddy dispersion coefficient (square meters/hour). For
example, in a simple (two surface water compartments) model of
a stratified lake, when XSTUR is the area of the thermocline and
CHARL is one-half the mean depth of the lake, this expression
describes the rate of exchange of water between the epilimnion
and hypolimnion.
EXAMS also computes sediment transport via the general
advective and dispersive equations, but an additional set of
transport rules is superimposed on the sediment equations. For
example, an advective pathway between water column
compartments is permitted to carry along suspended sediments
(and plankton) from one compartment to the next. An advective
pathway from the water column into a benthic sediment is not
allowed to carry particulate matter with it, however: This kind of
pathway is reserved for water seepage into the sediment (along
with dissolved and DOC-complexed chemicals), and
ground-water recharge leaving the system The sediment
transport rules give EXAMS the ability to include riverine
sediment wash-loads and bed-loads, groundwater seepage and
recharge, and complex exchanges between the water column and
benthic sediment deposits driven by physical and biological
processes dominant in non-fluvial systems.
EXAMS does not explicitly represent sequential deposition and
scour of benthic sediment zones, nor does it allow in most cases
for a secular accumulation of bottom sediments with consequent
burial of sorbed chemicals. In one sense, this limits EXAMS'
utility in evaluative safety investigations. For example,
run-of-the-river flood control reservoirs are very common in the
USA, and many of them rapidly accumulate sediments and bury
chemicals in their depths. The bottoms of many, if not most,
free-flowing rivers and larger lakes accumulate sediments quite
slowly, however. In addition, the synthetic chemicals captured
by benthic sediments probably are frequently re-exposed to the
overlying water column via water turbulence, physical
disturbance by demersal fishes, and the internal stirring of
sediments bybenthic organisms (bioturbation). EXAMS therefore
treats benthic sediments as bottom zone compartments with a
fixed volume, subject to continual (albeit slow) exchanges with
the overlying water column. At least for evaluation and
screening purposes, it seemed unwise to suppose that buried
synthetic chemicals will never reappear.
Where appropriate, however, net sedimentation and burial of
synthetic chemicals can be readily evaluated, for the burial
process can be represented via a first-order disappearance of the
compound from benthic sediments. For example, given an active
sediment depth of 10 cm, and a net burial of 1 mm/yr, the loss
coefficient for a synthetic compound in the sediment would be
0.1/10 = 0.01 /yr with a half-life of 69 years. This approach
should, however, only be used when the benthic zone is modeled
as a single sediment layer.
EXAMS was designed for evaluative purposes, rather than for
detailed site-specific applications. For this reason, EXAMS'
transport algorithms were written in a very general form that
uses an input description of the transport conditions in the
system, rather than attempting to compute hydraulics and solids
transport from first principles. Several cautions to the user
defining an ecosystem for entry to the program are therefore in
order.
First, because EXAMS is a compartment model, the representation
of advected flows necessarily introduces some numerical
dispersion into the computations. Although this poses little
problem for general evaluations, it shouldbe recognized that the
concentrations computed by the program for a particular location
are to a degree dependent on the spatial resolution used to
represent the system. For example, EXAMS computes the average
concentrations in a river reach, rather than the details of the
(usually decreasing) profile of concentrations within the reach.
As the number of compartments used to describe the reach
increases, the compartmentalized representation begins to
approach the more detailed profile predictable from an analytic
solution to the governing partial differential equations. In
general, for site-specific applications, simplified transport
representations can be adjusted or "calibrated" to more faithfully
depict transport detail, via initial matching of program outputs to
a conservative tracer substance. Chloride concentration is often
used as a calibration tracer in estuaries; temperature and
dissolved substances can be used for calibration studies in
lacustrine systems. Of course, if desired, EXAMS could be loaded
via an analysis of the outputs of detailed hydraulic and sediment
transport models.
Secondly, EXAMS does not impose a segment-by-segment mass
balance on the solid phases (sediments and biota) that transport
sorbed chemicals through the system. In most instances,
sediment (detrital materials), "dissolved" organic matter, and
biota are subject to significant creation and destruction in some
areas of aquatic systems, so that, unlike water, these quantities
cannot be treated as simple mass-conservative entities in a
generalized algorithm Thus, for example, mass balances for
suspended sediments in EXAMS are left to the user's discretion.
The EXAMS program, however, does not permit a failure to
conserve sedimentmass to perturb the mass balance for synthetic
organic chemicals.
20
-------
Exchanges with sediment beds are described primarily via a
dispersive exchange term (Chapter 2.3.1.4). This approach
involves a statistical summary of the multitude of physical and
biological transport mechanisms that have not been fully
characterized in the literature. It has disadvantages in that the
appropriate magnitudes for the input parameters (e.g., DSP) are
only approximately known. The magnitudes of the descriptive
parameters needed for a more mechanistic characterization of
these multiple physical and biological transport pathways are,
however, still less perfectly known. In general, the sensitivity of
EXAMS' outputs to variation in sediment-water column exchange
parameters should probably be routinely examined, at least for
extensively sorbed compounds.
2.3.1.1 Hydrology and advection
Hydrologic inputs are specified in EXAMS' environmental data
base via four variables: STFLO (stream flow), NPSFLO
(non-point-source flow), SEEPS (ground-waterinflows), and RAIN
(rainfall). After subtracting evaporative water losses (EVAP), the
sum of these terms is the total water flow entering a
compartment from external sources. EXAMS adds to this sum the
advected flows entering the compartment via other
compartments in the system. This grand sum is the total amount
of water advected into the compartment from all (internal and
external) sources combined. The total advective flow through the
compartment is then distributed among the flow paths specified
for that compartment in the JFRAD and ITOAD structural vectors.
Three of the hydrologic input variables (STFLO, NPSFLO, and
SEEPS) are vectors that include a separate entry for each
compartment of the aquatic system described by the data base.
The STFLO vector is used to enter point sources, tributary flows,
stream flows entering a lake, and the discharge entering the
uppermost reach of ariver system. TheNPSFLOs are used to enter
non-point-flows and overland runoff flowing into the
compartments. Ground- water seepage or subsurface flows are
entered via SEEPS. STFLO, NPSFLO, and SEEPS all have units of
cubic meters per hour; EXAMS allows each compartment to
receive an entry from each category. These hydrologic inputs can
in addition be specified for each month of the year.
Rainfall (RAIN) is a scalar variable in EXAMS, that is, the
environmental descriptor is a single (monthly) value rather than
a compartment-specific vector. RAIN has units of
millimeter/month. EXAMS converts this climatological datum to
a volume of water entering each compartment having an
air-water interface. EXAMS detects the air-water interface, and
decides whether to admit rainfall into the compartment, based
upon the structure of the system. Rainfall is not permitted to
enter compartment types (TYPE) "B" (benthic) or "H"
(hypolimnion). The decision mechanism for L(ittoral) and
E(pilimnion) compartments is more complex. For these
compartment types, EXAMS checks the compartment numbered
one less than the compartmentunder consideration (that is, if the
current compartment is J, the decision is based on the properties
of compartment (J-l)). If the preceding compartment is another
element of the water column, rainfall is not allowed to enter the
current (Jth) compartment.
At first glance, this decision seems trivial: after all, rain only
falls on the water surface, therefore only these compartments can
receive rainfall. The problem is complicated, however, by the
fact that EXAMS was designed to allow a user to interactively
modify any variable in the environmental data base. EXAMS
therefore must be able to detect any change in the system
structure and accurately recompute, in this case, a new
hydrologic regime. Changes in system structure affect a number
of other processes as well. For example, in computing vertical
light extinction through the water column, EXAMS detects the top
of the water column, compute the light levels in each
successively deeper water-column compartment, and then restart
the computation for each adjacent vertical section.
The problem, then, is somewhat more general than it first
appears, and is not entirely trivial: the three-dimensional
structure of the system must be decoded as an implicit function
of the input environmental data. The decoded structure then
serves as a guide for computing the kinetics of the transport and
transformation processes. The decoding problem could, of
course, be eliminated by requiring that the environmental data
include a more complete set of structural descriptors. This
approach, however, leaves the user in the uncomfortable position
of having to memorize a large set of arbitrary, model- specific
compartment descriptors of little intrinsic interest. EXAMS
instead relies on a set of simple conventions for numbering and
naming the compartments used to describe an aquatic system.
These conventions can be stated as 4 definition rules:
(1) Compartments can be named (TYPE) either "L" (littoral), "E"
(epilimnion), "H" (hypolimnion), or"B" (benthic). These names
carry their usual implications: an "H" compartment lacks an
air-waterinterface, a"B" compartment is a bottom sediment, etc.
All system zones can be entered as vertical stacks of the same
TYPE for added spatial detail.
(2) Compartment number 1 must be part of a water column and
must be of TYPE "E" or "L".
(3) Each vertical segment must be numbered in increasing order
with increasing depth. That is, when a vertical segment is
divided into, say, 4 compartments, if the topmost compartment
is numbered 5, the bottom compartment will be number 8.
(4) Every vertical segmentmust terminate in at least one bottom
sediment ("B") compartment.
EXAMS' computations are somewhat more efficient if one more
rule is observed:
21
-------
(5) Vertical blocks of compartments are arranged, and
numbered, along the main advective flow paths in the system.
inappropriate entries, that is, evaporation is allowed only for
compartments with an air- water interface.
EXAMS' internal decoding of the (implied) system structure thus
avoids the problem of multiple model-specific parameters, but at
some cost: EXAMS must assume that the user has scrupulously
adhered to these rules. Suppose, for example, that a 10-mile
stretch of river were described as 10 successive "L"
compartments, and the benthic sediments were omitted: EXAMS
would suppose the system to be a vertical, 10-mile -deep column
of water. Again, should a surface-water compartment be
designated "H", EXAMS will not allow rain to fall, nor chemicals
to volatilize, from the compartment. Examples of improper and
proper segmentations of a small river and lake system are given
in Figure 1 and Figure 2.
---Hi i I---M
* * i
I !
I *---
I---H I-
I I I
I I I
•* I < • I
Figure 1. Improper segmentation and numbering: compartment
number 1 is (B)enthic, benthic sediments are incomplete, and
numbering is horizontally rather than vertically organized.
I---M I---M
* i i i
i > i i i
* ........ » i :
i---n i
i * .........
i
i i
i
Figure 2. Proper segmentation: vertically organized numbering,
all vertical segments include a bottom sediment, and
compartment number 1 is of surface water type ("L" or "E").
EXAMS can be operated with very generalized descriptions of
aquatic systems typical of broad geographic regions, or with
more detailed descriptions of particular sites and locales. The
mechanics of EXAMS' transport computations can best be
appreciated via a specific example. This example will be
developed in some (site-specific) detail, so as to illustrate
methods for preparing environmental data for EXAMS,
computational mechanics, and the range of transport processes
that can be accommodated by the program.
2.3.1.2 Advected water flows
EXAMS' advective flow computations are a direct application of
the law of conservation of mass. Because changes in storage
volumes are not permitted in Modes 1-3, the total inputs to each
compartment must be balanced by advected outflows. The
advective transport regime thus can be computed by imposing a
water (mass) balance on the hydro logic inputs to the system The
logic of these computations can best be seen within the context
of a specific example:
Figure 3 is a 9-compartment model of a portion of a slowly
moving slough or river system. Upstream discharge into
compartment 1 is 10 cubic meters/h. The flow is split by an
island into compartments 3 and 5; 75% of the flow goes to
compartment 3, the remainder to compartment 5. Compartment
3 also receives a tributary discharge of an additional 10 cubic
meters/hour. Each segment is 100 m long and 2 m deep. The
segment widths are 10 m for 1 and 7, but compartment 3 is 8 m,
and compartment 5 is 2 m, wide. The rate of evaporative water
loss (EVAP) is 146. 1 mm/mo for all segments; no rain falls on the
system. EXAMS' hydrologic input data, thus far, looks like this:
Table 2. Hydrologic input data for Noname Slough
CMPT STFLO AREA EVAP VOL DEPTH
1
•f
3
5
1
10 1000
10 800
200
1000
146.1
146.1
146.1
146.1
2000
1600
400
2000
2
2
2
2
EXAMS' final hydrological input quantity is a
compartment-specific vector of monthly evaporative water loss
rates, EVAP, with units (millimeter/month). A value of EVAP can
be entered for any compartment, but EXAMS ignores
EXAMS now converts STFLO and EVAP (using AREA) to the net
liters/hour entering each compartment from external sources
(WATINL), and sums the total available flow (TOTIN), arriving
at the hydrologic analysis shown in Table 3.
22
-------
ID |
1
V
3 I.
* H
E M
1
\\
I \
>->\ 7 X, | >
/ 1 1
1 | 9 a |
+ 4. 4.
1
Figure 3. Nine-compartment model of Noname Slough.
Table 3. Advective inputs to Noname Slough
CMPT STRMFL EVAPL
WATINL
1 10,000
3 10,000
5 0
7 0
200
160
40
200
TOTIN
9,800
9,840
-40
-200
19,400
The structure of the advective flow field is given by parameters
JFRAD, ITOAD, and ADVPR. These names are acronyms for J
FRom ADvection (JFRAD), I TO ADvection (ITOAD), and
ADvection PRoportion (ADVPR), respectively. Vector JFRAD is
the list of the source compartments for each flow. The
corresponding member of ITOAD holds the number of the
compartment receiving the flow, and ADVPR gives the fraction of
the total flow through compartment JFRAD that follows the path
to ITOAD. A zero (0) entered in ITOAD denotes an export from the
system. EXAMS' report of the (partial) structure given for the
slough system in Figure 3 would appear as in Exams Output
Table 1.
Name of environment: Noname Slough - Advection Test Data
Total number of segments (KOUNT) = 9
Segment Number: 123456789
Segment "TYPE": LBLBLBLBB
Table 9.
J FR AD
I TO AD
ADV PR
Path No .
Input specifications -- advec
1
3
0.750
: 1
1
5
0.250
2
7
1.00
3
tive transport field.
7 0
1.00 1.00
4 5
EXAMS now combines the information given in Table 3 and
Exams Output Table 1 into a set of input/output equations
reflecting the conservation of water mass in the system. Taking
X(I) to denote the total output flow from compartment (I), these
equations can be written as:
OUTPUT =
X(l)
X(3)
X(5)
X(7)
SUM OF INPUTS
9800
9840 + 0.75 X(l)
-40 + 0.25 X(l)
-200 + 1.00 X(3)
+ 1.0 X(5)
This is a set of 4 simultaneous equations in 4 unknowns; it can
be solved by successive elimination of terms in the usual way. In
this case, it is easy to see that:
X(3) = 9840 +
X(5) = -40 +
X(7) = -200 +
0.75 (9800)
0.25 (9800)
17190 + 2410
= 9800
= 17190
= 2410
= 19400
EXAMS was written to accommodate N equations in N
unknowns. In other words, the program solves the input/output
advective equations for an arbitrary number (N) of
compartments, any of which can receive, deliver, or exchange
advected flows with any or all of the other compartments in the
system. The N input/output equations are solved by a Gaussian
elimination algorithm (Stuart 1970:304-311). This algorithm is
a matrix solution of a normalized form of the input/output
equations. The matrix (AMAT) is loaded in three stages: First,
each value of WATINL is divided (normalized) by TOTIN, and
loaded on the (N+l) column of AMAT. Next, the coefficients on
the output (X) terms (which are always unity (1)) are loaded on
the diagonal of AMAT. Finally, the ADVPR values are entered
into AMAT with a row index given by ITOAD and a column
index given by JFRAD. (The system export terms (ITOAD = 0) are
not required for this part of the analysis.) Leaving aside Noname
Slough's benthic compartments, the coefficient matrix would be
(Table 4):
Table 4. Normalized coefficient matrix, advection equations
JFRAD WATFL
TOTIN
I
T
O
A
D
1
3
5
7
1.00
-0.75
-0.25
0.00
0.0
1.0
0.0
-1.0
0.0
0.0
1.0
-1.0
0.0
0.0
0.0
1.0
0.505155
0.507216
-0.002062
-0.010309
Exams Output Table 1. Partial advective structure of Noname
Slough.
The solution of this system of equations is:
23
-------
X(l) = 0.505155
X(3) = 0.886082
X(5) = 0.124227
X(7)= 1.000000
where "X" is now the fraction of TOTIN passing through each
compartment.
For each compartment, EXAMS computes the actual discharges
along each flow path as the product of X, TOTIN, and the
ADVPR for the pathway. This information is entered in a matrix
(WATFL) of flows among segments. The row and column
indices of WATFL are the same as those of AMAT, that is, the
WATFL indices give the source and destination compartment
number for each flow. Exports from the system (in liters per
hour) are entered into a vector (WATOUL) of exports from each
compartment. The exports from each (JFRAD) compartment are
computed as the product of X, TOTIN, and ADVPR for those
pathways having ITOAD = 0.
In the Noname Slough example, the discharges from segment
number 1 are:
WATFL(3,1) = (0.505155)(19400)(0.75) = 7350,
and
WATFL(5,1) = (0.505155)(19400)(0.25) = 2450.
The only non-zero outlet flow is
WATOUL(7) = (1.0)(19400)(1.0) = 19400.
The WATFL matrix and the WATOUL vector forthese sections
of Noname Slough are shown in Table 5. EXAMS' outputs
include a "transport profile" of the system, showing the advected
flows through each segment. EXAMS also computes a (local)
turnover or "water renewal" time (the volume/discharge ratio)
for each compartment. EXAMS' transport profile for Noname
Slough, as defined thus far, is given in Exams Output Table 2.
Table 5. Example WATFL matrix and WATOUL vector (L/h)
JFRAD
13 57
I
T
O
A
D
WATOUL
CHEMICAL: Unspecified Chemical
ECOSYSTEM: Noname Slough - Advection test data
TRANSPORT PROFILE OF ECOSYSTEM.
1
•f
3
5
1
0
7350
2450
0
L 0
0
0
0
17190
0
0
0
0
2410
0
0
0
0
0
19400
CP T* VOLUME SEDIMENT WATER FLOW SED. FLOW RESIDENCE TIME (DAYS)
Y (CUBIC M) MASS (KG) (CU. M/DAY) (KG/DAY) WATER SEDIMENTS
* COMP. TYPE: "L"=LITTORAL; "E"=(EPI) AND "H"=(HYPO)LIMNION; "B"=BENTHIC
Exams Output Table 2. Partial transport profile for Noname Slough.
2.3.1.3 Advective sediment transport
Sedimentary materials (detritus) can be produced and destroyed
biogenically within an aquatic ecosystem. Sediment transport
thus is not computed in EXAMS via the simple mass balance
constraints used to compute the transport of advected water
masses. EXAMS instead treats advected sediment as a
non-conservative substance whose transport is simply driven by
the hydrodynamics of the system. The point- (STFLO) and
non-point- (NPSFLO) external hydrologic inputs do contain
coupled sediment loads (STSED and NPSED, kg/h). These
variables are used to evaluate the chemical loadings (see Chapter
2.4); they do not enter the chemical transport equations.
EXAMS computes advective sediment transport as the product of
the rates of water transport and the sediment/water ratio
(SEDCOL, Eq. (2-21)) of the source compartments. WATFL
and WATOUL are used to load an analogous sediment flow
matrix (SEDFL) and export vector (SEDOUL), both having
units of kg sediment transported per hour. The equation for
advective sediment transport among compartments is (2-62):
SEDFL(i,j} = WATFL (i, j) x SEDCOL(j) (2-62)
and the equation for advected sediment export is
SEDO UL(j) = WATOUL(j)x.SEDCOL(j) (2-63)
These equations are not blindly executed for the entire system,
however: The execution of the sediment transport equations
(Eqs. (2-62) and (2-63)) is constrained by a series of special
conditions, which can be expressed as a set of 5 sediment
transport rules:
(1) An advected water mass leaving any water column
compartment carries an entrained sediment washload, unless
the flow enters a benthic compartment. A flow from the
24
-------
water column into a benthic sediment is an infiltration flow
that does not transport sediments.
(2) Benthic sediment ("B") compartments can always export
water across systemboundaries, and can advect waterto any
other compartment in the system.
(3) A benthic compartment can export sediment (in addition to
water) only when it occupies the sediment-water interface
(that is, the (J-l) compartment is not of TYPE "B").
(4) Sediment cannot be advected from a benthic compartment to
any element of the water column.
(5) When benthic compartments are not vertically adjacent
(actually, when their compartment numbers differ by 2 or
more), sediments can be advected from one to another (for
example, along a bedload transport path).
These sediment transport rules allow EXAMS to include sediment
washloads and bedloads, and seepage of groundwater both into
and out of the system. One additional system definition rule must
be observed, however, when a groundwater recharge is
portrayed: The groundwater flow path must include at least 2
vertical benthic sediment compartments. If this definition rule is
ignored, EXAMS will interpret the export flow leaving the benthic
compartment as a bedload (rule 3) rather than as a groundwater
recharge.
These concepts can be illustrated by expanding the definition of
Noname Slough. In the expanded definition of Figure 4, one
segment receives a groundwater input (SEEPS) of 0.2 cubic
meters/hour. The groundwater seep passes through benthic
compartment 6 and is advected into the overlying water (rule 2);
by rule 4 the water flow does not entrain a sediment flow. The
downstream segment (7) loses water to a groundwater recharge.
The infiltration flow is 2% of the total advected flow through
compartment 7 (ADVPR = 0.02). By rule 1, the infiltration does
not entrain the suspended sediments. The groundwater recharge
must be carried on from compartment 8 into compartment 9
before it can be exported from the system, however, or EXAMS
would interpret the export as a bedload (rule 3) rather than as a
flow of water only (rule 2). The bedload path (compartment 2 to
4 to 8), by rule 5, carries both water and sediments downstream.
The bedload export at compartment 8 is enabled under rule 3.
The washload (compartments 1 to 3, 5 to 7) moves through the
system under rule 1; EXAMS computes the downstream transport
of suspended sediment from Eq. (2-62).
i
I S
i \
•+ --¥ *
1
•*•
*| fi
1
^*
D |
1 s = I
"* "f
| 3k
Figure 4. Noname Slough bedload, washload, and
groundwater flows.
To complete this example, some additional properties of the
sediments must be specified. Let, therefore, Noname Slough
have a suspended sediment concentration (washload, SUSED) of
100 ppm, and stream-borne sediment loadings (STSED(l) and (3))
of 1.0 kg/h. (Note that this treatment imposes a mass balance on
suspended sediments.) The depth of the surficial sediments
(compartments 2, 4, 6, and 8) is 5 cm; each has a bulk density
(BULKD) of 1.2 g/cc, and a water content (PCTWA) of 180%.
Segment 9 is a 30 cm layer of sand with a bulk density of 1.95
g/cc, and a water content of 115%.
The bedload is, of course, another measurable property of the
Slough. Suppose, for example, the measured bedload leaving the
downstream segment of the Slough were 100 kg/h. The ADVPR
for the crossed infiltration and bedload transport through
compartment 8, and the upstream bedload inputs, can be
developed from this datum and an assumed solids balance for the
system.
The sediment/water ratio for the surficial sediments is 1.25
kg/liter (Eq. (2-22)); the water export associated with the
bedload is, therefore, (100)7(1.25) = 80 liters per hour = 0.08
cubic meters/h. The groundwater infiltration was given as 2% of
the available discharge passing through segment 7 (Figure 4).
The total segment 7 discharge is 19.4 cubic m/h (Table 5), plus
the groundwater seep entering via segment 6 (Figure ), or 19.6
cubic meters/hour. The 2% infiltration is therefore (0.02)(19.6)
= 0.392 cubic m/h, and the total flow through compartment 8 is
0.392 + 0.08 = 0.472 cubic m/h. The ADVPR for the throughput
of infiltrated water is therefore (0.392)7(0.472) = 0.83; the
ADVPR for the bedload is 0.17.
Finally, presuming the bedload originates in equal measure from
the influent flows to compartments 2 and 4, the bedload inflows
25
-------
to both segments are 0.04 cubic meters of water per hour
(STFLO(2) and (4)), with a parallel sediment load (STSED) of 50
kg/h. EXAMS' retrieval of the full advective specifications is
shown in Exams Output Table 3, and EXAMS' computed
advective "transport profile" is given in Exams Output Table 4.
Name of environment: Noname Slough - Advection Test Data
Total number of segments (KOUNT) = 9
Segment Number: 123456789
Segment "TYPE": LBLBLBLBB
Table 9. Input specifications -- advective transport field.
J FR AD 1
I TO AD 3
ADV PR 0.750
Path No.: 1
J FR AD 7
I TO AD 8
ADV PR 2.OOOE-02
Path No.: 6
J FR AD 8
I TO AD 9
ADV PR 0.830
Path No.: 11
4
8
.00
Exams Output Table 3. Full advective structure of Noname
Slough.
The corresponding members of the JTURB and ITURB vectors
specify the pair of compartments that are exchanging materials
via each dispersion pathway. For example, the 4th entry in the
vectors could be used to specify an exchange between
compartments 7 and 10, by setting JTURB(4) = 7, and ITURB(4)
= 10. Because dispersion, unlike advection, is a symmetrical
process, this pathway could also be specified as JTURB(4) =10
and ITURB(4) = 7. Boundary conditions (dispersive exchanges
with the external world) are specified by a 0 setting on either
vector. In other words, the vectors can specify a turbulent
exchange of, for example, compartment 10 (an embayment),
with an external reservoir, via either (JTURB = 10; ITURB = 0) or
(JTURB = 0; ITURB = 10). EXAMS computes the boundary
exchanges as a simple displacement of contaminated, by
chemical-free, water and sediments. Non-zero chemical
boundary conditions are loadings, and thus would interfere with
EXAMS' estimates of persistence (defined as the time to cleanse
the system after all loadings terminate) in Mode 1. Non-zero
(dispersive) chemical boundary conditions can if desired be
introduced via an artificial point-source or non-point-source
advective input, coupled with a symmetrical advective export
from the compartment, or via a DRFLD (see Chapter 2.4).
CHEMICAL: Unspecified Chemical
ECOSYSTEM: Noname Slough - Advective transport regime
TRANSPORT PROFILE OF ECOSYSTEM.
CP T* VOLUME SEDIMENT WATER FLOW SED. FLOW RESIDENCE TIME (DAYS)
Y (CUBIC M) MASS (KG) (CU. M/DAY) (KG/DAY) WATER SEDIMENTS
3L
4B
5L
6B
7L
8B
9B
* COMP. TYPE: "L"=LITTORAL; X1E"=(EPI) AND "H"= (HYPO) LIMNION; "B"=BENTHIC
Exams Output Table 4. Advective transport regime in Noname
Slough.
2.3.1.4 Dispersive transport
The mean advected flow is not the only process governing the
transport of synthetic chemicals in aquatic systems. Turbulence
and shear flow in rivers, for example, combine to generate a
wide spectrum of meso-scale advective processes. Similarly, in
stratified lakes exchange across the thermocline is driven by
molecular diffusion, wind-induced mixing, storm surges, and
internal waves and seiches. These meso-scale processes can
usually be described via a statistical summary (the dispersion
equation) of their effects on the average transport of dissolved
and entrained suspended substances (see, for example, Fischer
etal. 1979). EXAMS' environmental data base includes 5 vectors
that specify the direction (JTURB, ITURB), and strength (DSP,
XSTUR, CHARL) of dispersive transport pathways in an aquatic
system; dispersivity can be varied by month.
The conventional dispersion equation used in EXAMS describes
the rate of exchange of environmental volume across a boundary
between two compartments. EXAMS' formula is:
DSPx XSTUR
CHARL
(2-64)
In this equation, XSTUR is the cross-sectional area of the pathway
(in square meters), CHARL is the "characteristic length" of the
path (m), and DSP is the dispersion coefficient or eddy diffusivity
(square meters/hour). F is then a flow of environmental volume,
with dimensions of cubic meters per hour. Equation (2-64) is
homologous with the mathematics of simple Fickian molecular
diffusion. The dispersion equation, however, is a statistical
summary of the large-scale effects of meso-scale advective
processes; it is used when the meso-scale processes are so
complex or sporadic that a detailed treatment is intractable. The
net result of the meso-scale processes is similar to molecular
diffusion in that dissolved constituents are transported along the
gradients of concentration in the system. The apparent rate of
transport of materials from areas of high, to areas of low,
concentration is much faster than rates of molecular diffusion,
however. For this reason, the kinetic parameter in Eq. (2-64)
(DSP) is usually called a "dispersion coefficient," "turbulent
diffusivity," or "eddy diffusivity," rather than a "diffusion"
constant (Bird et al. 1960:629). The events described by
dispersion are not fully homologous with Fickian diffusion,
however, and the use of dispersion terms to depict chemical
transport in sediments requires careful adjustment for effects of
porosity, tortuosity, sorption, and ion exchange (Berner 1976).
26
-------
From the dispersion equation (2-64), EXAMS computes a flux of
water and sediments along the pathway specified by JTURB and
ITURB. The fluxes between compartments are added to the
advective flows in matrices WATFL and SEDFL, thereby
completing EXAMS' description of the system's internal flow
field. The boundary fluxes are added to the appropriate elements
of WATOUL and SEDOUL; the exchange brings in a
replacement volume of uncontaminated water and sediments.
The "characteristic length" (CHARL) of a pathway conventionally
represents the distance between compartment centers, measured
along the axis of the exchange. A single segment thus may have
several characteristic lengths, depending on the geometric
orientation of its linkages to adjoining segments. The
cross-sectional area (XSTUR) for the exchange path also depends
upon the orientation of the compartments. For a vertical
exchange, as for example transport across the thermocline of a
lake, XSTUR is usually the AREA of the hypolimnion
compartment. Longitudinal dispersion in a river conventionally
takes the flow cross section as XSTUR, however, and this does
not correspond to any value of AREA. Adherence to these
conventions is a responsibility of model construction. EXAMS
does not attempt to evaluate the geometry of the system, but
simply inserts the user's entries for CHARL and XSTUR into Eq.
(2-64).
Although EXAMS does not evaluate the orientation of the
dispersive exchange pathways, the program does adjust its
computations according to the nature (that is, the TYPE) of the
exchanging compartments. These adjustments are primarily
important for the exchanges across the benthic boundary layer,
because of the very different physical properties of the water
column and a sediment bed. In this case, a simple symmetric
exchange of environmental volumes would transport very
different sediment masses and volumes of water. EXAMS allows
for the possibility of biogenic production and decay of detrital
sedimentary materials, and thus generally does not impose
explicit internal mass-balance constraints on the transport of
sediments in the system. In this case, however, the massive
injection of bed sediments into the water column, with little or
no resettlement, would amount to a gross distortion of sediment
transport dynamics beyond the realm of biogenic possibility. The
simple dispersion equation (2-64) thus requires situational
adjustments. EXAMS therefore divides its dispersion
computations into 3 distinct cases: (1) dispersion between water
column compartments, (2) dispersion between benthic
compartments, and (3) dispersion between a benthic sediment
and the overlying water column.
2.3.1.4.1 Dispersion within the water column
The volumetric displacement of suspended sediments can almost
always be neglected, so the water volumes and environmental
volumes of water column compartments can be assumed not to
differ. For example, a 15,000 mg/L washload with a density of
1.5 g/cc would perturb this assumption by only 1%. EXAMS
therefore computes the exchange flow of water between
water-column compartments (in liters/hour) as:
FLOW=
CHARL
(2-65)
This value is added to the advective flows already in
WATFL(I,J) and WATFL(J,I). (I is the compartment number
held in ITURB; J is the compartment number held in JTURB.) The
dispersive exchange is equivalent to a symmetric pair of
advected (pseudo) flows between the compartments. If FLOW
is a boundary condition, it is added to WATOUL(J), where J is
the compartment number held by the non-zero member of the
(JTURB, ITURB) couple.
The SEDFL matrix is updated via
SEDFL(I,J) <- SEDFL(I,J) + (FLOW)x(SEDCOL(J)) and
SEDFL(J,I) <- SEDFL(J,I) + (FLOW)x(SEDCOL(I));
the SEDOUL vector is updated via
SEDOUL(J) <- SEDOUL(J) + (FLOW)x(SEDCOL(J))
(SEDCOL is the sediment/water ratio, Eq. (2-22)). In this case,
sediment mass need not be conserved. When the exchanging
compartments have differing concentrations of suspended
sediments, EXAMS permits a net flow of sediments along the
concentration gradient. EXAMS assumes thatbiogenic production
and decay of detrital materials within the compartments serves
to maintain the gradient.
EXAMS computes lateral, vertical, and horizontal dispersion via
this procedure. The equations thus account for several rather
different processes. The effects of shear flow in rivers are
computed via a "longitudinal dispersion coefficient."
Depending upon the geometry and slope of the channel, riverine
longitudinal dispersion coefficients can vary from 2700 (Yuma
Mesa A Canal (Schuster 1965)) to 5.4x 106 (Missouri Rivernear
Blair, Nebraska; (Yotsukura et al. 1970)) square meters per hour
(cited from Fischer et al. 1979:126). Some small lakes develop
nearly uniform vertical density gradients that inhibit vertical
exchange, while allowing rapid lateral dispersion, at all depths
in the lake. Usually, however, the most important barrier to
vertical transport in lakes is a localized region (the thermocline
or metalimnion) with a steep temperature (density) gradient. The
exchange of epilimnetic and hypolimnetic water masses is driven
by wind-induced eddies, storm surges, and internal seiches (see,
for example, Wetzel 1975:89-122). The net effect of these
processes can usually be summarized via a dispersion equation.
For example, Snodgrass (1977) used a "vertical diffusivity
coefficient" to "integrate the effects of molecular diffusion, eddy
27
-------
diffusion, internal waves, seiches, standing waves, [and]
hypolimnetic entrainment... into a net transport process" across
the thermocline of Lake Ontario. The average DSP during the
stratified period (April to November) ranged from 1.0 to 4.1
square meters per day, over 6 years of measurements. A useful
summary of the observed values of dispersion coefficients can
be found in (Schnoor et al. 1987).
2.3.1.4.2 Dispersion within the bottom sediments
In some cases, exchanges among benthic compartments require
adjustment for strongly differing properties of the exchanging
segments. The surficial sediments of Noname Slough (Figure 4)
are laterally homogeneous, for example, but the deeper sandy
layer (compartment 9) differs substantially from the surficial
layers in bulk density, water content, and organic carbon content.
The sediment/water ratios of these layers can be compared by
inserting their water contents (PCTWA) into Eq. (2-22). In the
surficial sediments, the given water content is 180%, and
SEDCOL= 1.25 kg sediment per liter of water. The sandy layer
has a water content of 115%; its SEDCOL (6.67 kg/liter) is 5
times that of the surficial layers. Dispersion between benthic
compartments therefore must allow for exchanges between
segments of very different physical properties.
Furthermore, the water volume and the environmental volume of
a benthic sediment are by no means the same. The surficial
sediments of Noname Slough, to continue the example, have a
given bulk density of 1.2 g/cc. The volumetric (liter/liter) water
content ("porosity") can be computed via Eq. (2-21); the
surficial sediments contain only 0.53 liters of water per liter of
environmental volume. The porosity of the sandy layer (bulk
density 1.95 g/cc) is only 0.25 L/L. Equation(2-65) thus cannot
be used for a direct computation of dispersive transport of a
synthetic chemical between benthic sediment compartments.
The distribution of chemicals within lacustrine and marine
sediments has been successfully modeled via a vertical
one-dimensional treatment of transport and chemical dynamics
in this subsystem (Berner 1976, Imboden and Lerman 1978,
Jones and Bowser 1978). These one-dimensional ("diagenetic")
equations include the usual dispersive, advective, and reaction
terms. The advective terms in these equations are often used to
describe the net deposition of sedimentary materials, and the
effective vertical flow of interstitial waters produced by
compaction of the deposits. EXAMS precludes deposition and
permanent burial of synthetic organic chemicals as inappropriate
to an evaluative model, so its advective terms (Chapter 2.3.1.3)
represent ground-water flows and, where appropriate, irrigation
of benthic deposits by burrowing organisms.
The activities of burrowing organisms (bioturbation), physical
disturbance by demersal fishes, and intermittent strong water
turbulence tend to physically mix solids deposited on the
sediment surface to appreciable depths. These actively mixed
zones generally extend from about 2, to as deep as 50,
centimeters in natural systems (Jones and Bowser 1978 and
references therein). This physical reworking modifies observed
concentration profiles in sediments, and has led Schink and
Guinasso (1977) to propose the use of explicit solids mixing
terms in the one-dimensional treatment of early diagenesis in
sediments.
EXAMS makes use of a compartmentalized realization of these
one-dimensional equations, but does not permit explicit mixing
of sediment solids between benthic compartments. The depth of
the sediment compartments used to describe a system is taken to
be a depth through which the sediment can be regarded as
"well-mixed." The mixing of solids is thus implicitly
incorporated into the specifications of the structure of the
system, rather than as direct terms in the simulation equations.
In effect, therefore, EXAMS assumes that vertical concentration
gradients within the benthic compartments do not greatly disturb
the results of its evaluations.
There is some experimental evidence that the speed of internal
mixing processes in surficial sediments is sufficiently rapid to
justify EXAMS' discretized treatment. For example, McCall and
Fisher ((1977), quoted from (Jones and Bowser 1978)) have
shown that typical population densities of tubificid oligochaetes
can completely rework the top 5 cm of sediments every 2 weeks
in a laboratory setting. Vanderborght and Wollast (1977) found
that the upper 3 cm of marine (North Sea) sediments exhibited
an internal dispersion coefficient of 1x10 4 cmVs; this value
implies a reworking time of only 25 hours.
When a dispersion term (lateral or vertical) is specified for
exchange between benthic compartments, EXAMS computes a
symmetrical exchange of water (only) between the
compartments. This exchange flow is used to update the
WATFL matrix. Generally the input dispersion coefficient in
such a case should be corrected only for tortuosity; the program
itself corrects for the average porosity of the 2 compartments
involved, and for sorption of the chemical to solid phases.
EXAMS imposes no limitations on the magnitude of DSP.
Irrigation of deep sediment zones by burrowing organisms can
thus be represented via dispersion terms, if desired. A boundary
condition for a bottom sediment compartment is computed in
much the same way, except, of course, the porosity of the
specified compartment is the only datum available for correction
of the nominal DSP.
2.3.1.4.3 Exchanges between bed sediments and overlying
waters
Capture of organic chemicals by sediment beds can occur via
several processes. A dissolved phase can sorb directly to the
surface of the bed, with the sorbed material being then subducted
28
-------
into the bed via bioturbation. Irrigation of the sediments by
tube-dwelling animals can directly entrain a flow of
contaminated water through the bed; the sediment solids will
then tend to strip chemical from the water flow. Filter-feeding
organisms can aggregate compounds sorbed with suspended fine
particles and add material to the bed, as may the sequential
deposition, internal mixing, and scour events characteristic of
riverine systems. In lakes and oceans, sediment "bursting"
(Heathershaw 1974) results in frequent saltation of bed solids,
leading to sorption/desorption events and entrapment of free
boundary waters in the redeposited sediment matrix.
Although direct sorption/desorption to the sediment surface is a
continuous process, many of the interactions between the water
column and benthic sediments are highly intermittent. For
example, Heathershaw (1976) has estimated that, in well-mixed
areas of the Irish Sea, as much as 70% of the Reynolds stress in
the benthic boundary layer results from events occupying only
5% of the total time of record. Interactions mediated by the biota
are presumably also intermittent and highly variable in their
intensity. The most practical and efficient means of representing
this array of interactions between the water column and benthic
sediments is to use a statistical summary of their macro-scale
effects, that is, a dispersion equation (Berner 1976). This
strategy was adopted for EXAMS.
A dispersive exchange between a water column (L, E, or H) and
a benthic (B) compartment is described to EXAMS via
specification of a characteristic length (CHARL), cross-sectional
area (XSTUR), and dispersion coefficient (DSP) for the water
column-benthic element exchange pathway. The volumetric
exchange given by FLOW (Eq.(2-65)) can be regarded
(heuristically) as the saltation of a unit volume of the bed
(containing water and solids), followed by equilibration with the
water column and resettlement on the bed. EXAMS thus separates
the rate of exchange of environmental volume given by
(DSP)(XSTUR)/(CHARL) into distinct water and solids exchange
components. The porosity of the benthic sediment is coupled to
the dispersion equation to give a water-exchange term, via the
expression:
WATYOL (DSP)x(XSTUR)
VOL
CHARL
This water flow term (units of liters/hour) is then used to update
symmetric locations in the WATFL matrix, giving an apparent
rate of exchange of fluids between the water column and the
interstitial pore waters of the benthic sediment compartment.
Chemical transport can then be computed by treating these water
flows as simple carriers for the dissolved fraction resident on
either side of the benthic boundary layer.
In some cases, exchanges across the benthic boundary layer can
be treated as being driven by gradients in dissolved chemical
concentrations alone. Many synthetic organic chemicals,
however, have very high partition coefficients, so that sorption
onto the surface of the bed followed by bioturbational
subduction is probably a significant mechanism of chemical
transport for these compounds. The remaining exchange volume
is therefore taken to represent a direct interaction of the bed
solids with the water column compartment. First, an apparent
"resuspension" or "bursting" term for the exposure of the bed
solids to the water column is computed as the product of the
sedimentwater ratio (SEDCOL) of the benthic zone and the
fluid exchange rate. The SEDFL matrix is thus updated by an
apparent flow of bed sediments (TEMSED, units kilogram/hour)
into the water column via the expression:
TEMSED - SSDCCL x
VOL
CHARL
(2-66)
A naive "solids balance" now requires that an equal mass of
sediment resettle on the bed, in order to maintain the notion of
a stable (steady-state) bed thickness. The transport of a chemical
across the benthic boundary layer could then be computed by
regardingthe solid phase as asimple carrier of sorbed chemicals,
using sorbed concentrations (mg/kg solids) on either side of the
boundary.
The foregoing is, for the most part, conventional compartment
modeling. Still, because a multitude of processes have been
summarized in a single kinetic expression, a careful independent
trial of the approach seemed warranted. Although a number of
experimental tests of the equations can be imagined, initial tests
were conducted by comparing the output from the EXAMS
program to a example situation constructed on theoretical
grounds alone. This test was conducted on a reduced subsystem
of the Noname Slough ecosystem. Consider, for example, the
vertical segment of Noname Slough that includes a 2m water
column underlain by a 5 cm mud deposit and a 30 cm layer of
sand (Figure 4). If the transport characteristics of this subsystem
are redefined to eliminate the bedload and groundwater
infiltration, the dispersion equation can be used to describe
vertical movement of a chemical in the subsystem.
Retaining the physical sediment characteristics (BULKD, PCTWA)
developed in Chapter 2.3.1.3, the sorptive properties of the
sediments must now also be specified. For the example, let the
organic carbon content of the washload be 2% (FROC = 0.02),
that of the mud layer 5%, and let the sand contain only 0.1%
organic carbon. The characteristic length (CHARL) and exchange
cross section (XSTUR) for the dispersive exchanges can in this
instance be developed directly from the geometry of the system.
These values, along with the kinetic exchange coefficients (DSP)
are given in Exams Output Table 5. Vanderborght and Wollast
29
-------
(1977), working with rhodamine dye in North Sea sediments,
found that physical turbulence induced benthic boundary layer
exchange coefficients of 2.9x10 6 to 6.2x 4 cmVs. The test DSP
for Noname Slough were selected from this range of values.
Name of environment: Noname Slough - Dispersion Equation Test Data
Total number of segments (KOUNT) = 9
Segment Number: 123456789
Segment "TYPE": LBLBLBLBB
Table 10.13. Mean dispersive transport field.
CHEMICAL: Unreactive neutral compound -- Koc = 3.E5
ECOSYSTEM: Noname Slough — Dispersion equation test data
J TURB
I TURB
XS TUR m2
CHARL m
DSP m2/hr*
Path No.:
Exams Output Table 5. Dispersive interconections - test data.
In order to test the dispersive transport algorithm in isolation, the
test chemical can be specified as a completely unreactive,
non-volatile neutral compound, with a Koc of 3*105.
When the test chemical is introduced into the system, an
equilibrium state could be rapidly generated by suspending the
benthic layers in the water column, and thoroughly agitating the
mixture. At equilibrium, this 4-phase system would exhibit a
single aqueous concentration, and sorbed-phase concentrations
differing as the ratio of their partition coefficients (i.e., in
proportion with the organic carbon contents of the sediment
phases). If the bed solids were then allowed to resettle, the
simple separation of the materials would not result in any change
in the dissolved (mg/L of water) or sorbed (mg/kg dry solids)
concentrations. The environmental concentrations (mg/Lof total
environmental volume) could, of course, differ between the
water column and the (restored) bed layers.
This situation also applies to an open system in a dynamic
equilibrium or steady state. Suppose, for example, that water
contaminated to a level of 1 (J.g/L (ppb) with the test chemical
flows continuously through the Noname Slough subsystem. The
dispersion equation controls only the rate of exchange of
chemical between the water column and the benthic subsystem.
At steady state, no concentration gradient remains to drive
further net chemical exchange. In this instance of an unreactive
compound, the final dynamic equilibrium is equivalent to the
static case.
The resulting computer output is shown in Exams Output Table
6. The computations lead to a steady-state end point of equal
sorbed concentrations for the washload and the mud layer, rather
than equal dissolved concentrations. Although the concentration
distribution between the bed sand and mud layers follows the
theoretical exp ectations, this result for the washload and the mud
layer is exactly opposite the expected outcome of the test.
**** TOXICANT CONCENTRATIONS ****
TOTAL DISSOLVED SEDIMENTS BIOTA
MG/* MG/L MG/KG UG/G
* TOTAL CONCENTRATION AS MG/L IN WATER COLUMN, AS MG/KG IN SEDIMENTS.
Exams Output Table 6. Test of dispersive exchange equation.
Although the computed outcome could be rationalized in many
ways, the fact remains that the program output did not reflect the
assumptions and reasoning used to build the model; a naive
"sediment balance" failed to provide an accurate simulation of
chemical behavior and thus required revision.
Closer consideration of the heuristic logical structure used to
adapt the dispersion equation to this application suggested an
appropriate revision. At least nominally, in the case under
consideration the suspended and bedded solids need never even
come in contact, so there is no plausible way for sorbed chemical
to experience a direct concentration gradient. The capture of
chemical by the bed is driven by fluid exchange, and by saltation
of the bed solids, which allows them to interact directly with
chemical dissolved in the water column. The root of the problem
thus seems to lie with the difference in organic carbon content
(i.e., partition coefficient) of the suspended and bedded
sediments.
The sorbed concentrations computed for each compartment are
based on properties of the sediments specified for the
compartment, rather than on the properties of a saltatory
transient. Therefore, it might serve the case to simply adjust
chemical transport according to the ratio of the partition
coefficients (Kp). In this way, for example, if the Kp of the bed
sediment were 5 times that of the suspended sediment, the rate
of capture of chemical by the bed would be proportionally larger
than that suggested by Eq. (2-66) simply coupled to the
concentration on the washload, and conversely.
Although organic carbon content governs the ability of a
sediment to sorb neutral (uncharged) molecules, anorganic acid
or base will occur as both neutral and charged species, with a
speciation governed by the pH of the system. The sorptive
capacity of a sediment may thus depend on its carbon content,
ion exchange capacity, and the pH of the compartment. Thus
when necessary, the relative sorptive capacity of sediment
phases can be computed via the distribution coefficients (a) and
sediment:water ratios (SEDCOL) of the water-column and
30
-------
benthic compartments specified for an exchange pathway. Given
a(d,w) and a(d,b) as the total dissolved fraction in the water
column (w) and benthic (b) compartments, respectively, and
a(s,w) and a(s,b) as the sediment-sorbed fractions, the return
"flow" (SEDFL) of suspended sediment across the benthic
boundary layer to the sediment compartment can be computed
as:
TEMSED-x
a(d,w) xSEDCOL(w)
a(s, w)
z(d,b)-x.$EDCOL(b)
This calculation yields the ratio of the sorptive capacity (overall
partition coefficient, Kp) of the benthic sediment, to that of the
washload. Thus for example, if the benthic sediments have a Kp
twice that of the washload, the rate of capture of chemical by the
bed (that is, the apparent pseudo-settlement rate of saltatory bed
materials) must occur at twice the rate that would be inferred
directly from the properties of the washload itself.
The effect of this revision can now be tested by execution of the
test case described above; the results are given in Exams Output
Table 7. The consequences of the calculation are now quite
satisfactory: The calculated dissolved concentrations are
uniformly 0.625 ppb, and the sorbed concentrations reflect the
differences in organic carbon content of the system sediments.
Note, however, that this computation is valid within the context
of the EXAMS program only because sediment transport is not an
explicit state variable in the program, i.e., the SEDFL matrix is
not a description of sediment transport per se, but merely a
computational device for computing the exchange of synthetic
organic chemicals across the benthic boundary layer. "Solids
balances" and stable bed thicknesses are the responsibility of the
user when assembling an environmental description to drive the
program; EXAMS simply processes these data (via the SEDFL
matrix) to arrive at a proper characterization of chemical
transport in the system.
2.3.1.5 Transport of synthetic organic chemicals
EXAMS uses the transport field defined by WATFL, SEDFL,
WATOUL, and SEDOUL to compute first-order coefficients
that describe transportof chemical through the ecosystem. These
coefficients describe the export of chemicals from the system,
and the internal transport of the compound among the
compartments used to define different physical sectors of the
system.
The WATOUL and SEDOUL vectors are used to compute
exports from each compartment. A value of EXAMS' internal
vector EXPOKL, with dimensions of (liters/hour), is calculated
for each compartment as
EXPOKL = («(29)+a(31)+a(32))xWATOUL +
«(30)xSEDOUL/SEDCOL
where a(29), (30), (31), and (32) are the total fractions of the
chemical in dissolved, sediment-sorbed, DOC-complexed, and
planktonic biosorbed states, respectively.
Pollutants also leave each compartment via water and sediment
flow pathways that connect the compartment to other sectors of
the ecosystem. From the perspective of the donor compartment,
these flows can be represented as a pure export of chemical
across the boundaries of the compartment, despite the fact that
the material may be returned from the receptor compartment at
some later time. Each row of the WATFL and SEDFL matrices
gives the flows of water and sediments leaving a compartment,
and the row sums are the total local (that is, within-system)
outflows from the compartments. For each compartment, EXAMS
computes a value of an internal variable (INTOUL, liters/hour)
analogous to the export vector EXPOKL. This computation can
be represented as
CHEMICAL: Unreactive neutral compound — Koc = 3.E5
ECOSYSTEM: Noname Slough -- Dispersion equation test data
DISTRIBUTION OF CHEMICAL AT STEADY STATE: IN THE WATER COLUMN:
COMP STEADY-STATE RESIDENT MASS
2
G/M KILOS %
**** TOXICANT CONCENTRATIONS ****
TOTAL DISSOLVED SEDIMENTS BIOTA
MG/* MG/L MG/KG UG/G
TOTAL CONCENTRATION AS MG/L IN WATER COLUMN, AS MG/KG IN SEDIMENTS.
Exams Output Table 7. Test of modified dispersive exchange
procedure.
INTOUL = (
«(30)xSUMSED/SEDCOL
a(32))xSUMWAT
where SUMWAT and SUMSED are the appropriate row sums
in the WATFL and SEDFL matrices, and SEDCOL is the
sediment:water ratio for the donor compartment.
These transport terms (EXPOKL and INTOUL) must now be
converted to pseudo-first-order coefficients that express their
effect on the concentration of chemical in the source (donor)
compartment. This coefficient (CONOUL, dimensions /hour) is
computed as:
CONOUL = (EXPOKL + INTOUL)/WATVOL
where WATVOL is the volume of water (liters) present in the
donor compartment. This coefficient (CONOUL) is the
31
-------
contribution of transport processes to the overall loss constant
"K"ofEq. (2-1).
Intra-system transport also imposes chemical loadings on the
compartments receiving the contaminated flows (factor "Li" in
Eq. (2-1)). EXAMS combines the WATFL and SEDFL matrices
into a new matrix (INTINL, dimensions liters/hour) needed for
computing the internal loadings (Li) on each compartment. Each
element of INTINL is first calculated from the sum of
corresponding elements in WATFL and SEDFL via the
expression:
(a(29)+a(31)+a(32))x WATFL + a(30)xSEDFL/SEDCOL
using values of a, and SEDCOL for the donor compartment.
Multiplication of each element of INTINL in a given row, by the
concentration of chemical in the donor compartment given by the
successive column indices of the row, yields the magnitude of
the loadings passed to the receptor compartment by each of the
donors. The sum of these loadings is the internal load (Li, mg/h)
on the receiving compartment. Li must also be divided by the
aqueous volume of the receptor (V in Eq. (2-1)), in order to
express the effect of the internal loadings on the concentration
of chemicals in the receptor. EXAMS therefore divides each
elementof INTINL by the volume of the receiving compartment:
INTINL <- INTINL/WATVOL
and retains the resulting matrix of pseudo-first-order
(dimensions/h) coefficients for subsequent use in its steady-state
and kinetic simulation equations (Chapter 2.5).
References for Chapter 2.3.1.
Berner, R. A. 1976. The benthic boundary layer from the
viewpoint of a geochemist. Pages 33-55 in I. N. McCave,
editor. The Benthic Boundary Layer. Plenum Press, New
York and London.
Bird, R. B., W. E. Stewart, and E. N. Lightfoot. 1960. Transport
Phenomena. John Wiley and Sons, Inc., New York.
Fischer, H. B., E. J. List, R. C. Y. Koh, J. Imberger, and N. H.
Brooks. 1979. Mixing in Inland and Coastal Waters.
Academic Press, New York.
Heathershaw, A. D. 1974. "Bursting" phenomena in the sea.
Nature 248:394-395.
Heathershaw, A. D. 1976. Measurements of turbulence in the
Irish Sea benthic boundary layer. Pages 11-31 in I. N.
McCave, editor. The Benthic Boundary Layer. Plenum
Press, New York and London.
Imboden, D. M., and A. Lerman. 1978. Chemical models of
lakes. Pages 341-356 in A. Lerman, editor. Lakes-
Chemistry, Geology, Physics. Springer-Verlag, New York.
Jones, B. F., and C. J. Bowser. 1978. The mineralogy and
related chemistry of lake sediments. Pages 179-235 in A.
Lerman, editor. Lakes-Chemistry, Geology, and Physics.
Springer-Verlag, New York.
McCall, P. L., and J. B. Fisher. 1977. Vertical transport of
sediment solids by Tubifex tubifex (Oligochaeta) (abstract).
in 20th Conf. Great Lakes Res.
Schink, D. R., and N. L. Guinasso, Jr. 1977. Effects of
bioturbation on sediment-seawater interaction. Marine
Geology 23:133-154.
Schnoor, J. L., C. Sato, D. McKetchnie, and D. Sahoo. 1987.
Processes, Coefficients, and Models for Simulating Toxic
Organics and Heavy Metals in Surface Waters. EPA/600/3-
87/015, U.S. Environmental Protections Agency, Office of
Research and Development, Athens, Georgia.
Schuster, J. C. 1965. Canal discharge measurements with
radioisotopes. Journal of the Hydraulics Division, Proc.
ASCE 91:101-124.
Snodgrass, W. J. 1977. Relationship of vertical transport across
the thermocline to oxygen and phosphorus regimes: Lake
Ontario as a prototype. Pages 179-202 in R. J. Gibbs, editor.
Transport Processes in Lakes and Oceans. Plenum Press,
New York and London.
Stuart, F. 1970. FORTRAN Programming. John Wiley and
Sons, Inc., New York.
Vanderborght, J. P., and R. Wollast. 1977. Mass transfer
properties in sediments near the benthic boundary layer.
Pages 209-219 in J. C. J. Nihoul, editor. Bottom
Turbulence. Proceedings of the 8th International Liege
Colloquium on Ocean Hydrodynamics. Elsevier Scientific
Publishing Co., Amsterdam.
Wetzel, R. G. 1975. Limnology. W.B. Saunders Co.,
Philadelphia.
Yotsukura, N., H. B. Fischer, and W. W. Sayre. 1970.
Measurement of Mixing Characteristics of the Missouri
Riverbetween Sioux City, Iowa and Plattsmouth, Nebraska.
U.S. Geological Survey Water-Supply Paper 1899-G.
2.3.2 Volatilization
EXAMS uses a two-resistance model to compute transport across
the air-water interface. EXAMS calculates the rate of interphase
transport by computing the sequential resistance to movement
through an aqueous and a gaseous "film" at the air-water
interface. Although originally developed for industrial
applications (Whitman 1923), these models have been adapted
to environmental problems (Liss 1973, Liss and Slater 1974,
MackayandLeinonen 1975, Mackay 1978, Burns 1982, 1985).
Whitman (1923) visualized the aquatic interface as "stagnant
films" of air and water, bounded by well-mixed bulk phases on
either side of the interface. Figure 6 illustrates the conceptual
model of chemical transport across the air-water interface.
32
-------
Specifically, inFigure6, Cl is the concentration of contaminant
(mol-m 3) in the bulk water phase; Pg is its partial pressure
(atmospheres) in the bulk air above the interface; Csl is its aque-
ous concentration at the interface; and Psg is its partial pressure
INCREASING C AND P
CONNECTIVE
TRANSPORT
LIQUID FILM
*
Cs/ MOLECULAR
DIFFUSION
WATER
CONNECTIVE
TRANSPORT
Figure 6. Whitman (1923) two-resistance or two-film model of a
gas-liquid interface (after Liss and Slater 1974).
on the atmospheric side of the interface. The flux of compound
F (mol-m 2-h ') through the aqueous film can be described using
Pick's first law
dC
F= D —
dZ
(2-67)
where D is the aqueous diffusion constant of the chemical in the
film (m2 -h '), C is the concentration of unionized, unsorbed
compound (mol-m 3), and dC/dZ is the concentration gradient in
the film.
Similarly, the flux of chemical through the stagnant atmospheric
layer is
F =
D dP
WdZ
(2-68)
coefficient," and "piston velocity." The flux of gas F through
the stagnant layers is then
F = k dC
in the fiquid phase, and
(2-69)
(2-70)
in the gas phase, where dC is the concentration difference across
the film, dP is the partial pressure difference across the film, and
kf:) — D(yz(.j, Zy the film thicknesses. The reciprocals (r — k'1) of
the exchange constants give the transport resistances r of the
aquatic and atmospheric films.
Given negligible dynamic storage capacity in the films and
consequent steady-state transport of gas through the interface1,
the mass fluxes through the stagnant layers of air and water (see
Figure 6) mustbe the same. Therefore, F^Fg, and we can set (2-
69) equal to (2-70) and substitute the concentration differences
(Figure 6), obtaining
k
k,(C., - (7,1 = -*L(P- - P..\ (2-/1)
where kt is the liquid phase, and kg the gas phase, exchange
coefficient. The partitioning of the exchanging (unionized)
substance across the air-water interface is given by Henry' s Law:
Psg = H Csi, where His the Henry's Law constant (atm-mVmol).
Using Henry's Law, Csl and Psg can be eliminated from (2-71),
yielding an equation relating the transport flux F to the bulk
phase concentrations only:
\H }
where Kb the transport conductance, is defined by
1 1
RT
K, k, Hka
(2-72)
(2-73)
where D is now the diffusion constant of the compound in the
air layer, dP/dZ is the partial pressure (atmospheres) gradient in
the film, R is the gas constant (8.206xlO"5 m3-atm/mol-K), and
T is Kelvin temperature.
Environmental gas exchange processes are often formulated in
terms of an exchange constant "k", that is, as & conductivity (the
inverse of the film transport resistance). The exchange constant
has dimensions of velocity (m -h '); it is also known as the "mass
transfer coefficient," "permeability coefficient," "adsorption/exit
The total resistance to transfer of a gas across the air-water
interface (Rt = K{1) is thus the sum of the series of resistances in
the liquid (k{1) and gas (RT/(H kgj) phases of the interface. The
two-resistance model assumes that transport resistance at the
interface can be neglected; although generally this is the case,
under very turbulent conditions or in the presence of
surface-active contaminants (surfactants) this assumption is less
tenable (Birdetal. 1960:652)).
Note that this is a microscopic, rather than a macroscopic, assumption—that
is, these extremely thin interfacial films are merely assumed to maintain a rapid equilibrium
with the adjacent, themselves fully dynamic, bulk layers.
33
-------
The "two-film" picture of the air-water interface (Figure 6) is
physically inexact, although events at molecular scales
undoubtedly affect interphase transport. Both atmospheric and
hydrodynamic eddy turbulence often extend to the air-water
interface, however, so the idea of a discontinuous transition from
turbulent flow to a stagnant film near the air-water interface
cannot be seriously entertained. The supposition that the
interface is composed of stable, uniform films is still less
plausible. The two-resistance models do, however, explicitly
recognize that transport resistance occurs both in the aqueous
and in the atmospheric regions of the air-water interface. There
is ample precedent (see, for example, Fischer et al. 1979) for
amalgamating the effects of intermittent turbulent and advective
transport events occurring in the interfacial zone, into an
empirical dispersion coefficient D or exchange constant k.
Furthermore, predictions derived from two-resistance models
usually differ very little from the predictions of more complex
(e.g., surface-renewal theory) models (Danckwerts 1970).
Laboratory studies of the volatilization of chlorohydrocarbons
from dilute aqueous solution (Billing et al. 1975, Billing 1977)
have provided further evidence that two-resistance models are
good predictors of fluxes of organic chemicals across the
air-water interface.
A two-resistance model has been used to compute the transport
of atmospheric contaminants (sulfur dioxide, carbon
tetrachloride, etc.) into the world ocean (Liss and Slater 1974).
Such an application requires a knowledge of Pg, the bulk
atmospheric partial pressure. Lacking a measured value of Pg, it
can in some circumstances (general circulation models of the
atmosphere, plume (stack gases) dispersion models) be
calculated and coupled to a two-resistance interphase transport
model. Usually, however, bulk atmospheric transport of
synthetic organic s volatilized from aquatic systems rapidly
removes them from the vicinity of the interface, so that Pg can be
neglected (Mackay and Leinonen 1975, Mackay 1978). This
approach was adopted for EXAMS, resulting in a simplification
of (2-71), yielding
F=-K1C1 (2-74)
Because EXAMS was designed, among other things, for
pre-manufacture evaluation of new chemicals and pesticides,
there was, in any case, little likelihood that measured values of
Pg would be readily available for use in the model. EXAMS does
not entirely preclude atmospheric inputs, however. EXAMS'
loading functions allow for entry of spray drift (DRFLD), and for
rain-out (PCPLD) loadings, where these can be computed.
For use in EXAMS, (2-74) must be rephrased to give the effect of
volatilization on the concentration of pollutant in each sector of
the ecosystem (compartment, segment) having an air-water
interface. EXAMS' concentration variable [C] is the total
concentration of pollutant in units of mg/Liter of aqueous
volume. Multiplication of both sides of (2-74) by MWT A/V,
where MWT is the gram molecular weight of the compound, A
is the area of the air-water interface (square meters), and Vis the
volume (m3) of the compartment, gives
fi\n\ A
-K,—a,\C\ (2-75)
dt
The factor a; is the fraction of the total pollutant concentration
[C] present as a volatilizable (unionized, unsorbed) chemical
species. The group K^a/Vis a pseudo-first-order rate constant
with units h"1. This rate constant is computed by EXAMS in a
specific module, and the volatilization contribution is then added
to the total pseudo-first-order rate constants used internally by
EXAMS to simulate pollutant dynamics within environmentally
stable time segments.
EXAMS' two-resistance model reduces at this point to a
computation of the transport resistances (or exchange constants)
of chemical pollutants in the liquid (&,"') and atmospheric (RT/(H
k^) zones of the air-water interface. These transport resistances
are governed by the intensity and duration of physical turbulence
and convective motions in the interface zones. Expanding upon
a suggestion of Liss and Slater (1974), EXAMS indexes the
transport resistance of chemical pollutants against the exchange
properties of well-studied environmental substances: oxygen and
water vapor.
The transport of oxygen across the air-water interface of aquatic
systems (reaeration) has been studied for many years. Oxygen
transport is controlled by resistance in the liquid phase (Liss
1973). The exchange constant for dissolved oxygen thus
provides a measure of turbulence on the liquid side of the
air-water interface. Water itself, as the solvent for chemical
pollutants in aquatic systems, has no transport resistance in the
liquid phase of the interface: its transport is controlled by events
in the atmospheric zone of the interface.
Given exchange constants for oxygen and for water vapor (note
that the latter is not the same as the evaporation rate), it remains
to index the transport resistances of the pollutant to those of the
environmental referents. Several indexing methods have been
proposed. Kinetic theory suggests that the ensemble molecular
kinetic energies KE of all chemicals present in a given zone of
the interface are the same, and thus, as KE = ± mv 3 , that average
molecular velocities in a multi-component mixture must be
distributed in proportion to the square root of the molecular
weights of the components.
EXAMS uses this method for relating the exchange constant for
water vapor to the vapor-phase volatilization resistance of
pollutants. The temperature of the vapor film is assumed to be
the same as the water temperature specified for the appropriate
aquatic compartment. EXAMS thus computes the vapor-phase
transport resistance Rg from the equation
34
-------
R. =
RT
(2-76)
where T is Kelvin temperature, Vv is the water vapor exchange
constant (piston velocity, m -h '), //the Henry's Law constant,
the molecular weight of water is taken as 18 g/mol, and MWT is
the molecular weight ofthe volatilizing chemical.
To arrive at the total transport resistance, we must also compute
the liquid-phase resistance in the interface zone. Reasoning from
the Stokes-Einstein equation, Tsivoglou (1967) suggested that
the (liquid-phase dominated) exchange constants for molecular
oxygen vs. the normal atmospheric gases (Kr, Ra, He, etc.) are
linearly related to their relative molecular diameters or,
equivalently, their molecular diffusion constants in water. A
literal interpretation ofthe Whitman "two-film" derivation gives
much the same result. In contrast, models based on
surface-renewal theories suggest that relative exchange constants
should vary as the square root of diffusivities (Danckwerts
1970:100). Dobbins (1964) constructed an elegant hybrid of film
and surface-renewal theory that collapses to a Whitman model
under quiescent conditions, and to a surface-renewal model
under more turbulent conditions. He also found, via laboratory
studies, that the appropriate root ofthe diffusivity ratio for the
nitrogen/helium gas pair tended from 0.985 to 0.648 with
increasing water turbulence, as expected from his theoretical
equations. Given the uncertainties in estimating or averaging
oxygen exchange constants, however, a full development ofthe
Dobbins model for inclusion in EXAMS has thus far seemed
unwarranted.
The molecular diffusivity of a new organic chemical is not often
known, although it can be estimated from other chemical
properties (Reid et al. 1977). The molecular weight of an organic
compound is almost always available, however, so EXAMS uses
Liss and Slater's (1974) molecular weight corrector as its default
technique for relating the liquid-phase transport resistance of a
pollutant to exchange constants of dissolved oxygen (EXAMS
input parameter KO2, a piston velocity for molecular oxygen).
The liquid-phase transport resistance Rt is then simply
1
(2-77)
where K02 is the oxygen (molecular weight 32) exchange
constant.
The total transport resistance ofthe pollutant is the simple sum
of the individual phase transport resistances, Rt + Rg. The
exchange constant of the contaminant (Kb the conductivity) is
the reciprocal of that sum:
1
Kl = ^ 7T <2-78)
Thus, EXAMS completes computation of the pseudo-first-order
volatilization rate constant as Kff.1AJV.
2.3.2.1 Chemical Data Entry
The chemical parameters governing volatilization of a pollutant
from aquatic systems can be entered into EXAMS' chemical data
base in several ways. The gram molecular weight ofthe pollutant
MWT (EXAMS parameter MWT) is required, for computation of
the vapor-phase transport index (2-76). The Henry's Law
constant (H, input parameter HENRY) can be loaded, however,
either as a single value of HENRY (atm-mVmol), or as a
function of temperature. When input parameter EH (input datum
EHEN) is loaded as a non-zero value, EXAMS computes the
Henry's Law constant at local temperatures T (TCEL) from the
relationship
1000^,
4.58(1+273.15)
(2-79)
When no data for the Henry's Law constant is available at run
time, but EXAMS detects the presence of a non-zero value ofthe
vapor pressure ofthe contaminant, EXAMS internally computes
the Henry's Law constant from the vapor pressure/solubility
ratio (Mackay and Wolkoff 1973, Mackay and Leinonen 1975).
If either the vapor pressure or the solubility of the compound
have been entered as functions of temperature, these data are
adjusted to local (TCEL) temperatures (via Eq. (2-80) and/or Eq.
(2-81)), prior to computation of HENRY.
= E4ER-CLQOQ x £f/¥?]/[4J8(rC£X+27315)] (2-80)
(2-81)
SOL=
Note that, although a simple (temperature invariant) pollutant
solubility is entered in units of mg/L (ppm), EXAMS expects
solubility as a function of temperature to be entered via the ideal
solubility law, that is, as the dependence of molar solubility on
temperature.
2.3.2.2 Exchange Constants for Water Vapor
The water vapor exchange constant (Vw, Eq. (2-76)) used to
compute the vapor-phase transport resistance of pollutants is not
itself a direct user input to EXAMS. Liss (1973), in a series of
wind-tunnel experiments, found the piston velocity of water
vapor to be a linear function of wind speed. EXAMS takes, as its
user input variable, the average wind speed at a height of 10 cm
above the water surface (input variable WIND, m -s '). The
exchange constant for water vapor (F^) is computed separately
for each compartment from these data. Liss' results can be
represented via a linear regression equation that includes the
shift in units from m -s ' for wind speed to m -h ' for the water
vapor exchange constant
35
-------
Vw = 0.1857 + 11.36 x WIND
(2-82)
KOI L = (KOI. 1100) • 1.Q24 (ra£-2
(2-83)
in which changes in wind velocity at 10 cm above the water
surface (WIND) account for 98.3% of the variance in the
exchange constant for water vapor (Vw, m -h ') over a range of
wind speeds (at 10 cm height) from 1.6 to 8.2 m -s '.
Wind speeds observed at other heights canbe converted to wind
speed at 10 cm via the usual assumption of a logarithmic wind
profile (Israelsen and Hansen 1962). Wind speeds U1 and U2 at
heights Zl and Z2 are related by
U,/U2 = log(Z/Zo) / log(Z/Zo)
where Zo is the "effective roughness height." The roughness
height is generally on the order of millimeters; wind
measurement heights can conveniently be expressed in mm in
order to achieve a vertical translation of an observed wind speed
datum. For example, many terrestrial USA weather stations
measure wind speeds at 18-20 feet (6 m) above ground level.
Wind speed at 10 cm can be estimated by multiplication of this
datum by (log 100)/(log 6000), that is, by reducing the
observation by a factor of 1.89. The standard observational
height for wind speed data in oceanographic investigations is 10
m, in this case requiring reduction of the data by a factor of 2, to
generate values of WIND for EXAMS. Wind speeds read by
EXAMS from a PRZM meteorological file are automatically
translated to 10 cm height.
2.3.2.3 Exchange Constants for Molecular Oxygen
Hydrodynamic turbulence near the air-water interface is
generated by a variety of mechanisms. In swiftly flowing streams
and rivers, bed shear stress on the moving waters generates eddy
turbulence that can keep the entire water column in a state of
constant agitation. Where rivers widen into coastal estuaries,
advection velocities decrease, but the motion of the tides tends
to maintain strong turbulence in the surface waters. In lakes and
in the open ocean, wind stress is a primary force producing
turbulent motions in the upper part of the water column. Wind
waves travel far beyond the storm systems producing them in the
largest lakes and in the oceans, and the great ocean currents and
upwelling zones generate upper water turbulence beyond that
attributable to the winds alone. In smaller lakes, wind stress may
be directly responsible for most of the hydrodynamic motion in
the system.
EXAMS requires an oxygen exchange constant as an input datum
for each compartment from which a pollutant can volatilize. The
input datum (KO2, cm-h ') is assumed to be the exchange
constant measured at 20 °C, or corrected to that temperature.
EXAMS uses the conventional engineering correction (equivalent
to an Arrhenius expression) for converting KO2 to the
temperature (TCEL) of each compartment (Kramer 1974)
(KO2 is also divided by 100 to convert cm-h ' to m -h ').
Reaeration rates can be measured in the field in a number of
ways, including tracer techniques (Tsivoglou et al. 1972) and
oxygen release into a nitrogen-sparged dome (Copeland and
Duffer 1964, Hall 1970). Lacking measured values, oxygen
exchange constants can in many instances be estimated from
other properties of the system. Kramer (1974) has briefly
reviewed the available predictive equations for estimating
oxygen exchange coefficients in streams and rivers. Most of
these contain terms for flow velocities and depth. Many also
include longitudinal dispersion coefficients, energy grade lines,
and channel widths. Although predictive equations have been
successfully used for riverine systems, generally these equations
significantly under-predict reaeration in estuarine systems.
Oxygen exchange constants in rivers are generally on the order
of 5 to 20 cm-h '. In estuaries, exchange constants of 4 to 25
cm-h ', and as large as 100 cm-h ', have been observed. Liss and
Slater (1974) estimated an average exchange constant for the
open sea of 20 cm-h '.
In lakes and ponds, reaeration may be primarily determined by
the local winds. Banks (1975, Banks and Herrera 1977) showed
that the effect of wind on reaeration rates can be separated into
two distinct zones. At wind speeds (at 10 m height) less than
about 5.5 m -s ', exchange constants correlate with the square
root of wind speed. At higher wind speeds, the exchange
constant increases as the square of the wind velocity. Banks
(1975) gives, for U< 5.5 m -s ',
(2-84)
(2-85)
and, for 5.5 m -s ' < U< 30 m -s ',
KL = 3.2 x 1CT7 U2
where KL is the oxygen exchange constant (in m -s ') and C/is
wind speed (m -s ') at 10 m above the water surface. Over a
range of wind speeds from 1 to 30 m -s ', the oxygen exchange
constant thus would change from 1.5 to 104 cm-h '. When the
oxygen piston velocity is not entered, EXAMS uses Eq. (2-84) or
Eq. (2-85) to calculate its value.
2.3.2.4 Validation and Uncertainty Studies
A number of reports on volatilization from natural water bodies
have been published. These studies provide good illustrations of
the general utility and level of reliability of the two-resistance
model developed for EXAMS.
36
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2.3.2.4.1 Radon in small lakes in the Canadian Shield
Emerson (1975) conducted an experimental investigation of the
loss of radon gas (Rn222) from small lakes in Canada's
Experimental Lakes Area (ELA). He reportedhis results in terms
of exchange constants for Rn gas; the "best estimate" was 0.16
to 0.40 m/d. Average wind velocities, measured 1 m above the
water surface, were about 1.5 m -s '. Summer epilimnion
temperatures in these lakes are about 20 °C (Schindler 1971).
Wind speed and temperature suffice, given the Henry's Law
constant for Rn, to derive an independent estimate of the Rn
exchange constant from EXAMS' two-resistance model.
EXAMS computes both a gas- and liquid-phase transport
resistance. Rn transport is usually controlled by the liquid-phase
resistance. Under a sufficiently stagnant air mass, however,
gas-phase resistance can be greatly magnified. In this instance,
computation of the gas-phase resistance serves to illustrate
EXAMS' procedure, and to demonstrate that Rn transport is
controlled by events in the liquid phase of the air-water interface
of these lakes.
The water vapor exchange constant (from Eq. (2-82)) is then Vw
= 0.1857+(11.36)(1.0)=11.5 m -h ', and the gas-phase transport
resistance (Rg in Eq. (2-76)) is
Table 6. Rn Solubility
T°C 104X 102H
Wilhelm, Battino, and Wilcock
(Wilhelm et al. 1977) give the
aqueous solubility of radon gas
as the mole fraction X under 1
atm partial pressure (Table 6).
The Henry's Law constant (H,
atm-m3 mol ') of Rn gas between
0 and 50 °C can be computed
from these data.
Regression of these data on the
model H — A exp(-B/R7) where
R is the gas constant (1.9872
cal/deg mole) and T is Kelvin
temperature, yields A = 660.5
and B/R = 2615, accounting for
99% of the variation in the
Henry's Law constant with
temperature. EXAMS' input data
thus could include
HENRY=logA=2.82, and EHEN = (B/R)xRxQ.001 (kcal/cal) =
5.197 kcal/mol. EXAMS would then compute local
(compartment-specific) values of the Henry's Law constant for
radon, as a function of environmental temperatures (TCEL), via
Eq. (2-79). In what follows, the Henry's Law constant at 20 °C
will be taken as 0.09239 atm-mVmol.
A piston velocity for water vapor (Vw, Eq. (2-82)) can be
computed from the observed wind speed (1.5 m -s 'at 1000 mm
height). EXAMS' input (WIND) is referenced to a 10 cm height
above the water surface. The observed wind speed can be
corrected to a 10 cm height by assuming a logarithmic wind
velocity profile, giving WlND= 1.5 (log 100/log 1000)= 1.0 m • s '.
0
10
15
20
25
30
35
40
45
50
4.24 4.25
3.40 5.31
2.77 6.50
2.31 7.81
1.95 9.24
1.68 10.8
1.46 12.3
1.29 14.0
1.16 15.6
1.05 17.2
0.96 18.8
293.15x8.206x10
-5
11.5 x0.09239 x-,/18/222
(2-86)
= 0.079 h-m '
Emerson's (1975) investigations were conducted in small
(3.6-5.6 ha), shallow (mean depth 3.6-5.6 m) dimictic lakes with
relatively long hydraulic residence times (3.2-4.2 yr) (Brunskill
and Schindler 1971). The hydrodynamics of these lakes is
clearly dominated by wind stress, and Banks' (1975) equations
(Eqs.(2-84) and (2-85)) can be used to estimate an exchange
constant for molecular oxygen. The input datum for these
equations should be referenced to a height of 10 m above the
water surface. The observed datum thus must be translated to
10-m height via U =1.5 (log 10000/log 1000) =2.0 m-s '. U
is less than 5.5 m -s '; Eq.(2-84) therefore applies and
KL-4.19*io"fi-jzE = 5.93x 10"6 m -s '. EXAMS' input datum
(KO2) has dimensions of cm-h '; the units conversion yields
KO2 = 2.13 cm-h ' for direct entry. (Recall, however, that
EXAMS will use Banks' equations to generate KO2 when only the
wind speed is entered (l.Om-s 'at 10 cm).)
EXAMS' estimates the liquid-phase transport resistance using the
molecular weight of the pollutant as an indexing factor. The
temperature of the epilimnion (20°C) in this case obviates the
need for conversion of KO2 to a differing value at the
temperature of the environment (Eq. (2-83)). The liquid-phase
Rn transport resistance (R] in Eq. (2-77)) can be computed as
1
(2-87)
0.0213^32/222
= 123.66 h-m'.
Resistance in the liquid phase thus amounts to 99.9% of the total
Rn transport resistance (Rt = Rg + Rl = 123.74 h-m '). The
estimated exchange constant for Rn gas is therefore Rt"' =
8.08xlO"3 m -h ' = 0.19 m/d, which value can be compared to
Emerson's (1975) experimental estimate of 0.16 - 0.40 m/d.
Other models considered for indexing oxygen piston velocity to
a study compound use the relative diffusivities of oxygen and the
material of interest (see page 35). Under these models, Eq. (2-
77) for the liquid phase transport resistance becomes
1
Rl = (2-88)
KO2 x Kvo
Where Kvo is a liquid-phase transport index measured by the
techniques of Hill et al. (1976), or estimated from the aqueous
diffusivity of the pollutant, expressed as a ratio of diffusivities
or as some fractional power of that ratio. A comparison of
37
-------
results from these models applied to Rn evasion provides a
measure of "model uncertainty;" here we will contrast the
sensitivity of estimated volatilization to the model chosen, as
against the values chosen for the parameters used to calculate
model results (i.e., parameter uncertainty).
Emerson (1975), citing Rona (1918) via Peng (1973) gives a
diffusion constant for Rn of 1.37xlO'5 cm2 -s ' at 25 °C. The
diffusion constant of molecular oxygen in water at 25°C is
2.41xlO"5 cm2 -s ' (Vivian and King (1964), cited from Reid,
Prausnitz, and Sherwood (1977:576)). The diffusivity ratio
(D(Rn)/D(O2)) is thus 1.37/2.41 =0.568. Application of Eq. (2-
88) then yields a liquid-phase transport resistance in these small
lakes of R, = 17(0.0213x0.568) = 82.6 h-m ' and a Rn exchange
constant (l/(R,+Rg)) of 0.29 m/d. Application of surface renewal
theory would giveR,= l/(0.0213x4).568) = 62.3 h-m 'andaRn
exchange constant of 0.38 m-d '.
Tsivoglou (1967) measured simultaneous exchange constants for
oxygen and Rn in laboratory experiments, arriving at a ratio
between them of 0.70. (This value corresponds to the 0.63 root
of the diffusivity ratio.) Application of Eq. (2-88) in this case
yields R, = l/(.0213xQ.70) = 67.1 h-m ', and a Rn exchange
constant of 0.36 m/d. The model estimates of the Rn exchange
constant (0.19, 0.29, 0.36, and 0.38 m/d) thus all fall within the
range of Emerson's (1975) experimental "best estimates" of 0.16
to 0.40 m/d; there is no basis to prefer one model to another in
this application.
Liss and Slater (1974) have estimated average exchange
constants for oxygen (20 cm-h ') and water vapor (3000 cm-h ')
applicable to the surface of the open sea. These values have on
occasion been recommended as appropriate to estimation of the
volatilization of pollutants from inland waters. For Rn transport
in these small ELA lakes, use of Liss and Slater's (1974) oceanic
exchange constants would give
Rg = (293.15x8.206xlO-5)/(30.0x0.09239xy[18/222])
= 0.0305 h-m '
R, = 17(0.2x432/222]) = 13.17 h-m '
R, = Rg + R,= 13.2 h-m '
and a Rn exchange constant (1/Rt) of 1.8 m/d. The dangers of
uncritical extrapolation of environmental driving forces (in this
case the reaeration rate or oxygen piston velocity) between
systems is apparent: The ELA Rn exchange constant, estimated
from oxygen and water vapor transport in the open sea, is an
order of magnitude too large, as compared with either the
measured values, or to estimates from volatilization models
parameterized via wind speed and Banks'(1975) compilation of
oxygen exchange rates as a function of wind velocity. The
critical element in an accurate application of EXAMS to this
situation is therefore, the selection of appropriate values for the
environmental driving variables (WIND and KO2), rather than
the choice of a model for indexing Rn liquid-phase transport
resistance against EXAMS' environmental descriptors.
2.3.2.4.2 1,4-Dichlorobenzene in Lake Zurich
Schwarzenbach et al. (1979) conducted a one-year study of the
fate and transport of 1,4-dichlorobenzene (DCB) in Lake Zurich,
Switzerland. Contaminated effluents from waste-water treatment
plants are the primary source of DCB loadings entering the lake
(the material is used as a residential toilet cleanser). The
concentration of DCB in these effluents is relatively constant
among treatment plants and over time, providing an opportunity
for a case-history trial of EXAMS' use (in Mode 1) as a
steady-state evaluative model.
DCB is not subject to appreciable degradation by chemical or
biochemical processes in aquatic systems (Callahan et al. 1979);
its behavior is therefore governed by volatilization, transport,
and sorption phenomena. EXAMS in this instance requires 4
chemical descriptors: the molecular weight (MWT), solubility
(SOL), octanol-water partition coefficient (KOW), and Henry's
Law constant (HENRY). The molecular weight of DCB
(C6H4C12) is 147.0. The aqueous solubility and octanol-water
partition coefficient of DCB have been measured by Banerj ee et
al. (1980). DCB is soluble to 0.502 mM in water at 25 °C (SOL
= 73.8 ppm). Its octanol-water partition coefficient (KOW) is
2340.
The Henry's Law constant of DCB has not been measured, but
it can be estimated (for 25 °C, the temperature of the solubility
observation) from the vapor pressure/solubility ratio (Mackay
and Wolkoff 1973, Mackay and Leinonen 1975). Para-DCB is
a solid at normal environmental temperatures (mp 53.1 °C
(Weast 1971). The vapor pressure (Pv) of solid 1,4-DCB at 10,
30, and 50 °C is 0.232,1.63, and 8.435 torr, respectively (Darkis
et al. 1940). Regression of these data on the model Pv = A
exp(-B/T) yields A = 9.63x10", B = 8223, and accounts for
99.99% of the variation in Pv with temperature. EXAMS could be
loaded with the results of this regression analysis, i.e.,
VAPR=logA=11.98,andEVPR=(B)(R)(0.001)=16.34kcal/mol.
Alternatively, the Henry's Law constant can be estimated via
interpolation of the observed vapor pressure to 25°C. The latter
procedure was used for this analysis of DCB in Lake Zurich.
The interpolated value of Pvis 1.011 torr at 25 °C. The Henry's
Law constant is therefore (1.011/760)/0.502 = 2.66xlO'3
atm-mVmol.
EXAMS also requires environmental input data describing Lake
Zurich. In this case, a "canonical" data set need only include
information relevant to transport, sorption, and volatilization of
neutral organics. Schwarzenbach et al. (Schwarzenbach et al.
1979) restricted their investigation to the central basin of Lake
Zurich. Both the upper and the central basins receive
38
-------
DCB-contaminated waste- water effluents. The centralbasin can
be modeled in isolation, however, by treating inputs from the
upper basin as advected loadings to the (downstream) central
basin. Except as noted otherwise, the environmental description
given below was drawn from Schwarzenbach et al. (1979).
The central basin has an average hydraulic residence time of 1.2
years. Banks' (1975) method for estimating KO2 from wind
speed thus seems most appropriate, lacking extensive direct field
measurements of the oxygen exchange constant. The annual
mean wind speed fortheperiod!955-63, 1965-69 was 2.6m -s '
(5.1 knots) (unpublished data for Zurich, Switzerland/Kloten,
summarized by the U.S. Air Force Environmental Technical
Applications Center, supplied courtesy of NOAA). Although a
station history was not available, these data were in all
probability collected at the conventional meteorological screen
height (6 m). Wind speed at 10 m height would be 2.6(log
10000/log 6000) = 2.75 m -s '. Computation of KO2 via Eq.(2-
84) then gives 2.5 cm-h ' via
KO2 = 4.19 x ICT* v^TT? x 3600(5 / h) x 100(cm / m)
Average wind speed at 10 cm height (EXAMS' input parameter
WIND) would be 2.6(log 100/log 6000) = 1.38 m -s '.
The mean depth of the central basin is 50 m, and the surface area
is 68 km2, giving a total volume of 3.4*109 m3. The lake
stratifies during the summer (May through September); the
thermocline sets up at a depth of 10 m by early May and remains
at about that depth until fall turnover (Li 1973). A simple "box"
model of the lake can be constructed for EXAMS by dividing the
lake into 3 vertical zones, each with an area (AREA) of 68 km2
or 6.8*107 m2. For the epilimnion segment (compartment 1,
TYPE(l) = "E"), DEPTH(l) = 10 (m), and VOL(l) = 6.8xl08 (m3).
The hypolimnion ( compartment 2, TYPE(2) = "H") then has
DEPTH(2) = 40, and VOL(2) = 6.8xl07x40 = 2.72x10" m3.
Assuming a 2 cm depth of active benthic sediments (
compartment 3, TYPE(3) = "B") gives DEPTH(3) = 0.02, and
VOL(3) = 1.36xl06m3.
Li (1973) computed the vertical eddy diffusion coefficient in
Lake Zurich, as a function of depth and season, from observed
monthly temperature profiles averaged over 10 years of record.
The annual mean temperature of the epilimnion (0-10 m depth,
TCEL(l)) was 11°C; the mean hypolimnion temperature was
5.6°C (TCEL(2)). The eddy dispersion coefficient at 10 m depth
averaged 0.058 cm2 -s ' during the stratified period and was
about 1 cm2 -s ' during the balance of the year. The annual mean
value (DSP for parameterizing average transport between the
epilimnion and hypolimnion) was 0.6 cm2 -s '; EXAMS' input
value of DSP = 0.2 m2 -h '. The dispersion coefficient for
exchange between the hypolimnion and benthic sediments was
taken as 10-4m2-h '.
In order to compute sorption of DCB to sediment phases, EXAMS
requires a description of the benthic and suspended sediments in
the system (FROC, BULKD, PCTWA). Given the size of Lake Zurich
(i.e., the relatively small overall sedimentwater ratio), and the
small octanol:water partition coefficient of DCB (2340), DCB
will occur primarily in the dissolved state in the water column of
this lake. The values chosen for FROC, BULKD, and PCTWA are
therefore not critical to the outcome of the simulation, so long as
they are representative. Table 7 summarizes the observed and
assumed values that were used to describe the central basin of
Lake Zurich to EXAMS.
Table 7. Environmental Data for Central Basin of Lake Zurich
Parameter Compartment
Number
Type
Area, m2
Depth, m
Vol, m3
Sused (mg/L)
Bulkd (g/cm 3)
Pctwa, %
Froc
Wind,
m -s '@10cm
1
E
6.8x107
10
6.8x10s
5
0.02
1.38
2
H
6.8x107
40
2.72x109
5
0.02
3
B
6.8x107
0.02
1.36x10=
1.5
150
0.02
The combined flow from the upper basin (2,500), small creeks
draining into the central basin (100), and treatment plant
effluents (28) was 2,628x 106rnVyr, giving STFLO(l) = 3x 105 m3
•h '. The total load of DCB on the central basin was 88 kg/yr, of
which 25 kg derived from the upper basin, 62 kg from treatment
plant effluents discharged into the central basin, and 1 kg/yr
from other minor sources. For the EXAMS simulation, these
loadings were summed to give a STRLD(l) to the epilimnion of
the central basin of 0.010 kg -h '. (Chapter 3.4 demonstrates the
entry of these data into EXAMS, and the command sequences
used to conduct the analysis.)
EXAMS (Exams Output Table 8 and Exams Output Table 9)
predicted a total mass of 36.8 kg DCB resident in the water
column (DCB concentration 10.7 ng/L), a flux of DCB to the
atmosphere of 59.4 kg/yr, and water-borne export of 28.2 kg/yr.
By comparison, Schwarzenbach et al. (1979) estimated a
resident mass of 3 8 kg (11.2 ng/L) in the lake, and, from a mass
balance for DCB, estimated the flux to the atmosphere to be 60
kg/yr, with a water-borne export of 28 kg/yr.
During the stratified period, contaminated treatment plant
effluents spread laterally through the metalimnion of the lake
and mix with both the hypolimnion and the epilimnion
39
-------
(Schwarzenbach et al. 1979). Assuming that the summertime
loadings mix upward and downward in equal measure, EXAMS'
loadings can be modified to account for this phenomenon. The
summer (5 month) DCB load to the hypolimnion would amount
to (5/12)(62)/2 kg/yr, which can be entered to EXAMS as a
"drift" load (DRFLD(2)) of l.SxlO'3 kg -h '. Proportionate
reduction of the DCB load on the epilimnion gives STRLD(l) =
8.5xlO"3 kg -h '. Given this modification, EXAMS predicted a
larger concentrationof DCB in the hypolimnion (13.5 ng/L), and
a resident mass of 44.0 kg DCB; the predicted fluxes and DCB
concentration in the epilimnion (10.7 ng/L) were unchanged.
A test of other transport indices requires knowledge of DCB
diffusivity. The aqueous diffusivity of DCB can be estimated
from molar volume at the normal boiling point (Vb), and Vb can
itself be estimated from Vc, molar volume at the critical
temperature (Reid et al. 1977). Vc for p-DCB is 372 cc/g-mole;
Vb = 0.285(VCL048) (Tyn and Calus method) = 140.9 cc/g-mole.
The aqueous diffusivity of p-DCB at 25 °C, computed via the
Hayduk-Laudie revision of the Othmer-Thakar relationship, is
(13.26x10 5) (0.8904 14)(140.9 °0589) = 8.46x10 6 cm2 -s ',
taking the viscosity of water at 25 °C as 0.8904 cp (Weast
1971:F-36). The diffusivity ratio D(DCB)/D(O2), given the
diffusivity ofmolecular oxygen D(O2) as 2.41x10 5 cm2 -s ', is
0.35; its square root is 0.59.
Substituting these values for EXAMS' molecular weight transport
index (^2 /147 =0.47) gave estimated resident DCB masses
of 43.8 kg (12.9 ng/L), and 31.0 kg (9.1 ng/L), respectively.
Simulation using Liss and Slater's (1974) open-sea transport
parameters (with the molecular weight transport index) predicted
a resident mass of only 6.6 kg (2.0 ng/L). These comparisons are
summarized in Table 8.
Table 8. Results of EXAMS simulations of the behavior of DCB (1,4-
dichlorobenzene) in Lake Zurich, Switzerland
Method
Measured
^32/147
Partial load
hypolimnion
Cone
ng/L
11.2
10.7
10.7(E)
13.5(1-1)
Mass
kg
38.
36.5
44.4
Volatile
kg/yr
60.
59.4
59.4
Export
kg/yr
27+1
28.2
28.2
DDCB/D0
t/32/147
K02=20cm/h
Vw=30m/h
12.9
9.1
1.95
44.2
31.3
6.69
53.7
63.7
82.5
33.9
24.0
5.13
Predicted concentration, resident mass in water column, and
fluxes vary as a function of load routing, method used to index
interphase transport against its environmental referents, and
environmental transport parameters. The default molecular
weight transport index provided the most accurate prediction of
the volatilized flux of DCB from Lake Zurich. As was the case
for Rn transport in ELA lakes, however, the selection of proper
values for environmental driving forces seems to be more
critical, than is the choice taken among methods of indexing
pollutant transport across the air-water interface against its
environmental referents.
Seg Resident Mass
t
Kilos %
Exams Output Table 8. Predicted concentration and resident
mass of DCB.
Exams Output Table 9. EXAMS summary of DCB in Lake Zurich.
References for Chapter 2.3.2.
Banerjee, S., S. H. Yalkowsky, and S. C. Valvani. 1980. Water
solubility and octanol/water partition coefficients of
organics. Limitations of the solubility-partition coefficient
correlation. Environmental Science and Technology
14:1227-1229.
40
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Batiks, R. B. 1975. Some features of wind action on shallow
lakes. Journal of the Environmental Engineering Division,
Proc. ASCE 101(EE5):813-827.
Banks, R. B., and F. F. Herrera. 1977. Effect of wind and rain
on surface reaeration. Journal of the Environmental
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Brunskill, G. J., and D. W. Schindler. 1971. Geography and
bathymetry of selected lake basins, Experimental Lakes
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Burns, L. A. 1982. Identification and evaluation of fundamental
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Modeling the Fate of Chemicals in the Aquatic
Environment. Ann Arbor Science Publ., Ann Arbor,
Michigan.
Burns, L. A. 1985. Models for predicting the fate of synthetic
chemicals in aquatic systems. Pages 176-190 in T. P. Boyle,
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for Testing and Materials, Philadelphia, Pennsylvania.
Callahan, M. A., M. W. Slimak, N. W. Gabel, I. P. May, C. F.
Fowler, J. R. Freed, P. Jennings, R. L. Durfee, F. C.
Whitmore, B. Maestri, W. R. Mabey, B. R. Holt, and C.
Gould. 1979. Water-Related Environmental Fate of 129
Priority Pollutants. Vol. II: Halogenated Aliphatic
Hydrocarbons, Halogenated Ethers, Monocyclic Aromatics,
Phthalate Esters, Polycyclic Aromatic Hydrocarbons,
Nitrosamines, and Miscellaneous Compounds. EPA-440/4-
79-029b. U.S. Environmental Protection Agency,
Washington, D.C.
Copeland, B. J., and W. R. Duffer. 1964. Use of a clear plastic
dome to measure gaseous diffusion rates in natural waters.
Limnology and Oceanography 9:494-499.
Danckwerts, P. V. 1970. Gas-Liquid Reactions. McGraw-Hill
Book Co., New York.
Darkis, F. R., H. E. Vermillion, and P. M. Gross. 1940. p-
Dichloro-benzene as a vapor fumigant. Physical and
chemical studies. Industrial Engineering Chemistry 32:946-
949.
Dilling, W. L. 1977. Interphase transfer processes. II.
Evaporation rates of chloro methanes, ethanes, ethylenes,
propanes, and propylenes from dilute aqueous solutions.
Comparisons with theoretical predictions. Environmental
Science and Technology 11:405-409.
Dilling, W. L., N. B. Tefertiller, and G. J. Kallos. 1975.
Evaporation rates and reactivities of methylene chloride,
chloroform, 1,1,1-trichloroethane, trichloroethylene,
tetrachloroethylene, and other chlorinated compounds in
dilute aqueous solutions. Environmental Science and
Technology 9:833-838.
Dobbins, W. E. 1964. Mechanism of gas absorption by turbulent
liquids. Pages 61-76 in W. W. Eckenfelder, editor.
Advances in Water Pollution Research. Proceedings of the
International Conference held in London September 1962.
Vol. 2. The MacMillan Company, New York.
Emerson, S. 1975. Gas exchange rates in small Canadian Shield
lakes. Limnology and Oceanography 20:754-761.
Fischer, H. B., E. J. List, R. C. Y. Koh, J. Imberger, and N. H.
Brooks. 1979. Mixing in Inland and Coastal Waters.
Academic Press, New York.
Hall, C. A. S. 1970. Migration and Metabolism in a Stream
Ecosystem. Ph.D. University of North Carolina, Chapel
Hill.
Hill, J., IV., H. P. Kollig, D. F. Paris, N. L. Wolfe, and R. G.
Zepp. 1976. Dynamic Behavior of Vinyl Chloride in
Aquatic Ecosystems. EPA-600/3-76-001. U.S.
Environmental Protection Agency, Athens, Georgia.
Israelsen, O. W., and V. E. Hansen. 1962. Irrigation Principles
and Practices. John Wiley and Sons, Inc., New York.
Kramer, G. R. 1974. Predicting reaeration coefficients for
polluted estuary. Journal of the Environmental Engineering
Division, Proc. ASCE 100 (EEl):77-92.
Li, Y.-H. 1973. Vertical eddy diffusion coefficient in Lake
Zurich. Schweizerische Zeitschrift fuer Hydrologie 35:1-7.
Liss, P. S. 1973. Processes of gas exchange across an air-water
interface. Deep-Sea Research 20:221-238.
Liss, P. S., and P. G. Slater. 1974. Flux of gases across the air-
sea interface. Nature 247:181 -184.
Mackay, D. 1978. Volatilization of pollutants from water. Pages
175-185 in O. Hutzinger, I. H. v. Lelyveld, and B. C. J.
Zoeteman, editors. Aquatic Pollutants: Transformation and
Biological Effects. Pergamon Press, Oxford.
Mackay, D., and P. J. Leinonen. 1975. Rate of evaporation of
low-solubility contaminants from water bodies to
atmosphere. Environmental Science and Technology
9:1178-1180.
Mackay, D., and A. W. Wolkoff. 1973. Rate of evaporation of
low-solubility contaminants from water bodies to
atmosphere. Environmental Science and Technology 7:611-
614.
Peng, T.-H. 1973. Determination of gas exchange rates across
sea-air interface by the radon method. Ph.D. Thesis.
Columbia University, New York.
Reid, R. C., J. M. Prausnitz, and T. K. Sherwood. 1977. The
Properties of Gases and Liquids, 3rd edition. McGraw-Hill
Book Company, New York.
Rona, E. 1918. Diffusionsgrosse und Atomdurchmesser der
Radium-emanation. Zeitschrift fuer Physikalische Chemie
(Leipzig) 92:213-218.
Schindler, D. W. 1971. Light, temperature, and oxygen regimes
of selected lakes in the Experimental Lakes Area,
northwestern Ontario. Journal ot the Fisheries Research
Board of Canada 28:157-169.
41
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Schwarzenbach, R. P., E. Molnar-Kubica, W. Giger, and S. G.
Wakeham. 1979. Distribution, residence time, and fluxes of
tetrachloroethylene and 1,4-dichlorobenzene in Lake
Zurich, Switzerland. Environmental Science and
Technology 13:1367-1373.
Tsivoglou, E. C. 1967. Tracer Measurement of Stream
Reaeration. PB-229 923, Fed. Water Poll. Contr. Admin.,
Washington, B.C.
Tsivoglou, E. C., M. A. McClanahan, and W. M. Sanders, III,
editors. 1972. Proceedings of a Symposium on Direct
Tracer Measurement of the Reaeration Capacity of Streams
and Estuaries. U.S. Environmental Protection Agency,
Washington, B.C.
Vivian, J. E., and C. J. King. 1964. AIChE J. 10:220.
Weast, R. C., editor. 1971. Handbook of Chemistry and Physics.
51st edition. The Chemical Rubber Co., Cleveland, Ohio.
Whitman, W. G. 1923. A preliminary experimental confirmation
of the two-film theory of gas absorption. Chemical and
Metallurgical Engineering 29:146-148.
Wilhelm, E., R. Battino, and R. J. Wilcock. 1977. Low-pressure
solubility of gases in liquid water. Chemical Reviews
77:219-262.
2.3.3 Direct Photolysis
EXAMS includes two entirely separate methods to compute rates
of direct photolysis. These methods are mutually exclusive, and
accept different kinds of input data. The first, mechanized in
procedure "PHOTO 1," begins from a pseudo-first-order rate
constant (KDP) representing the photolytic decomposition rate in
near-surface waters under cloudless conditions at a specified
reference latitude (RFLAT). This input rate constant datum is
taken as the annual mean value averaged over the entire diel (24-
hour) cycle.
The second method, in procedure "PHOTO2," works from
measured light absorption spectra and reaction quantum yields
of the compound. Because this method is intrinsically more
accurate, it is to be preferred whenever possible.
EXAMS selects the appropriate procedure via an audit of the
structure of the chemical input data. For the existing ionic
species (RH3 et al. - see SPFLG and Chapter 2.2.1), when at
least one value of ABSORG, the light absorption spectrum of the
molecule, is non-zero, EXAMS calls on procedure PHOTO2 to
compute the photolysis rate of the chemical. (Technically this
test is executed on a summation of the ABSORG vector of the
ionic species; a positive value of this sum (internal variable
ABSTOL) invokes the call to PHOTO2.) Note that the structure of
this decision in effect gives ABSOR a higher computational
priority than KDP, that is, if any ABSOR are positive, EXAMS will
use PHOTO2 and the absorption spectrum for its computations,
and will ignore all entries in the KPD vector.
The techniques used within EXAMS for computing rates of direct
photolysis have been derived in large part from the work of Zepp
and coworkers (Zepp et al. 1975, Zepp et al. 1976, Zepp and
Cline 1977, Zepp et al. 1977, Zepp 1978, Zepp and Baughman
1978, Miller and Zepp 1979a, b, Zepp 1980). Zepp (1980) and
Zepp and Baughman (1978) summarized techniques for
predicting direct photolysis in natural waters; a computer code
for evaluating this transformation pathway was described by
Zepp and Cline (1977).
2.3.3.1 Direct photolysis in aquatic systems
Direct photochemical reactions are a consequence of the
absorption of electromagnetic energybyapollutant molecule. In
this "primary" photochemical process, absorption of a photon
promotes the molecule from its ground state to an electronically
excited state. The excited molecule then either reacts to yield a
photoproduct, or decays via some other mechanism
(fluorescence, phosphorescence, etc.) back to the ground state.
The efficiency of each of these energy conversion processes is
called its "quantum yield" ; the law of conservation of energy
requires that the primary quantum efficiencies sum to 1.0. These
ideas are expressed by two fundamental laws of photochemistry.
The first, the "Grotthus-Draper" law, states: "Only the light
which is absorbed by a molecule can be effective in producing
photochemical change in the molecule." Simple irradiation of a
system does not necessarily result in photochemical reactions;
the light must be of wavelengths that can be absorbed by the
chemical. Conversely, laboratory irradiation of a chemical with
wavelengths that are not found in natural waters (<280 nm) is of
little value for predicting the behavior of the compound in the
environment. The second law of photochemistry, the
"Stark-Einstein" law, was formulated after the discovery that
interactions of light and matter are restricted to discrete
(quantized) events. This second law in its modern form (Calvert
and Pitts 1966:20) states: "The absorption of light by a molecule
is a one-quantum process, so that the sum of the primary process
quantum yields must be unity."
The rate of photolytic transformations in aquatic systems
depends upon both the light intensity in the medium (in other
words, the dose rate), and on the response of the irradiated
pollutant. The chemical response is composed of two factors: the
pollutant's absorption spectrum ex (EXAMS' input ABSORG), and
its quantum efficiency for photochemical transformations
(reaction quantum yield, EXAMS' input QYIELD). The logic of the
situation can be developed in terms of monochromatic light, with
spectral effects subsequently incorporated via integration or
summation across the solar spectrum. In EXAMS, the solar
spectrum is subdivided into 46 wavelength intervals (Table 9),
and the total rate constant is computed as the sum of
contributions from each spectral interval. In what follows,
however, the spectral subscripts have in most cases been
omitted, in the interest of notational simplicity.
42
-------
Light intensity decreases exponentially with depth in any
absorbing medium. This phenomenon is known as the
Beer-Lambert law, and can be stated mathematically as:
dSo
~dz~
= -KSo
(2-89)
where Eo = photon scalar irradiance, photons cm 2s '
z = depth, m (EXAMS variable DEPTHG)
K — diffuse attenuation coefficient for irradiance, /m, and
K = Df. a + (Bb) (2-90)
(Smith and Tyler 1976), where
D is the mean optical path per unit z (dimensionless),
a is the absorption coefficient for the medium (/m), and
(Bb) is the back-scattering coefficient.
Photon scalar irradiance (Eo) is the sum of two contributing light
fields in natural waters, the downwelling (Ed) and upwelling
(Eu) irradiances. Field measurements, although for the most part
restricted to marine systems, have in almost all cases resulted in
measured values of Eu of only 2% or less of Ed. Upwelling
irradiance can, however, contribute significantly to Eo at visible
wavelengths in the clearest ocean waters, where molecular
back-scattering can be significant, andover white sandy bottoms
of high albedo (Jerlov 1976). Although seldom measured in
freshwater systems, these studies in marine waters indicate that
back-scattering is generally very small and can safely be
neglected (Jerlov 1976). In the following discussion, photon
irradiance is therefore designated E and is treated as being
identical with Ed or Eo; Eu is neglected.
Integration of Eq. (2-89) yields an expression for the residual
irradiance after transmission through a homogeneous layer of
depth z:
£(z) = £(0) exp(- Kz) = £(0) exp(- Daz") (2-91)
where E(0) is the irradiance at the top of the layer. The rate of
light absorption Ew (photons cm 2 s ') in the layer is
(E(O)-E(z)), or
Ew= 5(0)0- exp(-fe))
(2-92)
For photochemical purposes, it is most convenient to express
light absorption on a volumetric molar basis (Bailey et al.
1978:223) (one mole of photons is an Einstein, E). The rate of
light absorption Iw, in EL 's ', is:
Ew
-xlOOO-
0.01£
(2-93)
where A = 6.023*1023 photons/mole (Avogadro's number).
Denoting E(0)/A as lo (Einsteins cm 2s '), the volumetric
absorption rate Iw (in E L ' s ') is thus
—
z
(2-94)
The electronic absorption spectra of synthetic organic chemicals
are usually reported as (decadic) molar absorptivities or
extinction coefficients e, with units (cm ' (mole/liter) ') or
(cm 'M ') (EXAMS' input variable ABSORG). The defining
equation is:
Ab
£ = - (2-95)
where Ab is the absorbance measured in a spectrophotometer, /
is the path length in cm, and [P] is the concentration of the
chemical in moles per liter. The presence of the chemical in a
natural water body increases the absorption coefficient (units
m ') of the water from (a) to (« + I00(^)(ln 10>*[.F] ). The
total rate of light absorption in the water body then becomes, by
substitution into Eq. (2-94),
10 — l-
100x2. 303 xe[P]~jz)} (2-96)
where D is the relative optical path in the water body (Eq. (2-
90)). The fraction of this light absorbed by the pollutant itself is:
2.30 3 x 10 Oi[ P]
a + 2.303 xlQQs[P]
and the rate of light absorption by the pollutant la, in E L ' s ',
is:
At trace levels of the pollutant (EXAMS' operating range), by
definition the quantity (2 30.3 e[P])« a, and (a + 230.3e[P]) can
be approximated by the natural absorption coefficient of the
water body, a. Equation (2-97) in these circumstances reduces
to:
la =
0)0)
. .
[ P] (2-98)
The quantity la/fPJ is called the "specific sunlight absorption
rate" of the pollutant, Ka (Zepp 1980).
43
-------
Table 9. Spectral intervals used in EXAMS, and spectral absorption coefficients of water nw (m '), chlorophylls + pheophytins np (m 1("ig/L) ')<
(humic) dissolved organic carbon ndoc (m 1(mg/L) '). Suspended sediments n, taken as uniformly 0.34 m 1(mg/L) '. See section 2.3.3.2
A center A A r|w r|p r|doc
(nm) (nm)
280.0
282.5
285.0
287.5
290.0
292.5
295.0
297.5
300.0
302.5
305.0
307.5
310.0
312.5
315.0
317.5
320.0
323.1
330.0
340.0
350.0
360.0
370.0
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
3.
10.
10.
10.
10.
10.
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
8
0
0
0
0
0
0.288
0.268
0.249
0.231
0.215
0.194
0.174
0.157
0.141
0.133
0.126
0.119
0.105
0.0994
0.0952
0.0903
0.0844
0.0793
0.0678
0.0561
0.0463
0.0379
0.0300
145
138
132
126
120
115
109
106
101
95
90
85
80
78
75
72
70
68
64
59
55
55
51
8.35
8.05
7.77
7.49
7.22
6.97
6.72
6.48
6.25
6.03
5.81
5.61
5.41
5.21
5.03
4.85
4.68
4.47
4.05
3.50
3.03
2.62
2.26
Ka can also be computed from the average light intensity in any
layer of the water body (Miller and Zepp 1979a). In this
approach, the average light intensity Em (photons cm 2s ') is
found by integration of Eq. (2-91) over the depth of the layer z,
followed by division of the resulting integral by z:
A center
(nm)
380.0
390.0
400.0
410.0
420.0
430.0
440.0
450.0
460.0
470.0
480.0
490.0
503.75
525.0
550.0
575.0
600.0
625.0
650.0
675.0
706.25
750.0
800.0
AA
(nm)
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
17.5
25.0
25.0
25.0
25.0
25.0
25.0
25.0
37.5
50.0
50.0
r,w
0.0220
0.0191
0.0171
0.0162
0.0153
0.0144
0.0145
0.0145
0.01566
0.0156
0.0176
0.0196
0.0295
0.0492
0.0638
0.0940
0.244
0.314
0.349
0.440
0.768
2.47
2.07
where E(0) is the intensity at the top
cm 2s ') can be converted to molar
division by Avogadro ' s number A :
Em JYQ) 1 - esq
rip r
46
42
41
39
38
35
32
31
28
26
24
22
19
14
10
8
6
5
8
13
3
2
0
of the layer. Em
units (Im, E cm
Id,
1.
1,
1,
1.
1.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
(1
2
X
,96
,69
,47
,27
,10
,95
,82
,71
,61
,53
,46
,40
,33
,24
,17
,12
,08
Dhoton
s ') vi
(2-1 00
Sm =
(2-99)
44
-------
which, as lo — E(0)/A,
The term/o(l-exp(Z)flz))/az in this equation is embedded in Eq.
(2-98); /a and Ka can thus be computed from the average light
intensity Im via the equivalent expressions (2-101) and (2-102):
(2-101)
(2-102)
Ja= 2.303* 100Q
Ka= 23Q30)(Im)(£>)
Light absorption is a one-quantum process, so la (E L 's ') also
gives the rate of electronic activation of the pollutant (M/s). Ka,
the specific sunlight absorption rate, thus has units s '. If each
photon absorbed by the chemical pollutant resulted in
photochemical transformation of one molecule, Ka would
amount to a pseudo-first-order rate constant for photolysis of the
pollutant. This, however, is rarely the case in solution-phase
systems.
The efficiency of the (secondary) photochemical trans formation
process is called the "reaction quantum yield" (EXAMS input
parameter QYIELD), with ("dimensionless") units of
moles/Einstein. Zepp (1978) has described procedures for
measuring <3> of organic chemicals in dilute air-saturated aqueous
solutions; the measurement of is described in EPA Guideline
OPPTS 835.2210 (Direct Photolysis Rate in Water by Sunlight,
report EPA 712-C-98-060, January 1998). The rate of
photochemical transformation of a pollutant is given by:
d[P]
(2-103)
The quantity (<3>)(£a) is the pseudo-first-order photolysis rate
constant. Multiplication of this quantity by 3600 s/h gives the
photolytic contribution to the overall transformation rate
constant K in Eq. (2-1).
Although the foregoing discussion has been phrased in terms of
monochromatic light, the effect of spectral differences can be
readily incorporated via integration or summation (in discrete
wavebands) across the solar spectrum. The rate of photolysis of
a synthetic organic compound thus can be computed from the
absorption spectra and reaction quantum yields of the several
ionic species of the chemical, via a coupling of these parameters
to the (spectrally-dependent) behavior of light in natural waters
(EXAMS' procedure PHOTO2).
When the absorption spectrum of a compound has not been
quantified (although the spectral position of absorption maxima
may be known). EXAMS provides an additional procedure,
PHOTO 1, designed to accept pseudo-first-order photolysis rate
constants as its primary input data. For example, Smith et al.
(1978) attempted to measure the absorption spectrum of Mirex,
but the absorptivity was below the detection limit of their
instrument (0.1 cm 'M '). Experimental studies, conducted via
continuous exposure of an aqueous solution of Mirex to ambient
sunlight at Menlo Park, CA for a period of 6 months,
demonstrated that Mirex is photochemically reactive in aqueous
solution with apseudo-first-order rate constant of 3.7xlO"3d"'. (A
pseudo-first-order rate constant determined via a brief
experiment, for example at midsummer local noon, must be
adjusted for annual mean sunlight intensity and day length prior
to entry in EXAMS.)
So long as the reaction mixtures absorb a negligible fraction of
the ambient light (Im « lo ), this observed rate constant
(KDP(0), i.e., K atz=0) is equivalent to:
KDP(0) = Ka = 2303 e lo D
integrated across the solar spectrum. The average photolysis rate
constant KDP(z) in a layer of appreciable depth z is then
1 - exp(- DOE)
Daz
(2-104)
where the absorption coefficient (a) for the water body is some
appropriate single value. EXAMS also computes a correction term
for effects of cloudiness and geographic latitude. EXAMS' input
variable (KDP, units h ') is taken as the near-surface, 24-hour
annual average pseudo-first-orderphotolysis rate constant under
cloudless conditions at a specified latitude RFLAT. RFLAT is
expressed in degrees + tenths. For example, Menlo Park,
California, is at 37°27'N; EXAMS' input would be RFLAT = 37.4.
2.3.3.2 Light attenuation in natural waters
The attenuation of irradiance in natural waters is described by
the diffuse attenuation coefficient or "K-function," K (Eq. (2-
90)), with units m '. The numerical value of K depends upon
both the absorbance of the medium (a), and upon the relative
optical path in the water body (D). The absorbance of a natural
water body results from absorption of light by the water itself,
plus absorption by green plants, dissolved organic matter
(primarily humic materials), and suspended sediments. The
optical path parameter D depends on the angle of incidence of
the light source(s), and on forward scattering of the light within
the water body itself.
2.3.3.2.1 Distribution functions (D) in natural waters
The optical parameter/) is the mean optical path per unit vertical
depth in the system; D maybe called a "distribution function" as
proposed by Priesendorfer ((1958b, 1958a), quoted from Smith
and Tyler (1976)), or an "inverted value of an average cosine"
where the average cosine is defined as a/K (Jerlov 1976). (The
45
-------
term "inverted value of an average cosine" originated from a
generalization of the fact that the path length of the solar beam
is given by the secant (I/cos) of the angle of refraction of the
beam.) In the case of a collimated light beam incident normal to
the water surface, D= 1.0. In the case of a completely diffused
light field, D reaches its maximum value of 2.0 (Leighton
1961:24ff).
In the clearest natural waters, the distribution function is
dominated to appreciable depths by the geometry of incident sky
radiation and the solar beam. When the sun is in the zenith, the
solar D — 1.0; D increases with increasing zenith angle. When
the sun is near the horizon, D reaches a limiting value of about
1.5, because of refraction of the solar beam as it crosses the
air-water interface. At low solar elevations, however, the
underwater light field in the photochemically significant portion
of the solar spectrum is dominated by contributions from diffuse
skylight.
The collimating effect of transmission across the air-water
interface reduces the distribution function for diffuse skylight
from &D of 2.0 in the atmosphere, to a submarine value of about
1.19 ((Poole and Atkins 1926), quoted from (Hutchinson
1957:391)). This value corresponds to an equivalent solar
elevation of about 44°. The total incidentphotochemically active
irradiance (wavelengths <370 nm) is dominated by skylight at
solar altitudes less than 45°, and irradiance atwavelengths <330
nm is dominated by sky radiation at all solar elevations
(Leighton 1961:23ff). A distribution function of 1.19 is thus an
adequate approximation for the near-surface zone, and to some
considerable depth for the clearest natural waters.
In most natural waters, and in the deeper parts of clear waters,
the radiance distribution approaches an asymptotic value in
which forward scattering by suspended particles is balanced by
light absorption, and the distribution coefficient attains a stable
value. At shallow depths in natural waters containing scattering
particles, D is usually larger than the value suggested by the
solar elevation. For example, in the Baltic Sea and in the
Mediterranean, D has been measured at 1.40 and 1.25,
respectively ((Jerlov (Johnson) and Liljequist 1938, Hojerslev
1973,1974); quoted from (Jerlov 1976:88)). The corresponding
solar beam D values were only 1.14 and 1.04 respectively,
indicating a strong effect of particle scattering in these waters.
Miller and Zepp (1979a) measured light scattering by 6 natural
sediment suspensions. The distribution function ranged from 1.3
to 2.0, but showed little correlation with the suspension
concentrations of the sediments (17-105 mg/L).
The distribution function for each element of the water body is
an (environmental) input parameter to EXAMS. These parameters
(DFACG) can be set at any value between 1.0 and 2.0. If an input
value is <1 or >2, however, EXAMS resets the distribution
function of the segment to DFACG = 1.19 for surficial (type L, E)
waters, and to 1.50 for profundal (H) segments.
2.3.3.2.2 Absorption coefficients (a) in natural waters
EXAMS computes separate values of the spectral absorption
coefficients (a) for each sector of the water body. Absorption is
computed from the sum of the contributions of water itself, plant
pigments, dissolved organic carbon (primarily attributable to
humic materials of molecular weight >1000 (Mickle and Wetzel
1978)), and suspended sediments. Absorption coefficients for
water itself, and the specific absorption coefficients for the other
absorbing species, are given in Table 9.
EXAMS computes the total absorption co efficient (a, units /m) for
each spectral interval in each water column compartment viaEq.
(2-105):
a =
[ CHL] + 7 to [ DOC] + q, [SUSED] (2-1 05)
The spectral specific absorption coefficients supplied with the
program (r|w, r|p, r|doc, r|s) are given in Table 9. The
environmental concentrations of the absorbing species (CHL,
DOC, and SUSED (mg/L)) are entered as part of the
environmental data base for each water-column compartment of
each ecosystem.
Absorption by plant pigments is keyed to the concentration of
total chlorophyll-like pigments (chlorophylls +pheopigments) in
each sector of the water column (input variable CHL, units
mg/L) . Under very eutrophic conditions, Chi a can attain 2 mg/L
((Tailing et al. 1973), quoted from (Wetzel 1975:337)). In
oligotrophic alpine and arctic lakes, pigment concentrations can
be as low as 0.001 mg/L (Wetzel 1975:334). Smith and Baker
(1978, Baker and Smith 1982) determined the contribution of
total chlorophyll-like pigments (CHL) to the K- function of
marine systems via regression analysis; the resulting spectral K
values differed little from spectrophotometrically determined
absorbance of phytoplankters. EXAMS' specific absorption
coefficients (r|p in Eq. (2-105) and Table 9) were developed by
division of Smith and Baker's ((1978)) "k2" by an assumed
average distribution function of 1.20.
EXAMS' specific absorption coefficients for DOC (t~|doc) (Table
9), representative of freshwater aquatic humus, were calculated
from the expression
T|doc = 0.71 exp (0.0145(450-^))
where A is the wavelength in nm (Zepp and Schlotzhauer 1981).
Light absorption by suspended sediments may vary across the
solar spectrum, but interference by particle scattering has
hampered investigation of this phenomenon. Miller and Zepp
(1979a) determined both D- and K- functions for 33 1 nm light,
via actinometer experiments conducted on a series of 6 natural
sediment suspensions. The specific absorbance coefficient
46
-------
((K/D)/[S], where [S] (i.e., SUSED) is the concentration of
suspended sediments in mg/L) varied from 0.19 to 0.59, with a
mean value of 0.34, m '(mg/L) '. EXAMS includes a 46-element
vectorof specific sedimentabsorption coefficients r|s; this vector
is currently uniformly filled with a value of 0.34.
When the absorption spectrum of the compound is not available
for coupling to the absorption spectrum of the water body,
procedure PHOTO! requires appropriate single absorption
coefficients "a" for each aquatic segment. These values are
calculated from (2-105). The wavelength interval selected for
this computation can be specified via the input chemical data
describing the compound: The chemical data base includes a
variable (LAMAX, nm) that can be used to specify the desired
wavelength interval for computing a. The region of greatest
overlap of the absorption spectrumof the chemical with the solar
spectrum is perhaps the most appropriate value for LAMAX.
Usually, however, it is the spectral location of peak absorbance
that is readily available, in which case this value can be used for
LAMAX. If LAMAX is outside the relevant portion of the solar
spectrum (that is, <280 or >825 nm), EXAMS computes LAMAX
via (2-105) at 300 nm (interval 9 in Table 9). Input values of
LAMAX need not be restricted to the centers of the wavebands in
Table 9; EXAMS selects the specific absorption coefficients for
computing LAMAX via a matching of LAMAX to the appropriate
spectral intervals in the Table. For example, if LAMAX = 306,
EXAMS selects absorption coefficients from waveband 11 in
Table 9; a LAMAX of 442.2 selects waveband 30; etc.
2.3.3.3 Reaction quantum yields (; Qyield)
A photoactivated organic molecule can undergo a variety of
secondary (thermal) transformations, including photoaddition
and substitution reactions, cycloadditions, isomerizations and
rearrangements, and photofragmentations and eliminations
(Turro 1978). The efficiency of photochemical processes is
expressed in terms of the "quantum yield" , that is, the number
of moles of photochemical activity per mole of photons
(Einsteins) absorbed. Photoactivated molecules are subject to
numerous physical and chemical processes, and the efficiency of
each of these processes can be expressed as a quantum yield
(examples include the fluorescence quantum yield,
phosphorescence quantum yield, quantum yield for formation of
a specific product molecule, etc.) The quantum yield of
significance for EXAMS is the "disappearance" quantum yield
(input parameter QYIELD), that is, the number of moles of parent
compound transformed to daughterproducts per mole ofphotons
absorbed.
The (secondary) trans formation processes often involve thermal
reactions, and the disappearance quantum yield therefore can be
slightly temperature dependent. These thermal reactions are very
fast, as they must be in order to compete with thermal reversion
of the unstable intermediates to the original pollutant molecule.
The activation energies of the thermal transformation reactions
are on the order of only 1-2 kcal/mol; EXAMS therefore does not
include an option for description of temperature effects on
disappearance quantum yields.
Unlike vapor-phase photochemical reactions, the disappearance
quantum yields of organic molecules in aqueous solution are
generally independent of the wavelength of incident radiation, at
least within the environmentally significant portion of the solar
spectrum. This difference arises from the enhanced opportunity
for energy transfer to the solvent in condensed-phase systems. In
solution-phase irradiation, the rapid (radiationless) decay of
excited molecules from second or higher electronic states, to
their first excited state, normally precludes reaction from the
higher states (Zepp 1978). Some organic dyes, stable under
visible light but photochemically labile under UV, are notable
exceptions to this rule. This phenomenon should be suspected
when a >100 nm gap is present in the compound's absorption
spectrum. In such a case the assumed lack of wavelength
dependence of should be tested by experiments conducted in
both absorbing regions of the spectrum. If necessary, the
photochemically inactive section of the compound's absorption
spectrum can be omitted from the input data (ABSORG). Very
small organic molecules, and some coordination compounds, can
exhibit a wavelength dependence of . The photochemistry of
iron (II) cyanide complexes is one example of this phenomenon
((Balzani and Carassiti 1970), quoted from (Zepp 1978)). Most
small organic molecules in aqueous solution are photochemically
unreactive in sunlight, however. The disappearance quantum
yields used as input to EXAMS should in all cases be derived
from experiments using wavelengths that are part of the
environmentally relevant portion of the solar spectrum, rather
than far UV (< 280 nm).
<3> can also be affected by the chemistry of the water solution
itself. The quantum yields of the ionic species of an organic acid
or base generally differ. This phenomenon leads to an apparent
dependence of on the pH of the medium. EXAMS allows for the
entry of a separate value of (QYIELD) for each ionic species of
the compound. This information can be deduced from a
knowledge of the pK(s) of the compound, absorption spectra of
its ionic species, and quantum yield experiments conducted at
several pH values. For example, Zepp (1978) has described
methods for determining disappearance quantum yields via
comparison of the rate of transformation of a test pollutant P to
that of a reference compound R, of known absorptivity e(R) and
quantum yield (3>(R). The method depends on a comparison of
the slopes S of the (pseudo- first-order) ln[P] and ln[R]
disappearance curves over time, under irradiation at a selected
wavelength (e.g., 313 nm). The reference compound serves to
normalize the experiment for light intensity and the geometry of
the photochemical apparatus. For a neutral molecule,
(2-106)
S(R)
The apparent <3> of an organic acid or base will depend on the pH
47
-------
of the system. For a fixed pH, Eq. (2-106) can be rewritten to
include the ionization equilibria and separate absorptivities and
quantum yields of the various ionic species of the pollutant P.
For example, a monoprotic organic acid will distribute between
its uncharged RH3 molecule and its anion RH2 (see Chapter
2.2.4) as:
1
Ka. (2-107)
1 +
and
1 +
(2-108)
where a is the fraction of the total pollutant present as each of
the molecular species (Ka here is the acidity constant). Equation
(2-106) then becomes
(2-109)
*(*)
Suppose, for example, that P is a monoprotic organic acid whose
pKa = 5.0. At pH 5, the compound would distribute equally
between RH3 and the RH2 anion. At pH 4.5, the compound
would distribute as 76% RH3 and 24% RH2 . Given, for
example, e of RH3 as 5000, and e of RH2 as 10000 at 313 nm,
and experimentally determined values for the right side of Eq.
(2-109) as 10 and 20 at pH 4.5 and 5.0 respectively; the quantum
yields of the molecular species can be calculated via
simultaneous solution of the equations:
1 : (0.76)(5000)($(m,)) + (0.24)(10000)($(m, )) = 10
2: (0.50)(5000)($(m,)) + (0.50)(10000)($(m, )) = 20
giving a reaction quantum yield for the uncharged molecule
RH3of 1.5x10 4, and ®(RH2) = 3. 9x10 3.
Zepp and Baughman (1978) have discussed the effect of other
dissolved species on the direct photolysis of synthetic organic
molecules. Dissolved oxygen in some instances "quenches"
photochemical reactions via energy transfer from a pollutant
molecule to the oxygen molecule at the expense of
transformation pathways. Disappearance quantum yields should
be determined in air-saturated pure water to allow for this effect;
EXAMS does not include an explicit algorithm to make
allowances for varying dissolved oxygen concentrations (as
sunlit waters are very rarely anaerobic). Photochemical
reactions with dissolved nucleophilic species such as hydroxide,
chloride, bromide, and sulfide can change the reaction quantum
yield of a pollutant from the value determined in pure water
system. The concentrations of these species in natural fresh
waters are, however, typically much too small for them to
compete with nucleophilic displacements by water itself. In
marine systems, chloride and bromide occur at concentrations
(0.6 and 0.001 molar, respectively) high enough to compete, in
principle, with water.
Some organic compounds that are not photoreactive in pure
water are photoactive when complexed with metal ions. For
example, NTA and EDTA are photochemically activated by
complexation with iron (III). Photoactive co-dissolved
substances (e.g., dissolved humic materials) can also mediate
photoreactions of pollutant chemicals via direct energy transfer
to the pollutant molecule, or by generation of reactive
intermediates (e.g., singlet oxygen). These phenomena are
properly termed "indirect" or "sensitized" photolysis, however,
as opposed to the "direct" photo trans formations that are the
subject of this Chapter.
2.3.3.4 Absorption spectra (ABSORG)
The absorbance of organic acids and bases in aqueous solution
varies with pH, as a result of differential light absorption by the
neutral molecule and its ions. The rate of photolytic
transformation of an organic acid or base in natural waters thus
depends on the pH of the system. EXAMS' computations are
keyed to the species-specific absorption spectra of the pollutant
chemical (uncharged parent molecule and its ionic species),
measured in pure water. EXAMS links the average spectral solar
intensities in each layer of the water body to the absorption
spectra of the compound, and thereby computes a value of Ka
(Eq. (2-102)) for each dissolved molecular species.
EXAMS' dynamic state variable is the total concentration of
pollutant in each ecosystem segment, referred to the aqueous
phase of the compartment. The fraction of this total
concentration present as each molecular species is computed via
the equilibrium ionization and sorption distribution coefficients
a (Chapter 2.2.4). Onceinpossessionof Ka(Eq. (2-102)) values
for the several ionic species, EXAMS computes the contribution
of each to the total pseudo-first-order direct photolysis rate
constant via the expression (compare Eq. (2-
103)) a . x $ • x Ka- where the subscript (i) refers in turn to
each of the ionic species.
This computational loop is actually iterated 3 times for each
ionic species, because EXAMS incorporates the effects of
sorption to sediments and complexation with DOC via a set of 3
values of QYIELD for each ionic species. In other words, for each
ionic species r, EXAMS computes the total contribution of that
species to the overall direct photolysis rate constant by fixing the
value of r, and summing the expression
(a(i,k) x QYlELD(i,k) x Ka(i)}
over the k=l (dissolved), k=2 (sediment sorbed), plus k=3 (DOC-
complexed) forms of the compound in each sector of the
ecosystem. The mechanics of this computation allow the user to
control the photoreactivity of sorbed molecules via the entries in
the QYIELD chemical input vectors.
48
-------
Sorption of the pollutant to suspended sediments and
complexation with "dissolved" organic carbon (DOC) can induce
complex changes in both the light absorption spectra and the
reaction quantum yields of the compound, and can "protect" the
molecule from exposure to sunlight via migration or dissolution
into the (darkened) interior of sediment particles. EXAMS'
first-order evaluations use the quantum yield parameter (QYIELD)
as a mechanism for approximating the effects of sorption on the
photoreactivity of pollutant chemicals; the phenomena involved
are discussed in greater detail in Chapter 2.3.3.5.
In order to compute the effects of pH on light absorption and on
photolytic rate constants, EXAMS requires a specific absorption
spectrum (e, EXAMS ABSORG vector) for each (dissolved)
molecular species of the pollutant chemical. These absorption
spectra can be deduced from the pK(s) of the compound and the
absorption spectra of aqueous solutions of the chemical,
measured at several pHs. The procedure is analogous to that
described for reaction quantum yields in Chapter 2.3.3.3. For
example, Smith and coworkers ((1978)) measured the absorption
spectrum of p-Cresol at pH 5.1, 7.0, and 8.9 (Table 10).
Para-cresol is a weak organic acid (pKa= 10.2); atpH 5.1, 7.0,
and 8.9 the anion is present as only 0.00079, 0.063, and 4.77
percent of the total concentration. The observed spectra (Table
10) clearly indicate that the anion is the more strongly absorbing
species.
Table 10. Light absorption spectra of p-Cresol in pure water.
Measured values at pH 5.1, 7.0, and 8.9 from Smith et al. 1978.
Calculation of molecular species absorbance and estimated pH 7
solution absorbance described in text. pKa=10.2
Center Spectral Absorption Coefficients e, cm" M
A, nm
297.5
300.0
302.5
307.5
310.0
312.5
315.0
317.5
320.0
323.1
330.0
pHS.l pHV.O
14 18
3.8 7.2
2.4 3.8
0 2
2
1
0
pH8.9
193
173
150
92.6
63.5
40.3
24.1
13.0
6.2
3.8
0
RH3 RH2"
14 3767
3.8 3551
2.4 3097
0 1941
1331
845
505
272
130
80
pHV.O
16
6.0
4.4
1.2
0.8
0.5
0.3
0.2
0.08
0.05
The observed absorption coefficient at any fixed wavelength is
the sum of absorbance attributable to the uncharged (RH3)
molecule, plus that attributable to the anionic (RH2 ) species.
Denoting the observed absorption coefficient as £. , this sum
can be expressed algebraically:
This equation can be used to estimate the separate absorbances
of the RH3 and RH2 molecules. For example, at 297.5 nm, for
pH7.0:
eo= 18 = (0.999369) e(RH3) + (6.31x10 4) e(RH2 )
and atpH 8.9,
eo = 193 = (0.952) e(RH3) + (0.0477) e(RH2 )
Simultaneous solution of these equations yields
e(RH3)= 15. 6 and
e(RH2 ) = 3734.
The uncharged molecule is present as 99.9992% of the total
concentration at pH 5.1, so the pH 5.1 spectrum can betaken as
a direct experimental measurement of e(RH3). Given this, the
absorption spectrum of the (RH2 ) anion can be calculated using
the pH 8.9 spectrum and Eq. (2-1 10). For example, at 297.5 nm,
193 = (0.952)(14) + (0.0477) e (RH2 )
gives e (RH2 ) = 3767. The accuracy of this procedure can now
be evaluated by using Eq. (2-110) to predict absorbance of the
solution at pH 7.0:
(0.999369)(14) + (6.31x10 4)(3767) = 16 (cm 'M ')
as compared to the experimental value of 18 cm 'M '. The
computed absorption spectra of the uncharged RH3 molecule and
the RH2 anion of p-Cresol are also shown in Table 10, along
with the projected absorption coefficients at pH 7.0. The
absorptionspectraoftheindividualmolecules are the appropriate
data for entry into EXAMS' chemical data base (input vectors
ABSORG).
2.3.3.5 Effects of sorption on photolysis rates
The effects on direct photo lysis of sorption to suspended material
are incorporated into EXAMS via separate disappearance quantum
yields (QYIELD) for the sorbed forms of each dissolved species
(RH3, RH2 , etc.) of the compound. Although sorption can
produce subtle and complex changes in the photochemistry of
pollutants, light extinction by suspended particulate matter, and
capture of sorption-prone chemicals by bottom sediments,
relegate the effects of sorption to a secondary role. It should not
be assumed, however, that sorption inevitably "protects" a
chemical from direct interaction with solar radiation, so EXAMS
allows for alternatives to this assumption via the QYIELD
chemical input vector. (This capability is only available for
49
-------
compounds for which the absorption spectrum is known.)
(Oliver et al. 1979).
The "sorption" process includes adsorption of a chemical onto
particle surfaces, dissolution of uncharged species into suspended
organic materials, and migration into the interior of particles via
diffusion along pore channels. The particle microenvironment is
often very different from that of the surrounding aqueous milieu.
For example, the surface acidity of clay minerals can result in
protonation of organic bases beyond that predicted by
solution-phase pH and pK (Karickhoff and Bailey 1976). This
effect can, at least in principle, be represented via EXAMS'
separated partition coefficients for the ionic species of a pollutant
(Chapter 2.2.2). The extent of the sorption/protonation process
is influenced, however, by particle size, interactions with the
solution-phase carbonate system, and inorganic cations adsorbed
on the clay surface. These complexities, and the fact that mineral
clays constitute a varying proportion of natural sediments, restrict
EXAMS to the phenomenological approach described in Chapter
2.2.2. The photoreactivity of an organic acid or base can be
affected by ionic speciation at the surface of adsorbent particles.
Bailey and Karickhoff (1973) demonstrated this effect via its
converse: these authors showed that UV spectroscopy can be
used to monitor surface acidity of clay minerals via the shift in
the absorption spectrum of adsorbed organic bases from that of
the uncharged, to the protonated, molecular species.
The dissolution of neutral organic pollutants into suspended
organic matter or surface organic films also removes the
chemical from aqueous solution to a very different
micro enviro nment. This organic microenvironment may alter the
absorption spectrum, quantum yields, and photochemical
reactions of a sorbed molecule, for organic sorbents usually
differ from water in both refractive index and polarity. A change
in the refractive index of the absorbing medium implies changes
in spectral radiation density (erg cm 3 per unit frequency range)
in the medium, and therefore in the apparent absorption spectrum
of a pollutant chemical. Although the effects of such phenomena
can be computed in some cases (Strickler and Berg 1962), the
heterogeneity of natural suspended organic matter defies
description. Particle size distribution (i.e., light extinction in the
interior of the particle) would also affect the average radiation
density experienced by a sorbed-phase pollutant.
In addition, reaction conditions within a suspended particle
microenvironment must differ from those of aqueous solution;
these differences can alter the reaction quantum yields and the
kinds of photochemical products that result from irradiation of
the system (Miller andZepp 1979b). Suspended sediments may
also retard sorbed-phase photoreactions by quenching excited
states of the molecule, and may enhance photoreaction via
indirect processes or "sensitized" reactions. Possible indirect
reactions include the production of excited states or free radicals
via irradiation of the organic matrix of the suspended particles,
and photoelectric excitation of semiconductors, such as TiO2,
which are a common constituent of many natural sediments
A few phenomenological investigations of the effects of natural
suspended sediments on photochemical kinetics have appeared
in the literature; the complexity of the phenomena has led their
authors to describe their findings via apparent effects on reaction
quantum yields. Oliver, Cosgrove, and Carey (Oliver etal. 1979)
compared the effect of purified TiO2 semiconductor to the
photochemical efficacy of Ti ores (ilmenite and rutile) and
natural river suspensoids. Although purified TiO2 was an
efficient photochemical catalyst, the Ti ores and the natural
sediments alike simply suppressed the photochemistry of the test
materials via competitive light absorption. Miller and Zepp
(1979b), from studies of the photolysis of DDE and
m-trifluoromethylpentadecanophenone (TPP) in natural
suspended sediments, concluded that the sediment
microenvironment is similar to a saturated hydrocarbon solvent.
These authors reported an apparent increase in the disappearance
quantum yield of sorbed DDE to 1.5 times its value in aqueous
solution, and a factor of 2 to 6 decrease in the apparent quantum
yield of TPP. Both results were consistent with the behavior of
these compounds in organic solvents. Miller and Zepp (1979a)
concluded that the competitive absorption of light by suspensoids
is nonetheless the dominant effect of suspended particles on
photolysis in natural waters.
2.3.3.5.1 Sensitivity analysis of sorption effects on direct
photolysis
Photolytic transformation of pollutant chemicals is an important
process in many aquatic systems, most especially when
photochemical transformation is the only degradation process
capable of limiting organism exposures under environmental
conditions. This process is critically dependent on competitive
light absorption by suspended materials, and on the effects of
sorption on the availability of a pollutant to photochemical
processes.
In a natural water body, capture of a pollutant by benthic
sediments removes the compound to a photochemically inactive
(dark) sector of the ecosystem. The net photochemistry of a
pollutant chemical thus depends on solution- and sorbed-phase
photoreactivity, competitive light absorption by suspensoids, and
capture of the pollutant by the benthic subsystem. The
interactions among these processes can be explored via EXAMS.
For the example simulations, the ecosystem was a small (1 ha)
static pond, 2 m deep, with a 1 cm active benthic subsystem of
bulk density 1.75 g/cc and water content 150%. The example
compound had a near-surface half-life of 1 hour in solution, but
was a full order of magnitude more photoreactive when sorbed
with suspended sediments (QYIELD(!,!) = 1.0, QYIELD(2,1) =
10.0). The distribution function (DFACG) was taken as 1.20;
EXAMS used the sum of light absorption by water itself (at 300
nm), plus the absorbance attributable to suspended sediments, to
compute the average light intensity in the water column (Eq. (2-
104)). Systematic changes in the partition coefficient of the
50
-------
chemical (KPS) and the concentration of suspended sediments
(SUSED) were then used to elucidate their net effect on the
photochemistry of the compound, under conditions most
favorable for enhancement of phototransformation kinetics by
sorption.
The results of simulations using a suspended sediment
concentration of 10 mg/L and varying partition coefficients
(0-lxl06L/kg) are given in Table 11. These re suits are given in
terms of pseudo-first-order half-lives for the water column
subsystem alone, and for the entire pond ecosystem. (The
whole-system half-lives assume that the rate of transport from the
benthic subsystem into the water column does not limit the rate
of photochemical transformation of the compound.) Under these
environmental conditions, less than 1% of the compound in the
water column is in the sorbed state for a partition coefficient less
than 1000 L/kg. When the partition coefficient is greater than
1000, however, more than 85% of the resident material is
capturedby the (dark) benthic subsystem. The system-level half-
life of the compound increases steadily with increasing Kp,
despite the greater photo reactivity of the sorbed material.
The effect of competitive light absorption by suspended materials
was examined by fixing the partition coefficient at 100 L/kg, and
systematically increasing the concentration of suspended
sediments from 0 to 10000 mg/L (Table 12). In this case, light
absorption by the suspended sediments rapidly increased the
photochemical half-life of the compound. This effect
overwhelmed the enhanced photoreactivity of the sorbed
material, despite the fact that more than half of the total
compound in the system resided in the water column (at steady
state).
Table 11. Effect of partition coefficient Kp (L/kg) on net
photoreactivity when suspended sediment concentration = 10
mg/L (Average light intensity in water column 11.8% of surface
value)
Table 12. Effect of suspended sediment concentration [S] on net
photoreactivity when partition coefficient Kp = 100 L/kg
[S]
mg/L
0
1
10
100
1000
10000
% of Water % of Total
Column in Resident in
Dissolved Benthic
State Zone
100
100
99.9
99.0
90.9
50.0
36.96
36.96
36.93
36.73
34.77
22.67
Mean
Iz/Io
(%)
84.8
59.3
11.8
1.22
0.12
0.012
Pseudo-First-Order
Halflives, h
Water Whole
Column System
1.18
1.68
8.42
75.2
449
1484
1.87
2.67
13.4
119
688
1918
The system-level pseudo-first-order half life indicates the
outcome of the interaction between competitive light absorption
by suspended materials, capture of the compound by bottom
sediments, and enhanced photoreactivity in the sorbed state.
Table 13 gives the system-level photochemical half life of a
compound that is 100 times more reactive in the sorbed, than in
the dissolved, state, for a range of Kp and sediment
concentrations unlikely to be exceeded in nature. As compared
with the 1.18 hour half-life of the unsorbed, clean-water baseline,
an increase in sorption leads to a net suppression of the rate of
photochemical transformation of the compound in all cases,
despite the uniquely (and unrealistically) favorable conditions
assumed for the analysis. In sum, the effects of residence in the
sorbed state on photoreactivity are of secondary importance, and
can be adequately summarized via a simple descriptive parameter
(QYIELD).
Table 13. Effect of suspended sediment concentration (mg/L) and
partition coefficient (Kp, L/kg) on system-level pseudo-first-order
photochemical half-life (hours) when sorbed chemical is 100 times more
Kp
L/kg
0
1
10
100
1000
10000
100000
1000000
% of Water % of Total
Column in Resident in
Dissolved Benthic
State Zone
100
100
100
99.9
99.0
90.9
50.0
9.09
0.29
0.87
5.8
36.9
85.2
98.2
99.7
99.8
Pseudo-First-Order Half-
lives, h
Water Whole
Column
8.50
8.50
8.49
8.42
7.80
4.68
1.55
0.93
System
8.52
8.57
9.01
13.4
52.9
253
452
492
Kp
L/kg
0
1
10
100
1000
10000
100000
1000000
Suspended Sediment Concentration, mg/L
0
1.18
1.19
1.25
1.87
8.06
69.9
689
6876
1
1.69
1.70
1.79
2.65
10.5
50.0
89.6
97.4
10
8.52
8.57
8.93
12.3
29.1
45.9
49.2
49.6
100
82.2
81.8
79.1
65.4
51.7
49.0
48.6
48.6
1000
819
749
437
125
63.3
56.6
55.9
55.8
10000
8182
4157
861
209
137
130
129
129
51
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The effects of sorptionto organic slicks at the air-water interface
are not included in EXAMS for similar reasons: Although the
concentration in the slick can be large, the total fraction of the
pollutant resident in the film is probably too small to have much
effect on the system-wide kinetics of pollutant chemicals (Zepp
andBaughman 1978).
EXAMS represents the photochemical effects of sorption to
suspended sediments, and complexation with DOC, via separate
entries in the QYIELD vectors. Note that these vectors represent
the effect of residence in the biosorbed state on the direct
photolysis of the pollutant; they are not intended to represent
photosensitization or photobiological transformation of
pollutants by phytoplankton. Although qualitative studies have
indicated that this pathway exists, as yet a quantitative basis for
predicting its magnitude and importance in natural systems has
not been developed (Zepp 1980).
2.3.3.6 Near-surface solar beam and sky irradiance
The spectral light field is calculated by EXAMS as a vector
("WLAM" for Wx) of 46 photon irradiances. Wx is the total
(solar beam plus skylight) irradiance under clear (cloudless)
conditions, just below the air-water interface (that is, after
subtracting reflected light from the light incident on the surface).
EXAMS corrects the input irradiance for effects of cloud cover
using the empirical relationship:
Wi = Wi(\- 0.056(Cfoi*/)) (2-111)
(Biittner 1938), cited from (Zepp 1980); see also (Schultz and
Grafe 1969).
The cloudiness (EXAMS' input CLOUD) term is the average
opaque sky cover in tenths, with a range from 0 (clear sky) to 10
(full cover). Opaque sky cover may also be reported in oktas; an
okta is one eighth of the celestial dome. Conversion from oktas
to tenths may be made as follows:
7 oktas or more,
but not 8 oktas
8 oktas
9/10 or more, but not
10/10
10/10
WMO
Code
0 (clear)
1
2
3
4
5
6
Oktas
0
1 okta or less,
but not zero
2 oktas
3 oktas
4 oktas
5 oktas
6 oktas
Tenths
0
1/10 or less, but not zero
2/10 -3/10
4/10
5/10
6/10
7/10 -8/10
9 sky obscured by fog or other meteorological
phenomenon
15 cloud cover indiscernible for reasons other than
fog or other meteorological phenomenon, or
observation not made
Clear-sky irradiance at the earth's surface depends on latitude,
site elevation, atmospheric turbidity and water vapor content, and
the ozone content of the stratosphere (Leighton 1961).
Computational methods for estimating spectral irradiance have
been explored by a number of authors (e.g., Leighton 1961,
Green et al. 1974, Dozier 1980, Green et al. 1980, Green and
Schippnick 1980, Green and Schippnick 1982). Zepp and Cline
((1977)), using a computer program ("SOLAR") available on
request from those authors, calculated photon scalar irradiance
Wx at 39 of the wavelength intervals adopted for EXAMS. (They
reported "midseason," "midday" spectral irradiance at 40° N
latitude. For EXAMS, such values must be reduced by a day length
factor (daylight hours/24), and sinusoid averaging over the
photoperiod (i.e., multiplied by 2/7i).
Irradiance spectra are often computed or measured in energy
units. These spectra can be converted to photon irradiances via
Planck's law. For example, given an energy spectrum ("Es") in
fiWatts cm 2 nm ', WA in photons cm 2 s ' (N nm bandwidth) '
can be computed from:
Wx = Es x5.047x!09
(nm) x N (nm)
where A is the wavelength of the incident radiation, and
5.047xl09 ={10 6 (J/s)/(uWatt)xlO 9m/nm}/{6. 6262x10 34 J-s
X2.99m/s} . At 297.5 nm, for example, the appropriate conversion
factor for EXAMS' bandwidth of 2.5 nm is:
Wx = Es x5.047x!09 x 297.5 x 2.5 = 3.754 xlQ12 Es
Procedure PHOTO! begins from near-surface pre-computed
photolysis rate constants (KDP), and thus does not use the spectral
irradiance vector Wx in its computations. PHOTO 1 adjusts KDP via
a cloudiness correction (2-111), and also corrects KDP for the
geographic translation from the latitude at which each KDP was
measured (RFLAT) to the latitude of the ecosystem (LAT). The
correction term is computed from the total annual solar + sky
radiation received at latitudes RFLAT and LAT. Ground-level
radiation was computed as a function of latitude at 10 degree
intervals via procedures described by List (1966) using an
atmospheric transmission coefficient of 0.90. Estimated total
radiation increased from 1.038xl05 cal cm 2 yr ' at the pole, to
52
-------
2.744*105 langley yr ' at the equator. Regression of the (8)
estimates on:
Y = a + bcos(2L)
where L is the latitude in radians, yielded a = 1.917xl05, b =
8.705xl04, and accounted for 99.7% of the variation in total
irradiance (Y) with latitude. In procedure PHOTO 1, the correction
term for KDP is computed as:
KDP t- KDP x •
For example, for a rate constant measured at the equator and a
polar ecosystem,
KDP <- KDP x a+ cos(°-0349 x9°)
fl+&cos(0.0349 xO)
This procedure is based on total solar energy input, and thus may
underestimate the latitude dependence of photochemical
transformation of pollutants that absorb sunlight most strongly
at wavelengths < 320 nm.
2.3.3.7 Input data and computational mechanics - Summary
Procedure PHOTO! was designed to evaluate chemicals whose
absorption spectrum has not been quantified. Its primary input
datum, KDP, is a vector of pseudo-first-order photolysis rate
constants. A separate value of KDP can be entered for each
existing ionic species (RH3, RH4+, etc.). KDP must be calculated
as an annual average value applicable to near-surface waters,
under cloudless conditions and full-day average solar irradiance.
EXAMS then adjusts the rate constant for light extinction in the
water column, effects of cloud cover, and departure of the
latitude of the environment (LAT) from the latitude at which the
rate constant was measured (RFLAT). Effects of sorption and
DOC-complexation cannot be represented; only the dissolved
form of the chemical photolyzes.
The computations executed by procedure PHOTO! are inherently
less accurate than wavelength-specific computations based on
the chemical's absorption spectrum (PHOTO2), because PHOTOl
cannot evaluate the effects of spectrally differentiated light
extinction in the water column.
Procedure PHOTO2 couples the absorption spectra of the
compound (ABSORG( 1 -46,k)) to the irradiance spectrum incident
on the water body (WA). The QYIELD matrix contains measured
disappearance quantum yields; it is also used in EXAMS to
account for effects of sorption and complexation with DOC.
Both photolysis procedures operate on the incident light field to
compute the average light intensity in each compartment of the
ecosystem. (In PHOTOl, the incident light field has a nominal
value of 1.0, because KDP has built into it the clear-sky
irradiance at latitude RFLAT.) PHOTOl uses a spectral
composite light absorption coefficient for each sector of the
water body computed via Eq. (2-105) at the user-specified
wavelength (LAMAX); PHOTO2 computes the absorption
coefficient for every spectral waveband (Table 9) from the
pigment (CHL), dissolved organic carbon (DOC), and suspended
sediment (SUSED) concentrations in each compartment
(Equation (2-105)).
The water column ("E" and "H" compartment types) can be
subdivided into as many horizontal slices as seems appropriate
for a particular water body. EXAMS traces light extinction
vertically through the slices by computing irradiance at the
bottom of each compartment, as well as the average irradiance
used for computing photolysis rate constants. When the (J-l)
compartment is another E or H segment, irradiance at the bottom
of the (J-l) compartment is taken as the starting point for
irradiance computations in the current (J) sector. This scheme
will fail, however, if the system definition rules given in Chapter
2.3.1.1 are disregarded.
An example of a logical, but improper, segmentation scheme is
shown in Figure 7.
1
1 1 ]
1 I !
1
1
1
"1 1
Ill 1
^ _ „ __, „__„„___ _.|,
1
1 S b
1
1 E E
Figure 7. Improper segmentation: incomplete vertical definition
Calls to EXAMS' photolysis procedures are executed in
compartment order. For this ecosystem, the first call computes
photolysis in compartment 1, a (L)ittoral water column
compartment. The incident light field at the air-water interface
is identified as the light intensity at the top of the compartment,
and EXAMS computes both average light intensity and irradiance
at the bottom of compartment 1. Compartment 2 is a (B)enthic
compartment; EXAMS simply sets the photolysis rate to 0 and
does not call a photolysis procedure. Upon reaching
compartments, EXAMS checks the compartment type of the (J-l)
compartment. As compartment 2 is "B" (benthic), the incident
light field is used to start the computations. A photolysis
procedure is not invoked for (B)enthic compartment 4. When
EXAMS now calls for irradiance computations for the
(H)ypolimnion compartment (5), an error occurs. EXAMS
inspects (J-l) compartment 4, finds it to be (B)enthic, and starts
53
-------
its computations for the hypolimnion using the surface incident
light field, rather than intensity at the bottom of the epilimnion.
The proper system definition is shown in Figure 8.
1
1 1 ]
1 I !
1 1
1 1
1 115
1 E * 1
1
I I t
1
1 ! 1
1
1
!
i
i
i
i
i
Figure 8. Proper segmentation: complete vertical definition.
In this case, upon initiation of computations for the hypolimnion
(now numbered 6), EXAMS finds that the (J-l) compartment (5)
is not (B)enthic, as it is an (E)pilimnion segment. The light
intensity at the bottom of compartment 5 (internal variable
BOTLIT in PHOTO 1, vector BOTLAM in PHOTO2) is therefore
retrieved and used to start the irradiance computations for the
hypolimnion.
EXAMS reports the average light intensity in each sector of the
ecosystem as part of its "canonical profile" of the system. These
reports are given as apercentage of the light field incident on the
water body, integrated over the ultraviolet portion of the
spectrum (278.75-395 nm).
References for Chapter 2.3.3.
Bailey, G. W., and S. W. Karickhoff. 1973. An ultraviolet
spectroscopic method formonitoring surface acidity of clay
minerals under varying water content. Clays and Clay
Minerals 21:471-477.
Bailey, R. A., H. M. Clarke, J. P. Ferris, S. Krause, and R. L.
Strong. 1978. Chemistry of the Environment. Academic
Press, New York.
Baker, K. S., and R. C. Smith. 1982. Bio-optical classification
and model of natural waters. 2. Limnology and
Oceanography 27:500-509.
Balzani, V., and V. Carassiti. 1970. Photochemistry of
Coordination Compounds. Academic Press, London and
New York.
Biittner,K. 1938. Physikal Bioklimatologie (Physik. Bioklimat),
Leipzig.
Calvert, J. G., and J. N. Pitts, Jr. 1966. Photochemistry. John
Wiley and Sons, Inc., New York.
Dozier, J. 1980. A clear-sky spectral solar radiation model for
snow-covered mountainous terrain. Water Resources
Research 16:709-718.
Green, A. E. S., K. R. Cross, and L. A. Smith. 1980. Improved
analytic characterization of ultraviolet skylight.
Photochemistry and Photobiology 31:59-65.
Green, A. E. S., T. Sawada, and E. P. Shettle. 1974. The middle
ultraviolet reaching the ground. Photochemistry and
Photobiology 19:251-259.
Green, A. E. S., and P. F. Schippnick. 1980. UV-B reaching the
ground, in Conference in Ultraviolet Light in Marine
Ecosystems, Copenhagen.
Green, A. E. S., and P. F. Schippnick. 1982. UV-B reaching the
surface. Pages 5-27 in J. Calkins, editor. The Role of Solar
Ultraviolet Radiation in Marine Ecosystems. Plenum Press,
New York.
Hojerslev, N. K. 1973. Inherent and apparent optical properties
of the western Mediterranean and the Hardangerfjord. 21,
Univ. Copenhagen, Inst Phys. Oceanogr., Copenhagen.
Hojerslev, N. K. 1974. Inherent and apparent optical properties
of the Baltic. 23, Univ. Copenhagen, Inst. Phys. Oceanogr.
Hutchinson, G. E. 1957. A Treatise on Limnology. Vol. I.
Geography, Physics, and Chemistry. John Wiley and Sons,
Inc, New York.
Jerlov (Johnson), N. G., and G. Liljequist. 1938. On the angular
distribution of submarine daylight and the total submarine
illumination. Sven. Hydrogr.- Biol. Komm. Skr., Ny Ser.
Hydrogr. 14:15.
Jerlov, N. G. 1976. Marine Optics. Elsevier Sci. Publ. Co,
Amsterdam-Oxford-New York.
Karickhoff, S. W., and G. W. Bailey. 1976. Protonation of
organic bases in clay-water systems. Clays and Clay
Minerals 24:170-176.
Leighton, P. A. 1961. Solar radiation and its absorption. Pages
6-41 in Photochemistry of Air Pollution. Academic Press,
New York and London.
List, R. J. 1966. Smithsonian Meteorological Tables, 6th edition.
Smithsonian Institution, Washington, D.C.
Mickle, A. M., and R. G. Wetzel. 1978. Effectiveness of
submersed angiosperm-epiphyte complexes on exchange of
nutrients and organic carbon in littoral systems. II.
Dissolved organic carbon. Aquatic Botany 4:317-329.
Miller, G. C., and R. G. Zepp. 1979a. Effects of suspended
sediments on photolysis rates of dissolvedpollutants. Water
Research 13:453-459.
Miller, G. C., and R. G. Zepp. 1979b. Photoreactivity of aquatic
pollutants sorbed on suspended sediments. Environmental
Science and Technology 13:860-863.
Oliver, B. G., E. G. Cosgrove, and J. H. Carey. 1979. Effect of
suspended sediments on the photolysis of organics in water.
Environmental Science and Technology 13:1075-1077.
Poole, H. H., and W. R. G. Atkins. 1926. On the penetration of
light into sea-water. Journal of the marine biological
Association of the U.K. 14:177-198.
Preisendorfer, R. W. 1958a. Directly observable quantities for
54
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light fields in natural hydrosols. Visibility Laboratory
Report SIO Reference 58-46, Scripps Institution of
Oceanography, La Jolla, California.
Preisendorfer, R. W. 1958b. Unified Irradiance Equations.
Visibility Laboratory Report SIO Reference 58-43, Scripps
Institution of Oceanography, La Jolla, California.
Schultz, R., andK. Grafe. 1969. Consideration of sky ultraviolet
radiation in the measurement of solar ultraviolet radiation.
Pages 359-373 in F. Urbach, editor. The Biologic Effects of
Ulraviolet Radiation. Pergamon Press, Oxford.
Smith, J. H., W. R. Mabey, N. Bohonos, B. R. Holt, S. S. Lee,
T.-W. Chou, D. C. Bomberger, and T. Mill. 1978.
Environmental Pathways of Selected Chemicals in
Freshwater Systems: Part II. Laboratory Studies. EPA-
600/7-78-074, U.S. Environmental Protection Agency,
Athens, Georgia.
Smith, R. C., and K. S. Baker. 1978. Optical classification of
natural waters. Limnology and Oceanography 23:260-267.
Smith, R. C., and J. E. Tyler. 1976. Transmission of solar
radiation into natural waters. Photochemistry and
Photobiology Reviews 1:117-155.
Strickler, S. J., and R. A. Berg. 1962. Relationship between
absorption intensity and fluorescence lifetime of molecules.
Journal of Chemical Physics 37:814-822.
Tailing, J. F., R. B. Wood, M. V. Prosser, and R. M. Baxter.
1973. The upper limit of photo synthetic productivity by
phytoplankton: Evidence from Ethiopian soda lakes.
Freshwater Biology 3:53-76.
Turro, N. J. 1978. Modern Molecular Photochemistry. The
Benjamin/Cummings Publ. Co., Inc., Menlo Park,
California.
Wetzel, R. G. 1975. Limnology. W.B. Saunders Co.,
Philadelphia.
Zepp, R. G. 1978. Quantum yields for reaction of pollutants in
dilute aqueous solution. Environmental Science and
Technology 12:327-329.
Zepp, R. G. 1980. Assessing the photochemistry of organic
pollutants in aquatic environments. Pages 69-110 in R.
Haque, editor. Dynamics, Exposure and Hazard Assessment
of Toxic Chemicals. Ann Arbor Science Publishers, Ann
Arbor.
Zepp, R. G., and G. L. Baughman. 1978. Prediction of
photochemical transformation of pollutants in the aquatic
environment. Pages 237-263 in O. Hutzinger, I. H. v.
Lelyveld, and B. C. J. Zoeteman, editors. Aquatic
Pollutants: Transformation and Biological Effects.
Pergamon Press, Oxford.
Zepp, R. G., and D. M. Cline. 1977. Rates of direct photolysis
in aquatic environment. Environmental Science and
Technology 11:359-366.
Zepp, R. G., and P. F. Schlotzhauer. 1981. Comparison of
photochemical behavior of various humic substances in
water: III. Spectroscopic properties of humic substances.
Chemosphere 10:479-486.
Zepp, R. G., N. L. Wolfe, L. V. Azarraga, R. H. Cox, and C. W.
Pape. 1977. Photochemical transformation of the DDT and
methoxychlor degradation products, DDE and DMDE, by
sunlight. Archives of Environmental Contamination and
Toxicology 6:305-314.
Zepp, R. G., N. L. Wolfe, J. A. Gordon, and G. L. Baughman.
1975. Dynamics of 2,4-D esters in surface waters:
hydrolysis, photolysis, and vaporization. Environmental
Science and Technology 9:1144-1150.
Zepp, R. G., N. L. Wolfe, J. A. Gordon, and R. C. Fincher.
1976. Light-induced transformations of methoxychlor in
aquatic systems. Journal of Agricultural and Food
Chemistry 24:727-733.
2.3.4 Specific Acid, Specific Base, and Neutral
Hydrolysis
Many organic compounds react directly with water in the
aqueous solvent system. In addition, water dissociates into
hydronium and hydroxide ions, and the concentrations of these
subsidiary species often affect the rates of chemical
transformations. EXAMS includes kinetic constants for three
kinds of hydrolytic pathways: specific-acid (H3O+) catalyzed,
neutral hydrolysis, and specific-base (OH ) catalyzed
transformations.
The breakdown of esters to yield a carboxylic acid and an
alcohol is a convenient example of hydrolytic transformations.
The overall reaction is:
RCOOR'+ Kj O -* RCOOH + R1 OH (2-112)
where R and R' can be any alkyl or acyl moiety. Although this
equation accurately depicts the stoichiometry of ester hydro lysis,
it does not serve as a defining equation for computing the
velocity of the reaction. The rate of transformation of any
specific ester is fundamentally dependent on its chemical
structure. The speed of the reaction often depends on the pH of
the medium as well, however, because ester hydrolysis can
proceed via three distinct pathways (acid/base catalysis, neutral)
with identical net stoichiometry.
Because chemical reactions involve shifts in electronic bonding
orbitals, organic compounds are most readily attacked by groups
that can donate or accept electrons from the target molecule.
Electron-deficient chemical species (e.g., hydronium ions, H3O+)
are called "electrophiles." Electtophiles are particularly attracted
to atoms with negative charge, to alone pair of electrons, and to
the electron-dense region of a double bond. Chemical groups
with extra non-bonding electrons are called "nucleophiles." The
electronegative hydroxide ion is a relatively strong nucleophile.
The water molecule is itself nucleophilic, because the oxygen
55
-------
atom of this "polar" molecule has a lone pair of electrons.
The mechanisms of the formation and hydrolysis of organic
esters have beenreviewedbyKirby). Tinsley(Tinsley 1979:105
ff) has summarized the routes and mechanisms of ester
hydrolysis under environmental conditions, where in all
probability only the "A(AC)2"and "B(AC)2"mechanisms are
significant. Although all three routes of ester hydrolysis involve
nucleophilic phenomena, the mechanism of the pathway is
somewhat different in each case.
Acid-catalyzed ester hydrolysis is a true "catalysis" reaction in
that the hydronium ion participates in the reaction but is not
consumed by the reaction sequence. In the first step of this
pathway, the electrophilic hydronium ion protonatesthe carbonyl
(C=O) oxygen. The protonated ester then undergoes a
nucleophilic addition of water to give atetrahedral intermediate;
this step is catalyzed by a second water molecule acting as a
general base. Finally, the tetrahedral intermediate breaks down
via two additional (fast) steps to yield the product carboxylic
acid and alcohol, and to regenerate the catalytic hydronium ion.
Because two water molecules are involved in the formation of
the tetrahedral intermediate, the specific-acid catalyzed
hydrolysis of the ester bond is "second-order" in water. With
water as the solvent medium for the dissolved phase of the
compound, however, the water concentration does not change
during the course of the reaction and need not be incorporated in
the kinetic expression. The observed rate constants are
nonetheless subject to solvent effects and should be measured
using pure water as the solvent whenever possible. The direct
involvement of the water molecule in the reaction also imposes
a need for care in the extrapolation of hydrolysis rate constants
(measured in pure water) to compounds sorbed with sediments.
In EXAMS, all concentrations are referenced to the water phase
of each compartment (Eq. (2-1)). The total pollutant
concentration [C] (units mg/liter of water), when multiplied by
the appropriate distribution coefficient a, gives the dissolved
concentration of pollutant in the aqueous phase of each
compartment, including that in the interstitial pore water of a
benthic sediment. This fraction of the pollutant hydro lyzes at the
rate measured via a homogeneous phase (pure water)
experiment. EXAMS computes the effects of residence in a sorbed
state (where water concentration can be quite small) on the
reactivity of the compound via an additional set of input
parameters (Chapter 2.3.4.3).
Neutral hydrolysis of organic esters proceeds via a mechanism
similarto that of the second stage of the acid-catalyzed pathway.
Two molecules of water are again involved in the formation of
a tetrahedral intermediate, with one molecule of water acting as
the nucleophile and a second water molecule acting as a general
base catalyst. Although the neutral reaction is usually treated as
a simple first-order process (Wolfe 1980), it is in fact technically
second-order in water concentration. This reaction is thus also
subject to solvent effects, and requires the same caution in
extrapolation to sorbed states as was mentioned above for
acid-catalyzed hydrolysis.
Alkaline hydrolysis, the third hydro lytic mechanism considered
in EXAMS, is not strictly speaking a hydroxide-"catalyzed"
reaction, for hydroxide ion is consumed in the reaction sequence,
yielding the ester's constituent alcohol and the anion of its
carboxylic acid. At trace concentrations of the ester in buffered
natural waters, however, this distinction is of no practical
significance. As in the neutral and acid-catalyzed pathways, a
tetrahedral intermediate species is probably involved in alkaline
hydrolysis. In this case, however, the intermediate is usually
envisioned as resulting from a direct nucleophilic attack of
hydroxide ion on the carbonyl carbon, with formation of the
tetrahedral intermediate as the rate -limiting step in the reaction
pathway. The tetrahedral intermediate then breaks down via
release of an R'O anion, and rapid proton transfer (to R'O ), to
yield the products of the reaction (R'OH and RCOO ).
The overall rate of hydro lytic transformation is thus the sum of
three competing reactions, and the observed rate constant (Kobs,
units /h) can be computed as the sum of contributions from
acid-catalyzed (KAH), neutral (KNH), and base-catalyzed (KBH)
reactions (Wolfe 1980):
K^, = KAH[H + ] + K^IH+ KBH[OH~ ] (2-1 1 3)
Kobs is a pseudo-first-order (h ') rate constant underthe specified
environmental conditions (pH, pOH) in pure water. After
incorporation of the effects of ionization and sorption on
reactivity, Kobs becomes the hydro lytic contribution to the overall
pseudo-first-orderrateconstant^ofEq. (2-1). The second-order
transformation rate constants are part of EXAMS' input chemical
data base. KAH and KBH have units of reciprocal molarity and
reciprocal hours (M 'h '). KNH, the neutral hydrolysis rate
constant, has units h '. The environmental data for this
computation are entered as a separate pH and pOH for each
sector (compartment) of the ecosystem; the EXAMS variables are
"PH" and "POH," respectively.
The utility of this approach is not, of course, limited to chemical
transformations of carboxylic acid esters. For example, amides,
carbamates, and organophosphates break down via hydrolytic
mechanisms (Tinsley 1979). Also, many halogenated compounds
are subject to unimolecular and bimolecular nucleophilic
substitution (SN[ and SN2) or elimination (E[ and E2) reactions
whose kinetics can be represented via Eq. (2-113).
Experimental studies of the rate of hydrolysis in buffered
aqueous solution can be used to determine Kobs at fixed pH
levels. In many cases KAH and KBH can be determined at low and
high pH, respectively, where only one reaction pathway is
significant. The pH-rate profile, a plot of log Kobs vs. pH, then
56
-------
includes a descending (acid catalyzed) and ascending (alkaline)
limb, with slopes of -1.0 and +1.0, respectively ( Figure 9).
These lines intersect at the theoretical rate minimum, giving the
best pH for an experimental determination of any neutral
contribution (KNH). One or more of the reaction pathways may
be undetectably slow for a particular compound, resulting in a
simplified pH-rate profile. The use of pH-rate profiles for
calculating hydrolysis rate constants has been described in more
detail by Kirby(Kirby 1972:153) and by Mabey and Mill(1978).
-8 -
Figure 9. Hydrolysis pH-rate profile of phenyl acetate at 25°C.
Profile constructed via rate constant data summarized by
Mabey and Mill 1978.
Reported chemical rate constants are often based on the second
or minute as the unit of time. For use in EXAMS, such data must
be converted to units based on the hour. For example, the neutral
hydrolysis rate constant of phenyl acetate is 6.6* 10"8 s ' (Figure
9). EXAMS' chemical input parameter (KNH, units /h) would be
6.6xlQ-8(s 1)x3600(s/h)=2.38xlO-4h '.
2.3.4.1 Temperature effects
Each hydrolytic rate constant in EXAMS' chemical data base
(KAH, KNH, KBH) has a paired input parameter that allows for
alternative entry of the chemical information as an (Arrhenius)
function of temperature. The pairings are KAH-EAH, KNH-ENH,
and KBH-EBH, respectively. The rate constant variables (KAH
etc.) are interpreted as the (Briggsian logarithm of the) "pre-
exponential" or "frequency" factor in an Arrhenius function,
only when the parallel activation energies (EAH etc.) are
non-zero. When the input energy parameter is set to zero, EXAMS
accepts the kinetic parameter (KAH, etc.) as the rate constant
itself (compare Chapter 2.3.2.1). For example, an activation
energy (Ed) for the neutral hydrolysis of phenyl acetate (ENH) of,
say, 20,000. cal/mole would imply a frequency factor A =KNH /
exp(-Ea/RT), (T in Kelvin) or, given KNH = 2.38xlO'4h ' at 25°
C (Figure 9), A=2.38xlO-4/exp(-20000./(1.99x298.15)) =
1.04x10" h ', and log A, the EXAMS input (KNH) = 11.02 h '.
In this instance, if EXAMS were loaded with KNH=2.3 8x10 4and
ENH = 0.0, KNH would be used as the rate constant for neutral
hydrolysis irrespective of the temperature (environmental input
parameter TCEL) obtaining in the system compartments.
Alternatively, EXAMS could be loaded with KNH = 11.02 and ENH
= 20.0. In the latter case EXAMS would compute local
(compartment specific) values of the neutral hydrolysis rate
constant KNH via:
log KNH = KNH- ((1000xENH)/4.58(TCEL+273.15))
or, atTCEL=25°C,
log KNH=11.02-(1000x20)/(4.58x298.15) =-3.626
and
KNH = 2.36xlO'4h '.
In many cases the temperature dependence of a chemical rate
constant is reported in terms of transition state theory, that is, as
an enthalpy (H) and entropy (S) of activation. Given H in
calories/mole and S in cal/deg/mole (also called "entropy units,"
e.u.), data reported under this convention can be converted to
Arrhenius functions via (Bunnett 1961):
and
Ea = H+RT
log.4 = + log T+14319
(2-114)
(2-115)
4.58
in which Ea has units of calories/mole, and A is in h '.
(Because the RT term is only about 600 cal/mol at room
temperature, however, in many cases reported values of H are in
fact unconnected Arrhenius activation energies (Ed).)
For example, Wolfe and coworkers (Wolfe et al. 1977) found
that malathion (O,O-dimethyl-S- (1,2-dicarbethoxy)
ethylphosphorodithioate) breaks down via an acid catalyzed
degradation with an enthalpy of activation (H) of 22.3 kcal/mol
and an entropy of activation (S) of-4.1 eu. Using 15°C as the
temperature for conversion, Eq.(2-114) gives Ea = 22300 +
(1.987)(288.15) = 22872 cal/mol and an EXAMS input of EAH =
22.87 kcal/mol. The frequency factor,4 (Eq.(2-l 15)) would be:
log A = -4.1/4.58 + log(288.15) + 14.319, yielding log A =
When a compound is subject to more than one degradation
pathway, the rate constants must be combined prior to entry in
EXAMS' chemical data base. For example, malathion undergoes
an alkaline carboxyl ester hydrolysis plus an E2 elimination
reaction kinetically dependent on hydroxide ion concentration
[OH ] (Wolfe et al. 1977). These authors computed entropies
and (unconnected) enthalpies of activation for both reactions; this
information could be used to generate an Arrhenius
approximation for the total alkaline disappearance rate constant
KBH via the transition state theory equation
KBH (h ') = (7.5018xl013)(T)xexp(-H/RT)xexp(S/R),
57
-------
where H = Ea - RT, as in Eq.(2-114). Whenever possible,
however, it is probably better to rely on experimental
determinations of the total disappearance rate constant, measured
at environmentally relevant temperatures. In this case, the total
(second-order) disappearance rate constant was measured at 0°
and at 27° C; it was 0.067 and 5.5 M 's ', respectively. The
activation energy and frequency factor can be easily computed
from these data, giving KBH = 23.67 h ' and EBH = 26.6
kcal/mol.
2.3.4.2 lonization effects
Anions and cations of organic acids and bases can hydrolyze at
rates differing greatly from those of the parent unionized
species. This phenomenon can give rise to pH-rate profiles very
unlike the archetypal example of phenyl acetate (Figure 9),
including an apparent kinetic dependence on fractional powers
of {H3O+} or {OH }. In order to encompass these phenomena,
each of EXAMS' input kinetic parameters (and parallel activation
energies) is set up as a 3x7 matrix of variables. The second
index of these parameter matrices specifies the ionic species for
which a particular rate constant applies. The first index allows
for specification of the effects of sediment sorption and DOC
complexation on reactivity; this aspect of EXAMS' input data is
discussed in Chapter 2.3.4.3.
Consider, for example, the hydrolysis of the series of substituted
2- phenyl-1,3-dioxanes studiedby Bender and Silver (1963). All
the acetals in the series showed the usual acid-catalyzed
hydrolysis, but those members containing an o- or /"-phenolic
substituent were reactive at alkaline pH as well. The pH-rate
profile for one member of this series, 2-(4-hydroxy-5-
nitrophenyl)-1,3-dioxane ("HND"), is shown in Figure 10. This
profile includes a descending limb of slope -1.0 at low pH, a
zone of little change in K^ suggestive of a neutral mechanism,
followed by a second descending limb in the alkaline pH range,
which begins in the vicinity of pH = pKa.
-2
0-15 130
-8
pKa 6 63
2-{4-Hydroxy-5-Hitroplieiiyl}-
1,3-Dioxane
2468
pH
Figure 10. Hydrolysis pH-rate profile fora substituted
2-Phenyl-1,3-Dioxane. Data from Bender and Silver 1963.
From a comparison of the kinetics of o- and /"-phenolic
substituted acetals, and from the effect of deuterium oxide on
these kinetics, Bender and Silver (1963) concluded that the
hydrolysis of these compounds does not in fact proceed via a
neutral mechanism. Instead, the initial descending limb is the
result of the usual acid-catalyzed hydrolysis of the unionized
species, and the descending limb in the alkaline pH region
results from a similar acid- catalyzed hydrolysis of the phenolate
anion (Figure 10). The anion is 860 times more reactive than the
unionized species. The uniformity of Kobs at intermediate pH
results from the increasing importance of the phenolate anion, as
a fraction of the total concentration, in this pH range.
The hydrolytic kinetics of HND can be completely specified in
EXAMS' chemical data base via the pKa of the compound and the
rate constants for acid hydro lysis (KAH) of the uncharged and the
anionic species. The existence of the unionized species and the
phenolate anion is signaled by setting SPFLG(l) and SPFLG(5)
to 1 (Chapter 2.2.1), and the pKa of HND is specified via PK(1)
= 6.63. The second-order rate constants are 540 and 4.68*105
M 'h ' for the parent compound and the anion respectively
(Figure 10). This information is loaded to EXAMS by setting
KAH(1,1) to 540, andKAH(l,5)=4.68E5. The first index (Chapter
2.3.4.3) simply denotes the formof the compound (l=dissolved).
The second index specifies the ionic species to be transformed.
Subscript (1,1) thus specifies the unionized dissolved HND as
reacting at rate (540)[H3O+] h ', and subscript (1,5) specifies the
phenolate anion as reacting at rate (4.68xl05)[H3O+] h '.
2.3.4.3 Sorption effects
Sorption of a compound with sediment phases or complexation
with DOC removes the compound to a micro environment that can
be very different from the free water phase of the system. For
example, Miller and Zepp (1979) found that the daughter
product yields resulting from photolysis of DDE shifted from
p,p '-dichlorob enzophenone toward DDMU
(l,l-bis(p-chlorophenyl)- 2- chloroethylene) when the parent
DDE was sorbed with suspended sediments. Photolysis of DDE
dissolved in hexane also gave enhanced yields of DDMU. The
authors concluded that the sorbed compound experienced a
micro environment similar to that of an organic solvent, very
different from that of the aqueous phase.
Similar effects can be postulated for the majority of modes of
chemical reaction of organic compounds in the environment.
Presumably reactions like the neutral hydrolysis of carboxylic
acid esters, in which the water molecule participates in the
reaction, would be substantially inhibited by residence of the
target molecule in a sorbed state. An E[ or SN[ reaction might be
little affected, however. Furthermore, the possibility of
accelerated (sediment "catalyzed") reactions cannot be
disregarded. Although no definitive studies on the effects of
residence in a sorbed state on chemical reactivity have appeared
in the literature, such exploratory experiments as have been
58
-------
undertaken indicate that sorption "protects" many compounds
from hydrolytic transformation. This is not a universal
phenomenon, however, for in some cases the reactivity of
compounds was not affected by residence in a sorbed state
(Macalady and Wolfe 1985).
Obviously, then, the differences between the sorbed form of an
organic compound and its dissolved phase can be as profound as
the differences between, for example, an organic acid and its
anions (Chapter2.3.4.2). EXAMS therefore provides fortheentry
of rate constants (or Arrhenius functions) that specify the
kinetics of sorbed forms completely independently of the
reactivity of the aqueous dissolved phase of the compound. Each
named kinetic parameter is a matrix with 21 elements, allowing
for up to 7 ionic species with 3 forms (dissolved,
sediment-sorbed, andDOC-complexed) of the parent (uncharged)
compound and of its ions.
EXAMS computes the rate of reaction of the sorbed species under
the assumption that the reaction is first-order in sorbed substrate.
The model used to calculate these reaction rates can be described
via a simplified example. In general, the rate of transformation
of dissolved compound is given by:
(2-116)
and
(2-120)
where [Cw] is aqueous concentration (mg/L) and Kw is a
first-order (or pseudo-first-order) rate constant (units h ').
Similarly, the rate of transformation of the sorbed phase of the
compound can be taken as:
(2-117)
,.
dt
where [Cs] is the concentration of the sorbed phase of the
compound (mg/kg dry sediment) and Ks is a first-order rate
constant.
Under EXAMS' assumption that a rapid (i.e., faster than the
reaction rates) sorption equilibrium is maintained, [Cw] and [Cs]
are in constant proportion to [CT], where [CT] is the total
concentration of pollutant in the system. (In this context,
"system" is equivalent to a single ecosystem sector
(compartment), a well-mixed chemical apparatus, etc.) Within
EXAMS, chemical concentrations are carried internally in units of
mass/unit aqueous volume. Consequently the total rate of
transformation of a compound is represented as:
4frJ_ „ ^ i
dt
(2-118)
where p is the sediment/water ratio (kg/L), that is, SEDCOL in
the language of Chapter 2.2.2.
Given that the assumption of a rapid local sorption equilibrium
holds good, however,
[C7H,]-flf1[Cr] (2-119)
where a^l^l+pKp), and a2=pKp/(l+pKp)^p the partition
coefficient as described in Chapter 2.2.4. Substitution of Eq. (2-
119) andEq. (2-120) into Eq. (2-118) gives:
(2-121)
Equation (2-121) is (a simplified form of) the defining equation
used to build EXAMS' algorithm for combining the reactivity of
dissolved and sorbed forms into a single pseudo-first-order rate
constant.
This formulation makes the conversion of experimental
observations into a form suitable for EXAMS relatively
straightforward. Suppose, for example, that a neutral organic
compound ("NOC"), subject to a pH-independent
transformation, is observed to have a half-life in pure water of
33 hours. The rate constant describing this process is
^=-(^0.5)733=0.021 h '.
This rate constant enters EXAMS' chemical data base via
KNH(1,1) = 0.021. The first subscript of KNH denotes the
dissolved form (1) of the compound; the second denotes an
uncharged molecule. (As NOC is a neutral compound, only
SPFLG(l) is set (i.e., = 1), and SPFLG(2...7) are 0, denoting the
non-existence of any ionic species.)
Suppose, now, that NOC has a partition coefficient on a
particular sediment of 90000 (mg/kg)/(mg/L), and an
experimental determination is made of the half-life of the
compound after sorption equilibration on a 1 00 mg/L suspension
of the sediment. (So long as local sorption equilibrium is
maintained, the half-life can be ascertained by following either
Cw or Cs over time.) The fraction of CT present as dissolved and
as sorbed NOC can be computed using p and Kp. The
sediment/water ratio p is (100 mg/L)x(10-6 kg/ml) = 10'4 kg/L;
thedissolvedfractionisthusa1=l/(l+pKp)=l/(l+(90000xlO-4))
= 0.10. Similarly, the fraction sorbed is «2— pKp/(l+pKp) =
(90000xlO-4)/(l+(90000xlQ-4)) = 0.90.
This experiment has three obvious possible outcomes. First,
sorption may have no effect on the reactivity of the compound.
Second, the observed half- life may be increased in strict
proportion with a l , that is, sorption may "protect" the compound.
Finally, an altered half-life may be indicative of an altered, but
non-zero, reactivity of the sorbed phase of the compound.
In the first case, when the presence of the sediment suspension
has no effect on the rate of transformation of the compound, Ks
is identically the same as Kw. EXAMS could then be loaded with
KNH(2, 1) = 0.021 ; the subscript "2" denotes the sediment-sorbed
form of NOC.
59
-------
When residence in a sorbed state inhibits the transformation
reaction, the observed rate constant Kobs wil decrease in
proportion with the residual water concentration of the
compound. From Eq. (2-121), Kobs = o^Kw + a2Ks. In this
example, a: = 0.10, and, if Ks = 0, then Kobs = (0.10)(0.021) =
0.0021 h '.
The possibility that sorption "protects" the compound can thus
be evaluated via the compound's half-life in a sediment
suspension. Here, given Kobs = 0.0021, the half-life T is given
by:
T = -(In 0.5)7(0.0021) = 330 hours.
In other words, an observed half-life that was not significantly
different from 330 hours would be a clear indication that Ks =
0, and EXAMS could be loaded with KNH(2,1) = 0.
Finally, the observed half-life maybe neither 33 nor 330 hours,
indicating that the sorbed state, while reactive, does not
transform at the same rate as the aqueous-phase dissolved
compound. (In the event that the observed half-life were longer
than 330 hours, Ks would be negative. The most probable
explanation of such an event, however, is that either the
sediment was contaminated with the study compound prior to the
experiment, or that the assumption of rapid local equilibrium has
been violated.) Any half-life >0 and <330 hours can be
converted to a value of Ks via Eq. (2-121). If the half-life were
less than 33 hours, the transformation must have been
accelerated in the sediment phase and Ks > Kw. Suppose,
however, that the half-life of NOC in the 100 mg/L sediment
suspension was 150 hours. The first-order rate constant Kobs is
then:
Kobs = -(In 0.5)7(150) = 4.621X10'3.
Given Kobs = a, Kw + a2 Ks, then
4.621X10'3 = (0.1)(0.021) + (0.9) Ks
and Ks = 2.80xlO'3. The rate of reaction of NOC in the sorbed
state was thus 13.3% of that of the dissolved phase of the
compound. This information could now be entered into EXAMS
via the command SET KNH(2,l)=2.8E-3.
Second-orderpH-mediatedreactions occurring onsediments are
accommodated in EXAMS in a wholly analogous way. EXAMS
2.5.5 Indirect Photochemistry, Oxidation and
Reduction
Direct photo lysis is not the sole pollutant trans formationprocess
driven by the solar flux in aquatic systems. The simultaneous
occurrence of plant decomposition products ("humic materials"),
dissolved oxygen, and sunlight often results in an acceleration of
the rate of transformation of organic pollutants (Zepp 1988a).
uses the solution-phase pH and pOH to compute rates of
reaction, so of course the input data must also be developed from
this perspective. The full 21 -element matrices of KAH, KNH, and
KBH allow for the specification of effects of both sediment
sorption and DOC complexation on first-order, [H3O+] mediated,
and [OH ] mediated reactions. In addition, any of these rate
parameters can alternatively be specified via an Arrhenius
function, as described in Chapter 2.3.4.1.
References for Chapter 2.3.4.
Bender, M. L., and M. S. Silver. 1963. The hydrolysis of
substituted 2-phenyl-1,3 -dioxanes. Journal of the American
Chemical Society 85:3006-3010.
Bunnett, J. F. 1961. The interpretation of rate data. Pages 177-
283 in S. L. Friess, E. S. Lewis, and A. Weissberger,
editors. Technique of Organic Chemistry Vol. VIII Part I.
Interscience Publ. Inc., New York.
Kirby, A. J. 1972. Hydrolysis and formation of esters of organic
acids. Pages 57-207 in C. H. Bamford and C. F. H. Tipper,
editors. Comprehensive Chemical Kinetics Vol. 10—Ester
Formation and Hydrolysis and Related Reactions. Elsevier
Publishing Co., Amsterdam-London-New York.
Mabey, W., and T. Mill. 1978. Critical review of hydrolysis of
organic compounds in water under environmental
conditions. Journal of Physical and Chemical Reference
Data 7:383-415.
Macalady, D. L., and N. L. Wolfe. 1985. Effects of sediment
sorption on abiotic hydrolyses. I. Organophosphorothioate
esters. Journal of Agricultural and Food Chemistry 33:167-
173.
Miller, G. C., and R. G. Zepp. 1979. Pho tore activity of aquatic
pollutants sorbed on suspended sediments. Environmental
Science and Technology 13:860-863.
Tinsley, I. J. 1979. Chemical Concepts in Pollutant Behavior.
John Wiley and Sons, New York-Chichester-Brisbane-
Toronto.
Wolfe, N. L. 1980. Determining the role of hydrolysis in the fate
of organics in natural waters. Pages 163-178 in R. Haque,
editor. Dynamics, Exposure and Hazard Assessment of
Toxic Chemicals. Ann Arbor Science Publishers, Ann
Arbor.
Wolfe, N. L., R. G. Zepp, J. A. Gordon, G. L. Baughman, and
D. M. Cline. 1977. Kinetics of chemical degradation of
malathion in water. Environmental Scienc e and Te chnolo gy
11:88-93.
Zepp et al. (1977a), for example, found that methoxychlor, with
a direct photolysis halflife of more than 300 hours, had a halflife
of as little as 2.2 hours under irradiation in natural waters
containing dissolved humic materials. Further, Ross and Crosby
(1975) found that a solution of aldrin in water from a taro paddy
can be photochemically converted to dieldrin, despite the fact
that aldrin does not absorb sunlight.
60
-------
These kinds of reactions are usually termed "indirect" or
"sensitized" photolysis. Indirect photolysis can be subdivided
into two general classes of reactions. First, "sensitized
photolysis" per se involves sunlight absorption and electronic
excitation of a sensitizer (humic) molecule, followed by direct
chemical interaction between the excited state of the sensitizer
and a pollutant molecule. Possible chemical reactions include a
direct energy transfer to the pollutant molecule, hydrogen atom
transfer from pollutant to sensitizer to give free radicals, and
union of sensitizer and pollutant yielding an excited-state
complex or "exciplex" (Zepp and Baughman 1978). The
resulting free radicals or exciplexes can then react with dissolved
molecular oxygen, a process termed "type I sensitized
photooxidation" by these authors.
The second class of indirect photolysis involves the formation of
chemical oxidants in natural waters, primarily via the interaction
of sunlight, humic materials, and dissolved oxygen (type II
sensitized photooxidation (Zepp and Baughman 1978)). The
primary oxidants known to occur in natural waters are hydroxyl
(OH«) and peroxy (ROO) radicals (Mill et al. 1978), singlet
oxygen (1O2) (Zepp etal. 1977V). Alkoxy radicals and diradicals
may also contribute to environmental oxidation of some
compounds, but their presence in natural waters has not been
conclusively demonstrated (Mill 1980, Mill etal. 1980).
2.3.5.1 Oxidation
EXAMS represents the oxidative transformation of pollutants via
a phenomenological coupling of a second-order rate constant
(KOX, units M 'h ') to the molar concentration (OXRADL,
moles/liter) of oxidants in each ecosystem compartment. The
pseudo-first-order contribution of oxidation processes to the
overall transformation rate constant K of Eq. (2-1) is computed
for each (J) compartment via:
K (h ') = KOX X OXRADL(J)
The input parameter KOX is a 3x7 matrix, allowing for separate
entry of rate constants of ionic species and for control of the
effects of sorption on reactivity. The subscripts have the same
significance and use as described for KAH etc. in Chapter2.3.4.
Effects of temperature are entered via a parallel matrix of
Arrhenius activation energies BOX (kcal/mol), again as described
in Chapter 2.3.4 for hydrolytic reactions.
The occurrence and concentration of oxidants in natural waters
has been investigated via the irradiation of chemical "probes" in
a laboratory setting. Zepp et al. (1977V) studied the generation
of singlet oxygen using solutions of 2,5-dimethylfuran (DMFN)
in natural waters. DMFN gives 1,2- diacetylethylenes via
1,4-addition of singlet oxygen. The speed of this reaction upon
irradiation of the solution gives a quantitative measure of the
steady-state concentration of singlet oxygen in the solution.
Similarly, Mill et al. (1978, 1980) have used cumene
(isopropylbenzene) and pyridine as chemical probes for the
steady-state concentrations of peroxy and hydroxyl radicals in
irradiated natural waters.
Some results of these studies are given in Table 14 (Data from
Zepp etal. 1977b, Mill etal. 1980). These concentrations were
determined via photolysis of thin layers of solution, and are
presumably dependent on the intensity and spectral distribution
of the solar flux in the solution. The average steady-state molar
concentrations of oxidants were on the order of 10 13 for singlet
oxygen, 10 9 for peroxy radicals, and 10 17 for hydroxyl radicals.
More recent investigations have found surface concentrations of
singlet oxygen from about 9x10 13 M down to 4x 10 15 M (Haag
and Hoigne 1986). Even at these low concetratins, singlet
oxygen can be a significant factor in the environmental fate of
substituted alkenes, phenols, anilines, and mercaptans (Weber
1998). Hydroxyl radical concentrations have more recently been
reported to range from 10 15 to 10 18 M, with reaction with
hydroxyl radical potentially accounting for as much as 15% of
total carbaryl dissipation in some shallow water systems
(Armbrust 2000).
Table 14. Steady-state concentration (moles/liter) of oxidants in
some natural waters
Source Singlet Oxygen Peroxy Radical Hydroxyl Radical
(MxlO13) (MxlO') (Mx 10")
Aucilla
River, FL
Okefenokee
Swamp, GA
Mississippi
River, LA
Coyote
Creek, CA
Boronda
Lake, CA
18
22
2.8
1.8
9.1,5.0
9.5,0.45
1.6
0.15
Carbonate radical is a secondary oxidant, produced in natural
waters by reaction of hydroxyl radical with carbonate or
bicarbonate ion. Steady-state concentrations in Ontario, Canada
have been observed ranging from 5x10 15tolO 13M (Huang and
Mabury 2000); in the Greifensee (Switzerland) summer
carbonate radical concentrations are about 3x10 14M (Larson
and Zepp 1988).
Because light extinction in the water column reduces the
volumetric average solar flux below that at the air-water
interface, the oxidant concentrations given in Table 14 apply
only to the surface zone of natural bodies of water. The effect of
light extinction can be computed from the equation:
FIo = (l-exp(-kZ))/kZ
where I/Io is the average light intensity expressed as a fraction
61
-------
of its surface value (lo), k is a diffuse attenuation coefficient
(/m), and Z is the depth of the compartment (m). The factor I/Io
is calculated by EXAMS for the UV light important in generating
photochemical oxidants (280-395 nm), and then used to generate
depth-corrected oxidant concentrations:
OXRADL = OXRAD X (I/Io)
EXAMS allows for the entry of environmental concentrations of
only one kind of oxidant other than singlet oxygen, which is
treated separately (Chapter 2.2.5.2). For a compound reactive
with more than one oxidant species, the rate constant and
environmental concentration giving the most significant
transformation rate can be entered, or a composite rate constant
and environmental oxidant concentration can be computed and
loaded in the data bases.
(second-order process rate constant Kqnch with units M ' s ').
The second-order rate constant (Kox) for the production of
singlet oxygen by energy transfer to dissolved oxygen from S*
is between 1 x 109 and 3x 109 (R. G. Zepp 2000, oral comm.), for
EXAMS it is taken as 2xl09 M ' s ' (Zepp et al. 1985). Singlet
oxygen undergoes rapid physical quenching by water to ground
state dioxygen with a pseudo-first-order rate constant KdecO2 of
2.5xl05 s ' ((Rodgers and Snowden 1982) cited from (Haag et
al. 1984a)). These reactions can be summarized by Eq. (2-122)-
(2-124), where D is the distribution function and lave is the
average light intensity. The quantity (4> e lave) is integrated
across the sunlight spectrum during EXAMS' computations.
(2-122)
2.3.5.2 Singlet Oxygen
Singlet oxygen ('O2), an electronically excited state of the
dioxygen molecule (O2), is formed in natural waters by the
transfer of solar energy from illuminated DOM. EXAMS calculates
the concentration of 'O2 in each surface water segment, using
methods described by Zepp and coworkers (Zepp et al. 1985,
Zepp 1988b), corrects for depth and couples the result to a 3x7
matrix of second-order reaction rate constants Klo2 for
application in the mass balance Eq. (2-1). Generation of 'O2
begins with the production of electronically excited triplet states
(5*) via absorption of sunlight energy by dissolved organic
matter (S). The wavelength-specific quantum yields 4> used in
EXAMS for this process were computed from the relationship
4>=0.015 exp(0.01 (366-A), where A is the wavelength in nm. The
pre-exponential factor in this relationship was derived from the
average value of 4> (0.015) reported for 11 natural waters at 366
nm (Zepp et al. 1977b), corrected as per (Haag et al. 1984a).
The slope (0.01) was calculated from the observation that 4> at
313 nm is approximately 0.025 (Haag et al. 1984b, Zepp et al.
1985). Example values are given in Table 15. Computation of
light absorption by DOC uses the specific absorption
coefficients e of Table 9.
Table 15. Example quantum yields for production of triplet
sensitizer S*from DOC
(nm)
(nm)
280
300
312.5
350
0.035
0.029
0.026
0.016
400
450
490
650
0.011
0.006
0.004
0.0009
0
(2-123)
(2-124)
The steady-state concentration of 'O2 in sunlit surface waters is
given by
where
[S *
(2-125)
(2-126)
Kqnch[DOC]
Increasing the oxygen concentration from 2.6x10 4 M (air
saturated) to 1.2x10 3 M (oxygen saturated) decreases [S*(ss)J
by some 4x (Zepp et al. 1985). Given Kox[O2], the sum of the
other terms in the denominator of Eq. (2-126) (KdecS3 and
Kqnch[DOC]) is 1.6xl05. These experiments were conducted
using [DOC] of about 20 mg/L (R. G. Zepp 2000, oral comm.).
For EXAMS, the terms were equally partitioned, thus giving
KdecS3 = S.OxlO4 (s ') and Kqnch = 4,000 (s '(mg/L) ') for
calculating steady-state concentrations of triplet sensitizer and
singlet oxygen. Model performance can be gauged by
comparison of EXAMS' calculated values to published estimates
of surface concentrations of'O2 (Zepp et al. 1977b, Haag and
Hoigne 1986). The calculated 'O2 concentrations (Table 16) are
acceptable except in the case of the three USA rivers with
[DOC] < 15 mg/L; these waters (from the Wakulla, St. Marks,
and Mississippi rivers) contained significant quantities of non-
colored DOC (R. G. Zepp 2000, oral com).
S* decays back to its ground state by spontaneous decay (with
first-order rate constant KdecS3, with units s ') and self-
quenching interactions with other components of the DOM
62
-------
Table 16. Comparison of EXAMS' singlet oxygen estimates
with literature values
Water
Lentic
Tiirlersee1
Greifensee1
Liitzelsee1
Etang de la
Gruere1
Okefenokee2
Lotic
Rhine1
Kleine Emme1
Glatt1
Aucilla2
Ecofina2
Fenholloway2
Wakulla2
St. Marks2
Mississippi2
[DOC]
mg/L
8.3
3.5
7.9
13
24
3.2
3.2
4.1
24
41
77
6
9
14
['O2]ssxl014M
Lit Val
2.8
3.3
5.4
11.6
17.8
2.4
4.1
4.6
14.6
15.4
13.8
0.16
0.49
0.41
EXAMS
9.7
4.5
9.3
14.
6.9
4.1
4.1
5.2
6.9
9.0
11.
2.4
•-t •-}
3.3
4.8
1. June Swiss values from Haag and Hoigne 1986, corrected to
full day values by daylength and factor of 2/n
2. December USA values from Zepp et al. 1977b, corrected as
above, normalized to flat water body by division by 2, and
corrected for updated DMF rate constant via division by 1.6
2.3.5.3 Reduction
Anaerobic (reducing) environments are common in nature,
including many groundwaters and saturated soils, aquatic
sediments below the surface oxidized layer, waterlogged peats
and marshlands, and, at least periodically, the hypolimnion of
stratified lakes. Chemicals notoriously persistent in aerobic
environments can be remarkably less so under anaerobic
conditions (Macalady et al. 1986). In anaerobic sediments, the
trans formation half-lives ofnitroaromatics can be from minutes
to hours, with formation of aromatic amines as hazardous
daughter products (Weber 1998). The responsible reducing
agents may include ferrous iron, iron sulfides and dissolved
sulfidic species (Simon et al. 2000), biochemical agents
produced by the benthos, and direct biolysis. Reductive
transformations encompass a variety of reaction types and
chemical classes, including reductive dechlorination, (or, more
generally, reductive dehalogenation), reduction of nitroaromatic
and azo compounds, reduction of N-nitrosamines across either
the N-N or N-O bond, reduction of sulfoxides to thioethers, of
quinones, and reductive dealkylation (Larson and Weber 1994).
Prediction of the environmental concentrations of reducing
agents has thus far proved intractable. The situation is further
complicated by the observation that, although sorption inhibits
reduction reactions, the velocity of the reaction increases with
increasing sediment concentration. This result suggests that
regeneration of the reducing agent by "electron-transfer
mediators" may play in important role in governing the velocity
of reduction pathways (Weber 1998), and that reducing agents
maintain stable levels in the presence of low concentrations of
reacting pollutants.
As with oxidants, EXAMS provides a phenomenological coupling
between a reaction rate constant structure (the 3x7 matrices
KRED) and an array (monthly for each environmental segment)
of molar concentrations of reducing agents (REDAG). Effects of
temperature are entered via a parallel matrix of Arrhenius
activation energies ERED (kcal/mol), again as described in
Chapter 2.3.4 for hydrolytic reactions. EXAMS does not include
nominal values for REDAG.
References for Chapter 2.3.5.
Armbrust, K. L. 2000. Pesticide hydroxyl radical rate constants:
Measurements and estimates of their importance in aquatic
environments. Environmental Toxicology and Chemistry
19:2175-2180.
Haag, W. R., and J. Hoigne. 1986. Singlet oxygen in surface
waters. 3. Photochemical formation and steady-state
concentrations in various types of waters. Environmental
Science and Technology 20:341-348.
Haag, W. R., J. Hoigne, E. Gassman, and A. M. Braun. 1984a.
Singlet oxygen in surface waters - Part I: Furfuryl alcohol
as a trapping agent. Chemosphere 13:631-640.
Haag, W. R., J. Hoigne, E. Gassman, and A. M. Braun. 1984b.
Singlet oxygen in surface waters - Part II: Quantum yields
of its production by some natural humic materials as a
function of wavelength. Chemosphere 13:641-650.
Huang, J., and S. A. Mabury. 2000. Steady-state concentrations
of carbonate radicals in field waters. Environmental
Toxicology and Chemistry 19:2181-2188.
Larson, R. A., and E. J. Weber. 1994. Reaction Mechanisms in
Environmental Organic Chemistry. CRC Press, Boca Raton,
Florida.
Larson, R. A., andR. G. Zepp. 1988. Reactivity of the carbonate
radical with aniline derivatives. Environmental Toxicology
and Chemistry 7:265-274.
Macalady, D. L., P. G. Tratnyek, andT. J. Grundl. 1986. Abiotic
reduction reactions of anthropogenic chemicals in anaerobic
63
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systems: A critical review. Journal of Contaminant
Hydrology 1:1-28.
Mill, T. 1980. Chemical and photo oxidation. Pages 77-105 in
O. Hutzinger, editor. The Handbook of Environmental
Chemistry. Volume 2 Part A-Reactions and Processes.
Springer-Verlag, Berlin-Heidelberg-New York.
Mill, T., D. G. Hendry, and H. Richardson. 1980. Free-radical
oxidants in natural waters. Science 207:886-887.
Mill, T., H. Richardson, andD. G. Hendry. 1978. Oxidation of
organic compounds in aquatic systems: the free radical
oxidation of cumene. Pages 223-236 in O. Hutzinger, I. H.
v. Lelyveld, and B. C. J. Zoeteman, editors. Aquatic
Pollutants: Transformation and Biological Effects.
Pergamon Press, Oxford.
Rodgers, M. A. J., and P. J. Snowden. 1982. Journal of the
American Chemical Society 104:5541-5543.
Ross, R. D., and D. G. Crosby. 1975. The photooxidation of
aldrin in water. Chemosphere 4:277-282.
Simon, R., D. Colon, C. L. Tebes-Stevens, and E. J. Weber.
2000. Effect of redox zonation on the reductive
transformation of />-cyanonitrobenzene in a laboratory
sediment column. Environmental Science and Technology
34:3617-3622.
Weber, E. J. 1998. Abiotic transformation reactions. Pages 259-
281 in G. Schuurmann and B. Markert, editors.
Ecotoxicology: Ecological Fundamentals, Chemical
Exposure, and Biological Effects. John Wiley & Sons, Inc.,
New York, and Spektrum Akademischer Verlag,
Heidelberg.
Zepp, R. G. 1988a. Environmental photoprocesses involving
natural organic matter. Pages 193-214 in F. H. Frimmel and
R. F. Christmam, editors. Humic Substances and Their Role
in the Environment. John Wiley & Sons, New York.
Zepp, R. G. 1988b. Factors affecting the photochemical
treatment of hazardous waste. Environmental Science and
Technology 22:256-257.
Zepp, R. G., and G. L. Baughman. 1978. Prediction of
photochemical transformation of pollutants in the aquatic
environment. Pages 237-263 in O. Hutzinger, I. H. v.
Lelyveld, and B. C. J. Zoeteman, editors. Aquatic
Pollutants: Transformation and Biological Effects.
Pergamon Press, Oxford.
Zepp, R. G., P. F. Schlotzhauer, and R. M. Sink. 1985.
Photosensitized transformations involving electronic energy
transfer in natural waters: Role of humic substances.
Environmental Science and Technology 19:74-81.
Zepp, R. G., N. L. Wolfe, L. V. Azarraga, R. H. Cox, and C. W.
Pape. 1977a. Photochemical transformation of the DDT and
methoxychlor degradation products, DDE and DMDE, by
sunlight. Archives of Environmental Contamination and
Toxicology 6:305-314.
Zepp, R. G., N. L. Wolfe, G. L. Baughman, and R. C. Hollis.
1977b. Singlet oxygen in natural waters. Nature 267:421-
423.
2.3.6 Microbial Transformations
Microbial communities are a ubiquitous constituent of almost all
aquatic ecosystems. The microbiota play a central role in the
remineralization of plant and animal debris; they have evolved
the capacity to transform and harvest energy from an immense
array of naturally occurring organic compounds. The optimistic
hope that "the solution to pollution is dilution" to some extent
had its origin in a naive faith in the ability of saprobic microbes
to utilize any and all synthetic organic compounds in their
metabolic mills.
Total faith in the ability of natural systems to absorb and
detoxify synthetic chemicals was, of course, shaken by the
discovery of the world-wide dispersal (and bioconcentration) of
synthetic biocides, notably DDT and the related compounds
DDE and ODD. It is now "axiomatic that micro-organisms are
fallible and that many synthetic organic compounds are
recalcitrant ... and accumulating in some environments"
(Alexander 1979b). In consequence, qualitative
"biodegradability" tests (Swisher 1970) are now routine in the
detergent industry, and "sludge" tests have been suggested
(Buzzell et al. 1969) as a routine procedure for evaluating
industrial synthetic organic compounds. The latter tests can also
provide some assurance that the compound will not destroy the
microbial communities that serve man in sewage treatment
plants, as well as providing an evaluation of the biodegradability
of the compound (e.g., Baird et al. 1974).
The discovery of microbial fallibility has led to a more critical
and quantitative view of microbial metabolism of synthetic
organics; it no longer suffices to know that a compound is
"biodegradable." Instead the rates, products, microbial
populations, and environmental conditions surrounding an
observed biodegradation are of increasing interest to the
microbiological and ecological community. Given that the
natural microbial substrates can massively accumulate in special
circumstances (e.g., peat bogs and swamps), a degree of
skepticismis obviously warranted toward any claim of universal,
unconditional biodegradability of a synthetic organic compound.
The rates at which a compound is biodegraded must depend
upon both the structure of the compound, and on the metabolic
capacity of the microbial community resident in the ecosystem
receiving the compound.
Microbial communities derive energy for metabolism and growth
from the breakdown of organic compounds, and the kinetics of
growth and substrate utilization have been of interest to
microbiologists for many years. Both population growth and
substrate utilization rate have been described via Monod's
(1942) analogy with Michaelis-Menten enzyme kinetics (Slater
1979). In this approach, the growth of a microbial population in
a non-limiting environment is described by
(2-127)
64
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where fj, is called the "specific growth rate" and TV is microbial
biomass or population size. The Monod equation modifies Eq.
(2-127) via the recognition that consumption of resources in a
finite environment must at some point curtail the rate of increase
(dN/dt) of the population. This fact is typically incorporated into
Eq. (2- 127) via
[5]
(2-128)
in which [S] is the concentration of the growth limiting substrate,
fjLmax is the "maximum specific growth rate" obtaining when [S]
is present in excess (i.e., non-limiting), and Ks, the "half-
saturation constant" is that value of [S] allowing the population
to grow at rate nmJ2. An equation describing the behavior of the
growth substrate [S] over time, and thus by implication the
dynamics of a biodegradable synthetic compound, follows via a
simple derivation (Slater 1979): Assuming that a fixed amount
of growth results from metabolism (and therefore loss from
solution) of a unit quantity of S, then dN = -YdS, where Fis the
"yield coefficient" in cells or biomass produced per unit S
metabolized. Taking
(2-129)
dt di^J dt
gives dS/dt = -(jU/Y) N, or, substituting Eq. (2-128) for ju,
dt
7 KS+[S\
• N
(2-130)
The observed microbial growth yield Fis often calculated from
the amount of microbial biomass produced during the course of
transformation of a measured quantity of substrate. Because a
microbial population must satisfy maintenance requirements
before any growth can occur, the apparent yield Fcan fall quite
drastically at low concentrations of S. Expressions with
separated growth and maintenance yields have been developed
by Pirt (1975) and it is possible to compute the separate
magnitudes of growth and maintenance yields from microbial
growth experiments (Slater 1979).
The Monod formulation (Eq. (2-130)) has been successfully
applied to biotransformation of synthetic chemicals in a
laboratory setting. These studies have demonstrated that a
Monod analysis need not be restricted to single-species
populations, that is, the Monod equations can serve as adequate
descriptors of substrate transformation by "mixed" or
"heterogeneous" (i.e., multi-species) microbial populations. In
one example, Paris, Lewis, and Wolfe (1975) isolated a
4-species consortium of organisms able to use malathion as its
sole source of carbon. Analysis of the growth response of the
population and of concurrent malathion transformationrates then
gave the Monod parameters fimax = 0.37 /h, Ks — 2.17 nmol/L
(0.716 mg/L), and Y= 4.1xl010 cells/nmol (1.2xlOn cells/mg).
In a similar study, Smith and coworkers (1978) investigated the
degradation of p-cresol by mixed microbial cultures able to use
the compound as a sole carbon source. These authors expressed
their results in terms of Monod parameters, finding fimax = 0.62
/h, Y= 1.8x10" cells/mg, and£5 = 0.84 mg/L.
Unfortunately, these elegant applications of classical
microbiological methods to the biotransformation or "biolysis"
of synthetic organic compounds are difficult to extrapolate to a
broader ecological context. The first difficulty, which has
received some attention in the microbiological literature, is
primarily mechanical: The Monod formulation (Eq.(2-130)) is
non-linear in its parameters, and thus imposes a high cost in
computation time when used in a computer program or
mathematical model. For Irace concentrations of pollutants (i.e.,
[S] « Ks), however, the term (Ks + [S]) in the denominator of
Eq.(2-130) can be approximated by Ks, giving the linear
approximation
(2-131)
dt
YK,
This formulation is similarto the "second-order" equations used
to describe the kinetics of chemical reactions, and the term
^maJ(YK^ can by analogy be termed a second-order biolysis rate
constant KB2, with units /h/(cells/L) when population sizes TV are
expressed in cells/L.
The propriety of this substitution can be readily evaluated. For
malathion, jumw/(Y KJ = 0.37/(4.1xl010x2.17) =4.16xlQ-12
/h/(cells/L). Paris etal. (1975) also computed values ofKB2 from
microbial population sizes and malathion transformation rates in
their experimental studies, finding a mean value for KB2 of
(2.6±0.7)xlQ-12 (95% confidence interval) /h/(cells/L) for a
series of 8 experimental determinations spanning a concentration
range from 0.0273 to 0.33 nmol/L in malathion. The measured
KB2 differed from fj,maj/(Y Kg) by less than a factor of 2,
suggesting that simplified kinetic experiments could be used to
develop second-order rate constants, in lieu of a detailed
elaboration of (o.max, Y, and Ks, when the region of concern is
restricted to levels of compound « Ks.
Furthermore, dissolved pesticide concentrations in surface
waters are often very low indeed. For example, dieldrin, lindane,
and DDT have been found primarily at ng/L levels both in the
USA(1972) and in Britain (Brooks 1972). For industrial
chemicals, however, the situation can be somewhat different. For
example, the release of phenolic wastes into the St. Lawrence
River results in riverine concentrations of 0.01 to 0.15 mg/L
(Visser et al. 1977). Although these high concentrations are
restricted to a dispersion cone immediately downstream of the
effluents, Ks for p-cresol (0.84 mg/L) is uncomfortably close to
the highest measured concentrations.
65
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The need for a thorough evaluation of the propriety of Eq. (2-
131) as an adequate approximation to the Monod formula (Eq.
(2-130)) disappears, however, upon examination of the
conceptual difficulties standing in the way of the application of
either equation to environmental situations. A natural microbial
community derives its energy from a large variety of organic
detrital materials. A microbial species restricted to a trace-level
synthetic compound as its sole carbon source would be at a
severe competitive disadvantage; there is no way of predicting
a priori the population densities the degrader could attain in real
systems. Even when a synthetic compound is sufficiently similar
to natural substrates that it can be indifferently degraded by the
microbial community as a whole, the presence of multiple
energy-yielding substrates in the environment violates a
fundamental assumption underlying the Monod approach, that
is, that [S], the synthetic compound, limits growth. In many
instances, moreover, a compound may be transformed or
degraded in an energy-requiring detoxification process, making
the concept of cell yield (Y) of dubious utility. When the
compound, for whatever reason, is degraded in the absence of a
change in population size, the apparent zero yield forces
abandonment of Eq. (2-130) and (2-131) alike. This phenomenon
is sometimes called "cometabolism," e.g., (Alexander 1979a,
Jacobson et al. 1980).
Despite these difficulties, the rate of transformation of organic
pollutants must depend on the structure of the compound and the
metabolic capacity of microbial communities. The simplest
expression of this duality is the second-order equation
(2-132)
which asserts that the rate of biolysis (d[S]/df) is first-order in
compound concentration [S] and in microbial activity, biomass,
or population size B, and in which the identification of KB2 with
(a.mM/(Y Ks) is discounted. This approach requires that the
second-order rate constant be determined via laboratory studies
of relatively undisturbed samples drawn from natural microbial
communities. It also requires demonstration that dfSJ/dt is
linearly proportional to [S] for fixed levels of B, and that
appropriate measures of B be found.
One conventional microbiological technique for characterizing
microbial populations is simple population size, measured, for
example, by colony counts on pour-plate agars (APHA 1976.).
To the extent that population size is an adequate measure of
community metabolic capacity, simple microbiological
techniques could serve to characterize the rate of
biotransformation of synthetic compounds in natural
environments. This hypothesis has been explored for three
compounds - the butoxyethyl ester of 2,4-D (2,4-DBE),
malathion, and chlorpropham - by Paris and coworkers
(Baughman et al. 1980, Paris et al. 1981). These authors
investigated biotransformation of their study compounds in
samples of natural waters drawn from 40 locations in the
continental USA. Ambient water temperatures at the collection
points ranged from 1° to 29° C, and the laboratory studies were
conducted at the observed ambient temperature of the sampled
environments. Ambient bacterial populations, as measured by
48-hour incubation on TGE (tryptone glucose extract) agar at
22° C, ranged from4x!02 to 9xl05 cells/ml (Paris etal. 1981).
Biolysis of these chemicals was demonstrably first-order in
compound concentration and in population size, and was
relatively independent of temperature. The 95% confidence
intervals (based on among-site variation) forKB2 (/h/(cell/L)) and
the number of sites for these compounds were (5.42±0.97)x 10"10
(2,4-DBE, 31 sites),(4.54±0.74)xlO-u (malathion, 14 sites), and
(2.63±0.72)xlO~14 (chlorpropham, 11 sites).
Evaluation of biolysis in such "river die-away" studies is
particularly difficult when a combination of small populations
and slow rates of biodegradation leads to inconveniently long
biolysis halflives. In such cases population densities must be
augmented via centrifugation or supplemental nutrient broth.
The latter procedure can have the disadvantage of favoring
opportunistic heterotrophs at the expense of organisms better
equipped to degrade more recalcitrant compounds. For example,
the second-order biolysis rate constant (KB2) f°r chlorpropham
is unaffected when population densities are augmented via
nutrient broth supplementation, but the measured KB2 for
phenanthrene decreases in approximate proportion with the
population increase (Paris 1982, oral comm.).
Baughman, Paris, and Steen (1980) also reported second-order
rate constants for biotransformation of synthetic chemicals by
populations derived from aquatic sediments. The observed rate
constants for 2,4-DBE and malathion were consistent with the
rate of degradation by water-column populations, at KB2 of
2.3x 10"10and 4.0x10"" /h/(cell/L) respectively. Aerobic biolysis
of chlorpropham by the sediment-derived populations was,
however, almost an order of magnitude faster (KB2 — 1.42x 10"13)
than biolysis by the water-column populations (KB2 = 2.4xlO"14
/h/(cell/L)). By varying the sediment/water ratio, these authors
also demonstrated that chlorpropham, methoxychlor, and several
phthalate esters were unavailable to biolytic organisms when
sorbed to suspended sediments.
Plate-count estimates of bacterial population densities are
extremely selective and can underestimate total population sizes
by three or more orders of magnitude (Jannasch and Jones 1959,
Wetzel 1975:571, Fletcher 1979). Furthermore, although
increases in pollutant flux (that is, kg m"3 s"1 or mass
degraded/time) with increasing chemical concentration
(consonant with pseudo-first-order kinetics) have been observed
in nature (e.g., Sherrill and Sayler 1980), it is also Irue that, at
elevated pollutant concentrations, both a reduction in the
apparent first-order rate constant (Tinsley 1979:149ff), and
66
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zero-order kinetics (Visser et al. 1977) have been observed. The
latter phenomena are consonant (mathematically) with Eq. (2-
130). It is not clear, however, whether these kinetics are a
reflection of the maximum growth rate of a specialized
sub-population of degraders or result from toxic effects of
elevated concentrations on the microbial community.
In any case, it is clear that the studies of second-order kinetics
cited above are a useful beginning in the detailed quantitative
study of rates of biotransformation of pollutant chemicals. Little
more extrapolative power can be developed until such time as
the environmental coupling variables that govern the metabolic
capacity of natural and laboratory microbial populations have
been rigorously determined and measured in both kinds of
systems.
As a first approximation, EXAMS utilizes a simple second-order
equation (Eq.(2-132)) to compute the rate of biotransformation
of pollutant chemicals. Microbial population densities (as
"colony forming units" (abbrev. cfu)) for each sector of the
ecosystem enter EXAMS' environmental data base via the BACPL
and BNB AC vectors. For water column compartments, BACPL has
units of cfu/mL; BNBAC for benthic compartments has units
cfu/100 g dry weight of sediment. (A useful summary of
observedbacterial population densities in aquatic systems canbe
found at pp. 571-596 of (Wetzel 1975).) Second-order biolysis
rate constants enter EXAMS' chemical data base via separated
parameters for water-column (KBACW) and benthic (KBACS)
populations. The nominal units are h'^cfu/mL)"1 in both cases.
EXAMS internally converts benthic microbialpopulation densities
to units (cfu/mL of water) commensurate with the units of the
rate constant KBACS. This conversion is executed via the
expression (BNBAC x SEDMSL)/(100*WATVOL) where SEDMSL is
the mass of sediment in the compartment (kg), WATVOL is the
volume of water contained in the compartment (Liters) (Eq. (2-
21)), and the numerical factor (100) is a units conversion term
(1000(mL/L)/l 0(decagrams/kg).
Despite the ruthless parsimony imposed on EXAMS'
representation of biolysis kinetics, the degree of chemical detail
provided by EXAMS' allowance for ionic and sorptive speciation
leads to fairly substantial opportunities for the inclusion of
biolytic kinetic detail. Both KBACW and KBACS are 4x7 element
matrices for each chemical under study; each element represents
a separate rate constant for the ionic and sorbed species of the
pollutant. (The use of the matrix indices to specify chemical
species is described in Chapter 2.3.4.) In addition, biolysis rate
constants can be entered either as temperature-independent
single values, or a Q10 function can be invoked to depict the
effects of temperature on biolysis rates.
The Q10 values (i.e., increase in biolysis rate per 10° C change in
temperature) for KBACW and KBACS occur as the parallel
matrices QTBAW and QTBAS, respectively. When the value of
parameter QTBAW or QTBAS is non-zero, EXAMS takes the
corresponding member of KBACW or KBACS as the biolysis rate
constant at 25° C (in accordance with Subpart N guidelines), and
recalculates the second-order rate constant via the Q10 equation:
~ 5 X Q0^-^ (2-133)
Becausemicrobial communities frequentlyadapttheirmetabolic
capacity to keep pace with slow secular (e.g. seasonal) changes
in environmental temperatures, temperature responses measured
in a laboratory setting do not always apply to environmental
conditions. This limitation should be recognized when
interpreting the results of EXAMS simulations that include
temperature effects on biolysis rate constants.
Although the nominal units for bacterial numbers and for KBAC
include bacterial population densities expressed as numbers/mL,
clearly these variables can in both instances be redefined to
encompass any environmental coupling variable of utility in
estimating biodegradation rates in aquatic ecosystems. It is,
however, especially importantthat these units be commensurate:
For example, if the rate constants were determined via viable
plate counts, and the natural population estimated via direct
counts, biolysis rates would probably be grossly overstated.
Furthermore, the simple second-order equation allows for a
multiplicity of estimators of microbial capacity. For example,
Neely (1980:117ff)) lists 7 commonly used estimators of
microbial biomass or activity, including counting techniques,
ATP and DNA analyses, and oxygen uptake. By suitable (user)
redefinition of the nominal units of KBACs, BACPL, and BNBAC,
EXAMS can be used to compute pseudo-first-order rate constants
as a function of environmental variation in the presumed
governing variable, so long as the rate constants and the bacterial
population sizes are entered into EXAMS' data bases in
commensurate units.
The mechanics of EXAMS' conversion of second-order biolysis
rate constants and the compartment-specific environmental
coupling variables to pseudo-first-order form are completely
homologous with the equations used for chemical reactions. The
total pseudo-first-order biolysis rate constant K is accumulated
as the sum of expressions of the form K = a x KBAC x B,
where a is the fraction of the total pollutant concentration
present in each existing ionic or sorbed chemical species, KBAC
is the appropriate element of matrix KBACW or KBACS (corrected
as needed for environmental temperatures using the Q10
equation), and B is the degrader population density or metabolic
capacity of the microbial community in each ecosystem
compartment. The sum of these pseudo-first-order (/h)
expressions then becomes the biolysis contribution to the overall
(compartment-specific) pseudo-first-order rate constant K of Eq.
(2-1).
67
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References for Chapter 2.3.6
Alexander, M. 1979a. Biodegradation of toxic chemicals in
water and soil. Pages 179-190 in R. Haque, editor.
Dynamics, Exposure, and Hazard Assessment of Toxic
Chemicals. Ann Arbor Science Publishers, Ann Arbor,
Michigan.
Alexander, M. 1979b. Recalcitrant molecules, fallible micro-
organisms. Pages 246-253 in J. M. Lynch andN. J. Poole,
editors. Microbial Ecology: A Conceptual Approach.
Blackwell Scientific Publications, Oxford.
APHA. 1976. Standard Methods for the Examination of Water
and Wastewater (Fourteenth Edition). American Public
Health Association, Washington, D.C.
Baird, R. B., C. L. Kuo, J. S. Shapiro, and W. A. Yanko. 1974.
The fate of phenolics in wastewater—determination by
direct-injection GLC and Warburg respirometry. Archives
of Environmental Contamination and Toxicology 2:165-
178.
Baughman, G. L., D. F. Paris, and W. C. Steen. 1980.
Quantitative expression of biotransformation rate. Pages
105-111 in A. W. Maki, K. L. Dickson, and J. C. Cairns, Jr.,
editors. Biotransformation and Fate of Chemicals in the
Aquatic Environment. American Society for Microbiology,
Washington, D.C.
Brooks, G. T. 1972. Pesticides in Britain. Pages 61-114 in F.
Matsumura, G. M. Boush, and T. Misato, editors.
Environmental Toxicology of Pesticides. Academic Press,
New York and London.
Buzzell, J. C., Jr., C. H. Thompson, and D. W. Ryckman. 1969.
Behavior of Organic Chemicals in the Aquatic
Environment. Part III—Behavior in Aerobic Treatment
Systems (Activated Sludge). Manufacturing Chemists
Association, Washington, D.C.
Fletcher, M. 1979. The aquatic environment. Pages 92-114 in J.
M. Lynch and N. J. Poole, editors. Microbial Ecology: A
Conceptual Approach. Blackwell Scientific Publications,
Oxford.
Jacobson, S. N., N. L. O'Mara, and M. Alexander. 1980.
Evidence for cometabolism in sewage. Applied and
Environmental Microbiology 40:917-921.
Jannasch, H. W., and G. E. Jones. 1959. Bacterial populations
in sea water as determined by different methods of
enumeration. Limnology and Oceanography 4:128-139.
Matsumura, F. 1972. Current pesticide situation in the United
2.4 Input Pollutant Loadings
The flux of a pollutant chemical entering an ecosystem (term
"Le" in Eq. (2-1)) is a primary determinant of the ultimate
exposure experienced by resident organisms. EXAMS does not
compute pollutant loadings. Loadings may be developed via
projected or measured industrial effluent fluxes, agricultural
runoff, by transfer from the PRZM model, landfill seepages, etc.,
but these computations must be executed externally to EXAMS.
States. Pages 33-60 in F. Matsumura, G. M. Boush, and T.
Misato, editors. Environmental Toxicology of Pesticides.
Academic Press, New York and London.
Monod, J. 1942. Recherches sur la croissance des cultures
bacteriennes. Herman et Cie, Paris.
Neely, W. B. 1980. Chemicals in the Environment: Distribution,
Transport, Fate, Analysis. Marcel Dekker, Inc., New York
and Basel.
Paris, D. F., D. L. Lewis, and N. L. Wolfe. 1975. Rates of
degradation of malathion by bacteria isolated from aquatic
system. Environmental Science and Techno logy 9:135-138.
Paris, D. F., W. C. Steen, G. L. Baughman, and J. T. Barnett, Jr.
1981. Second-order model to predict microbial degradation
of organic compounds in natural waters. Applied and
Environmental Microbiology 41:603-609.
Pitt, S. J. 1975. Principles of microbe and cell cultivation.
Blackwell Scientific Publications, Oxford.
Sherrill, T. W., and G. S. Sayler. 1980. Phenanthrene
biodegradation in freshwater environments. Applied and
Environmental Microbiology 39:172-178.
Slater, J. H. 1979. Microbial population and community
dynamics. Pages 45-63 in J. M. Lynch and N. J. Poole,
editors. Microbial Ecology: A Conceptual Approach.
Blackwell Scientific Publications, Oxford.
Smith, J. H., W. R. Mabey, N. Bohonos, B. R. Holt, S. S. Lee,
T.-W. Chou, D. C. Bomberger, and T. Mill. 1978.
Environmental Pathways of Selected Chemicals in
Freshwater Systems: Part II. Laboratory Studies. EPA-
600/7-78-074, U.S. Environmental Protection Agency,
Athens, Georgia.
Swisher,R.D. 1970. Surfactant Biodegradation. Marcel Dekker,
Inc., New York.
Tinsley, I. J. 1979. Chemical Concepts in Pollutant Behavior.
John Wiley and Sons, New York-Chichester-Brisbane-
Toronto.
Visser, S. A., G. Lamontagne, V. Zoulalian, and A. Tessier.
1977. Bacteria active in the degradation of phenols in
polluted waters of the St. Lawrence River. Archives of
Environmental Contamination and Toxicology 6:455-469.
Wetzel, R. G. 1975. Limnology. W.B. Saunders Co.,
Philadelphia.
EXAMS provides input vectors for 5 kinds of loadings to each
compartment of the system. These are: point-source or
stream-borne loadings (STRLD), non-point-source loadings
(NPSLD), contaminated ground-water seepage entering the system
(SEELD), precipitation washout from the atmosphere (PCPLD),
and spray-drift (or miscellaneous) loadings (DRFLD). The
loadings have units of kg (of chemical)/hour in all cases. These
loadings are taken as time-invariant constants in EXAMS'
steady-state computations, or as monthly values when EXAMS is
68
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ran in mode 3. In addition, both Mode 2 and Mode 3 provide for
event loadings by specification of the date, chemical, and kg
mass of the event.
Each non-zero pollutant loading must conform to the hydrologic
definition of the ecosystem, or EXAMS will not implement the
loading. For example, EXAMS will cancel a STRLD, NPSLD, or
SEELD for a given compartment, if that compartment does not
receive an appropriate carrier flow STFLO, NPSFL, or SEEPS
respectively. (Definition and entry of the hydrologic variables is
discussed in Chapter 2.3.1.1.) Precipitation (RAIN) is a monthly
climatic variable in EXAMS; a PCPLD is therefore allowed only for
compartments possessing an air-water interface. Non-zero PCPLD
s are automatically canceled in the case of B (benthic) or H
(hypolimnion) compartments. A PCPLD is unconditionally
permitted for compartment number 1, which will always have an
air-water interface if the system definition rales discussed in
Chapter 2.3.1.1 are followed. For all other water-column
compartments, EXAMS simply looks back at the (J-l)
compartment. If this (J-l) compartment is also part of the water
column, it is assumed to be directly above the current (J)
compartment and any non-zero PCPLD is removed.
Compartments with an air-water interface are always preceded
by a benthic (B) compartment when EXAMS' system definition
conventions (Chapter 2.3.1.1) are observed.
EXAMS' subroutine CKLOAD evaluates the propriety of the four
kinds of loadings that enter via carrier flows. These loadings are
evaluated against the volume of the carrier flows and the water
quality characteristics of the target compartment. These
evaluations were designed to prevent inadvertent specification
of a loading outside EXAMS' operating range. In particular,
EXAMS makes no provision for crystallization of the compound
from solution, nor does the program allow for a gradual
dissolution of chemical from a condensed (solid or liquid) phase.
In addition, the non-linearities potentially present at high
chemical concentrations (non-linear sorption isotherms,
appreciable light absorption by the compound, zero-order
biolysis, etc.) are not incorporated in the code. EXAMS' loading
check computations are divided into two groups: checks based
on carrier flows of water alone (PCPLD and SEELD), and checks
based on carrier flows of water plus an entrained sediment
loading (STRLD and NPSLD).
Ground-water seep and rainfall loadings are simply constrained
to aqueous solubility. In these computations, the temperature
(TCEL) and pH (PH and POH) of the compartment receiving the
load are used to compute (as appropriate) the solubilities of each
ionic species of the compound, and the distribution of the
pollutant among its ionic species (distribution coefficients a,
computed as described in Chapter 2.2). EXAMS then computes
the concentration of pollutant in the carrier flow. If the solubility
criterion is exceeded, EXAMS reduces the load to the extent
necessary to conform to the upper limit of EXAMS' operating
range, notifies the user of the modification(s), and returns
control to the user without executing a simulation. The loadings
are recomputed as the product of the limiting concentration and
the carrier flow rate, that is,
Load =
(2-134)
where Load is in kg/ti, Inflow in L/h, Limit (kg/L) is one-half the
solubility of the least soluble chemical species, and p is the
fraction of the total concentration present in the least soluble
dissolved form.
Each streamflow (STFLO) or non-point-source water flow (NPSFL)
entering a compartment may have an associated stream sediment
(STSED) or non-point-source sediment (NPSED) loading (kg
sediment/h) to the compartment. STSED and NPSED are not used
in transport computations (see Chapter 2.3.1.3). In the course of
evaluating stream-borne and non-point-source pollutant
loadings, EXAMS computes a sorption equilibrium for capture of
the pollutant by entrained sediments in the stream or
non-point-source flows entering each compartment. These
computations use the temperature, pH, and sedimentpartitioning
parameters (e.g., organic carbon content (FROC) and ion
exchange capacities (AEC and CEC)) of the receiving
compartment. From the sediment/water ratio of the carrier flow,
EXAMS computes the distribution coefficients (a) of the chemical
in the carrier flow.
If the residual aqueous concentration of any dissolved species
exceeds its concentration limit, the offending loading is reduced
via Eq. (2-134), the simulation is aborted, and control is returned
to the user for evaluation. EXAMS' method for calculating
concentration limits that ensure linearity of sorption isotherms
is described in Chapter 2.2.2.
After checking the loadings and making any necessary
modifications, summing all external loads and units conversion
from kg/h to mg/h yields term Le of Eq. (2-1).
"Drift" loadings (DRFLD) are initially implemented uncritically.
If, however, EXAMS' computations result in final steady-state
chemical concentrations above EXAMS' operating range, any
DRFLD s are then sequentially reduced until the computationally
invalid estimates are corrected. (If MODE>1, or no drift loads
were specified and the results are outside the operating range,
EXAMS aborts the ran and returns control to the user for
corrective action.)
The DRFLD vector can be used to specify miscellaneous loadings
not encompassed in EXAMS' four other monthly loading types.
EXAMS, for example, does not allow for entry of pollutant across
the air-water interface from a polluted atmosphere (Chapter
69
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2.3.2). The impact of a polluted atmosphere can, however, be
computed from the bulk atmospheric partial pressure of the
contaminant, and entered into EXAMS via the DRFLD vector. The
net flux of pollutant across the air-water interface (F, moles m 2
h ') is given (see Eq. (2-71)) by F = K, (P/H- Q where K, is
the exchange constant (m h '), and both (P/H) and have Cl units
of (moles m 3). By assuming the bulk atmosphere to be
uncontaminated (Pg = 0.0), the term (P/H) was discarded in the
development of EXAMS' algorithm for computing volatilization
losses of pollutants from aquatic systems. Inmuchthe same way,
the gross pollutant loadings imposed by a contaminated
atmosphere can be computed by taking Cl = 0.0.
Non-zero boundary conditions can be loaded into EXAMS via the
DRFLD vector. EXAMS' environmental input data can include a
characteristic length (CHARL), cross-sectional area (XSTUR), and
dispersion coefficient (DSP) at any boundary of the system (by
setting either JTURB or ITURB to zero, as described in Chapter
2.3.1.4). The exchange flow of water across the boundary is
given by
FLOW (liters/h) = (1000)(DSP)(XSTUR)/(CHARL)
If the inlet exchange flow is contaminated to a level of [C] mg/L,
the loading (kg/h) on the system of interest is simply:
LOAD = FLOW x [C] xlO 6 (kg/mg)
This load can be imposed on the receptor compartment via the
appropriate element of the DRFLD vector.
The final term in Eq. (2-1) is "Li," the sum of the internal
loadings on the system compartments. Internal loads areis from
two sources: chemical and biological transformation processes,
and internal transport processes. The transport loadings arise
from flows of contaminated water, sediments, and plankton
among the physical sectors (compartments) of the ecosystem.
Their magnitudes can be computed from the magnitudes of the
flows, the distribution of the compound among its dissolved and
sorbed species, and the concentration of the pollutant in the
source compartments (expressed as mass/unit aqueous volume).
EXAMS uses the WATFL (L/h) and SEDFL (kg/h) flow matrices
(see Chapter 2.3.1.5) to compute a matrix of internal loading
factors (EXAMS' internal variable INTINL), by associating
carrier flows with appropriate elements of the a matrix (Chapter
2.2.4). This computation results in terms which, when multiplied
by the total concentration of pollutant in the source
compartments (mg/L), yield the mass loadings Li (mg/h) on the
target compartments, constituents of the final term in Eq. (2-1).
2.4.1 Product Chemistry
Transformation products are an additional element in the internal
loadings of some compounds. These are specified to EXAMS by
setting the activity database (ADB) number of the parent and
daughter within the current simulation specifications, theprocess
generating the product, the reactive form of the parent molecule,
the process yield, and, if desired, the enthalpy of the
transforming process. The EXAMS input parameters are CHPAR,
the chemical parent ADB number, TPROD, the transformation
product ADB number, NPROC, the number of the process, RFORM,
the reactive form of the parent molecule, yield, the (M/M)
process yield, and EAYLD, the enthalpy (kcal).
The generatingprocess is specified usingthe folio wing codes for
NPROC:
1 = specific acid hydrolysis
2 = neutral hydrolysis
3 = specific base hydrolysis
4 = direct photolysis
5 = singlet oxygen
6 = free radical oxidation (e.g., hydroxyl radical)
7 = water column bacterial biolysis
8 = benthic sediment bacterial biolysis
9 = reduction
Twenty-eightreactive forms are available for specifying RFORM,
in addition to which total dissolved etc. can be specified using an
RFORM of
29 = all dissolved species
30 = all sediment-sorbed species
31 = all DOC-complexed species
32 = all biosorbed species
(The complete list of RFORM specifications is given at the RFORM
entry in the EXAMS data dictionary in Chapter 6 of this
document.) The process YIELD may be entered as the simple
mole of product per mole reacted, or as an Arrhenius function,
in which case the enthalpy of the reaction is entered as EAYLD.
Entering trans formation process chemisty requires specification
of these parameters using a "pathway number" as the index for
each complete reaction path. For example, if the parent
compound (parathion) is recalled as ADB chemical 1, and the
product (paraoxon) is recalled as ADB chemical 2, then a reaction
scheme in which direct photolysis of DOC-complexed parathion
yields a 50% generation of paraoxon , and hydroxyl radical
oxidation of dissolved parathion produces paraoxon in mole-for-
mole formation, would be specified to EXAMS as follows:
Given these specifications, EXAMS calculates the resultant
CHPAR
TPROD
NPROC
RFORM
YIELD
EAYLD
Pathway:
1
2
4
3
0.5
0
1
70
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generating flux (in this case of paraoxon, chemical number 2)
from the breakdown of the parent material(s) (in this example,
parathion, chemical number 1), and adds the resulting terms to
the mass loadings Li (mg/h) in each segment, thus completing
Eq. (2-1).
EXAMS allows for multiple transformations; any one of the
chemicals being studied can generate any of the others. Among
the possibilities thus available are regeneration of parent from
product, generation of multiple products from a single starting
material, reaction chains, etc.
2.5 Data Assembly and Solution of Equations
EXAMS includes three "modes" of operation: direct calculation
of the "steady-state" outcome of long-term contamination (Mode
1), step-wise computation of the time course of contaminant
exposure under a specified set of environmental conditions,
(Mode 2), and computation of daily exposure concentrations
using monthly updates of environmental conditions (Mode 3).
The numerical integration techniques are the same, and the
output summaries analogous, for each operating "mode" of the
program.
2.5.1 Exposure
After computing all terms in Eq. (2-1), the resulting system of
mass-balance equations can be divided through by the volume of
the compartments to give a set of equations describing the rate
of change of chemical concentration over time:
r v
dt
(EXAMS allows the volumes of system elements to be altered by
the user during the course of an analysis (except in Mode 1), and
makes appropriate modifications to exposure levels. This feature
is difficult to use, however, and requires care in interpretation.
A version of EXAMS that includes dynamic changes in storage
volumes (Mode 4) is in preparation.)
As time passes, the system evolves toward an ultimate
"steady-state" condition at which the concentrations achieve
stable values. This endpoint is defined by the condition d [C]/dt
= 0.0 for every compartment. At steady state, then, the
concentration of pollutant in each sector of the ecosystem is
given by
M.
r
(2-135)
K
Numerical integration to steady state is notoriously profligate of
computational resources, so EXAMS contains an explicit
procedure (subroutine STEADY) designed to solve the equations
for these concentrations, which define long-term exposure levels
of the pollutant and can serve as a useful adjunct to any exposure
analysis.
The logic of the situation can be illustrated via an elementary
example. For the example, consider the behavior of a
non-sorbing chemical, subject to neutral hydrolysis as its sole
transformation process, in a static one-hectare pond. The pond
is 1 meter deep, with VOL V therefore 10,000 m3 (107 L). The
benthic subsystem consists of a 5 cm active depth of material
with a bulk density (BULKD) of 1.5 g/cc and a water content
(PCTWA) of 150%. The environmental volume of the benthic
zone (VOL) is 500 m3; its aqueous volume is 250,000 liters of
water (Eq. (2-21)). Defining exchange between the benthic
subsystem and the water column via XSTUR = 10,000 m2,
CHARL = (1.05/2) = 0.525 m, and DSP = 10 4 mVh, the rate of
exchange of fluid volume between the water column and the
interstitial pore water (Chapter 2.3.1.4) is
500
~4 xlQ.OQQ
0.525
4 Lik
This exchange flow of water can be reduced to its
pseudo-first-order effect on chemical concentrations (Kt, /h) in
the water column (w) and the benthic (b) subsystem via:
Kt(w) = 952.4/107 = 9.524x10 5 /h, and
Kt(b) = 952.4/250,000 = 3.810x10 3 /h
As the compound does not sorb, transport of sediment and
plankton can in this instance be ignored.
The internal loadings on the system arise from pollutant
contamination of the 952.4 L/h exchange flow between the
system compartments. All the compound is present in dissolved
form in this example; sorbed and complexed exchange processes
are here immaterial. EXAMS computes its internal load factors
Li/Vby dividing the elements of INTiNL by the volume of the
target compartment. In this instance, the load factor on the
benthic subsystem resulting from contamination of the water
column is
INTINL(b,w) <~ (952.4 x 1.00)7250,000 = 3.810x10 3 /h
and the load factor on the water column resulting from
contamination of the benthic interstitial water is
INTINL(w,b) <- (952.4 x 1.00)7107 = 9.524x10 5 7h
(INTINL and Kt are equal in this much simplified example; this
is not usually the case.)
Finally, given an external load on the water column (here a
DRFLD) of, say, 0.02 kg/h, and a neutral hydrolysis rate
71
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;emis
constant of 0.01 /h, the behavior of the chemical in the syst
given by:
d[Cw]/dt=(0.02x10V107)+9.524x105(C,bH0.01 +9.524x10 s)Cw
d[0b]/dt= 3.810x10 3(Cw) - (0.01 + 3.810x10 3)Cb
where Cw is the aqueous concentration in the water column and
Cb is the aqueous concentration in the interstitial pore water
(mg/L). At steady state, d[Cw]/dt = d[CZ>]/dt = 0.0, resulting
2 equations in 2 unknowns:
in
- 0.01009524 Cw + 0.00009524 Cb = - 0.002
0.00381 Cw-0.01381 Cb = 0.0
This elementary example can be easily solved to give Cw —
0.1986 and Cb = 0.0548 mg/L. EXAMS solves the simultaneous
linear equations that describe steady-state concentrations by
Gaussian elimination (Chapter 2.3.1.2). EXAMS' output for the
example system described above is given in Exams Output Table
10.
Seg Resident Mass
t
Kilos %
Exams Output Table 10. EXAMS output tabulation of steady-state
concentrations.
As Gaussian elimination is not an infallible mechanism for
executing these computations, EXAMS includes a second
technique for computing the solution to the system of equations
(Eq. (2-135)), which is invoked in those cases for which
Gaussian elimination fails. This algorithm is an iterative "linear
cascade" method that applies Eq. (2-135) to each compartment
in turn, and then repeats the entire process until such time as the
successive estimates for each compartment change by less than
0.0001%. EXAMS allows for up to 100,000 iterations of the full
"linear cascade" computation. If the linear cascade terminates
without full convergence, EXAMS aborts the run; this event
generally can be taken as an indication that steady-state
concentrations are unbounded. For example, a
non-transformable chemical in a static pond without any export
pathways will accumulate indefinitely; in this situation no
"steady-state" condition can be computed. The degree of
convergence achieved by EXAMS for the full ecosystem can be
examined in the mass balance check printed as the final entry
("Residual Accumulation Rate") in EXAMS' output table entitled
"Analysis of Steady-State Fate of Organic Toxicant."
In the elementary example given above, the linear cascade
solution would proceed:
Iteration 1, step 1: solve for Cw (compartment number 1)
Cw = (Le/V + Li/V)/K = (0.002+9.524x10 5(Cb)) / 0.01009524
giving, as Cb at the moment = 0.0,
Cw = 0.002/0.01009524 = 0.19811
Iteration 1, step 2: solve for Cb (compartment number 2)
Cb = (Le/V+ Li/V)/K = 0.00381 (CwyO.01381
= (0.00381x0.19811)70.01381 =0.05467
From the initial estimates, the second iteration proceeds to
compute a refined estimate of Cw and Cb:
Cw=(0.002+(9.524x105x0.05467)y0.01009524=0.19863
Cb=(0.00381xQ. 19863X0.01381 = 0.054799
The convergence test is computed for each compartment by
calculating the relative change in the estimate. The change in Cw
was
1 -0.19811/0.19863 = 0.0026
and the change in Cb was
1 - 0.05467/0.054799 = 0.0026
As these test values are greater than EXAMS' convergence
criterion (10 6), EXAMS continues with a third iteration of the
linear cascade. EXAMS judges convergence to be complete only
when the relative change in every compartment is less than 10"6.
In mode 2, EXAMS integrates from time "UNIT" to time "TFINAL"
with output and intervals of "CINT" in units specified by
"TCODE." At the end of an integration ("RUN"), the simulation
is paused and the user can evaluate the results and choose to
"CONTINUE" the simulation to some new value of TFINAL. In
Mode 3, the minimum run time is one year, with monthly
updates of environmental properties. Multiple years ("NYEAR")
can be run as a single unit, or the continue command can be used
to run blocks of years. When EXAMS is used in conjunction with
the PRZM model, EXAMS reads the climate time-series used with
72
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PRZM and build a climatic data set consistent with the PRZM data.
2,5,2 Fate
After computing exposure concentrations, EXAMS evaluates the
impact of each transport and transportation process on the
behavior of the compound. During the course of reducing its
input data to pseudo-first-order form, EXAMS preserves the value
of each process' contribution to the overall pseudo-first- order
rate constant K (Eq. (2-1)) for each compartment. The flux of
chemical transformed or transported (mass/time) is then given by
the product of the process rate constant, the concentrations, and
the aqueous volume of each compartment. In the instance under
review, the chemical fluxes attributable to neutral hydrolysis are
Fw = Khyd x Cw x WATVOL(w) x 10'6 (kg/mg)
= (0.01)(0.1986)(107)(10-6) = 0.01986 kg/h
in the water column, and
Fb = (0.01)(0.05479)(2.5x105)(10-6) = 0.000137 kg/h
in the benthic subsystem.
EXAMS sums the fluxes (by process) over the entire system, and
computes the significance of the process via division of the
process flux by the sum of the external loadings on the system,
folio wed by conversion of this result to apercentage basis. In the
example, the total flux is (0.01986 + 0.000137) kg/h, the total
loading was 0.02 kg/h, thus hydrolytic transformation accounts
for 100(0.01986 + 0.000137)/0.02 = 100%ofthe input loadings,
as of course it must in this elementary example.
EXAMS' output table containing the results of the flux analysis,
entitled "Sensitivity Analysis of Chemical Fate," also includes
estimated half-lives for removal or dissipation of the chemical
from the system. These half-lives are computed under the
assumption that internal transport delays are insignificant, giving
a supplemental view of the general significance of each process.
The half-life computations are executed via division of the total
process fluxes by the total mass of pollutant resident in the
system to give a system-wide pseudo-first-order rate constant
Kpr. The half-life is then simply
T1/2 = - In(0.5)/Kpr
In the example case, the total resident mass (computed as the
sum of volumes and concentrations) is 2.00 kg (Exams Output
Table 10), and the projected hydrolytic half-life is therefore
T1/2 = (0.69315 x 2.00)7(0.01986 + 0.000137) = 69.3 hours.
EXAMS does not inflexibly report fluxes and half lives in hours,
for in many instances the hour is an inconveniently small
reporting unit. Instead, the program makes a preliminary
evaluation of the total transport and transformation flux through
the ecosystem, and computes a first-order estimate of the total
half-life of the compound. This preliminary estimate is then used
to select the most appropriate (hours, days, months, or years)
time scale for reporting the results of all succeeding
time-dependent computations. This estimate of the
"system-level" half-life is also used to set the time intervals for
EXAMS' Mode 1 "persistence" computations, as described in
Chapter 2.5.3.
EXAMS' flux computations, along with its table of
compartment-specific pseudo-first-order rate constants (output
table "Kinetic Profile of Synthetic Chemical") provide a
sensitivity analysis of the behavior of the pollutant in the
particular ecosystem under study. These tables indicate the
relative strength of the transformation processes, and thereby
indicate which processes are in need of the most scrupulous and
exact experimental determinations of rate constants. In addition,
EXAMS interactive capabilities allow a user to vary the input data
over a reported error bound, and thus determine, for example,
the degree of uncertainty implied for exposure concentrations.
2.5.3 Persistence
EXAMS' final round of computations deal directly with a third
(after exposure and fate) aspect of exposure evaluation in
aquatic systems, that of the "persistence" of the compound.
(These are a feature of Mode 1 only.) It should perhaps be
emphasized that EXAMS computes local, rather than global,
persistence, that is, EXAMS' computations address the persistence
of compounds in the specific ecosystem under review, and do
not address the global issue of the persistence of a compound
after it leaves the local ecosystem. Thus, for example, a
compound that is not subject to any transformation processes is
ipso facto (globally) persistent. Within the more limited context
of a particular ecosystem, however, export processes will
ultimately result in a "cleanup" of the system, and the time
required for this cleanup process can be computed. (As the
ultimate exposure concentrations for a transformationally
persistent chemical in a static (closed) system are unbounded,
EXAMS never encounters the resulting infinite cleanup times.)
EXAMS begins its Mode 1 persistence computations by using the
steady-state concentrations of pollutant in the system as a set of
starting values or "initial conditions." These initial conditions
are presented to the numerical integration package (subroutine
DRIVER et seq.). The relevant set of differential equations,
describing the behavior of the pollutant over time, is essentially
Eq. (2-1) with the external loadings (Le) set to zero or struck
from the equations:
dt V
In Mode 1, EXAMS computes the dissipation of the compound
73
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over time, taking the time required to encompass 2 (estimated)
system-level half-lives as the endpoint of the simulation. The
results of this simulation are summarized in EXAMS' output table
entitled "Exposure Analysis Summary."
EXAMS makes use of two integration packages (Malanchuk et al.
1980). EXAMS initially calls upon a variable stepsize, variable
order (1-12) Adams PECE2 method. In the event that the
equations prove to be mathematically "stiff," the partially
complete integration is remanded to an alternate package that
integrates stiff equations by a variable-order, variable stepsize
code employing multistep backward differentiation methods of
order 1 through 6. The Adams method code was taken from
(Shampine and Gordon 1975); the stiff equations method was
derived from an algorithm originally developed by Gear (Gear
1971c, b, a).
EXAMS limits the expense incurred in the integration via a
limitation on the total number of steps which these routines are
permitted to execute. EXAMS writes out the secular dissipationof
the compound at 12 equally spaced times, up to the endpoint
(TFINAL) defined by 2 estimated system-level half-lives. If the
integrators exceed their allotted expense allowance prior to
integration to TFINAL, control is relinquished by the integrator for
evaluation of the situation. If the integrators have failed to reach
the first output point (TFINAL/12), EXAMS aborts all further
persistence computations and so notifies the user. If at least one
output point has been passed, EXAMS uses the latest point
reached by the integrators in its persistence computations, and
notifies the user that the dissipation simulation was abbreviated.
Integration expense is also influenced by the precision
demanded. This can be controlled by the user by alteration of
ABSER and RELER, the absolute andrelative error criteria used by
the numerical integration package. The smaller their values, the
greater the precision of the analysis, but the greater the
performance costs incurred. These parameters cannot be made
arbitrarily small due to the intrinsic limitations of digital
computing machinery; upon invocation EXAMS evaluates the
numerical precision limitations of the computing platform and
establishes limits for ABSER and RELER. If these are too large, the
integrators will encounter stability problems, so it is sometimes
worth experimenting with ABSER and reler to optimize the
conditions of an analysis.
In the vast majority of cases, EXAMS' dissipation simulations
conclude with a successful integration to TFINAL. EXAMS' output
summary of the time course of dissipation via neutral hydrolysis
in the static pond used as an example in Chapter 2.5.1 is given
in Exams Output Table 11.
So called from their successive operations of prediction,
derivative evaluation, correction, and final derivative evaluation.
Exams Output Table 11. Sample EXAMS output for dissipation of
chemical after removal of external loadings.
EXAMS prints a summary of the exposure and fate information
generated by the program and also estimates and reports the
length of time required for cleanup of the ecosystem. EXAMS
reports, in the first instance, the percentage of the initial
chemical masses in the entire water column and benthic
subsystem that had been dissipated by time TFINAL. EXAMS then
weights these dissipations according to the initial distribution of
the chemical in the system, and reports a first-order estimate of
the time required for the system to cleanse itself of the chemical
mass accumulated at steady state. This estimate is computed as
5 (pseudo-first-order, weighted) half-lives; in a true first-order
system this would correspond to dissipation of 97% of the mass
of chemical initially present in the system. (The actual
(mathematical) "order" of the system is defined by the number
of compartments used to describe the ecosystem. For example,
when a water-body is described to EXAMS via 20 segments,
EXAMS compiles 20 linked first-order differential equations, and
solves this system of equations to generate its outputs. The data
used in EXAMS' persistence time computations is generated by
summing the residual chemical masses over compartments,
thereby following the dissipation of the chemical in the entire
system. The computations are in this sense reduced order
approximations; thus EXAMS reports are given as "rough"
estimates (e.g., Exams Output Table 12).)
This computation can be illustrated via the results of the sample
simulation given in Exams Output Table 11. At the expiration of
144 hours, the resident pollutant mass had fallen from 1.986 to
0.467 kg in the water column. This dissipation of the pollutant
represents a loss of
100(1 -(0.467/1.986)
or 76.5% of the original material. Similarly, the benthic
subsystem has lost (0.0137 - 0.00682) = 0.00688 kg or 50.2% of
its original mass of chemical. (The benthic sediment exhibits a
slower loss of chemical as a result of continuing recontamination
74
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(Li, Eq. 10) of this subsystem from the water column.)
estimate of decontamination time.
In a first-order system, the decrease of an initialmass of material
Qo over time is given by
Q(t) = Q(0) exp(-k x t), or
ln(Q(t)/Q(0)) = -kxt
where k is the first-order rate constant andt is time. The half-life
is defined as the time required for Q(t) to reach Q(0)/2 or for
Q(t)/Q(0) = 0.5,thatis,
H = -ln(0.5)/k
The first-order half-lives (H) for these water-column (Hw) and
benthic (Hb) subsystems are, therefore:
Hw = (0.69315)(144)/ln(0.467/1.986) = 69.0 hrs, and
Hb = (0.69315)(144) / ln(0.00682/0.0137) = 143.4 hrs
At steady state, 99.32% of the compound is present in the water
column, and only 0.68% is in the benthic subsystem (Exams
Output Table 10). EXAMS thus estimates the time required for
dissipation of the chemical as:
Td = 5 (0.9932 (Hw) + 0.0068 (Hb))
= 5 (0.9932 (69.0) + 0.0068 (143.4)) = 347.5 hrs
or 14.5 days. EXAMS output summary for this example is given
in Exams Output Table 12.
Ecosystem: Static 1-hectare pond, 1 meter deep
Chemical: Unsorbed chemical subject to neutral hydrolysis
Exams Output Table 12. Example summary output table (Mode 1).
If the majority of the chemical had been present in the benthic
zone, EXAMS would of course have given computational
precedence to dissipation in the sediment subsystem for its
A more detailed evaluation of the persistence of the chemical
can be executed via a graphical analysis of the time course of
pollutant dissipation, plotted from the results of EXAMS'
numerical simulation of this phenomenon (Exams Output Table
11). In interpreting EXAMS' estimate of the time required to
dissipate the chemical, it should be remembered that EXAMS'
estimate represents five half-lives, or about 97% removal of the
initial mass. If this removal suggests that the chemical would
still occur at unacceptable concentrations, a first-order
evaluation of the time required to achieve a specified reduction
can be computed from EXAMS' outputs. Suppose, for example,
that the time to reduce the chemical to 0.01% of its initial value
were the time of interest. This time is given by the expression
(-ln(Q/Q(0))/K), where Q/Q(0) is in this case 0.0001. EXAMS'
estimate of decontamination time is computed as (5)(0.69315)/K,
where K can be regarded as a weighted whole-system first-order
decay constant. EXAMS' estimate of dissipation time Td can thus
be expanded via the approximation:
T =
(5)(0.69315)
In this instance, Q/Q(0) = 0.0001, -ln(Q/Q(0)) = 9.21, and the
time to reduce the chemical to 0.01% of its steady-state value is
approximately T = • =37 days.
(5)(0.69315)
Note, however, that a continued first-order decay of chemical in
the benthic subsystem would, at 37 days (888 hours), result in a
residual of
100 exp(888 (In 0.5 / Hb))
or 1.4% of the original benthic pollutant mass. The system-wide
dissipation of the chemical may leave pockets of higher
concentration in zones of restricted physical transport.
Extrapolations of EXAMS' results beyond the designed operating
range of the program are probably ill-advised. If necessary,
however, a plot of the results of EXAMS' dissipation simulation
should be used to evaluate the propriety of a first-order
extrapolation of system self-purification times.
References for Chapter 2.5.
Gear, C. W. 197 la. Algorithm 407: DIFSUB for solution of
ordinary differential equations. Communications of the
ACM 14:185-190.
Gear, C. W. 1971b. The automatic integration of ordinary
differential equations. Communications of the ACM
14:176-179.
Gear, C. W. 1971c. Numerical Initial Value Problems in
Ordinary Differential Equations. Prentice-Hall, Englewood
Cliffs, N.J.
Malanchuk, J., J. Otis, and H. Bouver. 1980. Efficient
75
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Algorithms for Solving Systems of Ordinary
Differential Equations for Ecosystem Modeling. EPA-
600/3-80-037, U.S. Environmental Protection Agency,
Athens, Georgia.
Shampine, L. F., and M. K. Gordon. 1975. Computer Solution
of Ordinary Differential Equations: The Initial Value
Problem. W.H. Freeman, San Francisco.
76
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3.0 Tutorials and Case Studies
3.1 Tutorial 1: Introduction to Exposure Analysis with EXAMS - Laboratory 1
I. First computer run of EXAMS (the overall Lab plan)
• Follow the directions in the "Lab 1, Exercise 1: Steady-State Analysis (Mode 1)" (page 85) through line 30. This will introduce you
to the basics of running the program.
• Review the principles of running the program in "Mode 1" (the default mode, steady-state analysis).
• Stop and review the inputs, outputs, and interrelationships, complete the worksheets on pages 79 and 80 , and prepare for Lab. 2 by
entering the Lab. 1 data for methyl parathion chemistry and its behavior in the standard pond on pages 83 and 84.
la. Running the program: Inputs
EXAMS has "User Data Bases" (UDB) for Chemicals, Environments, Loads, and Products (i.e., products of transformation processes). Each
entry in the UDB is accessed by its number, e.g., CHEM 11. To run a simulation, you must
• Recall an environment (ENV)
• Recall at least one chemical (CHEM)
• Recall or set an input LOAD (i.e., the amount of pesticide added over time, or an event loading from spray drift or runoff).
• Set a MODE (default is Mode 1, steady-state analysis)
The RECALL command (REC) loads data from the UDB into the Activity Data Base (ADB), and changes the UDB number to an ADB
number. For example, when you RECALL CHEM 11, its number in the ADB becomes CHEM 1.
Only one ENVIR can be active at any time. More than one CHEM can be active; this is especially useful when you specify the generation
of a transformation product, e.g., production of 2,4-D by hydrolysis of 2,4DBE, the butoxyethyl ester of 2,4-D. In addition to the
continuous long-term loads of Mode 1, you can also specify load events (as direct initial conditions in Mode 2, or from PRZM transfer
files in Mode 3) and piece-wise (monthly) continuous loads.
Once you have specified the required information, the RUN command executes a simulation.
EXAMS Laboratory Exercises Page 77
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Ib. Running the program: Outputs
EXAMS produces at least 20 output tables for each simulation. For complex, multi-year studies the total output can rapidly become
overwhelming. In most cases, you will want to examine the ecotoxicological exposure information in one or a few tables and graphics,
as you did in the introductory tutorial. Warning: when requesting a plot, make note of the variables you have asked for and their units,
because the graphic will not provide the labels.
Type LIST HELP to display a list of the available input and output tables (as at lines 18 and 19 of the first introductory exercise). On some
computers, [ Shift] [PrintScrn] will print the list.
• Note that Table 1 is an echo of the chemical input data.
• Because transformation product chemistry was not entered, Table 2 is empty.
• Because no pulse loads were specified (and are in any case irrelevant to steady-state analyses), Table 3 is also empty.
• Tables 4 through 13 are the environmental model, again an echo of the input data although Table 12 and Table 13 contain some
inferred values. You will need to examine these tables to see what kind of an environment ENV 2 is so you can label your diagram
on page 4. The EXAMS "data dictionary" (pages 175 ff.) includes descriptions and units for the parameters given in the tables.
• Table 14 contains load information.
• Tables 15 through 20 are output results.
For reference, you can PRINT ALL to get a pap er copy of all the input and output tables (Tables 1 -20, which may in some instance s have
several sub-tables).
Warning: The output file "report.xms" is overwritten each time you make an EXAMS run.
Once you have completed the worksheets on pages 79 and 80 , you may return to the introductory exercises to explore Mode 2 (initial
value approaches) and Mode 3 (seasonal effects).
EXAMS Laboratory Exercises Page 78
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Name
EXAMS Lab Assignment - Introduction
1. Working from the Mode 1 analysis, make a half-page diagram of the EXAMS pond environment (on the next page). Include the
compartments, their sizes, numbers, and types, and the geometry and hydrology of the system.
2. What is the water solubility of methyl parathion?
3. What mass (kg) of chemical is resident in the littoral water column at steady state?
4. What concentration (mg/L or ppm) of the chemical is dissolved in the littoral?
5. What is the bulk mass (kg) of the chemical in the benthic sediment?
6. What is the analytical concentration (mg/kg dry weight) in the sediment?
7. What is the exposure of benthic organisms (i.e., ppm dissolved in pore water)?
Do the different locations of the chemical and its concentration in the various media (-water column, benthic sediments, pore water) make
sense to you? If not, please discuss with your instructor - this is an important part of the exercise.
8. Of the processes considered, which two were responsible for the greatest loss of chemical?
(1)
(2)
9. Of the processes considered, which two were least responsible for loss of chemical?
(1)
(2)
Go back and examine the properties of the chemical and the environment and determine why these processes had the relative importance
they did.
Given the properties of methyl parathion and the pond environment, propose at least three questions that the model could help you study
(for methyl parathion or a related chemical, for the pond or a related environment). For example, if the pond were more oligotrophic with
less numerous bacteria, what would be the steady-state exposure?
EXAMS Laboratory Exercises Page 79
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Diagram of EXAMS' Constructed Farm Pond
Your questions:
(1)
(2)
(3)
EXAMS Laboratory Exercises Page 80
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3.2 Tutorial 1: Introduction to Exposure Analysis with EXAMS - Laboratory 2
EXAMS' second tutorial (beginning on page 113) is a substantial exercise in assembling chemical data, building an environmental
model (of the lower basin of Lake Zurich, Switzerland), validating the model against observed data and exploring the dynamics of an
environmental pollutant. Here we will use the first part of that tutorial (chemistry of 1,4 dichlorobenzene) to illustrate chemical data
entry and basic studies of the environmental behavior of pesticides and other organic chemicals.
1. Restart EXAMS from the DOS prompt (It is sometimes safer to re-start the program when beginning a new problem to ensure that
all previous results are deleted.)
2. Review what you did in Lab 1. Note that the only required EXAMS commands were
RECALL CHEM 11
RECALL ENV 2
SETSTRL(1,1,13)-01
All the other commands you used (e.g., HELP, SHOW, CATALOG CHEMICAL, etc.) were optional entries to teach you how to use
the program.
3. Add another chemical to the user database (UDB) and use it in the standard pond.
Type the following series of commands (note the similarity to the first part of the Lake Zurich tutorial). Press the Enter key after each
line.
EXAMS (To re-start the program)
REC CHEM 1 (This entry in the UDB is an empty template for loading new data.)
CHEM NAME IS 1,4-Dichlorobenzene
setmwt(l)=147.0
change sol(l,l) to 73.8
setkow(l)=2340
set henry(l)=2.66e-3
cat chem (To see how many chemicals are in the UDB)
sto chem 12 (Assuming you have no CHEM 12 now)
REC ENV 2
SETSTRL(1,1,13)-01
RUN
PRINT 15
PRINT 18
PRINT 20
If your computer doesn't print, use the screen listings (LIST 15, etc.) to start filling out the tables on pages 83 and 84.
4. Modify (eutrophy) the pond to increase the organic carbon content of the sediment (both the suspended sediment in the water
column and the benthic sediment).
SHOW FROC(1,13) (Write down what you see)
SHOW FROC(2,13) (Write down what you see)
Look in the EXAMS User's guide for an explanation of the FROC parameter.
SETFROC(1,13)=0.4
SETFROC(2,13)=0.4
SHOW FROC(1,13) (Write down what you see)
SHOW FROC(2,13) (Write down what you see)
EXAMS Laboratory Exercises Page 81
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SHOW CHEM (Check that the values you added for the chemical are there; EXAMS may have added additional properties based
on its understanding of pesticide chemistry. If so, you can use the HELP function to gain an understanding of these new values.
SHOW LOAD (Check to see that the load is what you expect it to be.)
RUN
LIST 15 (Fill in some of the table data on pages 83 and 84)
LIST 6
LIST1
LIST 18
LIST 20
ENV NAME IS Hyper-eutrophic Pond (You are naming and will store this environment)
CAT ENV (To see how many you have)
STOR ENV 6 (Assuming you have 5)
CAT ENV (To make sure it took)
5. Determine how the first chemical (methyl parathion) would behave in the hvper-eutrophic pond.
REC CHEM 11
REC ENV 6
SETSTRL(1,1,13)=.01
RUN
LIST 15 (Write your answers on the Tables)
LIST 6
LIST 1
LIST 18
LIST 20
EXAMS Laboratory Exercises Page 82
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Name
1. Compare the chemical properties of methyl parathion and p-DCB. Provide number and units.
a. In what table are these variables found?
b. Where are the units listed?
c. Fill in the table. If necessary, use EXAMS' "help" functions to generate a consistent data set.
Chemical Property
Molecular Weight
Solubility
Kow (n-Octanol:water solubility ratio)
Henry's Law Constant
KBACW
KBACS
Units
Methyl Parathion
1 ,4-Dichlorobenzene
If a value has not been entered or calculated by EXAMS, the EXAMS table entry is blank and its value is zero.
d. List the major differences between these chemicals that affect their fate and exposure.
I.
111.
2. Compare the standard and the hyper-eutrophic ponds.
a. Define FROC
( , ) units
Variable
FROC (1,13)
FROC (2, 13)
Standard Pond
Hyper-Eutrophic Pond
EXAMS Laboratory Exercises Page 83
-------
3. Compare the distribution and transformation of the two chemicals in the two environments.
Variable
EXAMS
Table No.
Methyl Parathion
1,4-DCB
Std. Pond
Eutr pond
Std. Pond
Eutr pond
% in water column
Kg in water col.
% in benthic zone
Kg in benthic zone
Total mass (Kg)
Cone in plankton (|o,g/g)
Cone in benthos (ng/g)
Flux (%load) from
Bacterioplankton
Water-borne export
Volatilization
Persistence: time to 95%
dissipation
4. Using the numbers you filled in above, explain why increasing the organic carbon content of the suspended and bed sediment had a
major impact on 1.4-DCB. but not on methyl parathion. Is this realistic? If you think not, re-run the analysis and revise your
conclusions.
5. Which of these chemicals would have a different environmental fate and exposure if the numbers of bacteria in the water column or
benthic zone were changed. Why?
EXAMS Laboratory Exercises Page 84
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3.3 Command Sequences for Tutorial 1, Lab. 1
3.3.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1)
Introduction to EXAMS. Execute the following commands at the EXAMS prompt. The object of the exercises is to gain a basic
familiarity with the EXAMS command interface.
1 HELP
2 HELP HELP
3 HELP USER
4 HELP PAGES
5 SHOW VAR
6 HELP MODE
7 CATALOG CHEMICAL
8 CATALOG ENVIRONMENT
9 CAT
10 LO
11 HELP RECALL
12 RECCHEM11
13 RECALL ENV 2
14 HELP LOAD
15 SHOW LOAD
16 SET STRL(1,1,13)-01
17 RUN
18 LIST
19 HELP
20 20
21 LIST 18
22 LIST 15
23 HELP PLOT
24 PLOT
25 KI
26 PL
27 3
28 6
29 0
30 0
EXAMS Introductory Laboratory (Mode 1) Page 85
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3.3.2 Lab. 1, Exercise 2: Mode 2 Analysis of Initial Value Problems - Sixty Day RUn Time
1 REC CHEM 11
2 REC ENV 2
3 SETSTRLD(1,1,13)=0.01
4 SET MODE=2
5 SHOW TI FR
6 HELP TCODE
7 SET TCODE=2
8 SET TEND=60
9 SET CINT=1
10 SHOW TI FR
11 RUN
12 PLOT KI PL
13 3
14 6
15 0
16 0
17 SHOW LOAD
18 ZERO LOAD
19 SHOW LOAD
20 SET ICHEM(1)=1
21 SETISEG(1)=1
22 SETIMASS(l)-!
23 SHO PU
24 SET TEND=14
25 RUN
26 PL KI PL
27 3
28 6
29 0
30 0
31 CONTINUE
32 28
33 CONTINUE
EXAMS Introductory Laboratory (Mode 2) Page 86
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3.3.3 Lab. 1, Exercise 3: Time-varying seasonal analysis using Mode 3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
REC
SET
REC
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SET
SHO
CHEM 1 1
MODE=3
ENV3
ICHEM(1)=1
ISEG(1)=1
IMASS(l)=.l
IMON(1)=5
IDAY(1)=15
ICHEM(2)=1
ICHEM(3)=1
ISEG(2)=1
ISEG(3)=1
IMASS(2)=.l
IMASS(3)=.l
IMON(2)=6
IMON(3)=6
IDAY(2)=1
IDAY(3)=15
PULO
RUN
PL KI PL
o
3
6
0
0
PL KI PL
•-t
3
0
0
CONTINUE
PLOT KI PL
3
0
0
EXAMS Introductory Laboratory (Mode 3) Page !
-------
35 LIST 20
36 Y
37 QUIT
EXAMS Introductory Laboratory (Mode 3) Page 89
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3.4 Introduction to EXAMS - Lab. 1 Exercises with Complete EXAMS Responses
These three exercises illustrate the EXAMS command line interface, data entry, and operations "modes." The commands in the summary
listings are given here in BOLD/ITALIC. Note that EXAMS starts in Mode 1 when it is initially invoked, i.e., the default "Mode" is 1.
3.4.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1)
To begin, at the DOS prompt, enter
C:> EXAMS
Welcome to EXAMS Release
Exposure Analysis Modeling System
Technical Contact: Lawrence A. Burns, Ph.D.
U.S. Environmental Protection Agency
960 College Station Road
Athens, GA 30605-2700 USA
Phone: (706) 355-8119 (Fax) 355-8104
Internet: burns.lawrence@epa.gov
Latest Maintenance
Type HELP and press the RETURN key for command names,
HELP USER for a summary of command functions,
HELP PAGES for a list of information pages,
or HELP EXAMS for introductory information.
Please stand by while EXAMS checks the computational precision
of this computer and initializes the Activity Data Base.
EXAMS-> HELP
EXAMS includes these system commands:
AUDIT
DO (@)
PASSWORD
RECALL
WRITE
CATALOG
ERASE
PLOT
RUN
ZERO
CHANGE
HELP
PRINT
SET
CONTINUE
LIST
QUIT
SHOW (?)
DESCRIBE
NAME
READ
STORE
Help pages describe the commands and additional topics:
ADB, COMMAND, EXAMS, INPUTS, LOADS, LODNAM, NEWDAT, TABLE,
TABNUM, TUTOR, UDB, USER, WILD
Text describing the commands, and the text of the additional
topics, can be seen by entering HELP , where
is either a command, one of the additional topics above, or
the name of one of EXAMS' input data parameters.
USER gives a summary list of EXAMS' command functions.
PAGES gives an annotated list of EXAMS' information pages.
TUTOR is an introduction to using the HELP facility.
EXAMS-> HELP HELP
The HELP command invokes EXAMS' HELP facility to display
Introduction to EXAMS Page 90
-------
information about a particular EXAMS topic. For a
explanation of the HELP command, now type HELP TUTOR and press
the RETURN key. For more detail on the available information,
now type HELP USER or HELP PAGES and press the RETURN key.
Related topics: DESCRIBE
Syntax: HELP [command], or [EXAMS parameter]
HELP is also available from the interior of most EXAMS commands.
A list of the "parameters" available through HELP can be invoked
by typing "SHOW VAR". HELP provides definitions and dimensions
of all these input data and control variables.
EXAMS-> HELP USER
Command
Summary Description
AUDIT Start/Stop user notepad for recording procedures
CATALOG List the contents of User Databases (UDBs)
CHANGE/SET Enter/reset input data and program controls
CONTINUE Resume integration (Modes 2 and 3 only)
DESCRIBE Report dimensions and data type of parameter
DO Execute file of EXAMS commands (file.EXA)
ERASE Clear section of stored database (UDB)
HELP Describes access to EXAMS on-line HELP facility
LIST Show tabular results on the screen
NAME Specify the name of a UDB, e.g., CHE NAME IS ...
PASSWORD Restrict Recall/Store access to UDB datasets
PLOT Plot results on the screen
PRINT Queue tabular results for hardcopy printing
QUIT Abort command, or End interactive session
READ/WRITE Upload/download data from non-EXAMS disk files
RECALL Activate data from stored database (UDB)
RUN Begin simulation run
SHOW Display current data values or control settings
STORE Download current data into stored database (UDB)
ZERO Clear loadings, pulses, or current concentration
EXAMS-> HELP PAGES
ADB defines the term "activity database"
COMMAND tells how to use EXAMS' system commands
EXAMS describes the scope and operation of the program
INPUTS describes EXAMS input data and data manipulation
LOADS explains how to specify chemical loads on the ecosystem
LODNAM gives EXAMS' special names for the chemical loadings
NEWDAT tells how to begin entry of a new dataset
HELP gives more explanation of the HELP facility
TABLE explains why your initial outputs seem to disappear
TABNUM explains the numbering system used for EXAMS' outputs
TUTOR introduces EXAMS' HELP utility
UDB defines the term "user database"
USER gives a general command summary.
WILD describes the use of wild cards in SET/CHANGE commands
Enter HELP and the name of a topic for further information.
EXAMS-> SHOW VAR
Introduction to EXAMS
Page 91
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CHEMNA
KCHEM
TINIT
KOC
VAPR
KNH
ERED
JTURB
AEC
PLMAS
VOL
LONG
SEEPS
ICHEM
YIELD
ECONAM
MODE
ABSER
ROW
EVPR
ENH
KBACW
ITURB
PCTWA
BNMAS
AREA
WIND
STRLD
IMON
EAYLD
LOADNM
PRSW
RELER
KPB
QUANT
KBH
QTBAW
XSTUR
TCEL
K02
DEPTH
ELEV
NPSLD
I DAY
PRODNM
MONTH
SPFLG
KPDOC
KDP
EBH
KBACS
CHARL
PH
DOC
XSA
RHUM
PCPLD
IYEAR
TYPE
NYEAR
MWT
KPS
RFLAT
KOX
QTBAS
DSP
POH
CHL
LENG
ATURB
DRFLD
SPRAY
AIRTY
YEAR1
SOL
KIEC
ABSOR
EOX
KOUNT
SUSED
OXRAD
CLOUD
WIDTH
STFLO
PRBEN
CHPAR
FIXFIL
TCODE
ESOL
LAMAX
K102
JFRAD
BULKD
REDAG
DFAC
RAIN
STSED
SEELD
TPROD
IUNIT
CINT
PK
HENRY
KAH
EK102
ITOAD
FROC
BACPL
DIS02
EVAP
NPSFL
IMASS
NPROC
MCHEM
TEND
EPK
EHEN
EAH
KRED
ADVPR
CEC
BNBAC
OZONE
LAT
NPSED
ISEG
RFORM
EXAMS-> HELP MODE
MODE is an integer scalar.
MODE sets the operating "mode" of EXAMS. Three operating modes
are available; these are selected by SETting MODE to 1, 2, or 3.
MODE
1
2
Operational characteristics of EXAMS
Long-term (steady-state) analysis.
Pulse analysis--specifiable initial chemical mass
(IMASS) and time frame, time-invariant environment.
Monthly environmental data, daily pulse loads IMASS
and monthly chemical loadings of other types.
EXAMS-> CATALOG CHEMICAL
Catalog of CHEMICAL parameter sets
UDB No. Name of Entry Volume
1 Chemical Data Entry Template
2 p-Cresol
3 Benz[a]anthracene
4 Benzo[a]pyrene
5 Quinoline
6 Benzo[f]quinoline
7 9H-Carbazole
8 7H-Dibenzo[c,g]carbazole
9 Benzo[b]thiophene
10 Dibenzothiophene
11 Methyl Parathion
12 Mirex
13 Heptachlor (Heptachlorodicyclopentadiene)
14 Speciation Test Data
EXAMS-> CATALOG ENVIRONMENT
Catalog of ENVIRONMENTal models
UDB No. Name of Entry Volume
Environmental Data Entry Template
Pond — code test data — mean values only
Monthly pond — code test data
Lake Zurich (Untersee), Switzerland: annual means
Introduction to EXAMS
Page 92
-------
5 Georgia Pond-Stream (R. Lee)
6 GApond USEPA
EXAMS-> CAT
Enter Environment, Chemical, Load, Product,
Help, or Quit-> LO
Catalog of Chemical LOADings
UDB No. Name of Entry Volume
1 Load Data Entry Template
2 Test input loadings for EXAMS 2.97
EXAMS-> HELP RECALL
RECALL transfers data from permanent storage (UDB) to activity
databases (ADB). The data to be used by EXAMS for an analysis
are held in a foreground memory bank or ADB (activity database).
When EXAMS is started, the ADB is empty. Use the RECALL command
to transfer data from the User Databases (UDBs) to foreground
memory (ADB). Be sure to STORE all new data before you QUIT
from EXAMS: the ADB is discarded at the end of each session!!
Related topics: CATALOG, ERASE, NAME, STORE
Syntax: RECALL [AS ADB#]
where can be CHEM, ENV, LOAD, or PRODUCT
EXAMS uses these four kinds of datasets:
1. CHEMICAL reactivity and partitioning (up to MCHEM
2. ENVIRONMENTal physical and chemical parameters,
3. allochthonous chemical LOADings, and
4. PRODUCT chemistry for generating i:
among multiple chemicals in an analysis.
For more information: ADB, AS, UDB
EXAMS-> REC CHEM 11
Selected compound is "Methyl Parathion"
EXAMS-> RECALL ENV 2
Selected environment is "Pond -- code test data -- mean values only'
EXAMS-> HELP LOAD
Enter allochthonous loadings of synthetic chemicals either as
instantaneous pulses, or as stable (or average) values that
persist for at least one month. SHOW PULSE displays pulsed
loadings; SHOW LOADS displays the steady loadings. Steady
loadings are entered by specifying the type of load (STRLD,
etc.) , the segment, ADB number of the chemical, month of the
loading, and the mass loading (kg/hour). For example, command:
EXAMS-> SET STRLD(1,2,8) TO 0.01
sets an August load of 0.01 kg/h of chemical #2 on segment #1.
Pulse loads are entered by "event number." Each event includes
the ADB # of the chemical, the month and day of the event, the
Introduction to EXAMS Page 93
-------
target segment, and the mass (kg) of the pulse.
If, during a RUN, a loading violates assumptions of the model
(e.g., by super-saturating an incoming flow), or is found to be
impossible (e.g., a PCPLD to a (B)enthic segment), the load is
reduced or deleted. The corrected loadings are stored in the
LOADS matrix and returned to the user, i.e., the unusable
loadings are discarded and the new values are substituted.
EXAMS-> SHOW LOAD
Name of environment: Pond -- code test data -- mean values only
Total number of segments (KOUNT) = 2
Segment Number: 1 2
Segment "TYPE": L B
Exposure Analysis Modeling System -- EXAMS Version 2.97, Mode 1
Ecosystem: Pond -- code test data -- mean values only
Chemical: 1) Methyl Parathion
Mean of year 1989: allochthonous chemical loads (kg/h).
Load data—
Seg Streams Rainfall Seeps NFS Loads Drift
1
2
EXAMS-> SET STRL(1,1,13)=.01
Command complete; ready for input.
EXAMS-> RUN
Simulation beginning for:
Environment: Pond -- code test data -- mean values only
Chemical 1: Methyl Parathion
RUN command completed.
EXAMS-> LIST
At the prompt, enter a Table number, "Quit,"
or "Help" to see a catalog of the output tables.
Enter Table Number -> HELP
I Chemical inputs: FATE Data
2 Chemical inputs: PRODUCT Chemistry
3 PULSE Chemical Loadings
4 Environmental Input Data: BIOLOGICAL Parameters
5 Environmental Input Data: HYDROLOGIC Parameters
6 Environmental Input Data: SEDIMENT Properties
7 Environmental Input Data: PHYSICAL GEOMETRY
8 Miscellaneous Environmental Input Data: Wind, D.O., etc.
9 Input specifications: ADVECTIVE transport field
10 Input specifications: DISPERSIVE transport field
11 Environmental Input Data: GLOBAL site parameters
12 KINETIC PROFILE of Synthetic Chemical
13 Chemical REACTIVITY PROFILE of Ecosystem
Introduction to EXAMS Page 94
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14 Allochthonous Chemical LOADS and Pulses
15 DISTRIBUTION of Chemical in Environment
16 Chemical SPECIATION of Dissolved
17 Chemical Concentration MEANS, Maxima, and Minima
18 Sensitivity Analysis of Chemical FATE
19 Summary TIME-TRACE of Chemical Concentrations
20 Exposure Analysis SUMMARY
ALL Entire Report
At the prompt, enter a Table number, "Quit,"
or "Help" to see a catalog of the output tables.
Enter Table Number -> 20
Ecosystem: Pond — code test data — mean values only
Chemical: Methyl Parathion
Table 20.01. Exposure analysis summary.
Exposure (maximum steady-state concentrations):
Water column: 7.352E-02 mg/L dissolved; total = 7.364E-02 mg/L
Benthic sediments: 1.683E-02 mg/L dissolved in pore water;
maximum total concentration = 0.848 mg/kg (dry weight).
Biota (ug/g dry weight): Plankton: 27. Benthos: 6.3
Fate :
Total steady-state accumulation: 2.05 kg, with 72.01%
in the water column and 27.99% in the benthic sediments.
Total chemical load: l.OOE-02 kg/ hour. Disposition: 2.27%
chemically transformed, 78.10% biotransformed, 0.02%
volatilized, and 19.62% exported via other pathways.
Persistence:
After 288. hours of recovery time, the water column had
lost 84.39% of its initial chemical burden; the benthic zone
had lost 30.09%; system-wide total loss of chemical = 69.2%.
Five half-lives (>95% cleanup) thus require ca. 49. days.
EXAMS-> LIST 18
Introduction to EXAMS Page 95
-------
Ecosystem: Pond — code test data -
Chemical: Methyl Parathion
- mean values only
Table 18.01. Analysis of steady-state fate of organic chemical.
Mass Flux
Kg/ hour
of Load
Hydrolysis
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
1.4064E-04
8.6310E-05
1.41
Half-Life'
hours
1.0080E+04
1 . 6426E + 04
Bacterioplankton
Benthic Bacteria
Surface Water-borne Export
Seepage export
Volatilization
Chemical Mass Balance:
Sum of fluxes =
Sum of loadings =
Allochthonous load:
Autochthonous load:
Residual Accumulation =
6 .
1 .
1 .
1 .
1 .
1 .
. 7642E-
. 0455E-
. 9617E-
. 7646E-
. OOOOE-
. OOOOE-
9 . 31E-
03
03
03
06
02
02
10
67 .
10 .
19 .
0 .
100 .
0 .
0 .
. 64
. 45
. 62
. 02
. 0
. 0
. 0
209.6
1356 .
722 . 7
8 . 0339E+05
Pseudo-i
EXAMS-> LIST 15
Ecosystem: Pond -- code test data -- mean values only
Chemical: Methyl Parathion
Table 15.01. Distribution of chemical at steady state.
Seg Resident Mass ********
# Total
Kilos % mg/*
Chemical
Dissolved Sediments Biota
mg/L ** mg/kg ug/g
In the Water Column:
1 1.5 100.00 7.364E-02 7.352E-02
1 . 5
72.01
and in the Benthic Sediments:
2 0.57 100.00 0.848
0.57
27.99
1.
3E-02
Total Mass (kilograms) =
2.045
. 842
6.25
* Units: mg/L in Water Column; mg/kg in Benthos.
** Excludes complexes with "dissolved" organics.
EXAMS-> HELP PLOT
Use the PLOT command to display results of the current analysis.
Three kinds of PLOTs are available on-line from EXAMS: POINT,
PROFILE, and KINETIC. Each PLOT requires the specification of
several options; these can either be entered on the system
command line or entered in response to EXAMS' prompts. You can
enter HELP in response to any of these prompts; EXAMS will then
describe the available options.
Introduction to EXAMS Page 96
-------
Related topics: LIST, PRINT
Syntax: PLOT
For more information on optionl: type HELP POINT,
HELP PROFILE, or
HELP KINETIC
HELP for options 2 and 3 is available inside the PLOT command.
EXAMS-> PLOT
The following options are available:
POint - Vertical concentration profile
PRofile - Longitudinal concentration profile
Kinetic - List or plot kinetic outputs
Help - This message
Quit - Return to the EXAMS program prompt
Option-> KI
The following KINETIC options are available:
List - lists selected KINETIC output parameters
Plot - plots selected KINETIC output parameters
Help - this message
Quit - return to the EXAMS prompt
Option-> PL
Chemical: Methyl Parathion
Environment: Pond — code test data — mean values only
Simulation units: Hours
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
Introduction to EXAMS Page 97
-------
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Pond — code test data — mean values only
Chemical: Methyl Parathion
1.47 I*
I
I *
I
I *
0.982 I
I *
I *
I *
1+ + + + *
0.491 I +++++ +
I * * + + +
I * *
I *
I
I
-I -| -| -| -| -| -| -| -| -| +
0.000 57.6 115. 173. 230. 288.
28.8 86.4 144. 202. 259.
Time, Hours
Introduction to EXAMS Page
-------
3.4.2 Lab. 1, Exercise 2: EXAMS in Mode 2
Using EXAMS to solve initial value problems. Mode 2 can be used to study details of exposure during brief (60 days in the example)
periods, or through a series of episodic contaminant releases. If you have not continued on directly from Exercise 1, after starting
EXAMS you should first issue the commands to "RECALL CHEM 11" (methyl parathion), "REC ENV 2", "SET STRLD(1,1,13)=0.01" and
"RUN" to begin this exercise.
EXAMS-> SET MODE=2
Command complete; ready for input.
EXAMS-> SHOW TI FR
A RUN will integrate from 0. to 288. Hours
with output at intervals of 24.00 Hours
(CINT = 24.00 TINIT = 0.
TEND = 288. TCODE = 1)
EXAMS-> HELP TCODE
TCODE is an integer scalar.
The value of Time_CODE sets the units of TINIT, TEND, and CINT
SET TCODE to 1 (hours), 2 (days), 3 (months), or 4 (years).
TCODE is under full user control only in Mode 2. In mode 2 TCODE
controls the time frame of the study: e.g., given TINIT=0, TEND=
24, and CINT=2; CHANge TCODE from 1 to 3 to convert a 0-24 hour
study into 0-24 months (bimonthly reports). In mode 1, EXAMS
selects the units for reporting results from the probable half-
life of the study chemical(s). In mode 3, a RUN encompasses one
year or longer, and the timing is set to produce standard output
EXAMS-> SET TCODE=2
Command complete; ready for input.
EXAMS-> SET TEND=60
Command complete; ready for input.
EXAMS-> SET CINT=1
Command complete; ready for input.
EXAMS-> SHOW TI FR
A RUN will integrate from 0. to 60. Days
with output at intervals of 1.00 Days
(CINT = 1.00 TINIT = 0 .
TEND = 60. TCODE = 2)
EXAMS-> RUN
Simulation beginning for:
Environment: Pond -- code test data -- mean values only
Chemical 1: Methyl Parathion
RUN command completed.
EXAMS-> PLOT KI PL
Chemical: Methyl Parathion
Environment: Pond -- code test data -- mean values only
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
Introduction to EXAMS Page 99
-------
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Pond -- code test data — mean values only
Chemical: Methyl Parathion
1.47 I
I
I
I **
I **
0.979 I *
I *
I *
I *
I *
I
I *
I
I * +-
0.000 12.0 24.0 36.0 48.0 60
6.00 18.0 30.0 42.0 54.0
Time, Days
Introduction to EXAMS Page 100
-------
EXAMS-> SHOW LOAD
Name of environment: Pond -- code test data -- mean values only
Total number of segments (KOUNT) = 2
Segment Number: 1 2
Segment "TYPE": L B
Exposure Analysis Modeling System — EXAMS Version 2.97, Mode 2
Ecosystem: Pond — code test data — mean values only
Chemical: 1) Methyl Parathion
Mean of year 1989: allochthonous chemical loads (kg/h).
Load data—
Seg Streams Rainfall Seeps NFS Loads Drift
1 l.OOOE-02
2
EXAMS-> ZERO LOAD
All allochthonous loads removed.
EXAMS-> SHOW LOAD
Name of environment: Pond -- code test data -- mean values only
Total number of segments (KOUNT) = 2
Segment Number: 1 2
Segment "TYPE": L B
Exposure Analysis Modeling System -- EXAMS Version 2.97, Mode 2
Ecosystem: Pond -- code test data -- mean values only
Chemical: 1) Methyl Parathion
Mean of year 1989: allochthonous chemical loads (kg/h).
Load data—
Seg Streams Rainfall Seeps NFS Loads Drift
1
2
EXAMS-> SET ICHEM(1)=1
Command complete; ready for input.
EXAMS-> SET ISEG(1)=1
Command complete; ready for input.
EXAMS-> SET IMASS (!)=.!
Command complete; ready for input.
EXAMS-> SHO PU
Exposure Analysis Modeling System -- EXAMS Version 2.97, Mode 2
Ecosystem: Pond -- code test data -- mean values only
Chemical: 1) Methyl Parathion
Table 3. Chemical input data: pulse loadings.*
Introduction to EXAMS Page 101
-------
Load Entry—
ICHEM-ADB# 1
ISEGment 1
IMASS (kg) 0.100
Event Number 1
* N.B.: These values represent the input request stream only;
they may be revised during simulation.
EXAMS-> SET TEND=14
Command complete; ready for input.
EXAMS-> RUN
Simulation beginning for:
Environment: Pond -- code test data -- mean values only
Chemical 1: Methyl Parathion
RUN command completed.
EXAMS-> PL KI PL
Chemical: Methyl Parathion
Environment: Pond -- code test data -- mean values only
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
Introduction to EXAMS Page
-------
System: Pond — code test data — mean values only
Chemical: Methyl Parathion
0.100 I*
I
I *
I
I *
6.667E-02 I
I *
I *
I
I *
3.333E-02 I * *
I *
I * *
I * * *
I +++++ + ++++++ +
0.000 1+ +
-I -I -I -I -I -I -I -I -I -I +
0.000 2.80 5.60 8.40 11.2 14.
1.40 4.20 7.00 9.80 12.6
Time, Days
EXAMS-> CONTINUE
Initial time for integration will be 14.0 Days
Enter ending time of integration, Help, or Quit-> 28
Simulation continuing for:
Environment: Pond — code test data — mean values only
Chemical 1: Methyl Parathion
CONTINUE command completed.
EXAMS-> CONTINUE
Initial time for integration will be 28.0 Days
Enter ending time of integration, Help, or Quit-> 60
Simulation continuing for:
Environment: Pond -- code test data -- mean values only
Chemical 1: Methyl Parathion
CONTINUE command completed.
EXAMS-> PL KI PL
Chemical: Methyl Parathion
Environment: Pond — code test data — mean values only
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Introduction to EXAMS Page 103
-------
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to
cancel .
Enter segment number > 0 «T ,. . ,, ,
Notice how the pulse is
System: Pond -- code test data — mean values , , „ ...
onl repeated on each continue
Chemical: Methyl Parathion if it is not explicitly removed.
0.111 I * *
I
T * * *
I
T * * *
7.421E-02 I *
I * *
I * * *
I * *
I * * *
3.711E-02 I * ** *
T * * * *
T * * * * *
I
I
0.000 12.0 24.0 36.0 48.0 60.0
6.00 18.0 30.0 42.0 54.0
Time, Days
EXAMS-> PL KI PL
Chemical: Methyl Parathion
Environment: Pond -- code test data -- mean values only
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
Introduction to EXAMS Page 104
-------
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Pond — code test data — mean values only
Chemical: Methyl Parathion
1.574E-02 I *****
T * * *
I * **
I ******* * ***
j * * * *
1.049E-02 I * **
I * **
I **
I ******** **
j * * * *
5.247E-03 I * **
I
I *
I *
I
I*
H H H H H H H H H H +
0.000 12.0 24.0 36.0 48.0 60.
6.00 18.0 30.0 42.0 54.0
Time, Days
Introduction to EXAMS Page 105
-------
3.4.3 Lab. 1, Exercise 3: EXAMS in Mode 3
Mode 3 is used for exposure over a minimum period of one year, with monthly updates of the properties of the environment. It can be
used with PRZM to examine the exposure pattern arising from use of a pesticide over many years. In this exercise the load is entered
manually to illustrate the structure of the data.
EXAMS-> REC CHEM 11
Selected compound is "Methyl Parathion"
EXAMS-> SET MODE=3
Command complete; ready for input.
EXAMS-> REC ENV 3
Selected environment is "Monthly pond — code test data"
EXAMS-> SET ICHEM(1)=1
Command complete; ready for input.
EXAMS-> SET ISEG(1)=1
Command complete; ready for input.
EXAMS-> SET IMASS(!)=.!
Command complete; ready for input.
EXAMS-> SET IMON(1)=5
Command complete; ready for input.
EXAMS-> SET IDAY(1)=15
Command complete; ready for input.
EXAMS-> SET ICHEM(2)=1
Command complete; ready for input.
EXAMS-> SET ICHEM(3)=1
Command complete; ready for input.
EXAMS-> SET ISEG(2)=1
Command complete; ready for input.
EXAMS-> SET ISEG(3)=1
Command complete; ready for input.
EXAMS-> SET IMASS(2)=.1
Command complete; ready for input.
EXAMS-> SET IMASS(3)=.1
Command complete; ready for input.
EXAMS-> SET IMON(2)=6
Command complete; ready for input.
EXAMS-> SET IMON(3)= 6
Command complete; ready for input.
EXAMS-> SET IDAY(2)=1
Introduction to EXAMS Page 106
-------
Command complete; ready for input.
EXAMS-> SET IDAY(3)=15
Command complete; ready for input.
EXAMS-> SHO PU LO
Exposure Analysis Modeling System — EXAMS Version 2.97, Mode 3
Ecosystem: Monthly pond — code test data
Chemical: 1) Methyl Parathion
Table 3. Chemical
Load Entry--
IMONth
I DAY
ICHEM-ADB#
ISEGment
IMASS (kg) 0.
Event Number
5
15
1
1
.100
1
input data: pulse loadings.*
6
1
1
1
0.100
2
6
15
1
1
0.100
3
* N.B.: These values represent the input request stream only;
they may be revised during simulation.
EXAMS-> RUN
Simulation beginning for:
Environment: Monthly pond -- code test data
Chemical 1: Methyl Parathion
RUN command completed.
EXAMS-> PL KI PL
Chemical: Methyl Parathion
Environment: Monthly pond -- code test data
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
Introduction to EXAMS Page 107
-------
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Monthly pond — code test data
Chemical: Methyl Parathion
0.111 I *
I *
I * *
I *
I * *
7.425E-02 I * *
I * *
I * *
I * * *
I * * *
3.712E-02 I * * *
I * * + +
I * ++*++++
I
0.000
-I -I -I -I -I -I -I -I -I -I +
0.000 73.0 146. 219. 292. 365.
36.5 110. 183. 256. 329.
Time, Days
EXAMS-> PL KI PL
Chemical: Methyl Parathion
Environment: Monthly pond — code test data
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 0
The following parameters are available for each segment:
Introduction to EXAMS Page 108
-------
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Monthly pond — code test data
Chemical: Methyl Parathion
0.111 I *
I *
I * *
I *
I * *
7.425E-02 I * *
T * *
I * *
T * * *
T * * *
3.712E-02 I * * *
T * * *
I * * *
T *****
T * * * *
H H H H H H H H H H +
0.000 73.0 146. 219. 292. 365.
36.5 110. 183. 256. 329.
Time, Days
EXAMS-> CONTINUE
CONTinuing integration through 31 December 1990.
Simulation continuing for:
Environment: Monthly pond — code test data
Chemical 1: Methyl Parathion
CONTINUE command completed.
EXAMS-> PLOT KI PL
Chemical: Methyl Parathion
Environment: Monthly pond -- code test data
Simulation units: Days
Number of segments: 2
Segment Number: 1 2
Segment "TYPE": L B
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
1 - Water Column: average dissolved (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
4 - Benthic: average dissolved (mg/L)
5 average sorbed (mg/kg)
6 total mass (kg)
Introduction to EXAMS Page 109
-------
Enter parameters, one per line;
enter "0" to end data entry and proceed.
Parameter-> 3
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - Dissolved (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; or Quit to cancel.
Enter segment number > 0
System: Monthly pond -- code test data
Chemical: Methyl Parathion
0.Ill I * *
I * *
I * * * *
I * *
I * * * *
7.425E-02 I ** **
I ** **
I * * * *
I * ** * **
I *** ***
3.713E-02 I *** ***
I *** ***
I *** ***
I *** ***
I * ** * **
-I -| -| -| -| -| -| -| -| -| +
0.000 146. 292. 438. 584. 730.
73.0 219. 365. 511. 657 .
Time, Days
EXAMS-> LIST 20
Introduction to EXAMS Page 110
-------
3.5 Tutorial 2: Chemical & Environmental Data Entry
This tutorial example illustrates the entry of EXAMS chemical environmental data are not a complete characterization of Lake
and environmental data and the exploration of alternative Zurich; they include only a simple geometry plus the
process models and parameters. The EXAMS session duplicates parameters needed to construct a volatilization model for
the problem analysis in the text beginning on page 38; it EXAMS. The data entry sequence contains some errors; these
should be executed using that text as a guide. Note that the are intended as illustrations of error recovery methods.
3.5.1 Exercise 4: p-DCB in Lake Zurich
! Commands for Lake Zurich analysis of p-DCB
1 REC CHEM 1
2 CHEM NAME IS 1,4-Dichlorobenzene
3 set mwt(1)=147.0
4 change sol(l,l) to 73.8
5 set kow(l)=2340
6 set henry(1)=2.66e-3
7 cat chem
8 stor chem 2 0
9 rec env 1
10 env name is Lake Zurich - central basin (Untersee)
11 set elev=431
12 set airty(*)=r
13 set lat=47.5
14 set lon=8.5
15 SET K02 (1,13)=2.5
16 SET WIND(13)=1.38
17 HELP WIND
18 SET WIND(1,13)=1.38
19 SHOW KOUNT
20 SET KOUNT=3
21 SET TYPE(1)=E
22 SET TYPE(2)=H
23 SET TYPE(3)=B
24 SET DEPTH(1)=10
25 SET VOL(1)=6.8E8
26 SET DEPTH(2)=40
27 SET VOL(2)=2.72E9
28 SET DEPTH(3)=0.02
29 SET VOL(3)=1.36E6
EXAMS Lake Zurich Data Entry Laboratory Page 113
-------
30 SET AREA(*)=6.8E7
31 SET TCEL(1,13)=11
32 SET TCEL(2,13)=5.6
33 SET TCEL(3,13)=5.6
34 SET DSP (1, 13)=0 . 2
35 SET DSP(2,13)=1.E-4
36 SET XST (*)=6.8E7
37 SET JTUR(1)=1
38 SET ITUR(l) TO 2
39 CHA JTUR(2)=2
40 SET ITUR(2)=3
41 SHO DISP
42 HELP CHARL
43 SET CHARL(1)=25
44 SET CHARL(2)=20.01
45 SHO DISP
46 SET SUSED(*,13)=5
47 SET BULKD(3,13)=1.5
48 SET PCTWA(3,13)=150
49 SET FROC(*,13)=.02
50 SET STRFL(1,13)=3.E5
51 HELP STFL
52 SET STF(1,13)=3.E5
53 SET STSED(1,13)=1500
54 SET STRLD(1,1,13)=0.01
55 RUN
56 SET JFR(1)=1
57 SET ITO(1)=0
58 SET ADVPR(1)=1.0
59 SHO AD
60 RUN
61 LI 15
62 1 i s t 1 8
63 list 20
64 CAT ENV
65 ERA ENV 5
66 STOR ENV 5
67 SET DRFL(2,1,13)=1.5E-3
68 SET STRL(1,1,13)=8.5E-3
69 ENV NAME IS Lake Zurich with partial load to hypolimnion
70 run
EXAMS Lake Zurich Data Entry Laboratory Page 114
-------
71 li 15
72 1 i s t 1 8
73 li 20
74 zero load
75 set strl(1,1,13)=0 . 01
76 cat chem
77 rec chem 20
78 env name is Lake Zurich with oceanic wind and reaeration
79 show ko2 (1,13)
80 set ko2 (1, 13)=20
81 sho wind(1,13)
82 set wind(1,13)=2.6245
83 run
84 list 15
85 list 18
86 era chem 20
87 quit
EXAMS Lake Zurich Data Entry Laboratory Page 115
-------
5.5.2 Exercise 4 (Lake Zurich) with Complete EXAMS Responses
Welcome to EXAMS Release
Exposure Analysis Modeling System
Technical Contact: Lawrence A. Burns, Ph.D.
U.S. Environmental Protection Agency
960 College Station Road
Athens, GA 30605-2700 USA
Phone: (706) 355-8119 (Fax) 355-8104
Internet: burns.lawrence@epa.gov
Latest Maintenance
Type HELP and press the RETURN key for command names,
HELP USER for a summary of command functions,
HELP PAGES for a list of information pages,
or HELP EXAMS for introductory information.
Please stand by while EXAMS checks the computational
of this computer and initializes the Activity Data Base.
EXAMS-> REC CHEM 1
Selected compound is "Chemical Data Entry Template'
EXAMS-> CHEM NAME IS 1,4-Dichlorobenzene
EXAMS-> set mwt(1)=147.0
EXAMS-> change sol(1,1) to 73.8
EXAMS-> set kow(l)=2340
EXAMS-> set henry(1)=2.66e-3
EXAMS-> cat chem
Catalog of CHEMICAL parameter sets
UDB No. Name of Entry Volume
1 Chemical Data Entry Template
2 p-Cresol
3 Benz[a]anthracene
4 Benzo[a]pyrene
5 Quinoline
6 Benzo[f]quinoline
7 9H-Carbazole
8 7H-Dibenzo[c,g]carbazole
9 Benzo[b]thiophene
10 Dibenzothiophene
11 Methyl Parathion
12 Mirex
13 Heptachlor (Heptachlorodicyclopentadiene)
14 Speciation Test Data
EXAMS-> store chem 20
Chemical stored: "1,4-Dichlorobenzene"
EXAMS Lake Zurich Data Entry Laboratory Page 116
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EXAMS-> rec env 1
Selected environment is "Environmental Data Entry Template"
- central basin (Untersee)
EXAMS-> env name is Lake Zurich
EXAMS-> set elev=431
EXAMS-> set airty(*)=r
EXAMS-> set lat=47.5
EXAMS-> set lon=8.5
EXAMS-> SET K02(1,13)=2.5
EXAMS-> SET WIND(13)=1.38
Invalid number of subscripts.
EXAMS-> HELP WIND
WIND is a Real Matrix with 32 rows and 13 columns.
WINDspeed (segment, month) Units: meters/second
Average wind velocity at a reference height of ten centimeters
above the water surface. Parameter is used to compute a piston
velocity for water vapor (Liss 1973, Deep-Sea Research 20:221)
in the 2-resistance treatment of volatilization losses.
EXAMS-> SET WIND(1,13)=1.38
EXAMS-> SHOW KOUNT
KOUNT is 1
EXAMS-> SET KOUNT=3
EXAMS-> SET TYPE(1)=E
EXAMS-> SET TYPE(2)=H
EXAMS-> SET TYPE(3)=B
EXAMS-> SET DEPTH(1)=10
EXAMS-> SET VOL(1)=6.8E8
EXAMS-> SET DEPTH(2)=40
EXAMS-> SET VOL(2)=2.72E9
EXAMS-> SET DEPTH(3)=0.02
EXAMS-> SET VOL(3)=1.36E6
EXAMS-> SET AREA(*)=6. 8E7
EXAMS-> SET TCEL(1,13)=11
EXAMS-> SET TCEL(2,13)=5. 6
EXAMS-> SET TCEL(3,13)=5.6
EXAMS-> SET DSP(1,13)=0.2
EXAMS-> SET DSP(2,13)=l.E-4
EXAMS-> SET XST(*)=6.8E7
EXAMS-> SET JTUR(1)=1
EXAMS-> SET ITUR(l) TO 2
EXAMS-> CHA JTUR(2)=2
EXAMS-> SET ITUR(2)=3
EXAMS-> SHO DISP
Name of environment: Lake Zurich
Total number of segments (KOUNT)
Segment Number: 123
Segment "TYPE": E H B
central basin (Untersee)
3
EXAMS Lake Zurich Data Entry Laboratory Page 117
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Table 10.13.
Mean
transport field.
J TURB
I TURB
XS TUR m2
CHARL m
DSP m2/h*
Path No.:
1 2
2 3
6.800E+07 6.800E+07
1.00 0.000
0.200 1.OOOE-04
1 2
* Average of 12 monthly mean values.
EXAMS-> HELP CHARL
CHARL is a real vector with 300 elements.
CHARacteristic Length or mixing length (path) Units: meters
Average of segment dimensions normal to the exchange interface
linking segment numbers JTURB(p) and ITURB(p). The matching ("p"
subscript) members of JTURB, ITURB, CHARL, DSP, and XSTUR define
define a dispersive transport pathway. N.B.: A given segment
may have different mixing lengths at different interfaces.
CHARL can also be calculated from the distance along a path that
connects the centers of segments JTURB (p) and ITURB(p), passing
through the interface whose area is XSTURG(p).
See also: DSP, ITURB, JTURB, XSTUR
EXAMS-> SET CHARL(1)=25
EXAMS-> SET CHARL(2)=20.01
EXAMS-> SHO DISP
Name of environment: Lake Zurich - central basin (Untersee)
Total number of segments (KOUNT) = 3
Segment Number: 123
Segment "TYPE": E H B
Table 10.13. Mean dispersive transport field.
J TURB
I TURB
XS TUR m2
CHARL m
DSP m2/h*
Path No.:
1 2
2 3
6.800E+07 6.800E+07
25.0 20.0
0.200 1.OOOE-04
1 2
* Average of 12 monthly mean values.
SET SUSED(*,13)=5
SET BULKD(3,13)=1 . 5
SET PCTWA(3,13)=150
SET FROC(*,13)=.02
SET STRFL(1,13)=3.E5
EXAMS->
EXAMS->
EXAMS->
EXAMS->
EXAMS->
Option not identified.
EXAMS-> HELP STFL
STFLO is a real matrix with 100 rows and 13 columns.
STream_FLOws (segment,month) Units: cubic meters/hour
Flow into head reach of river or estuary; segment tributaries
and creeks or other streamflows entering a lake or pond. Note
that STFLO represents stream flow entering system segments from
external sources ONLY. EXAMS itself computes hydrologic flows
EXAMS Lake Zurich Data Entry Laboratory Page 118
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among segments that are part of the waterbody being studied, via
the specified advective and dispersive flow patterns (see JFRAD,
JTURB, etc.)- Therefore, DO NOT compute net water balances for
each segment and enter these into the database—enter ONLY those
flows entering the system across external boundaries!
EXAMS-> SET STF(1,13)=3.E5
EXAMS-> SET STSEDfl,13)=1500
EXAMS-> SET STRLDfl, 1,13)=0 . 01
EXAMS-> RUN
Simulation beginning for:
Environment: Lake Zurich - central basin (Untersee)
Chemical 1: 1,4-Dichlorobenzene
Hydrologic definition of segment 1 is improper.
There is a net advected flow leaving this segment,
but the flow pathway has not been specified. Simulation aborted.
RUN command completed.
EXAMS-> SET JFR(1)=1
EXAMS-> SET ITO(1)=0
EXAMS-> SET ADVPR(1)=1.0
EXAMS-> SHO AD
Name of environment: Lake Zurich - central basin (Untersee)
Total number of segments (KOUNT) = 3
Segment Number: 123
Segment "TYPE": E H B
Table 9. Input specifications — advective transport field.
J FR AD 1
I TO AD 0
ADV PR 1.00
Path No.: 1
EXAMS-> RUN
Simulation beginning for:
Environment: Lake Zurich - central basin (Untersee)
Chemical 1: 1,4-Dichlorobenzene
RUN command completed.
EXAMS-> LI 15
Ecosystem: Lake Zurich - central basin (Untersee)
Chemical: 1,4-Dichlorobenzene
Table 15.01. Distribution of chemical at steady state.
Seg Resident Mass ******** chemical Concentrations *******^
# Total Dissolved Sediments Biota
Kilos % mg/* mg/L ** mg/kg ug/g
In the Water Column:
EXAMS Lake Zurich Data Entry Laboratory Page 119
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1 7.3 20.00 1.074E-05 1.074E-05 2.060E-04 0.000
2 29. 80.00 1.074E-05 1.074E-05 2.060E-04 0.000
37. 99.22
and in the Benthic Sediments:
3 0.29 100.00 2.114E-04 1.074E-05 2.060E-04 0.000
0.29 0.78
Total Mass (kilograms) = 36.80
* Units: mg/L in Water Column; mg/kg in Benthos.
** Excludes complexes with "dissolved" organics.
EXAMS-> list 18
Ecosystem: Lake Zurich - central basin (Untersee)
Chemical: 1,4-Dichlorobenzene
Table 18.01. Analysis of steady-state fate of organic chemical.
Steady-state Values Mass Flux % of Load Half-Life*
by Process Kg/ day days
Hydrolysis
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
Bacterioplankton
Benthic Bacteria
Surface Water-borne Export 7.7315E-02 32.21 329.9
Seepage export
Volatilization 0.1627 67.79 156.8
Chemical Mass Balance:
Sum of fluxes = 0.2400
Sum of loadings = 0.2400
Allochthonous load:
Autochthonous load:
Residual Accumulation = 2.24E-08
* Pseudo-first-order estimates based on flux/resident mass.
EXAMS-> list 20
Ecosystem: Lake Zurich - central basin (Untersee)
Chemical: 1,4-Dichlorobenzene
Table 20.01. Exposure analysis summary.
Exposure (maximum steady-state concentrations) :
Water column: 1.074E-05 mg/L dissolved; total = 1.074E-05 mg/L
Benthic sediments: 1.074E-05 mg/L dissolved in pore water;
maximum total concentration = 2.114E-04 mg/kg (dry weight).
Biota (ug/g dry weight): Plankton: Benthos:
Fate :
Total steady-state accumulation: 36.8 kg, with 99.22%
in the water column and 0.78% in the benthic sediments.
Total chemical load: 0.24 kg/ day. Disposition: 0.00%
chemically transformed, 0.00% biotransformed, 67.79%
EXAMS Lake Zurich Data Entry Laboratory Page 120
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1, and 32.21% exported via other pathways.
After 216. days of recovery time, the water column had
lost 50.51% of its initial chemical burden; the benthic zone
had lost 19.54%; system-wide total loss of chemical = 50.3%.
Five half-lives (>95% cleanup) thus require ca. 36. months.
EXAMS-> CAT ENV
Catalog of ENVIRONMENTal models
UDB No. Name of Entry Volume
1 Environmental Data Entry Template
2 Pond -- code test data -- mean values only
3 Monthly pond -- code test data
4 Lake Zurich (Untersee), Switzerland: annual means
5 Georgia Pond-Stream (R. Lee)
6 GApond USEPA
EXAMS-> erase env 5
Environment
Pas sword
EXAMS-> STORE ENV 5
Environment stored: "Lake Zurich - central basin (Untersee)"
EXAMS-> SET DRFL(2,1,13)=1.5E-3
EXAMS-> SET STRL(l,l,13)=8.5E-3
EXAMS-> ENV NAME IS Lake Zurich with partial load to hypolimnion
EXAMS-> run
Simulation beginning for:
Environment: Lake Zurich with partial load to hypolimnion
Chemical 1: 1,4-Dichlorobenzene
RUN command completed.
EXAMS-> li 15
Ecosystem: Lake Zurich with partial load to hypolimnion
Chemical: 1,4-Dichlorobenzene
Table 15.01. Distribution of chemical at steady state.
Seg Resident Mass ******** chemical Concentrations *******•>
# Total Dissolved Sediments Biota
Kilos % mg/* mg/L ** mg/kg ug/g
In the Water Column:
1 7.3 16.59 1.074E-05 1.074E-05 2.060E-04 0.000
2 37. 83.41 1.350E-05 1.349E-05 2.589E-04 0.000
44. 99.19
and in the Benthic Sediments:
3 0.36 100.00 2.657E-04 1.349E-05 2.589E-04 0.000
.36 0.81
EXAMS Lake Zurich Data Entry Laboratory Page 121
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Total Mass (kilograms) = 44.37
* Units: mg/L in Water Column; mg/kg in Benthos.
** Excludes complexes with "dissolved" organics.
EXAMS-> list 18
Ecosystem: Lake Zurich with partial load to hypolimnion
Chemical: 1,4-Dichlorobenzene
Table 18.01. Analysis of steady-state fate of organic chemical.
Mass Flux % of Load Half-Life*
Kg/ day days
Hydrolysi s
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
Bacterioplankton
Benthic Bacteria
Surface Water-borne Export 7.7315E-02 32.21 397.8
Seepage export
Volatilization 0.1627 67.79 189.1
Chemical Mass Balance:
Sum of fluxes = 0.2400
Sum of loadings = 0.2400
Allochthonous load:
Autochthonous load:
Residual Accumulation = 0.0
* Pseudo-first-order estimates based on flux/resident mass.
EXAMS-> li 20
Ecosystem: Lake Zurich with partial load to hypolimnion
Chemical: 1,4-Dichlorobenzene
Table 20.01. Exposure analysis summary.
Exposure (maximum steady-state concentrations) :
Water column: 1.349E-05 mg/L dissolved; total = 1.350E-05 mg/L
Benthic sediments: 1.349E-05 mg/L dissolved in pore water;
maximum total concentration = 2.657E-04 mg/kg (dry weight).
Biota (ug/g dry weight): Plankton: Benthos:
Fate :
Total steady-state accumulation: 44.4 kg, with 99.19%
in the water column and 0.81% in the benthic sediments.
Total chemical load: 0.24 kg/ day. Disposition: 0.00%
chemically transformed, 0.00% biotransformed, 67.79%
and 32.21% exported via other pathways.
After 252. days of recovery time, the water column had
lost 54.35% of its initial chemical burden; the benthic zone
had lost 25.70%; system-wide total loss of chemical = 54.1%.
Five half-lives (>95% cleanup) thus require ca. 37. months.
EXAMS-> zero load
All allochthonous loads removed.
EXAMS Lake Zurich Data Entry Laboratory Page 122
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EXAMS-> cat chem
Catalog of CHEMICAL parameter sets
UDB No. Name of Entry Volume
Chemical Data Entry Template
p-Cresol
Benz [a] anthracene
Benz o [ a ] pyrene
Quinoline
Benz o [ f ] quinoline
9H-Carbazole
Methyl Parathion
1 , 4 -Dichlor obenzene
rec chem 20
compound is " 1 , 4-Dichlorobenzene"
rec env 5
environment is "Lake Zurich - central basin (Untersee)"
env name is Lake Zurich with oceanic wind and reaeration
show ko2(l,13)
is 2.500
1
2
3
4
5
6
7
11
20
EXAMS->
Selected
EXAMS->
Selected
EXAMS->
EXAMS->
K02 (1,13)
EXAMS-> set ko2(l,13)=20
EXAMS-> sho wind(l,13)
WIND (1,13) is 1.380
In this study, we wish to set Vw to 3000 cm/h. Use Eq. (2-82) (On page 36) to calculate the required value for WIND.
EXAMS-> set wind (1 ,13) =2 . 6245
EXAMS-> set strl (1 ,1 ,13)=0 .01
EXAMS-> run
Simulation beginning for:
Environment: Lake Zurich with oceanic wind and reaeration
Chemical 1: 1 , 4-Dichlorobenzene
RUN command completed.
EXAMS-> list 15
Ecosystem: Lake Zurich with oceanic wind and reaeration
Chemical: 1 , 4-Dichlorobenzene
Table 15.01. Distribution of chemical at steady state.
Seg
#
Resident
Kilos
Mas s
-6
Total
mg/*
Chemical Con
Di s solved
mg/L **
centr ations
Sediments
mg/ kg
Biota
ug/g
In the Water Column:
1 1.3 20.00 1.952E-06 1.952E-06 3.746E-05 0.000
2 5.3 80.00 1.952E-06 1.952E-06 3.746E-05 0.000
6 . 6
and in the Benthic Sediments:
3 5.23E-02 100.00 3.844E-05
5.23E-02 0.78
Total Mass (kilograms) =
1.952E-06 3.746E-05 0.000
6.691
* Units: mg/L in Water Column; mg/kg in Benthos.
** Excludes complexes with "dissolved" organics.
EXAMS-> list 18
Ecosystem: Lake Zurich with oceanic wind and
Chemical: 1,4-Dichlorobenzene
EXAMS Lake Zurich Data Entry Laboratory Page 123
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Table 18.01. Analysis of steady-state fate of organic chemical.
Mass Flux
Kg/ day
of Load
Half-Life'
days
Hydrolysi s
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
Bacterioplankton
Benthic Bacteria
Surface Water-borne Export 1.4058E-02
Seepage export
Volatilization 0.2259
5.86
94.14
329.9
20.53
Chemical Mass Balance:
Sum of fluxes = 0.2400
Sum of loadings = 0.2400
Allochthonous load:
Autochthonous load:
Residual Accumulation = 2.24E-(
* Pseudo-first-order estimates based on
EXAMS-> quit
EXAMS Lake Zurich Data Entry Laboratory Page 124
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4.0 EXAMS Command Language Interface (CLI) User's Guide
Introduction This Chapter describes the EXAMS command
language, including usage and reference information. The first
part provides an overview of the command language and its
grammar. The second part contains detailed descriptions of each
command. The commands are listed in alphabetical order.
4.1 Conventions Used in this Chapter
Convention Meaning
CTRL/X The phrase CTRL/X indicates that you must press
the key labeled CTRL while simultaneously pressing
another key, for example, CTRL/Q.
-> LIST 7 Vertical series of periods, or ellipsis, mean that
not all the data EXAMS would
display in response to
the particular command is shown,
or that not all
the data a user would enter is
shown.
keyword, ... Horizontal ellipsis indicates that additional
key-words, command parameters, or data can be
entered in a command sequence, or that EXAMS
displays additional data as part of the sample
output line.
[keyword] Square brackets indicate that the item enclosed is
optional, that is, the entity can be omitted from the
command line altogether.
-------
EXAMS-> ! This Command File should be renamed file.EXA
EXAMS->
OFF -- ends Audit recording of input commands,
Help -- this message,
Quit — return to the EXAMS prompt.
EXAMS analyzes the parts of the above example as follows.
EXAMS-> The EXAMS system prompt for command input; a
greater-than (->) means that EXAMS' command
interpreter is ready for a command to be entered.
AUDIT The command name, requesting that EXAMS
enable/disable the User Notepad/Command File
Creation facility.
ON An option of the AUDIT command, requesting that the
Notepad/Create facility be enabled.
All input will now be copied into the file
named "AUDOUT" on Fortran Unit Number 4
AUDIT-> ON
All input will now be copied into the
file named "AUDOUT" on Fortran unit number 4
In this example, no AUDIT option was entered, so EXAMS prompts
for a more complete specification of the intended action. The line
ending with a -> indicates that EXAMS is waiting for the
additional input.
In many cases, EXAMS' prompts do not include an automatic
description of the full range of possible response options. Often,
however, entering HELP in response to the prompt will display a
list of available choices, as in the following example.
A message from the AUDIT command, indicating that
the command completed successfully. The command
interpreter used the value of AUDOUT (4) to establish
communication with an external file.
EXAMS-> The next system command prompt, confirming that
the command has completed its operations (AUDIT
has opened communications with an external file
and started recording terminal inputs), and EXAMS
is ready for additional input.
! This Command File should be renamed file.EXA
EXAMS-> LIST
At the prompt, enter a Table number, "Quit," or "Help" to see a
catalog of the output tables.
Enter Table Number -> HELP
1 Chemical inputs: FATE Data
2 Chemical inputs: PRODUCT Chemistry
3 PULSE Chemical Loadings
A comment entered by the user. Comment lines must
begin with an exclamation point (!) or an asterisk (*).
You can use comments, as needed, to document EXAMS
analysis sessions or command procedures.
EXAMS-> The next EXAMS system command prompt,
confirming that the comment has been recorded in
the Notepad/Command file and EXAMS is ready to
accept another command.
4.4 Command Prompting
When you enter a command at the terminal, you need not enter
the entire command on a single line. If you enter a command that
requires that you specify its range or the object of the requested
action, and you do not include the needed information, EXAMS'
command interpreter prompts you for all missing information.
For example:
EXAMS-> AUDIT
The following AUDIT options are available
ON -- begins a new Audit file,
20 Exposure Analysis SUMMARY
ALL Entire Report
At the prompt, enter a Table number, "Quit,"
or "Help" to see a catalog of the output tables.
Enter Table Number -> 18
Ecosystem: Name of Water body
Chemical: Name of chemical
Table 18.01. Analysis of steady-state fate
(body of table)
In the example above, LIST is entered without the number of the
output table to be displayed. EXAMS prompts for the missing
information; typing HELP in response to the LIST prompt displays
a catalog of EXAMS output tables.
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4.5 EXAMS Messages
When a command is entered incorrectly, EXAMS displays a
descriptive errormessage indicating what is wrong. Forexample,
if a data subscript larger that the maximum available is entered,
EXAMS will respond
Subscript out-of-range.
You can then retype the command correctly.
Other error messages may be produced during the execution of
a command, or during a simulation or data display sequence.
These messages indicate such things as incomplete
environmental data, character data entered where numeric data
are required, or typographic errors during entry of commands.
EXAMS will respond to typographic errors in command entries by
displaying:
Command not recognized. Type HELP for command
information.
Because the messages are descriptive, it is usually possible to
determine what corrective action is required in order to proceed.
When this is not the case, EXAMS' HELP facility contains a large
body of additional and supplementary information available
through the HELP, DESCRIBE, and SHOW commands.
4.6 The HELP Command
Consulting a printed guide is not the most convenient way to get
a summary of the syntax of a command or a definition of an input
datum. EXAMS' HELP command provides this information in
EXAMS' interactive environment. Forexample, you can type the
command:
EXAMS-> HELP LIST
EXAMS responds by displaying a description of the LIST
command, its syntax, and the options needed to specify the range
of the command.
EXAMS' HELP facility supplies lists of individual topics and
subtopics. The HELP command is described in more detail later
in this Chapter, and a tutorial explanation of the command is
available online by entering
EXAMS-> HELP TUTOR
4.7 Command Procedures
A command procedure is a file that contains a sequence of
EXAMS commands, optionally interspersed with descriptive
comments (lines with"!" or"*" in column one). By placing sets
of frequently-used commands and/or response options in a
command procedure, all the commands in it can be executed as
a group using a single command. For example, suppose a file
called START.EXA were to contain these command lines and
comments:
SET MODE TO 3
SETKCHEM TO 4
SETNYEARTO 5
RECALL LOAD 7
! Loadings UDB Sector 7 is the spray drift study
The four commands in this file can be executed by entering the
command
EXAMS-> DO START
Or EXAMS-> @START
You do not have to specify the file type of a command procedure
when you use the @ command, so long as the file type is
".EXA"--the default file type for EXAMS' @ command. You can
use another file suffix, if you so inform EXAMS when you enter
the command request. For example, to execute commands in a
file named START.UP
EXAMS-> @START.UP
The HELP facility also provides on-line assistance for EXAMS'
input data, e.g.,
EXAMS-> HELP QYlELD
will display the subscript ranges, their meanings, the physical
dimensions, and the English definition of EXAMS chemical input
datum "QYlELD". This information is available online for all
EXAMS' input data and control parameters. The names of all of
EXAMS' input variables were selected as mnemonics for their
English-language names. (For example, QYlELD is the
photochemical quantum yield.) These mnemonics are used in
EXAMS' output tables; definitions are given in the Data
Dictionary (pages 175 ff.) as well as in the on-line HELP.
4.8 Wild Card Characters
Some EXAMS commands accept a "wild card" character in the
input command specifications. The asterisk (*) is the only
symbol having this function in EXAMS. Wild card characters are
used to refer to a range of data subscripts, or other entities, by a
general name, rather than having to enter a specific name for
each member of the group. Particular uses of wild cards in
EXAMS vary with the individual commands. The command
descriptions later in this Chapter indicate where wild cards are
allowed and describe their effects.
127
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4.9 Truncating Command Names and Keywords
All keywords and names of input data that are entered as
command input can be abbreviated. Only enough characters to
uniquely distinguish a keyword or datum from others with similar
names need be entered (often only one).
4.10 Summary Description of EXAMS' System
Commands
EXAMS Command
Summary Description
AUDIT Start/Stop user notepad for recording procedures
CATALOG List the contents of User Databases (UDBS)
CHANGE/SET Enter/reset input data and program controls
CONTINUE Resume integration (Modes 2 and 3 only)
DESCRIBE Report dimensions and datatype of parameter
DO or @ Execute file of EXAMS commands (file.EXA)
ERASE Clear section of stored database (UDB)
HELP Describes access to EXAMS on-line HELP facility
LIST Show tabular results on the screen
NAME Specify the name of a UDB, e.g., CHEM NAME is ...
PLOT Plot results on the screen
PRINT Queue tabular results for hardcopy printing
QUIT Abort command, or End interactive session
READ Upload data from non-EXAMS ASCII disk file
RECALL Activate data from stored database (UDB)
RUN Begin simulation run
SHOW Display current data values or control settings
STORE Download current data into stored database (UDB)
WRITE Download data to ASCII disk file
ZERO Clear chemical loadings, pulses, or residuals
128
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5.0 System Command Descriptions
AUD IT
Creates a copy of user input commands and responses in an external file.
Related: Control variables: AUDOUT
Commands: DO
Syntax: AUDIT
-------
2. EXAMS-> AUDIT OFF
The AUDIT option has been terminated.
This command ends copying of EXAMS commands and responses to the external medium (usually a disk file).
3. EXAMS-> AUDIT ON
All input will now be copied into the
file named "AUDOUT" on Fortran Unit Number 4
EXAMS-> RECALL ENV 2
Selected environment is: Phantom Inlet
EXAMS-> RECALL CHEM 2
Selected compound is: Dichloroexample
EXAMS-> RECALL CHEM 4 AS 2
Selected compound is: Tetrabromoexample
EXAMS-> AUDIT OFF
These commands build a file (AUDOUT) that can later be used as a command file upon entering the EXAMS system. In this instance, the
file would be renamed (e.g., MYCOMAND.EXA) and used to execute the above series of commands as a unit—
EXAMS-> DO MYCOMAND
130
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CATALOG
Lists, by accession number, the title of all current entries in the specified User Database (UDB).
Related: Control variables: none
Commands: ERASE, NAME, RECALL, STORE
Syntax: CATALOG
-------
1 Chemical Data Entry Template
2 p-Cresol
3 Benz[ a] anthracene
EXAMS->
This example use of the CATALOG command lists the contents of the current User Database for chemical data. Any of these datasets can
be loaded into the Activity Database (ADB) for study, using the RECALL command and the appropriate access number. The first entry
("Chemical Data Entry Template") is a blank data area reserved for entering new chemical data.
2. EXAMS-> CATALOG ENVIRON
Catalog of ENVlRONMENTal models
UDB No. Name of Entry Volume
1 Environmental Data Entry Template
2 Pond -- code test data
3 Connecticut River estuary
This example CATALOG command generates a listing of the environmental datasets present in the User Database. Any of these can be
retrieved for study using a RECALL command and the accession number. The first entry ("Environmental Data Entry Template") is a
template for entering a new environmental model.
132
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CHANGE
Use to enter data into the activity database (synonymous with SET).
Related: Commands: DESCRIBE, HELP, SET
Syntax: CHANGE TO
or SET =
Prompt: Enter name=value command->
Variable: The data entry or variable to be entered can be specified either as a single datum or, using wild cards (*), as an entire vector,
row/column of a matrix, etc.
Description: Use the CHANGE command to specify the values of data in the activity database. "Value" can be any numerical quantity or
literal characters, as appropriate. "Variable" specifies an individual element of input data or a program control parameter.
Entire vectors, rows/columns of matrices, etc. can be set to a single uniform value using wild cards (*).
Examples:
1. EXAMS-> CHANGE VOL(1 53) TO 7E5
Subscript out-of-range.
EXAMS-> DESCRIBE VOL
VOL is a Real Vector with 100 elements.
EXAMS> CHANGE VOL(2) TO E
Invalid numeric quantity after TO or =.
EXAMS-> CHANGE VOL(2) TO 7E5
This command sets the environmental volume of segment 2 to 7.0E+05 cubic meters. The initial attempt to set the volume of segment 153
was rejected by EXAMS because the version in use was setup for environmental models of 100 segments at most. The DESCRIBE command
was used to check the number of subscripts and the dimensional size of the variable "VOL". The accidental entry of an alphabetic character
("E") for the volume was trapped by the CHANGE command; VOL(2) was not altered.
2. EXAMS-> HELP TCEL
TCEL is a Real Matrix with 100 rows and 13 columns.
Temperature-CELsius (segment, month) Units: degrees C.
Average temperature of ecosystem segments. Used (as enabled by input data) to compute effects of temperature on transformation rates
and other properties of chemicals.
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EXAMS-> CHANGE TCEL(2,7)TO 24
This command changes the July temperature in segment 2 to 24° C. The HELP command was used to check subscript dimensions,
maximum values, the meaning of the subscripts (subscript #1 denotes the segment, subscript #2, the month), and the proper units for the
input datum (degrees Celsius).
3. EXAMS-> HELP POH
POH is a Real Matrix with 100 rows and 13 columns.
pOH (segment, month) Units: pOH units
The negative value of the power to which 10 is raised in order to obtain the temporally averaged concentration of hydroxide [OH"]
ions in gram-equivalents per liter.
EXAMS-> CHANGE POH(*,13) TO 6.2
This command sets the average pOH (sector 13) of every segment to 6.2. Note use of wild card "*" to specify that all segments are to be
changed. As in the previous example, HELP was used to check subscript dimensions, units, etc. This step, of course, is optional.
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CONTINUE
The CONTINUE command resumes EXAMS' simulation analysis of chemical dynamics beginning from the current state of the system.
Related: Control variables: CINT, TIN IT, TEND, TCODE, NYEAR
Commands: RUN, SHOW_TIME_FRAME
Syntax: CONTINUE
Prompt: (In Mode 2 only:)
Initial time for integration will be (nn.n) units
Enter ending time of integration, Help, or Quit->
Options: None. Reply to prompt with a value greater than (nn.n).
Description: The CONTINUE command resumes EXAMS' simulation analysis of chemical dynamics, beginning from the current state ofthe
system. Chemical loadings and other input data can be altered (CHANGEd or SET) between simulation time segments; EXAMS
will re-evaluate equation parameters as needed to incorporate the changed conditions into the analysis. CONTINUE cannot be
invoked from Mode 1, where it is not appropriate. The SHOW TIME FRAME (abbreviate to SH T F) command can be used to
examine the current state ofthe integrator timer controls. In Mode 2, the (Communications iNTerval CINT can be used to vary
the temporal resolution in different segments ofthe analysis (see Example 1). In Mode 3, NYEAR, the number of years in a
simulation time segment, can similarly be altered.
Examples:
1. EXAMS-> SETMODE=2
EXAMS-> SHOW TIME FRAME
A RUN will integrate from 0. to 24. Hours
with output at intervals of 2.00 Hours
EXAMS-> SETTCODE=2
EXAMS-> SETTEND=10
EXAMS-> SETCINT=1
EXAMS-> SH TIP
A RUN will integrate from 0 to 10 Days
with output at intervals of 1 Days
EXAMS-> RUN
Simulation beginning for:
Environment: Pond -- code test data
Chemical 1: Dichloroexample
Run complete
EXAMS-> PLOT KIN PL (3,0,0 - see PLOT command)
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System: Pond -- code test data
Chemical: Dichloroexample
2.00 j*****
j
j *****
I
I
I
I
I
0.667 I
I
I
I
I
I
-I ----- -| ----- -| ----- -| ----- -| ----- -| ----- -| ----- -| ----- -| ----- -| ----- +
0.000 2.00 4.00 6.00 8.00 10. (
1.00 3.00 5.00 7.00 9.00
Time, Days
EXAMS-> CONTINUE
Initial time for integration will be 10.0 Days
Enter ending time of integration, Help, or Quit-> 30
Simulation beginning for:
Environment: Pond -- code test data
Chemical 1: Dichloroexample
Run complete.
EXAMS-> SETCINT=10
EXAMS-> ZERO PULSE LOAD
EXAMS-> CONTINUE
Initial time for integration will be 30.0 Days
Enter ending time of integration, Help, or Quit-> 90
Simulation beginning for:
Environment: Pond -- code test data
Chemical 1: Dichloroexample
Run complete.
EXAMS-> PLOTKINETIC PLOT (3,0,0)
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System: Pond -- code test data
Chemical: Dichloroexample
3.49 I *
I **
I **
I *
I **
2.33 I **
T * * *
J * * * * * *
I ***
T * * *
1.161 *
I *
I * * *
I
I
0.000 I
-I -I -I -I -I -I -I -I -I -I +
0.000 18.0 36.0 54.0 72.0 90.0
9.00 27.0 45.0 63.0 81.0
Time, Days
These commands show the use of the CONTINUE command in Mode 2. The objective of the analysis was to introduce two pulses
of chemical separated by 10 days and to follow exposure over 90 days. Note the phased increase in the Communications iNTerval
CINT from 1 to 10 days. Note the use of the ZERO command to clear the pulse load ADB before the simulation of dissimilation from
day 30 through day 90. If this were not done, EXAMS would introduce an additional pulse on day 30.
EXAMS-> SETMODE=3
EXAMS-> SHO TI FR
A RUN will integrate from 1 January 1989
through 31 December 1989.
(YEARl = 1989, andNYEAR= 1.)
EXAMS-> RUN
Simulation beginning for:
Environment: Pond -- code test data
Chemical 1: Dichloroexample
Run complete.
EXAMS-> SHO TI FR
A RUN will integrate from 1 January 1989
through 31 December 1989.
(YEARl = 1989, andNYEAR= 1.)
CONTinuation will proceed through 31 December 1990
(NYEAR = 1.)
EXAMS-> SETNYEAR=3
EXAMS-> SH TIP
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A RUN will integrate from 1 January 1989
through 31 December 1991.
(YEARl = 1989, andNYEAR= 3.)
CONTinuation will proceed through 31 December 1992
(NYEAR = 3.)
EXAMS-> CONTINUE
CONTinuing integration through 31 December 1992.
Simulation beginning for:
Environment: Pond -- code test data
Chemical 1: Dichloroexample
Run complete.
EXAMS->
These commands illustrate the use of the CONTINUE command in Mode 3. "SHOW TIME FRAME" is used to check the state of the
integrator timer controls.
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DESCRIBE
Reports the data type, dimensionality, and implemented size of parameters.
Related: Control variables:
Commands: HELP
Syntax: DESCRIBE
Parameters:
Any "system parameter"—any chemical or environmental input datum, control parameter (e.g., MODE, CINT), etc.
Prompt: Enter name of input parameter->
Options: Any parameter accessible to the CHANGE and SET commands can be inspected using the DESCRIBE command.
Description: The DESCRIBE command returns information about EXAMS' input data and control parameters. All variables whose values
can be altered using the CHANGE and SET commands can be inspected by the DESCRIBE command. The information returned
by DESCRIBE includes the data type (real, integer, character), dimensionality (scalar, vector, matrix (2-dimensional), table
(3-dimensional matrix)) and implemented size in the version of EXAMS in use. The DESCRIBE command is the first recourse
when a CHANGE or SET command fails.
Examples:
1. EXAMS-> DESRMODE
Command not recognized. Type HELP for command information.
EXAMS-> DESCR
Enter name of input parameter-> MODE
MODE is an Integer Scalar.
These commands establish that "MODE" is an integer scalar. Note that the initial typing error (DESR) resulted in a "not recognized"
error message followed by return to the EXAMS prompt.
2. EXAMS-> CHANGE VOL(133) TO 7E5
Subscript out-of-range.
EXAMS-> DESCRIBE VOL
VOL is a Real Vector with 100 elements.
This command reports that VOL is a real variable, with 100 elements. In this example, the number of segments (NPX) in the
version of EXAMS currently in use is set for 100 almost. Any (intentional or accidental) attempt to set "KOUNT" to a value > 100,
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or to enter a value for the vomme of a segment > 100 (e.g., VOL(133)) will fail, as illustrated above. DESCRIBE can be used to
check the reason for a failure of the CHANGE or SET command when a problem with dimension sizes is suspected.
3. EXAMS-> DESCRIBE QYlELD
QYlELD is a Real Table with dimensions (3,7,4)
EXAMS-> HELP QYlELD
QYlELD is a Real Table with dimensions (3,7,4)
Quantum_YlELD (form, ion, chemical) Units: dimensionless
Reaction quantum yield for direct photolysis of chemicals—fraction of the total light quanta absorbed by a chemical that results
in transformations. Separate values (21) for each potential molecular type of each chemical allow the effects of speciation and
sorption on reactivity to be specified in detail. The matrix of 21 values specifies quantum yields for the (3) physical forms: (1)
dissolved, (2) sediment-sorbed, and (3) ooc-complexed; of each of (7) possible chemical species: neutral molecules (1), cations
(2-4), and anions (5-7). (QYlELD is an efficiency.)
These commands report the data type and dimensionality of EXAMS' input "QYlELD" (result of "DESCRIBE QYlELD") and then
report the meaning of the dimensions and the physical units of the variable (result of "HELP QYlELD"). The local implementation
of EXAMS used in this example has the capacity to simulate the behavior of no more than four chemicals simultaneously. Thus,
QUANT was DESCRlBEd as consisting of a set of four matrices, each of (fixed) size (3,7).
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D O
Executes a command procedure; requests that EXAMS read subsequent input from a specific file.
Related: Control variables:
Commands: AUDIT
Syntax: DO
Prompt: Enter name of file (no more than nn characters), Help, or Quit->
Parameters: name of file
Specifies the file from which to read a series of EXAMS commands. If you do not specify a file type suffix, EXAMS uses a
default file type of EXA (e.g., "filename.EXA"). Wild cards are not allowed in the file specification.
Description: Use command procedures to catalog frequently used sequences of commands. An EXAMS command procedure can contain
• Any valid EXAMS command. The command line can include all the necessary options and data to build a complete command
(exception: kinetic plots).
• Parameters or response options for a specific command. When the currently executing command requires additional parameters,
the next line of the command file is searched for appropriate input.
• Data. When the currently executing command requires numerical or character data entry, the next line of the command file is
searched for input.
• Comment lines. Any line that contains an exclamation point (!) or asterisk (*) in column one is ignored by EXAMS' command
interpreter. These lines can be used as needed to document the command procedure.
Command procedures must not contain a request to execute another command procedure. (In other words, a DO file must not
contain a DO (@) command; EXAMS' DO commands cannot be nested.) Command procedures can be constructed as external files
using your favorite editor, or they can be constructed interactively through the EXAMS system command processor, as illustrated
below. The default file type is "EXA", but files of any type (suffix) can be used if the entire file name is specified when entering
the DO command.
Examples: 1. EXAMs-> AUDIT ON
All input will now be copied into the
file named "AUDOUT" on Fortran Unit Number 4
EXAMS-> RECALL
Enter Environment, Chemical, Load, Product, Help or Quit-> ENV
Enter environment UDB catalog number, Help, or Quit-> 2
Selected environment is: Phantom Inlet
EXAMS-> RECALL CHEM 2
Selected compound is: Dichloroexample
EXAMS-> RECALL LOAD 2
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Selected load is: Aedes control spray drift
EXAMS-> ! Load 2 is the Phantom Inlet salt marsh study
EXAMS-> SETKCHEM TO 2
EXAMS-> RECALL CHEM 4 AS 2
Selected compound is: Tetrabro mo example
EXAMS-> AUDIT OFF
These commands build a file (AUDOUT.DAT) that can later be used as a command file upon entering the EXAMS system. In this
instance, the file could be renamed (e.g., SETUP.EXA) and used to execute the above series of commands as a unit:
EXAMS-> DO SETUP
Or, EXAMS-> @SETUP
The completed command file appears as follows
RECALL
ENV
2
RECALL CHEM 2
RECALL LOAD 2
! Load 2 is the Phantom Inlet salt marsh study
SETKCHEM TO 2
RECALL CHEM 4 AS 2
AUDIT OFF
Note that command files that are constructed interactively will include "AUDIT OFF" as the final instruction. This can, of course,
be removed by editing the file if it is undesirable.
2. EXAMS-> DO
Enter name of file (no more than nn characters), Help, or Quit-> HELP
The "DO" or "@" command provides a means of executing stored EXAMS commands. In response to the prompt, enterthe name
of the file that contains the stored commands. A three-character filename extension of "EXA" is added to the name if no period
is present in the name as entered. The maximum length for file names is nn characters; this limit includes the .EXA suffix.
Enter name of file (no more than nn characters), Help, or Quit-> AUDOUT
EXAMS/DO-> ! Audit trail of input sequence from EXAMS.
EXAMS/DO-> RECALL
Enter Environment, Chemical, Load, Product, Help, or Quit->
EXAMS/DO-> ENV
Enter environment UDB catalog number, Help, or Quit->
EXAMS/DO-> 2
Selected environment is: Phantom Inlet
EXAMS/DO-> RECALL CHEM 2
Selected compound is: Dichloroexample
EXAMS/DO-> RECALL LOAD 2
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Selected load is: Aedes control spray drift
EXAMS/DO-> ! Load 2 is the Phantom Inlet salt marsh study
EXAMS/DO-> SETKCHEMTO2
EXAMS/DO-> RECALL CHEM 4 AS 2
Selected compound is: Tetrabro mo example
EXAMS/DO-> AUDIT OFF
The AUDIT option has been terminated.
This command requests execution of the command procedure constructed in Example 1 above. The default name (AUDOUT) was not
altered, so the complete file specification was given to the DO command as the entry parameter. The DO file transfers a set of two
chemicals, an environmental model, and a load pattern from the stored UDB to the ADB for study and analysis.
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ERAS E
Deletes, by accession number, the data stored at a single sector of a User Database (UDB) library (chemical, environmental, loadings,
product chemistry).
Related: Control variables:
Commands: CATALOG, RECALL, STORE
Syntax: ERASE
-------
Environment 20 erased.
This command erases the data stored at Environmental UDB sectornumber twenty. The space is now available for storing another
dataset.
2. EXAMS-> ERASE
Enter Environment, Chemical, Load, Product, Help, or Quit-> HELP
The ERASE command requires that you specify either:
1. Environment,
2. Chemical,
3. Load,
4. Product,
5. Help (this option), or
6. Quit.
Enter Environment, Chemical, Load, Product, Help, or Quit-> LOAD
Enter allochthonous loading UDB catalog number, Help, or Quit-> 10
Load 10 erased.
This command erases the data stored at Loadings UDB sector number ten. The space is now available for another dataset.
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EXIT
EXIT can be used as a synonym for QUIT to end an interactive session.
Related: Control variables:
Commands: QUIT is used to abort commands in progress.
Syntax: EXIT
Prompt: None
Options: None
Description: If EXIT is entered from the EXAMS prompt command level, EXAMS stops and returns control to the computer operating system.
Examples:
1. EXAMS-> EXIT
This command terminates an interactive EXAMS session.
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HELP
Displays, on the terminal, information available in EXAMS' help files. EXAMS provides descriptions of its commands, input data, control
parameters, and general concepts and analysis procedures.
Related: Control variables:
Commands: DESCRIBE
Syntax: HELP [keyword]
Prompt: None
Keyword: Specifies a keyword (a topic or an element of EXAMS input data) that tells EXAMS what information to display.
• None—if HELP is typed with no keyword, EXAMS lists the keywords that can be specified to obtain information about other
topics.
• Topic-name--describes either a basic EXAMS command, an information page, or a "system parameter." System parameters
include chemical and environmental input data, system control parameters (e.g., CINT), and parameters that control the
current analysis (e.g., IMASS).
Ambiguous abbreviations result in a failure to achieve a match on the keyword, and an error message is displayed.
Description: The HELP command provides access to EXAMS' collection of on-line user aids and information texts. This material includes
• Brief discussions of the syntax and function of each of EXAMS' command words (RECALL, RUN, etc.)
• Definitions, physical dimensions, and meanings of subscripts for EXAMS' chemical and environmental input data and control
parameters.
• A series of information pages providing orientationto the concepts implemented in the EXAMS program, the range of capabilities
and analyses that can be executed with the program, and brief expositions on data structures and program control options.
Examples:
1. EXAMS-> HELP
EXAMS includes these system commands:
HELP message text and list of command and
information topics
Issuing the HELP command without any keywords produces a list of the HELP topics in EXAMS main command library. When
responding to one of the topics on the list, EXAMS displays a HELP message on that topic, and a list of subtopics (if any).
2. EXAMS-> HELP QUOIT
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No information available for this request.
EXAMS->
When you request information for a topic not on file, EXAMS displays a message to that effect and returns you to the EXAMS->
prompt.
3. EXAMS-> HELP QYlELD
QYlELD is a Real Table with dimensions(3,7,4)
Quantum_YlELD (form, ion, chemical) Units: dimensionless
Reaction quantum yield for direct photolysis of chemicals--fraction of the total light quanta absorbed by a chemical that results
in transformations. Separate values (21) for each potential molecular type of each chemical allow the effects of speciation and
sorption on reactivity to be specified in detail. The matrix of 21 values specifies quantum yields for the (3) physical forms: (1)
dissolved, (2) sediment-sorbed, and (3) DOC-complexed; of each of (7) possible chemical species: neutral molecules (1), cations
(2-4), and anions (5-7). (QYlELD is an efficiency.)
You can request information about any input datum (chemical, environmental, control parameters, analysis parameters) accessible
to the CHANGE and SET commands. EXAMS then displays on the screen the characteristics of the variable (equivalent to the results of
DESCRIBE), followed by a discussion of the variable that echoes the entry in the Data Dictionary (page 175).
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LIST
Displays an EXAMS output table on the terminal screen.
Related: Control variables: FIXFIL
Commands: PLOT, PRINT
Syntax: LIST
-------
18 Sensitivity Analysis of Chemical FATE
19 Summary TIME-TRACE of Chemical Concentrations
20 Exposure Analysis SUMMARY
ALL Entire Report
Table-> 18
Ecosystem: Name of Water body
Chemical: Name of chemical
TABLE 18.01. Analysis of steady-state fate
(body of table)
The LIST command requests that output Table 18 from an EXAMS results file be displayed on the terminal. For illustrative purposes,
it was assumed that the user had left EXAMS and then returned to inspect Table 18 generated in the previous session.
2. EXAMS-> LIST 20
Ecosystem: Name of Water body
Chemical: Name of FIRST chemical
TABLE 20.01. Exposure analysis summary: 1983--1985.
(body of table)
More? (Yes/No/Quit)-> Y
Ecosystem: Name of Water body
Chemical: Name of SECOND chemical
TABLE 20.02. Exposure analysis summary: 1983--1985.
(body of table)
In this example, EXAMS was used to investigate the behavior of two chemicals over a period of several years, using Mode 3
simulations. The analysis began with year 1983, andNYEAR was set to 3 to produce an analysis of the period 1983 through 1985. The
LIST command requests that all versions of Table 20 in the analysis file be displayed, with a pause between each for inspection of the
results. In the example, the analyst chose to examine the output for both chemicals. If the analysis is now CONTlNUEd, the current set
of tables will be replaced with new results. The PRINT command should be used to make copies of all intermediate results you want
to save.
The sub-table numbers of EXAMS' output tables identify the ADB number of the chemical, the indexes of any ions (see SPFLG in the
Data Dictionary on page 175), and the month of the year, as follows.
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NAME
Use the NAME command to attach unique names to datasets.
Related: Control Variables: MCHEM
Commands: CATALOG, ERASE, STORE, RECALL
Syntax: NAME IS a[aa...] (up to 50 characters), where can be CHEmical, ENvironment, LOad, or PROduct
Prompt: Options available are:
Help - this message.
Quit - return to EXAMS command mode.
= Help
- accepted as the new name.
Enter new name->
: EXAMS uses these four kinds of datasets:
1. CHEMICAL reactivity and partitioning,
2. ENVlRONMENTal physico/chemical parameters,
3. allochthonous chemical LOADings, and
4. PRODUCT chemistry for generating interconversions among multiple chemicals in an analysis
Description: The NAME command is used to associate unique names with datasets in the UDB. These names can be STOREd in the
CATALOGS; they are printed in the headers of EXAMS' output tables. When naming CHEMICAL datasets, the ADB number of
the chemical to be named is given by MCHEM; use "SET MCHEM TO n" before naming dataset "n".
Examples:
1. EXAMS-> CHEM NAME IS Tetrachloroexample
The NAME command associates the name "Tetrachloro..." with the chemical data in the sector of the activity database (ADB) given
by the current value of MCHEM . This name will be printed on all subsequent appropriate output tables, and it will be used as a
title for the database if the STORE command is used to download the data into the User Database (UDB).
2. EXAMS-> SET MCHEM = 2
EXAMS-> CHEM NAME IS Dichloroexample
The chemical name command always addresses the MCHEM sector of the chemical ADB, thus, this example names chemical
number 2 to "Dichloro...".
3. EXAMS-> ENVIR NAME is Pogue Sound
This command names the current environmental dataset "Pogue Sound". The name will now appear on output tables, and remain
with the dataset if it is downloaded to the UDB permanent files.
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PLOT
Used to plot character graphics for the chemical state of the ecosystem.
Related: Control Variables: MCHEM
Commands: LIST, PRINT
Syntax: PLOT
Options
POINT
PROFILE
KINETIC
Prompt: The following options are available:
POint - Vertical concentration profile
PRofile - Longitudinal concentration profile
Kinetic - List or plot kinetic outputs
Help - This message
Quit - Return to the EXAMS program prompt
Option->
Plot options: POINT
"POINT" plots are generalized pro files of chemical concentrations. These also require selection of a variable to be displayed (total
concentration, dissolved concentration, etc.) and a "statistical" class (average values, minima, or maxima).
PROFILE
"PROFILE" plots are longitudinal profiles of chemical concentrations. These require selection of a concentration variable (total
concentration, dissolved concentration, etc.) and an environmental sector (water column or benthic sediments). The abscissa of
the resulting plot is set up by increasing segment number, which in most cases should represent an up stream-down stream
progression. When the aquatic model includes both longitudinal and vertical segmentation, each section of the plot begins at the
air-water or water/benthic interface and proceeds vertically downward (the bars are presented along the abscissa).
KINETIC
"KINETIC" plots display the results of integration of the governing equations over the time spans selected for simulation. These
plots also require selection of concentration variables and either particular segments, or summary "statistics," for display. Time
is used as the abscissa for the plot.
Description: Use the PLOT command to display results of the current analysis. Three kinds of character graphic PLOTS are available on-line
from EXAMS: POINT, PROFILE, and KINETIC. Each PLOT requires the specification of several options; these can either be
entered on the system command line or entered in response to EXAMS prompts. The available second- and third-level options
153
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are illustrated in the examples below. The results available to POINT and PROFILE plots depend on the Mode used in the
simulation. In Mode 1, the outputs are steady-state concentrations. In Mode 2, the results are a snap-shot of concentrations
as of the end of the current temporal simulation segment. In Mode 3, the results are time-averaged concentrations over the
most recent temporal simulation segment of length NYEAR.
Examples:
1. EXAMS-> PLOT POINT
The following concentration options are available:
Total - mg/L in Water Column
mg/kg in Benthic Sediments
Dissolved - "Dissolved" (mg/L)
(aqueous + complexes with "dissolved" organics)
Particulate - Sediment-sorbed (mg/kg dry weight)
Biota - Biosorbed (ug/g dry weight)
Mass - Chemical mass as grams/square meter AREA
Help - This message
Quit - Return to the EXAMS prompt
Option-> DISSOLVED
The following statistical options are available:
MAX - Maximum concentration
MIN - Minimum concentration
AVE - Average concentration
Help - This message
Quit - Return to the EXAMS prompt
Option-> AVERAGE
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EXAMS-> SET MCHEM=2
EXAMS-> PL PO DIAV
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V E
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A R
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Benthic
This example illustrates EXAMS' internal prompting for POINT plots. Note that the analysis included two chemicals; the plot for
chemical number two was obtained by first SETting MCHEM=2. The second plot was requested via a single command line, thus
bypassing the PLOT prompts.
2. EXAMS-> PLOT PROF
The following concentration options are available:
Total - mg/L in Water Column
mg/kg in Benthic Sediments
Dissolved - "Dissolved" (mg/L)
(aqueous + complexes with "dissolved" organics)
Particulate - Sediment-sorbed (mg/kg dry weight)
Biota - Biosorbed (ug/g dry weight)
Mass - Chemical mass as grams/square meter AREA
Help - This message
Quit - Return to the EXAMS prompt
Option-> TOTAL
The following options are available:
WATER
SEDIMENTS
Help
Quit
Option-> WATER
Water Column concentrations
Benthic Sediment concentrations
This message
Return to the EXAMS prompt
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c
T 0
0 N
T C
A E
L N
T
M R
G A
/ T
L I
0
N
8 . OOE-01
6 . OOE-01
4.OOE-01
2 . OOE-01
0.OOE+00
012
013
E | E
E I E
001
010 Oil EIE E|E
E | E E | E E | E E | E
008 E|E E|E E|E E|E E|E
005 006 007 E|E E|E E|E E|E E|E E|E
002 003 004 E|E E|E E|E E|E E|E E|E E|E E|E E|E
_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE
WATER COLUMN
015
014
H | H
H | H
H | H
H | H
H | H
H | H
H | H
H | H
H | H
H I H
H
H
HHH HHH
The above example illustrates EXAMS' internal prompts for a PROFILE plot. As with the POINT option, this entire command could be
entered on a single line:
EXAMS-> PLOT PROF TOT WAT
3. EXAMS-> PLOT KIN
The following KINETIC options are available:
List - lists selected KINETIC output parameters
Plot - plots selected KINETIC output parameters
Help - this message
Quit - return to the EXAMS prompt
Option-> PLOT
Chemical: Methyl Parathion
Environment: Pond -- code test data
Simulation units: Days
Number of segments: 2
Type of segment (TYPE): L B
1 2
The following parameters are available for time-trace plotting
of values averaged over the ecosystem space:
("Dissolved" = aqueous + complexes with "dissolved" organics.)
1 - Water Column: average "dissolved" (mg/L)
2 - average sorbed (mg/kg)
3 - total mass (kg)
Benthic:
average "dissolved" (mg/L)
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5 average sorbed (mg/kg)
6 total mass (kg)
Enter parameters, one per line;
enter 0 to end data entry and proceed.
Parameter-> 3
Parameter-> 6
Parameter-> 0
The following parameters are available for each segment:
1 - Total concentration (Water Column, mg/L; benthic, mg/kg)
2 - "Dissolved" (mg/liter of fluid volume)
3 - Sorbed (mg/kg of sediment)
4 - Biosorbed (ug/g)
5 - Mass (grams/square meter of AREA)
Enter segment-parameter number pair, one number per line;
enter 0 when data entry is complete; Quit to abort.
Enter segment number—-> 0
System: Monthly pond -- code test data
Chemical: Methyl Parathion
0.160
0.106
, , , , , , , , , , ,
This example illustrates EXAMS' prompting in KINETIC plots. The numerical options cannot be entered on the command line, but must
be entered in response to the prompts.
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PRINT
Use the PRINT command to queue an output table for hardcopy printing.
Related: Control variables: FIXFIL
Commands: LIST
Syntax: PRINT
-------
QUIT
Use QUIT to abort a command in progress or to end an interactive EXAMS session.
Related: Control variables:
Commands: EXIT
Syntax: QUIT
Prompt: None
Options: None
Description: Entering QUIT at the EXAMS prompt command level will terminate an interactive session, returning control to the computer's
operating system. QUIT is included as an option of many EXAMS commands to allow the command to be aborted.
Examples:
1. EXAMS-> AUDIT
The following AUDIT options are available
ON -- begins a new audit file,
OFf -- ends Audit recording of input commands,
Help -- this message,
Quit -- return to the EXAMS prompt.
AUDIT-> QUIT
EXAMS->
This command terminates processing of the AUDIT command and returns control to the EXAMS prompt command level. The current
status of AUDIT is not altered.
2. EXAMS->QUIT
This command terminates an interactive EXAMS session.
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READ
Use the READ command to transfer data from a properly organized non-EXAMS file into the Activity Data Base (ADB).
Related: Control variables: MODE, MCHEM, PRBEN
Commands: WRITE
Syntax: READ
Prompt: Enter Environment, Chemical, PRZM, Meteorology, Help, or Quit->
Description: The RE AD command provides a facility for up-loading EXAMS datasets from external ASCII sequential files. These non-EXAMS
files can be stored entirely separately from the main EXAMS User Data Base (UDB), which is contained in a direct access file
named "EXAMS.DAF". Data are transferred directly to the Activity Data Base (foreground memory ADB) rather than to the
User Data Base (UDB) file area, so the STORE command must be used to transfer data to the UDB from the ADB after invoking
READ or they will be discarded when you exit from EXAMS.
Under the ENVIRONMENT option of READ, the entire ADB dataset ("months" 1 through 13) will be uploaded from the external file
called .
Under the CHEMICAL option of READ, the chemical dataset to be uploaded from is put into the MCHEM sector of
the Activity Data Base (ADB).
In the PRZM option of the READ command, EXAMS acquires a set of external loadings generated by the Pesticide Root Zone Model
(PRZM). This facility transfers chemicals exported from the land surface into an adjacent aquatic system. The PRZM transfer file
is a mode 3 construct, in which the first set of loadings contains the application rate of the pesticide, and the succeeding loadings
contain runoff events generating water-borne and sediment-borne chemical transfers to the aquatic system. The parameter PRBEN
(c.f.) controls EXAMS' treatment of sediment-borne materials. When PRBEN is zero, all sediment-borne materials are equilibrated
with the water column upon entry into the system. When PRBEN is 1.0, all sediment-borne materials are routed directly to the
benthic zone. PRBEN has a default value of 0.5, based on the observation that, in general, about 50% of sorbed chemical is usually
labile, and about 50% recalcitrant, to rapid re-equilibration in water. Runoff from PRZM is translated into monthly non-point-
source water and sediment input.
The METEOROLOGY option reads a PRZM meteorology file. These files contain daily values of precipitation, pan evaporation,
temperature, and wind speed. If an EXAMS environment has been selected, the period-of-record monthly averages are transferred
to the corresponding EXAMS variables. In addition, a digest of monthly mean values is prepared for each year of data in the file;
when a PRZM transfer file is read, the weather data for that year is loaded into EXAMS.
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Examples:
1. Transfer an environmental dataset from a file called "INLET.DAT" on the default directory; the dataset can then be then STOREd in
EXAMS' direct access UDB file.
EXAMS-> READ
Enter Environment, Chemical, Load, Help, or Quit-> EN
Enter name of file, Help, or Quit-> INLET.DAT
2. Read precipitation, pan evaporation, temperature, and wind speed from a meteorology file. Note that a directory other than the
default can be specified as part of the READ command option; the default suffix for meteorology files is ".met".
EXAMS-> SETMODE = 3
EXAMS-> READ MET C:\EXAMS\PROJECTX\W13873
3. To read a PRZM transfer file, first set MODE to 3, and then read the dataset. Note the convention for naming of PRZM transfer files--
the base name is always "PRZM2EXA" or "P2E-Cn" and the suffix indicates the year--in this case data from 1989 ("D89"). Because
EXAMS will accept any file name for acquisition by the READ command, these files can be renamed to any convenient file name for
archiving or to prevent subsequent PRZM runs from over-writing them.
EXAMS-> READ PRZM PRZM2EXA.D89
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RECALL
Use RECALL to upload data from the permanent database (UDB) into current foreground memory (ADB).
Related: Control Variables: MCHEM
Commands: CATALOG, ERASE, NAME, STORE
Syntax: RECALL [AS ADB#]
Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->
Command parameters:
can be Chemical, Environment, Load, or Product
(EXAMS uses these four kinds of datasets.)
AS ADB# is an optional explicit specification of MCHEM (see Example 1).
UDB# specifies the accession number or location in the User Database for the source data for transfer to the ADB (Example 2).
Description: RECALL transfers data from permanent storage (UDB) to activity databases (ADBS). The data in active use by EXAMS are held
in a foreground memory bank (Activity DataBase or ADB) with four sectors, one for each datatype required by EXAMS--
hemical reactivity and partitioning,
nvironmental physical and chemical parameters,
allochthonous chemical oadings, and
roduct chemistry for generating interconversions among multiple chemicals in an analysis.
When EXAMS is started, the ADB is empty. Use the RECALL command to transfer data from the permanent User Databases (UDBS)
to foreground memory (ADB). When an analysis session is ended (QUIT or EXIT), ADBS are discarded. Use the STORE command
to transfer new data from the ADB to the UDB sector of the same datatype for permanent retention of the data.
Examples:
1. Because EXAMS can process several chemicals in a single analysis, the target sector of the chemical activity database should be
specified when using the RECALL command to activate CHEMICAL data. (This section of the command should be omitted for other data
types.) When the ADB# (an integer between 1 and KCHEM) is omitted, the chemical data are transferred to the sector of the activity
database given by the current value of MCHEM. For example, to activate data from the chemical UDB, putting UDB datasetnumber 9
into ADB sector 1, and UDB #14 into sector 2:
Either:
EXAMS-> SET MCHEM TO 1
EXAMS-> RECALL CHEMICAL 9
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EXAMS-> SETMCHEM TO 2
EXAMS-> RECALL CHEMICAL 14
or, equivalently:
EXAMS-> RECALL CHEMICAL 9 AS 1
EXAMS-> RECALL CHEMICAL 14 AS 2
2. Long-term retention of data required by EXAMS is provided by storage in the "User Database" (UDB, generally resident on a physical
device--e.g., a hard disk) for Chemicals, Environments, Loads, or Products. Within each UDB sector, each dataset is catalogued via
a unique accession number (UDB#). When transferring data to foreground memory (the activity database or ADB) from a UDB, the
source location must be specified by the name of the UDB sector and the accession number within the sector. For example, to RECALL
an environmental dataset:
EXAMS-> RECALL ENVIR 2
Selected environment is: Phantom Inlet, Bogue Sound
EXAMS->
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RUN
The RUN command begins a simulation analysis.
Related: Control Variables: MODE
Commands: CONTINUE
Syntax: RUN
Prompt: None
Description: The RUN command executes an analysis and creates the output files accessed by the LIST and PLOT commands. The activity
database (ADB) must be loaded, either via entry of new data or by RECALL from the UDB, before a RUN can be started.
Examples:
1. EXAMS-> RECALL CHEMICAL 22
Selected compound is: Dibro mo example
EXAMS-> RECALL ENVIRON 17
Selected environment is: Albemarle Sound—Bogue Bank
EXAMS-> SET STRL(1,1,13)=.01
EXAMS-> RUN
Simulation beginning for:
Environment: Albemarle Sound--Bogue Bank
Chemical 1: Dibro mo example
Run complete.
EXAMS->
In this example, a steady-state (MODE=!) analysis is conducted by selecting a chemical and an environment, imposing a loading
of chemical 1 on segment 1 under average conditions (i.e., data sector 13, EXAMS initial default value) and invoking EXAMS'
simulation algorithms with the RUN command.
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SET
Use SET to specify the values of data in the activity database.
Related: Commands: CHANGE (synonym), DESCRIBE, HELP
Syntax: SET TO
or
SET =
Prompt: Enter name=value command->
Variable: The data entry or variable to be SET can be specified either as a single datum or, using wild cards (*), as an entire vector,
row/column of a matrix, etc.
Description: Use the SET command to specify the values of data in the activity database. "Value" can be any numerical quantity or literal,
as appropriate. "Variable" specifies an individual element of input data or a program control parameter. Entire vectors,
rows/columns of matrices, etc. can be set to single values using wild cards (*).
Examples:
1. EXAMS-> SETVOL(167) TO 7E5
Subscript out-of-range.
EXAMS-> DESCRIBE VOL
VOL is a Real Vector with 100 elements.
EXAMS> SET VOL(2) TO E
Invalid numeric quantity after TO.
EXAMS-> SET VOL(2) TO 7E5
This command sets the environmental volume of segment 2 to 7.0E+05 cubic meters. The initial attempt to set the volume of segment
67 was rejected by EXAMS because the version in use was set up for environmental models of 100 segments at most. The DESCRIBE
command was used to check the number of subscripts and the dimensional size of the variable "VOL". The erroneous entry of an
alphabetic for the volume was trapped by the SET command; the initial value of VOL(2) was not altered.
2. EXAMS-> HELP TCEL
TCEL is a Real Matrix with 100 rows and 13 columns.
Temperature-CELsius (segment, month) Units: degrees C.
Average temperature of ecosystem segments. Used (as enabled by input data) to compute effects of temperature on transformation
rates and other properties of chemicals.
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EXAMS-> SET TCEL(2,7)=24
This command changes the July temperature in segment 2 to 24°C. The HELP command was used to check subscript dimensions,
maximum values, the meaning of the subscripts (subscript #1 denotes the segment; subscript #2, the month), and the proper units for
the input datum (degrees Celsius).
3. EXAMS-> HELP POH
POH is a Real Matrix with 100 rows and 13 columns.
pOH (segment, month) Units: pOH units
The negative value of the power to which 10 is raised in order to obtain the temporally averaged concentration of hydroxide [OH"]
ions in gram-molecules per liter.
EXAMS-> SETPOH(*,13)TO 6.2
This command sets the average pOH (sector 13) of every segment to 6.2. Note use of wild card "*" to specify that all segments are
to be changed. As in the previous example, HELP was used to check subscript dimensions, units, etc. This step, of course, is optional.
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SHOW
Use SHOW to display current data values or control settings.
Related: Control Variables: MCHEM, MONTH
Commands: CHANGE, SET
Syntax: SHOW
-------
DSP m /h 4.676E-05 Eddy DiSPersion coefficient
Path No.: 1 Vector index for data entry
No more than NCON hydrologic pathways can be specified. If more are needed, this number can be increased and EXAMS recompiled.
CHEMISTRY
SHOW CHEMISTRY displays the chemical output data currently in the ADB (foreground memory bank). The sector of the ADB denoted
by the current value of MCHEM is displayed. Within each sector of the ADB (that is, for each chemical under active review), the data
for each ionic species are presented separately, and photochemical data are presented on separate screens.
GEOMETRY
SHOW GEOMETRY returns a segment-by-segment description of the geometry (volumes, areas, etc.) of the current ecosystem. The
segment number reported with each block of data is the first subscript for modifying the datum using CHANGE or SET. The month to
be displayed is set by the current value of MONTH (explicit mean values are denoted by MONTH number 13): the month is the second
subscript of such data as WIND, STFLO, etc.
GLOBALS
SHOWGLOBALS displays the input data that are "global" in extent, that is, "global" data apply to all segments of the current ecosystem.
LOADS
SHOW LOADS displays the current state of allochthonous chemical loadings. The form of the display depends on the current operational
MODE: initial values are ignored in Mode 1 as they have no effect on the analysis results. The value of PRSW also affects the display:
whenPRSW is 0, SHOW LOADS returns a summary of annual loadings; when PRSW=1, a month-by-month tabulation is displayed as well.
This display may not represent the final values used in the analysis, because EXAMS will modify loads that result in violation of the
linearizing assumptions used to construct the program. After a RUN has been executed, however, SHOW LOADS will display the
corrected values.
PRODUCTS
SHOW PRODUCTS displays the specifications for product chemistry currently in the ADB. Each entry is identified and loaded according
to a unique "pathway number." A single element of a dataset might look like this:
CH PAR 1 ADB number of CHemical PARent
T PROD 2 ADB number of Transformation PRODuct
NPROC 7 Number of transforming PROCess
RFORM 29 Reactive FORM (dissolved, etc.)
YIELD M/M 0.100 Mole/Mole YIELD of product
EAYLD Kcal 0.000 Enthalpy of yield (if appropriate)
Pathway: 1 Number of the pathway
More detail as to the numbering of NPROC and RFORM is given in the Data Dictionary (page 175), which can also be accessed on-line
using the HELP command. No more than NTRAN transformation pathways can be specified. If more are needed, a special version of
EXAMS can be created.
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PLOT
SHOW PLOT examines the contents of the concentration time-series and steady-state files, and reports the names of the chemicals and
ecosystem used in the analysis.
PULSE LOADS
SHOW PULSE LOADS displays the specifications for allochthonous pulses of chemicals entering the system. This display may not
representthe final values used in the analysis, because EXAMS will modify loads that result in violation of the linearizing assumptions
used to construct the program. Although faulty pulse loads are discarded, EXAMS does not correct the input pulse load data, because
the occurrence of load constraint violations depends on the context (i.e., the size of current stream loadings, etc.). Thus, unlike SHOW
LOADS, the SHOW PULSE display following execution of a RUN does not display corrected data. The pulses actually used during an
analysis are instead entered into EXAMS' output tables, where they can be examined using the LIST and PRINT commands.
QUALITY
SHOW QUALITY returns a segment-by-segment display of the canonical water-quality data included in the current Environmental ADB
dataset. The month to be displayed is set by the current value of MONTH (explicit mean values are denoted by MONTH number 13).
The month is the second sub script of such data as pH, pOH, etc. The first subscript is the segment number; thus these data are entered
(CHANGE/SET) as "datum(segment,month)".
TIME FRAME
SHOW TIME FRAME displays the current status of the parameters needed to control the temporal aspects of a Mode 2 or Mode 3
simulation.
VARIABLES
SHOW VARIABLES displays a list of the names of EXAMS input data and control parameters. These names must be used to SET/CHANGE,
SHOW values, HELP/DESCRIBE, etc.
Description: Use the SHOW command to examine the current contents of the ADB, that is, the foreground datasets used for the current
analysis. The SHOW command can be used to examine clusters of similar data, the values of individual parameters, or the
data contained in entire vectors. Typing SHOW without an option will display a list of the available options.
Examples: 1. The SHOW command can be used to examine the value of single parameters. For example, the pH of segment
7 of the current ecosystem during September could be inspected by entering:
EXAMS-> SHOW PH(7,9)
Using wild cards (*), the SHOW command can also be used to display the data in an entire vector or row/column of a data matrix.
For example, the pH in every segment of the current ecosystem during September could be displayed by entering:
EXAMS-> SHOW PH(*,9)
and the pH of segment 7 through the year could be displayed by:
EXAMS-> SHOW PH(7,*)
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STORE
Use STORE to download current (ADB) data into the permanent database (UDB).
Related: Control Variables: MCHEM
Commands: CATALOG, ERASE, NAME, RECALL
Syntax: STORE [ADB# IN]
Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->
Command parameters:
can be Chemical, Environment, Load, or Product
(EXAMS uses these four kinds of datasets.)
ADB# IN is an optional explicit specification of MCHEM (see Example 1).
UDB# specifies the accession number or location in the User Database for storage of the current ADB sector (Example 2).
Description: STORE downloads data from activity databases (ADBS) into the permanent User DataBases (UDBS). The data in active use by
EXAMS are held in a foreground memory bank (Activity DataBase or ADB) with four sectors, one for each datatype required
by EXAMS:
CHEMICAL reactivity and partitioning,
ENVlRONMENTal physical and chemical parameters,
allochthonous chemical LOADings, and
PRODUCT chemistry for generating interconversions among multiple chemicals in an analysis.
When an analysis session is ended (QUIT or EXIT), these data are discarded. Use the STORE command to transfer data from the
ADB to the UDB sector of the same datatype for permanent retention of the data.
Examples: 1. Because EXAMS can process several chemicals in a single analysis, the source sector of the chemical activity
database should be specified when using the STORE command to download CHEMICAL data. (This section of
the command should be omitted for other datatypes.) When the ADB# (an integer from 1 IOKCHEM) is omitted,
the chemical data are taken from the sector of the activity database given by the current value of MCHEM. For
example, to STORE data in the UDB, putting ADB sector 1 into the chemical UDB under catalog/accession 9 and
ADB sector 2 into UDB sector 14:
Either:
EXAMS-> SET MCHEM TO 1
EXAMS-> STORE CHEMICAL 9
EXAMS-> SET MCHEM TO 2
EXAMS-> STORE CHEMICAL 14
or, equivalently:
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EXAMS-> STORE CHEMICAL 1 IN 9
EXAMS-> STORE CHEMICAL 2 IN 14
2. Long-term retention of data required by EXAMS is provided by storage in the "User Database" (UDB, generally resident on a
physical device--e.g., a hard disk) for Chemicals, Environments, Loads, or Products. Within each of these UDB sectors, each
dataset is CATALOGued via a unique accession number (UDB#). When transferring data between foreground memory (the activity
database or ADB) and a UDB, the target location must be specified by the name of the UDB sector and the accession number within
the sector. For example, to STORE the current environmental dataset:
EXAMS-> STORE ENVIR 2
Environment record 2 is in use with
Pond -- code test data
Replace?-> no
Nothing changed.
EXAMS-> STORE ENVIR 14
Environment stored: Phantom Inlet-Bogue Sound Study Data
EXAMS->
Note that EXAMS provides a measure of protection against accidental overwriting of existing datasets, an important courtesy in
a multi-user environment.
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WRITE
Use the WRITE command to transfer data from the Activity Data Base (ADB) to an external (non-EXAMS) sequential file.
Related: Control variables: MODE, MCHEM
Commands: READ
Syntax: WRITE
Prompt: Enter Environment, Chemical, Load, Help, or Quit->
Description: The WRITE command provides a facility for off-loading EXAMS datasets into external ASCII sequential files. These non-EXAMS
files can be stored separately from the main EXAMS User Data Base (UDB). Data are transferred from the Activity Data Base
(foreground memory ADB) rather than directly from the User Data Base (UDB) file, so the RECALL command must be used
to transfer data from the UDB to the ADB before invoking WRITE.
Under the ENVIRONMENT option of WRITE, the setting of MODE controls how many data are stored in the external file. When MODE
is 1 or 2, only the dataset sector indicated by the current value of MONTH is transferred. For example, ifMODE=l andMONTH=13,
explicit mean values (only) will be downloaded. When MODE=3, the entire ADB dataset ("months" 1 through 13) will be
downloaded to the external file called .
Under the CHEMICAL option of WRITE, the chemical dataset to be downloaded to is chosen from the MCHEM sector
of the Activity Data Base (ADB).
In the LOAD option of WRITE, a set of external chemical loadings are written to an ASCII file. As with environmental data, the
setting of MODE controls the amount of data written to the file. In Mode 1, only long-term, average data are written from the ADB;
in Mode 2, initial conditions are added, and in Mode 3 a full set of monthly loads and daily pulse loads are written from the ADB
to the external file. The first item written to the external file is the Mode for which the loadings are designed. This datum serves
as a check value when EXAMS reads data from a file of external loadings (see discussion under READ command.
Examples:
1. Transfer of a single set of values of an environmental dataset takes place in Mode 1 and 2. In this example, the data is RECALLed
from the UDB, and MODE and MONTH are set to download the average data to a file called "INLET. DAT" on the default directory.
EXAMS-> RECALL ENVIRONMENT 12
Selected environment is: Chinquoteague Inlet
EXAMS-> SET MONTH=13
EXAMS-> SETMODE=1
EXAMS-> WRITE
Enter Environment, Chemical, Load, Help, orQuit-> EN
Enter name of file, Help, or Quit-> INLET.DAT
2. To continue the above example, the entire dataset could be stored in another file by changing mode to 3. Note that a directory
other than the default can be specified as part of the WRITE command option.
EXAMS-> SETMODE=3
EXAMS-> WRITE ENV C:\EXAMS\PROJECTX\INLET.DAT
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ZERO
Use the ZERO command to initialize (set to zero) loadings databases orthe concentration of pollutant chemicals throughout the ecosystem.
Related: Control variables: MODE
Commands: CONTINUE, RUN
Syntax: ZERO
-------
EXAMS-> SET ICHEM(1)=1
EXAMS-> RUN
Simulation beginning for:
Environment: Albemarle Sound--Bogue Bank
Chemical 1: Dibro mo example
Run complete.
EXAMS-> ZERO PULSE LOADS
EXAMS-> CONTINUE
In this example, an initial-value (MODE=2) analysis is begun by selecting a chemical and an environment, imposing an allochthonous
load of chemical 1 on segment 1 under average conditions (i.e., data sector 13, EXAMS' initial default value), and specifying the initial
presence (or introduction at time zero) of 2.0 kg of material in segment 14. At the end of the initial RUN segment, one might want to
examine the output tables, plot the results, etc. Then, before coNTiNuing, the ZERO command is used to remove the pulse load
specifications. If this were not done, EXAMS would introduce a second 2.0 kg pulse into segment 14 at the beginning of the
continuation segment. Alternatively, the other loadings could have been removed, and the effect of a series of pulse loads could be
studied by issuing a sequence of CONTINUE commands.
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6.0 EXAMS Data Dictionary
ABSER
ADVPR
ABSolute ERror tolerance of integrators
When the characteristics of the chemical and ecosystem are
such as to result in "stiff equations, numerical errors may
lead to small negative numbers in the time series. If desired,
the value of ABSER and RELER can be decreased in order to
achieve greater precision in the simulation outputs.
ADB
Activity DataBase
EXAMS provides for long-term storage of CHEMical,
ENvironmental, transformation PRODuct chemistry, and
allochthonous LOADings databases in a User DataBase or
UDB. The actual analyses are conducted on particular
datasets drawn from these files (or entered via
SET/CHANGE). Particular cases are loaded from the UDB into
the foreground transient memory of your computer in an
Activity DataBase or ADB, using the RECALL command.
Because EXAMS simulates the behavior of several (MCHEM)
chemicals simultaneously, the ADB for chemicals has
MCHEM separate sectors. These data are lost when you EXIT
from EXAMS, so be sure to STORE any new or corrected
datasets before leaving EXAMS.
ABSOR
ABSORption spectra (wavelength, ion, chemical)
Units: cm'^mole/L)"1
ADVection PRoportion (path)
Units: n/a Range: > 0 - 1.0
PRoportion of flow Aovected from segment JFRAD that
enters ITOAD. The matching (same subscript) members of
JFRAD, ITOAD, and ADVPR define an advective hydrologic
flow pathway. Although usually 1, ADVPR lets one enter
braided channels, etc. The total of ADVPR s for each segment
must sum to either 0 or 1, failing which, EXAMS aborts the
RUN. The flow data can be inspected by typing SHOW ADV;
path numbers are given above each active dataset. Enter
data via CHANGE or SET commands.
Additional information available: JFRAD, ITOAD
AEC
Anion Exchange (Capacity (segment, month)
Units: meq/100 g (dry)
Anion exchange capacity of sediment phase of each
segment. Useful in relating sediment sorption (partitioning)
of anions to a variable characteristic of system sediments.
AIRTY
AIR mass TYpe (month)
Units: letter codes
Select: Rural (default), Urban, Maritime, or Tropospheric
Mean decadic molar light extinction coefficients in 46
wavelength intervals over 280 - 825 nm. For wavelength
"w" and chemical "c":
AREA
AREA (segment)
Units: m
ABSOR(W,!,C) is molar absorption coefficient of RH3
(neutral molecule)
ABSOR(w,2,c) is molar absorption coefficient of RH4 (+1
cation)
ABSOR(w,3,c) is molar absorption coefficient of RH5 (+2
cation)
ABSOR(w,4,c) is molar absorption coefficient of RH6 + (+3
cation)
ABSOR(w,5,c) is molar absorption coefficient of RH2 (-1
anion)
ABSOR(w,6,c) is molar absorption coefficient of RH (-2
anion)
ABSOR(W,V,C) is molar absorption coefficient of R (-3
anion)
Top plan area of each model segment of the water body. For
Epilimnion and Littoral segments, AREA is the area of the
air-water interface; for Hypolimnion segments AREA is the
area of the thermocline; for Benthic segments it is the
surface area of the bottom. In the latter case AREA may
differ from XSTUR in a dispersive exchange pair because of
reduction in exchanging area due to rock outcrops, etc.
ATURB
Atmospheric TURBiditv (month)
Units: km
Equivalent aerosol layer thickness.
AUDOUT
While the AUDIT directive is in effect, a copy of user inputs
175
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and responses is written to the file connected to
FORTRAN Logical Unit Number AUDOUT.
CHEMNA
BACPL
BACterioPLankton population (segment, month)
Units: cfu/mL
Population density of bacteria capable of degrading
xenobiotics. The abbreviation "cfu" stands for a "colony
forming unit."
BNBAC
BeNthic BACteria (segment, month)
Units: cfu/lOOg dry sediment
Population density of benthic bacteria that degrade
xenobiotics. The abbreviation "cfu" stands for a "colony
forming unit."
CHEMical NAme of compounds (50 characters,chem)
Units: n/a
Do not use "CHANGE" or "SET" to enter names! The NAme
for a CHEMical is entered into the database via the command
sequence:
EXAMS-> CHEMICAL NAME IS nnn...
where "nnn... " can include as many as 50 characters. This
name is associated with chemical library entries and is
printed in the header information of the appropriate output
tables.
CHL
CHLorophvlls
Units: mg/L
pheophytins (segment, month)
BNMAS
BeNthic bioMASs (segment, month)
Units: g(dry)/m
Biomass of small benthos - infauna subject to biosorption.
Concentration of chlorophyll plus chlorophyll-like
pigments. Used to compute spectral light absorption
coefficients due to pigments which absorb light from the
water column and thus compete with photolysis of synthetic
chemicals.
BULKD
CHPAR
BULK Density (segment, month)
Units: g/cm3
Fresh weight per unit volume of benthic sediments.
CEC
Cation Exchange Capacity (segment, month)
Units: meq/lOOg (dry)
Cation exchange capacity of sediment phase in each
segment. Useful in relating sediment sorption (partitioning)
of cations to a variable characteristic of system sediments.
CHARL
CHARacteristic Length or mixing length (path)
Units: m
Average of segment dimensions normal to the exchange
interface linking segment numbers JTURB(P) and ITURB(P).
The matching (same "p" subscript) members of JTURB,
ITURB, CHARL, DSP, and XSTUR together define a dispersive
transport pathway. A given segment may have different
mixing lengths at different interfaces. CHARL can also be
calculated from the distance along a path that connects the
centers of segments JTURB(P) and ITURB(P), passing through
the interface whose area is XSTUR(P).
See also: DSP, ITURB, JTURB, XSTUR
CHemical PARent compound (path)
Units: n/a Range: 1- KCHEM
CHPAR(P) gives the ADB location of the parent source of
TPROD(p). The matching (same transformation path number
"p") members of CHPAR and TPROD give the location
numbers in the active database of the parent chemical and
the transformation product for pathway "p". For example,
"SET CHPAR(P) TO 1", and TPROD(P) TO 4, to show that the
chemical in ADB sector 4 is produced via transformation of
the chemical in ADB sector 1, via process data defined by
the remaining members of product chemistry sector "p".
See also: EAYLD, NPROC, RFORM, TPROD, YIELD
CINT
Communications iNTerval for dynamic simulations.
Units: see TCODE
CINT is the interval between output cycles from the
integrators. In Mode 2, CINT can be set to produce any
desired output frequency, so long as the resulting reporting
interval is >1 hour. When CINT is set to 0, EXAMS (Mode 2)
sets CINT to report at the 12 equal-increment periods most
closely matching the duration specified by (TEND - TINIT).
CINT is under full user control only in Mode 2; in Modes 1
and 3 EXAMS itself sets the value of CINT according to the
needs of the analysis.
176
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CLOUD
CLOUPiness (month)
Units: dimensionless Range: 0-10
Mean monthly cloudiness in tenths of full sky cover.
DEPTH
DEPTH (segment)
Units: m
Average vertical depth of each segment.
DFAC
Distribution FACtor (segment, month)
Units: dimensionless ratio
Ratio of optical path length to vertical depth, range 1.0 -
2.0. A vertical light beam has a DFAC of 1.0; a fully diffused
light field has a DFAC of 2.0. For whole days, a value of
1.19 is often adequate; EXAMS defaults to this value when
the entry for DFAC is outside the range 1.0 - 2.0.
DISO2
Dissolved O2 (segment, month)
Units: mg/L
Concentration of dissolved oxygen (O2) in each segment of
ecosystem.
DOC
Dissolved Organic Carbon (segment, month)
Units: mg/L
Used for computing spectral light absorption, singlet
oxygen concentrations, and complexation.
DRFLD
DRJFt LoaD (segment, chemical, month)
Units: kg/hour
Drift loadings: aerial drift, direct applications, stack fallout
(etc.) of chemical on each system element.
DSP
Dispersion coefficient (path, month)
Units: m /hour
Eddy diffusivity to be applied to dispersive exchange
pairing "p". The matching (same "p" subscript) members of
JTURB, ITURB, CHARL, and XSTUR together define a
dispersive transport pathway. In the case of horizontal
mixing, DSP is the longitudinal dispersion coefficient; for
vertical mixing it may represent exchange across the
thermocline or exchanges with bottom sediments. In the
latter case DSP is a statistical kinetic composite
incorporating direct sorption to the sediment surface,
mixing of the sediments by benthos (bioturbation), stirring
by demersal fishes, etc.
See also: CHARL, ITURB, JTURB, XSTUR
EAH
Ea for Acid Hydrolysis (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy Ea of specific-acid-catalyzed
hydrolysis of chemicals. Matrix indices match those of KAH,
giving, for each chemical, data for 3 forms (1: dissolved, 2:
solids-sorbed, 3: ooc-complexed) of 7 ionic species (1:
neutral; 2, 3, 4: cations; 5, 6, 7: anions). When EAH is
non-zero, the second-order rate constant K (M^h"1) is
calculated from:
1000 x EAH(farm,ian, chemicaF)
4 5 i( TC'BL( segm ent, m antH) + 2 73.15)
EAYLD
EA YieLD. (path)
Units: kcal
EAYLD (p) is activation energy Eato compute transformation
product yield as a function of environmental temperatures
(TCEL). When EA_YieLD(p) is zero, YIELD(P) gives the
dimensionless molar product yield. A non-zero EAYLD (p)
invokes a re-evaluation in which YIELD(P) is interpreted as
the Briggsian logarithm of the pre-exponential factor in an
Arrhenius-type function, giving product yield as a function
of temperature (varying with position and time)
(TCEL(segment, month)):
1000 x.
45%(TCEL(5egment,mo7!th) +27315)
See also: CHPAR, NPROC, RFORM, TPROD, YIELD
EBH
Ea for Base Hydrolysis (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy Ea of specific-base catalyzed
hydrolysis of chemicals. Matrix indices match those of KBH,
giving, for each chemical, data for 3 forms (1: dissolved, 2:
solids-sorbed, 3: ooc-complexed) of 7 ionic species (1:
neutral, 2, 3, 4: cations, 5, 6, 7: anions). When EBH is
non-zero, the second-order rate constant K (M" h" ) is
calculated from:
LogK= KBH(f,i,c)-
1000 x 8BH(farm,ian,chemicd)
45$(TCEL(segment, montH) + 273.15)
177
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EHEN
Enthalpy term for HENjy's law (chemical)
Units: kcal/mol
Used to compute Henry's law constants as a function of
TCEL (environmental temperature). When EHEN isnon-zero,
the Henry's law constant (H) affecting volatilization at a
particular (segment, month) is computed from TCEL:
. IQQQx EHEN(<:hemical)
log H = HENRY (chemical) -
EKl02
EaKlo2 (singlet oxygen) (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy for singlet oxygen
photo-oxygenation of chemicals. Matrix indices match those
of Klo2, giving, for each chemical, data for 3 forms (1:
dissolved, 2: solids-sorbed, 3: DOC-complexed) of 7 ionic
species (1: neutral, 2, 3, 4: cations, 5, 6, 7: anions). When
EKlo2 isnon-zero, the second-order rate cons tantK (M~ h" )
is calculated as:
1000 x EKl02(Jarm,ian,chemicaT)
45Z(TCEL(segment,month) +273.15)
ELEV
ELEVation
Units: meters above mean sea level
Ground station elevation.
ENH
E^ for Neutral Hydrolysis (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy for neutral hydrolysis of
chemicals. Matrix indices match those of KNH, giving, for
each chemical, data for 3 forms (1: dissolved, 2:
solids-sorbed, 3: DOC-complexed) of 7 ionic species (1:
neutral, 2, 3, 4: cations, 5, 6, 7: anions). When ENH is
non-zero, the pseudo-first-order rate constant (h"1) is
calculated from:
1000 x ENH(Jarm,ian,chemica!)
4 5 8( TCEL ( segment, month)+ 273.15)
BOX
E,, oxidation (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy for oxidative transformations of
chemicals. Matrix indices match those of KOX, giving, for
each chemical, data for 3 forms (1: dissolved, 2:
solids-sorbed, 3:DOC-complexed) of 7 ionic species (1:
neutral, 2, 3, 4: cations, 5, 6, 7: anions). When BOX is
non-zero, the second-order rate constant K (M~ h" ) is
calculated from:
10 00 x £ OX (form, ion, c he mic al)
4.58 (TC EL(seg me nt, month) +273.15)
EPK
45S( TCEL( segment, month) + 273.15) ^^
Enthalpy term for pK (ion, chemical)
Units: kcal/mol
When EPK is non-zero, pK is computed as a function of
temperature via:
1000 x EPKQon,chemcal)
4.5 8( TCEL(se gmen t, month) + 27 3.15)
The vector indices for EPK ("c" denotes the chemical) are
EPK(!,C) contains datum for generation of RH4+ from RH3
EPK(2,c) contains datum for generation of RH5 +from RH4+
EPK(3,c) contains datum for generation of RH63+from RH52+
EPK(4,c) contains datum for generation of RH2" from RH3
EPK(5,c) contains datum for generation of RH" from RH2"
EPK(6,c) contains datum for generation of R " from RH"
ERED
Ea REDUction (form, ion, chemical)
Units: kcal/mol
Arrhenius activation energy forreductive transformations of
chemicals. Matrix indices match those of KRED, giving, for
each chemical, data for three forms (1: dissolved, 2:
solids-sorbed, 3: DOC-complexed) of seven ionic species (1:
neutral, 2, 3, 4: cations, 5, 6, 7: anions). When ERED is
non-zero, the second-order rate constant K (M^h"1) is
calculated as:
1000 x ERED(form,ion, chemical)
4 5 S( TC'8L(segn erf, month) +27315)
ESOL
Enthalpy term for SOLubilitv (ion, chemical)
Units: kcal/mol
ESOL describes chemical solubility as a function of
temperature (TCEL). The matrix indices ("c" denotes the
chemical) denote:
ESOL(!,C) is datum for solubility of neutral molecules RH3
ESOL(2,c) is datum for solubility of singly charged cations
178
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RH4+
ESOL(3,c) is datum for solubility of doubly charged cations
RH52+
ESOL(4,c) is datum for solubility of triply charged cations
RH63+
ESOL(5,c) is datum for solubility of singly charged anions
RH2"
ESOL(6,c) is datum for solubility of doubly charged anions
RH=
ESOL(7,c) is datum for solubility of triply charged anions R
EVAP
EVAPoration (segment, month)
Units: mm/mo nth
(Monthly) evaporative water losses from ecosystem
segments.
EVPR
MolarhEat of vaPoRization (chemical)
Units: kcal/mol
Enthalpy term for computing vapor pressure as a function
of TCEL (environmental temperature (segment,month)).
When EVPR is non-zero, vapor pressure Va is computed
from:
]ag¥a =
100Ox EVPR(chemical)
45$(TCEL(segment,month) +27315)
FIXFIL
FIXFIL signals the existence of output data for LISTS and
PLOTS.
To access results from a prior run, "SET FIXFIL TO 1." FIXFIL
is set to zero when EXAMS is invoked, so that the LIST and
PLOT commands are protected from attempts to access
non-existent output data files. When results exist from a
previous simulation, you can reset FIXFIL to 1 in order to
gain access to them.
Used in computation of air/water exchange rates
(volatilization). If parameter EHEN is non-zero, HENRY is
used as the pre-exponential factor in computing the Henry's
law constant H as a function of environmental temperatures
(TCEL):
. \QQQx EHEWchemical)
bg H = JBMrfrA.™*!) - 4J8(reEI(iap«ftiaoHfl?+mi3)
ICHEM
I CHEMical (event)
Units: n/a Range: I--KCHEM
Event "e" is a pulse of chemical number lCHEM(e) in the
active database ICHEM identifies the location in the Activity
Database (ADB) of the chemical entering the ecosystem via
pulse load event "e". When, for example, chemical data are
loaded into ADB sector 3 (whether RECALLed from the User
Database Library (UDB) (via, for example, the command
sequence "RECALL CHEM 7 AS 3") or entered as new data),
iCHEM(e) can be SET to 3 to create a pulse load event of that
chemical.
See also: IDAY, IMASS, IMON, ISEG
IDAY
I DAY (event)
Units: n/a Range: 1--31
Pulse load event "e" takes place on day IDAY(B) of month
iMON(e). The pulse load data are organized by vertical event
columns, that is, the set of pulse load variables (iMASS(e),
iCHEM(e), iSEG(e), iMON(e), and IDAY(B)) with the same
vector subscript describes a single chemical pulse event.
Thus a pulse of chemical lCHEM(e), of magnitude
IMASS(e), is released into segment iSEG(e) on day iDAY(e)
of month iMON(e). During mode 2 simulations, IDAY and
IMON are inoperative.
See also: ICHEM, IMASS, IMON, ISEG
FROC
FRaction Organic Carbon (segment, month)
Units: dimensionless
IMASS
initial MASS (event)
Units: kg
Organic carbon content of solids as fraction of dry weight.
FROC is coupled to KOC to generate the sediment partition
coefficient for neutral chemicals (R-H3) as a function of a
property (organic carbon content) of the sediment.
HENRY
HENRY'S law constant (chemical)
Units: atmosphere-mVmole
IMASS gives the magnitude of chemical pulse load event
"e". In mode 2, pulses are entered at time 0 (i.e., as initial
conditions), and at the outset of each CONTlNUation of the
simulation. In mode 3, IMON and IDAY specify the date of
the load events. An event recurs in each year of the RUN or
CONTlNUed simulation. The pulse load data are organized by
vertical event columns; that is, the series of pulse load
variables (IMASS, ICHEM, ISEG, IMON, and IDAY) with the
same vector subscript describes a single event.
179
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See also: ICHEM, IDAY, IMON, ISEG
IMON
I MONth (event)
Units: n/a Range: 1--12
Pulse load event "e" takes place on day iDAY(e) of month
iMON(e). The pulse load data are organized by vertical event
columns; that is, the set of pulse load variables (iMASS(e),
iCHEM(e), iSEG(e), iMON(e), and iDAY(e)) with the same
vector subscript describes a single chemical pulse event.
Thus a pulse of chemical lCHEM(e), of magnitude iMASS(e),
is released into segment iSEG(e) on day iDAY(e) of month
iMON(e). During mode 2 simulations, IDAY and IMON are
inoperative.
See also: IDAY, ICHEM, IMASS, ISEG
ISEG
I SEGment (event)
Units: n/a Range: I--KOUNT
Segments ITURB(P) and JTURB(P) exchange via turbulent
dispersion. The matching (same "p" subscript) members of
ITURB, JTURB, CHARL, DSP, and XSTUR together define a
dispersive transport pathway; ITURB(P) and JTURB(P)
indicate which segments are linked by dispersive transport
pathway "p". A "0" in ITURB paired with a non-zero
segment number in JTURB denotes a boundary condition
with a pure (zero chemical) water-body. The input data can
be examined via SHOW TURBULENCE; pathway numbers are
shown with each dataset.
See also: CHARL, DSP, JTURB, XSTUR
IUNIT
IUNIT controls the printing of diagnostics from the
integrators.
Normally zero (off), it may be turned on when problems
occur. To manually set IUNIT to generate integrator
diagnostic messages, SET IUNIT TO 1. The message generator
can be disabled at any time by SETting IUNIT to 0.
Pulse load event "e" loads chemical iCHEM(e) on segment
iSEG(e). Any segment can receive a pulse load. Should the
pulse loads increase the free concentration of unionized
chemical above 10"5 M (or half its aqueous solubility,
whichever is less), the size of the event is reduced, to avoid
violating the linearizing assumptions used to create EXAMS.
The pulse load data are organized by vertical event
columns; that is, the pulse load variables having the same
vector subscript define a single chemical pulse event.
See also: ICHEM, IDAY, IMASS, IMON
JFRAD
ITOAD
I TO_ ADyection (path)
Units: n/a Range: O--KOUNT (0 = export)
J FRom ADyection (path)
Units: n/a Range: 1-KOUNT
Chemicals are advectedfrom segment JFRAD(p) to segment
ITOAD(P). The matching (same subscript) members of
JFRAD, ITOAD, and ADVPR define an advective hydrologic
flow pathway. EXAMS computes the total net flow available
for advection from segment JFRAD(p). Of the total flow, the
fraction ADVPR(p) flows from segment JFRAD(p) into
segment iTOAD(p). The hydrologic flow carries an entrained
mass of chemical along the pathway. The flow
specifications can be inspected by typing SHOW ADV;
pathway numbers are given above each active dataset. Enter
data with SET or CHANGE commands.
Chemicals are advectedto segment iTOAD(p) from segment
JFRAD(P). The matching (same subscript) members of
JFRAD, ITOAD, and ADVPR define an advective hydrologic
flow pathway carrying entrained chemicals and solids
through the water body. When iTOAD(p) is 0, the pathway
advects water and entrained substances across system
boundaries, i.e., iTOAD(p) = 0 specifies an export pathway.
The flow data can be inspected by typing "SHOW ADV"; path
numbers are given above each active dataset. Enter data
with SET or CHANGE commands.
See also: JFRAD, ADVPR
See also: ITOAD, ADVPR
ITURB
I TURBulent dispersion (path)
Units: n/a Range: O--KOUNT
JTURB
J TURBulent dispersion (path)
Units: n/a Range: O--KOUNT
Segments JTURB(P) and ITURB(P) exchange via turbulent
dispersion. The matching (same "p" subscript) members of
JTURB, ITURB, CHARL, DSP, and XSTUR together define a
dispersive transport pathway; JTURB(P) and ITURB(P)
indicate which segments are linked by dispersive transport
pathway "p". A "0" in JTURB paired with a non-zero
segment number in ITURB denotes a boundary condition
with a pure (zero chemical) water-body. The input data can
be examined via SHOW TURBULENCE; pathway numbers are
shown with each dataset.
180
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See also: CHARL, DSP, ITURB, XSTUR
Units: per mole [OH"] per hour
KAH
K Acid Hydrolysis (form, ion, chemical)
Units: per mole [H+] per hour
Second-order rate constant for specific-acid-catalyzed
hydrolysis of chemicals. When the matching (same
subscripts) Arrhenius activation energy (EAH) is zero, KAH
is interpreted as the second-order rate constant. When the
matching entry in EAH is non-zero, KAH is interpreted as the
(Briggsian) logarithm of the frequency factor in an
Arrhenius equation, and the 2nd-order rate constant is
computed as a function of segment temperatures TCEL.
Matrix indices refer to three forms--!: aqueous, 2:
solids-sorbed, and 3: DOC-complexed; by seven ions--l:
neutral, 2-4: cations, and 5-7: anions.
Second-order rate constant for specific-base-catalyzed
hydrolysis of chemicals. When the matching (same
subscripts) Arrhenius activation energy (EBH) is zero, KBH
is interpreted as the second-order rate constant. When the
matching entry in EBH is non-zero, KBH is interpreted as the
(Briggsian) logarithm of the frequency factor in an
Arrhenius equation, and the 2nd-order rate constant is
computed as a function of segment temperatures TCEL.
Matrix indices refer to three forms —1: aqueous, 2:
solids-sorbed, and 3: DOC-complexed; by seven ions--l:
neutral, 2-4: cations, and 5-7: anions.
KCHEM
Number of chemicals under review in current study.
Units: n/a
KB ACS
K BACteria benthos, (form, ion, chemical)
Units: (cfu/mL)"1 hour"1
KDP
K Direct photolysis (ion, chemical)
Units: hour"1
Second-order rate constants--benthic sediment bacterial
biolysis of chemicals normalized by "colony forming units"
(cfu) per mL. When the matching (same subscripts) Q10
(QTBAS) is zero, KBACS is interpreted as the second-order
rate constant. When the matching entry in QTBAS is
non-zero, KBACS is interpreted as the numerical value of the
second-order rate constant at 25°C, and local values of the
rate constant are computed as a function of temperature
(TCEL) in each ecosystem segment. Indices refer to four
forms--!: aqueous, 2: solids-sorbed, 3:DOC-complexed,and
4: bio-sorbed; by seven ions--!: neutral, 2-4: cations, and
5-7: anions.
KBACW
K BACterioplankton water (form, ion, chemical)
Units: (cfu/mL)"1 hour"1
Second-order rate constants K for water column bacterial
biolysis of chemicals normalized by "colony forming units"
(cfu) per mL. When the matching (same subscripts) Q10
(QTBAW) is zero, KBACW is interpreted as the second-order
rate constant. When the matching entry in QTBAW is
non-zero, KBACW is interpreted as the numerical value of
the second-order rate constant at 25°C, and local values of
the rate constant are computed as a function of temperature
(TCEL) in each ecosystem segment. Indices refer to four
forms--!: aqueous, 2: solids-sorbed, 3:DOC-complexed,and
4:bio-sorbed; by seven ions--!: neutral, 2-4: cations, and
5-7: anions.
Estimatedphotolysisrates--use only when ABSOR, theactual
light absorption spectra of the compound in pure water, are
unavailable. KDP is an annual average pseudo-first-order
photolysis rate constant under cloudless conditions at
RFLAT, where
KDP(!,C) are pseudo-first-order photolysis rate constants of
neutral molecules RH3
KDp(2,c) are pseudo-first-order photolysis rate constants of
singly charged cations RH4+
KDp(3,c) are pseudo-first-order photolysis rate constants of
doubly charged cations RH5 +
KDp(4,c) are pseudo-first-order photolysis rate constants of
triply charged cations RH63+
KDp(5,c) are pseudo-first-order photolysis rate constants of
singly charged anions RH2"
KDp(6,c) are pseudo-first-order photolysis rate constants of
doubly charged anions RH
KDp(7,c) are pseudo-first-order photolysis rate constants of
triply charged anions R
KIEC
KBH
K Base Hydrolysis (form, ion, chemical)
Kp for ion Exchange Capacity (ion, chemical)
Units: Kp (meq/lOOg dry)"1
Coefficient relating sediment partition coefficient Kp of
ions to exchange capacity of sediments. KIEC times the
cation exchange capacity CEC(seg, month) (or anion
exchange capacity AEC foranionic species) gives the Kp for
sorption of ions with solid phases. This computation is
overridden by explicit (non-zero) values of KPS, i.e., a non-
zero value of KPS takes precedence over a Kp computed by
181
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EXAMS USUlg KIEC.
KIEC(!,C) is datum for relating CEC and sorption of singly
charged cation RH4+
KlEC(2,c) is datum for relating CEC and sorption of doubly
charged cation RH5 +
KlEC(3,c) is datum for relating CEC and sorption of triply
charged cation RH6
KlEC(4,c) is datum for relating AEC and sorption of singly
charged anion RH2"
KlEC(5,c) is datum forrelating AEC and sorption of doubly
charged anion RH
KlEC(6,c) is datum for relating AEC and sorption of triply
charged anion R "
Kow
KINOUT
Logical Unit Number for writing results of numerical
integration to kinetics plotting file.
KNH
K Neutral Hydrolysis (form, ion, chemical)
Units: hour"1
Pseudo-first-order rate constants for neutral hydrolysis of
chemicals. When the matching (same subscripts) Arrhenius
activation energy (ENH) is zero, KNH is interpreted as the
first-order rate constant. When the matching entry in ENH is
non-zero, KNH is interpreted as the (Briggsian) logarithm of
the frequency factor in an Arrhenius equation, and the
1 st-orderrate constant is computed as a function of segment
temperatures TCEL. Matrix indices refer to three forms--!:
aqueous, 2: solids-sorbed, and 3: DOC-complexed; by seven
ions--l: neutral, 2-4: cations, and 5-7: anions.
KOC
Koc (chemical)
Units: [(mg/kg)/(mg/L)] (organic carbon fraction)"1
KOC is partition coefficient (Kp) keyed to organic carbon
content FROC(S, m) of the sediment solids in each (s)
segment, during each (m) month of simulation of chemical
behaviorinthe system. Multiplication of KOC by the organic
carbon fraction FROC(s) of the solids in each segment
yields the partition coefficient (Kp) for sorption of
unionized (R-H3) species with those solids:
Kp(chemical, segment, month)=
KOC(chemical) x FROC(segment, month)
KOUNT
Number of segments used to define current ecosystem.
Units: n/a
Octanol-Water partition coefficient (chemical)
Units: (mg/L)/(mg/L)
Kow is an experimentally determined chemical descriptor.
Kow (KOW(C)) can be used to estimate Koc (c.f.), and thus
relate the Kp of a chemical to the organic carbon content of
sediments.
KOX
K oxidation (form, ion, chemical)
Units: per mole [OXRAD] per hour
Second-order rate constants for free-radical (OXRAD)
oxidation of chemicals. When the matching (same
subscripts) Arrhenius activation energy (BOX) is zero, KOX
is interpreted as the second-order rate constant. When the
matching entry in BOX is non-zero, KOX is interpreted as the
(Briggsian) logarithm of the frequency factor in an
Arrhenius equation, and the 2nd-order rate constant is
computed as a function of segment temperatures TCEL.
Matrix indices refer to three forms--!: aqueous, 2:
solids-sorbed, and 3: DOC-complexed; by seven ions--!:
neutral, 2-4: cations, and 5-7: anions.
K02
KO2 (segment, month)
Units: cm/hour
Oxygen exchange constant or piston velocity at 20 degrees
C in each ecosystem segment.
KPB
KP for Biomass (ion, chemical)
Units: (ug/g) / (mg/L)
Partition coefficient (Kp) for computing equilibrium
biosorption. The "c" subscript denotes the chemical; the
"ion" subscripts identify:
KPB(!,C) datum for biosorption of neutral molecules RH3
KPB(2,c) datum for biosorption of singly charged cations
RH4+
KPB(3,c) datum for biosorption of doubly charged cations
RH/+
KPB(4,c) datum for biosorption of triply charged cations
RH63+
KPB(5,c) datum for biosorption of singly charged anions
RH2"
KPB(6,c) datum for biosorption of doubly charged anions
RH=
KPB(7,c) datum for biosorption of triply charged anions R
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KPDOC
neutral, 2-4: cations, and 5-7: anions.
KP_ Dissolved Organic Carbon (ion, chemical)
Units: (ug/g)/(mg/L)
Partitioncoefficient(Kp) for equilibrium complex ation with
DOC. The "c" subscript denotes the chemical; the "ion"
subscripts identify:
KPDOC(l,c) datum for complexation of neutral molecules
RH3
KPDOC(2,c) datum for complexation of singly charged
cations RH4+
KPDOC(3,c) datum for complexation of doubly charged
cations RH5
KPDOC(4,c) datum for complexation of triply charged
cations RH6 +
KPDOC(5,c) datum for complexation of singly charged
anions RH2"
KPDOC(6,c) datum for complexation of doubly charged
anions RH
KPDOC(7,c) datum for complexation of triply charged
anions R "
KPS
KP_ for Sediment solids (ion, chemical)
Units: (mg/kg)/(mg/L)
Partition coefficients (Kp) for computing sorption with
sediments. The "c" subscript denotes the chemical; the
"ion" subscripts identify:
KPS(l,c) datum for sorption of neutral molecules RH3
KPS(2,c) datum for sorption of singly charged cations RH4+
KPS(3,c) datum for sorption of doubly charged cations
RH52+
KPS(4,c) datum for sorption of triply charged cations RH63+
KPS(5,c) datum for sorption of singly charged anions RH2"
KPS(6,c) datum for sorption of doubly charged anions RH
KPS(7,c) datum for sorption of triply charged anions R3"
KRED
K REDuction (form, ion, chemical)
Units: per mole [REDAG] per hour
Klo2
Klo2 (singlet oxygen) (form, ion, chemical)
Units: per M ['O2] per hour
Second-order rate constants for singlet oxygen
photo-oxygenation of chemicals. When the matching (same
subscripts) Arrhenius activation energy (EKlo2) is zero,
K102 is interpreted as the second-orderrate constant. When
the matching entry in EK1O2 is non-zero, Klo2 is
interpreted as the (Briggsian) logarithm of the frequency
factor in an Arrhenius equation, and the 2nd-order rate
constant is computed as a function of segment temperatures
TCEL. Matrix indices refer to three forms--!: aqueous, 2:
solids-sorbed, and 3: ooc-complexed; by seven ions--l:
neutral, 2-4: cations, and 5-7: anions.
LAMAX
LAMbda MAximum (ion, chemical)
Units: nanometers
Wavelength of maximum absorption of light by each ionic
species, or wavelength of maximum overlap of solar
spectrum and chemical's absorption spectrum (of each ion).
Indices match with KDP matrix. LAMAX selects the
wavelengths used to compute light extinction factors for
photochemical transformation, in those cases where the
absorption spectrum of the compound is not available, but
the results of simple photochemical experiments can be
used as a coarse estimate of rates of photochemical
transformations (i.e., KDP > 0.0). When set to zero, LAMAX
defaults to 300 nm.
LAT
LATitude
Units: degrees and tenths (e.g., 37.24)
Geographic latitude of the ecosystem. EXAMS uses latitude
and longitude for retrieving data and for calculating
climatological parameters. When entering latitude and
longitude of a study site, enter south latitude as a negative
number; north latitude as a positive number.
Second-order rate constants for REDucing AGent chemical
reduction of compounds. When the matching (same
subscripts) Arrhenius activation energy (ERED) is zero,
KRED is interpreted as the second-orderrate constant. When
the matching entry in ERED is non-zero, KRED is interpreted
as the (Briggsian) logarithm of the frequency factor in an
Arrhenius equation, and the 2nd-order rate constant is
computed as a function of segment temperatures TCEL.
Matrix indices refer to three forms--!: aqueous, 2:
solids-sorbed, and 3: ooc-complexed; by seven ions--!:
LENG
LENGth (segment)
Units: m
Length of a reach - used to compute volume, area, depth.
LOADNM
LOADings database NaMe (50 characters)
Units: n/a
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Do not use "CHANGE" or "SET" to enter names! The NaMe
for a LOADings database is entered via the command
sequence:
EXAMS-> LOAD NAME IS nnn...
where "nnn... " can include as many as 50 characters. This
name is associated with chemical loadings database library
entries, so that load patterns can be found in the catalog.
The Ith character can be corrected with a CHANGE or SET
command. For example, to repair the 7th character, "SET
LOADNM(7) TO ... ."
LONG
LONGitude
Units: degrees and tenths (e.g., -83.2)
Geographic longitude of the ecosystem. EXAMS uses
longitude and latitude for retrieving data and for calculating
climatological parameters. When entering longitude and
latitude of a study site, enter west longitude as a negative
number; east longitude as a positive number.
MCHEM
M CHEMJCal
Units: n/a
Number of chemical in activity data base.
MODE
MODE sets the operating "mode" of EXAMS.
Three operating modes are available; these are selected by
SETting MODE to 1,2, or 3:
MODE Operational characteristics of EXAMS
1 Long-term (steady-state) analysis.
2 Pulse analysis -- specifiable initial chemical mass
(IMASS) and time frame, time-invariant environment.
3 Monthly environmental data, daily pulse loads IMASS
and monthly chemical loadings of other types.
MONTH
MONTH
Units: n/a
Set MONTH to inspect a specific block of environmental
data. Months 1 — 12 correspond to January—December;
month 13 is average data.
MP
MeltingPoint (chemical)
Units: degrees Celsius
Melting point of the chemical - used for calculating linear
range of sorption isotherm.
Melting Point is used in the calculation of the maximum
concentrations within the range of linear isotherms. When
computing the crystal energy term for chemicals that are
solids at the ambient temperature (Karickhoff 1984, J.
Hydraulic Eng. 110:707-735) the solute entropy effusion
is taken as 13 eu.
MWT
Gram Molecular weighT (chemical)
Units: g/mole
Molecular weight of the neutral species of each study
chemical. Changes in molecular weight due to ionization are
neglected.
NPROC
Number of PROCess (path)
Units: n/a Range: 1--9
Signals the type of process transforming CHPAR(p) into
TPROD(P). NPROC can be set to the following:
1 --> specific acid hydrolysis
2 --> neutral hydrolysis
3 --> specific base hydrolysis
4--> direct photolysis
5 --> singlet oxygen reactions
6 --> free radical oxidation
7 --> water column bacterial biolysis
8 --> benthic sediment bacterial biolysis
9 --> reductions, e.g., reductive dechlorination
See also: CHPAR, EAYLD, RFORM, TPROD, YIELD
NPSED
Non-Point-Source SEDiment (segment, month)
Units: kg/hour
Non-point-source sediment loads entering ecosystem
segments.
NPSFL
N_on-Point-!5ource FLOW (segment, month)
Units: m3/hour
Non-point-source water flow entering ecosystem segments.
NPSLD
N_on-Point-!5ource LoaD (segment, chemical, month)
Units: kg/hour
Chemical loadings entering segments vianon-point sources.
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NYEAR
Numb er of YEARS
Units: n/a
NYEAR is number of years to be simulated for a mode 3 run.
OXRAD
oxidant RADicals (month)
Units: moles/L
Concentration of environmental oxidants in near-surface
waters (e.g., peroxy radicals). EXAMS computes
segment-specific oxidant concentrations using ultra-violet
light extinction in the system.
OZONE
OZONE (month)
Units: centimeters NTP
Typically 0.2--0.4 cm
Mean (monthly) ozone (O3) content of atmosphere.
EXAMS includes a database (ozone.daf) of total column
ozone data summarized from the Total Ozone Mapping
Spectrometer (TOMS) that flew on theNimbusV spacecraft
from 1 l/Nov/78 through 06/May/93. From the latitude and
longitude of the environment, EXAMS finds its position in a
1 degree latitude by 1.25 degree longitude grid of the Earth
and retrieves monthly mean ozone for the site, hi this grid,
south latitude is negative, north latitude positive; west
longitude is negative, east longitude positive.
PK
The negative value of the power to which 10 is raised in
order to obtain the temporally averaged concentration of
hydronium ions [H3O+] in gram-equivalents per liter.
pK (ion, chemical)
Negative of base-10 logarithm of acid/base dissociation
constants. When the matching value in the EPK matrix is
zero, PK(I, c) is taken as the pK value. (To "match" is to
have the same sub script values.) When EPK(!, c) is non-zero,
PK is taken as the base-10 logarithm of the pre-exponential
factor in the equation forpK as a function of environmental
temperature TCEL.
The vector indices for PK ("c" denotes the chemical) are
PK(!,C) contains datum for generation of R-H4 from RH3
PK(2,c) contains datum for generation of R-H5 from RH4
PK(3,c) contains datum for generation ofR-H6 +from RH5 +
PK(4,c) contains datum for generation of R-H2" from RH3
PK(5,c) contains datum for generation of R-H~ from RH2"
PK(6,c) contains datum for generation of R " from RH
PLMAS
FLanktonic bioMASs (segment, month)
Units: mg (dry weight)/L
Total plankton subje ct to bio sorp tion of synthetic chem icals.
PCPLD
precipitation LoaD (segment, chemical, month)
Units: kg/hour
POH
pOH (segment, month)
Units: pOH units
Chemical loadings entering each segment via rainfall.
PCTWA
PH
PerCenT WAter (segment, month)
Units: dimensionless
Percent water in bottom sediments of benthic segments.
Elements of these vectors that correspond to water column
segments are not used (dummy values). PCTWA should be
expressed as the conventional soil science variable (the
fresh weight : dry weight ratio times 100); all values must
be greater than or equal to 100. An entry in PCTWA that is
less than 100.0 for a benthic segment raises an error
condition, and control is returned to the user for correction
of the input data.
pH (segment, month)
Units: pH units
The negative value of the power to which 10 is raised in
order to obtain the temporally averaged concentration of
hydroxide [OH"] ions in gram-equivalents per liter.
PRBEN
FRoportion of sorbed chemical delivered to BENthic zone
Unitless
The PRZM model generates an output file that can be read by
the RE AD command in EXAM S.PRZM reports, for each runoff
date, contaminant dissolved in the flow, and contaminant
sorbed to entrained particulate matter. Use PRBEN (SET to a
value between 0.0 and 1.0) to indicate how much of the
sorbed material is to sink through the water column and
become incorporated into the benthic sediments. Based on
the generalization that about 50% of sorbed contaminant is
typically quite labile, and 50% is refractory, the default
value of PRBEN is set to 0.50.
PRINTR
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Logical Unit Number used for printing results on a line
printer.
2-4:cations, and 5-7:anions. When QTBAW is non-zero, the
matching (same subscripts) rate constant is computed as:
PRODNM
KBACW(f,i,c)=QTBAW(f,i,c)(
(TCEL(se8'm°°tll>25'10
KBACW(f,i,c)
FRODuct chemistry database NaMe (50 characters)
Units: n/a
Do not use "CHANGE" or "SET" to enter names! The NaMe
for a PRODuct chemistry database is entered via the
command sequence:
EXAMS-> PRO DUCT NAME IS nnn...
where "nnn... " can include as many as 50 characters. This
name is associated with product chemistry database library
entries, so that databases can be found in the catalog. Use a
CHANGE or SET command to repair single characters in the
name. For example, to repair character seven, enter "SET
PRODNM(7) TO ... ."
)YlELD
Quantum_YlELD (form, ion, chemical)
Units: dimensionless
Reaction quantum yield for direct photolysis of
chemicals—fraction of the total light quanta absorbed by a
chemical that results in transformations. Separate values
(21) for each potential molecular type of each chemical
allow the effects of speciation and sorption on reactivity to
be specified in detail. The matrix of 21 values specifies
quantum yields forthe (3) physical forms: (l)dissolved, (2)
sediment-sorbed, and (3) DOC-complexed; of each of (7)
possible chemical species: neutral molecules (1), cations
(2-4), and anions (5-7). (QYlELD is an efficiency.)
PRSW
FRint switch
Units: n/a
RAIN
RAlNfall (month)
Units: mm/mo nth
PRSW is a switch for controlling printing options. In mode
3, when PRSW is set to 0 (the default), average values of the
environmental parameters are recorded in the run log. When
PRSW is 1, a separate table is produced for each (monthly)
data set, except for those values which are invariant (VOL
etc.).
JTBAS
O_Ten BActeria benthos, (form, ion, chemical)
Units: dimensionless
Q10 values for benthic bacterial biolysis (see KBACS) of
chemical. "Q10" is the increase in the second-order rate
constant due to a 10°C increase in temperature. Indices refer
to 28 molecular spp: 4 forms--!:aqueous, 2:solids-sorbed,
3:DOC-complexed, and 4: bio-sorbed; by 7 ions--l:neutral,
2-4:cations, and 5-7:anions. When QTBAS is non-zero, the
matching (same subscripts) rate constant is computed as:
Average (monthly) rainfall in geographic area of system.
(TCEL(seg,month)-25)/10 .
KBACS(f,i,c)
KBACS(f,i,c)=QTBAS(f,i,c)"CEL
JTBAW
O_Ten BActeria water (form, ion, chemical)
Units: dimensionless
Q10 values for bacterioplankton biolysis (see KBACW) of
chemical. "Q10" is the increase in the second-order rate
constant due to a 10°C increase in temperature. Indices refer
to 28 molecular spp: 4 forms--l:aqueous, 2:solids-sorbed,
3:DOC-complexed, and 4: bio-sorbed; by 7 ions--l:neutral,
RANUNT
Logical Unit Number for the UTILITY file support.
The UTILITY file is used for retrieving and storing chemical
and environmental parameters, for supporting the on-line
assistance facility, and to support the SYSTEM PARAMETERS
operations.
REDAG
REDucing AGents (segment, month)
Units: moles/L
Molar concentration of reducing agents in each system
segment.
RELER
RELative ERror tolerance for integrators.
When the characteristics of the chemical and ecosystem are
such as to result in "stiff" equations, numerical errors may
lead to small negative numbers in the time series. If desired,
the value of ABSER and RELER can be decreased in order to
achieve greater precision in the simulation outputs.
RFLAT
ReFerence LATitude (ion, chemical)
Units: degrees (e.g., 40.72)
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(RFLAT - LAT) corrects for North or South displacement of
the ecosystem LAiitude from the location (RFLAT) of a
photochemical study used to develop a matched (same
subscript) KDP pseudo-first-order rate constant.
RFLAT(!,C) refer to photolysis of neutral molecules RH3
RFLAT(2,c) refer to photolysis of singly charged cations
RH4+
RFLAT(3,c) refer to photolysis of doubly charged cations
RH52+
RFLAT(4,c) refer to photolysis of triply charged cations
RH63+
RFLAT(5,c) refer to photolysis of singly charged anions RH2"
RFLAT(6,c) refer to photolysis of doubly charged anions
RH
RFLAT(7,c) refer to photolysis of triply charged anions R
RFORM
Reactive FORM (path)
Units: n/a Range: 1--32
RFORM gives the reactive molecular form (ionic species in
each of the possible sorptive states) of CHPAR(P) resulting in
product TPROD(P). The following table shows the value of
RFORM for each molecular entity, including values for total
dissolved (29), solids-sorbed (30), etc.
See also: CHPAR, EAYLD, NPROC, TPROD, YIELD
Ionic species
Valence
Forms:
Dissolved
Solids-sorbed
DOC-complexed
Biosorbed
Neutral
0
1
2
3
4
Cations
1 +
5
6
7
8
2+
9
10
11
12
3 +
13
14
15
16
Anions
1-
17
18
19
20
2-
21
22
23
24
3-
25
26
27
28
Total
(all)
29
30
31
32
RHUM
Relative HUMiditv (month)
Units: %, i.e., saturation = 100% R.H.
Mean (monthly) relative humidity during daylight hours.
Data typical of daylight hours are needed because their
primary use is to characterize light transmission in the
atmosphere.
RPTOUT
Logical Unit Number for data written to tabular report file.
SEELD
SEEpage LoaD (segment, chemical, month)
Units: kg/hour
Chemical loadings entering the system via "interflows" or
seepage (all sub-surface water flows entering the system,
(usually) via a benthic segment).
SEEPS
SEEPage flows, (segment, month)
Units: m3/hour
Interflow (subsurface water flow, seepage) entering each
segment. SEEPS usually enter via a benthic segment. SEEPS
are assumed to lack an entrained sediment flow; that is, they
are flows of water only.
SOL
SOLubilitv (ion, chemical)
Units: mg/L
Aqueous solubility of each species (neutral molecule + all
ions). When the matching value in the ESOL matrix is zero,
SOL(I, c) is taken as the aqueous solubility in mg/L. (To
"match" is to havethe same subscriptvalues.)When ESOL(I,
c) is non-zero, SOL(I, c) is taken as the base-10 logarithm of
the pre-exponential factor of the equation describing the
molar solubility of the species as a function of
environmental temperature (TCEL). The vector indices for
SOL are given in the text describing ESOL. Solubility must
be specified, because it is used as a constraint on loads.
SPFLG
species FLaGs (ion, chemical)
Takes on values of "1" (exists) or"0"
This vector of "flags" or "switches" shows which ions exist.
Set the flags ("SET SPFLG(!, c)=l") when entering chemical
187
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data in order to show EXAMS the ionic structure of the
chemical. When EXAMS starts, only SPFLG(!,*) are set,
i.e., the default chemical structure is a neutral (non-
ionizing) molecule. As additional SPFLG are set, EXAMS
displays the additional chemical data tables needed to
display the properties of the ionic species.
set SPFLG(!,C)=I to signal existence of a neutral molecule
RH3
set SPFLG(2,c)=l to signal existence of a singly charged
cation RH4
set SPFLG(3,c)=l to signal existence of a doubly charged
cation RH5
set SPFLG(4,c)=l to signal existence of a triply charged
cation RH6 +
set SPFLG(5,c)=l to signal existence of a singly charged
anion RH2"
set SPFLG(6,c)=l to signal existence of a doubly charged
anion RH
set SPFLG(7,c)=l to signal existence of a triply charged
anion R3"
STRLD
STReam LoaD (segment, chemical, month)
Units: kg/hour
Chemical loadings entering ecosystem segments via stream
flow.
STSED
srream-borne SEDiment (segment, month)
Units: kg/hour
Stream-borne sediment load entering ecosystem segments.
SUSED
suspended SEDiment (segment, month)
Units: mg/L
Suspended particulate matter - applicable to the water
column only.
SYSTYP
SPRAY
Name of aquatic ecosystem TYPe (50 characters)
Units: n/a
SPRAY drift from agricultural chemicals
Unitless percentage
The PRZM model generates an output file that can be read by
the READ command in EXAMS. PRZM3 reports, for each
application date, the application rate and the percentage drift
to adjacent aquatic ecosystems. Use SPRAY to set a drift
percentage for earlier versions of PRZM. EXAMS defaults
SPRAY to 10%. Note that values of SPRAY are entered as
percentages rather than as fractions.
SSOUT
Logical Unit Number for data written to plotting file
containing EXAMS' steady-state chemical concentrations.
Do not use "CHANGE" or "SET" to enter names! The name of
a water body is entered into the database via the command
sequence:
EXAMS-> ENVIRONMENT NAME IS nnn...
where "nnn... " can include as many as 50 characters. This
name is associated with environmental library entries (the
UDB catalog) and is printed in the header information of the
appropriate output tables. Use SET and CHANGE to correct
single characters in the name. For example, to correct the
seventh character in a name,
EXAMS-> CHAN SYSTYP(7) TO ...
STFLO
srream FLOWS (segment, month)
Units: m3/hour
TCEL
Temperature in CELSJUS (segment, month)
Units: degrees C
Flow into head reach of river or estuary; segment tributaries
and creeks or other stream flows entering a lake or pond.
Note that STFLO represents stream flow entering system
segments from external sources only.EXAMS itself computes
hydrologic flows among segments that are part of the water
body being studied, via the specified advective and
dispersive flow patterns (see JFRAD, JTURB, etc.). Therefore,
do not compute net water balances for each segment and
enter these into the database—enter only those flows
entering the system across external boundaries!
Average temperature of ecosystem segments. Used (as
enabled by input data) to compute effects of temperature on
transformation rates and other properties of chemicals.
TCODE
The value of Time CODE sets the units of TINIT, TEND, and
CINT.
TCODE can be SET to 1 (hours), 2 (days), 3 (months), or 4
(years). TCODE is under full user control only in Mode 2. In
mode 2, TCODE controls the time frame of the study. For
188
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example, given TINIT=0., TEND=24., and CINT=2.;
CHANging TCODE from 1 to 3 converts a 0-24 hour
study into 0-24 months, with bimonthly reports. In
mode 1, EXAMS selects the units for reporting results,
from the probable half-life of the study chemical(s). In
mode 3, a RUN encompasses one year or longer, and the
timing is set to produce standard outputs.
TEND
Time END for a dynamic simulation segment.
Units: see TCODE
A simulation segment encompasses the period TINIT through
TEND. At the end of each integration, TINIT is reset to TEND.
The simulation can be extended by invoking the
"CONTINUE" command; EXAMS will then request a new
value of TEND. Pulse loads (IMASS) and longer-term
chemical loads (STRLD, NPSLD, etc.) can be modified or
deleted during the pause between simulation segments.
TINIT
Time iNlTial for a dynamic simulation segment.
Units: see TCODE
A simulation RUN encompasses the period TINIT through
TEND. At the end of each integration, TEND is transferred to
TINIT. The simulation results can be evaluated, and the study
continued via the "CONTINUE" command. EXAMS will note
the new value of TINIT and request a new endpoint. Pulse
and other chemical loadings can be modified or deleted
between simulation segments.
warnings, and for EXAMS' interactive responses.
TYPE
Segment TYPE (segment)
Units: letter codes
Letter codes designating segment types used to define
ecosystems.
Available types: Littoral, Epilimnion, Hypolimnion, and
Benthic.
UDB
User DataBase
Long-term retention of data required by EXAMS is provided
by storage in the "User Database" (UDB, generally resident
on a physical device, e.g., a hard disk) for CHEMICALS,
ENVIRONMENTS, LOADS, or PRODUCTS. Within each of these
UDB sectors, each dataset is CATALOGued via a unique
accession number (UDB#). When transferring data between
foreground memory (the activity database or ADB) and a
UDB, the target location must be specified by the name of
the UDB sector and the accession number within the sector.
For example, to STORE the current pattern of chemical
loadings: STORE LOAD 7. Similarly, to retrieve or RECALL
data from a UDB into the ADB for use in an analysis, one
could enter: RECALL LOAD 7.
VAPR
VAPOR pressure (chemical)
Units: Torr
TPROD
Transformation PRODuct (path)
Units: n/a Range: I-KCHEM
Used to compute Henry's law constant when HENRY datum
is zero (0) but VAPR is non-zero:
HENRY = (VAPR/760) / (SOL/MWT)
TPROD(p) -- ADB location of the transformation product of
CHPAR(p). The matching (same transformation path number
"p") members of CHPAR and TPROD give the location
numbers in the active database of the parent chemical and
the transformation product for pathway "p". For example,
SET CHPAR(P) TO 1, and TPROD(P) to 4, to show that the
chemical in ADB sector 4 is produced via transformation of
the chemical in ADB sector 1, via process data defined by
the remaining members of product chemistry sector "p".
See also: CHPAR, EAYLD, NPROC, RFORM, YIELD
TTYIN
Logical Unit Number for interactive input commands.
If the associated molar heat of vaporization (EVPR) is
non-zero, VAPR is taken as the base-10 logarithm of the
pre-exponential factor in an exponential function describing
vapor pressure as a function of temperature (TCEL).
VOL
VQLume (segment)
Units: m3
Total environmental volume of ecosystem segments.
WIDTH
WIDTH (segment)
Units: m
TTYOUT
Logical Unit Number for output error messages and
Average bank-to-bank distance-for computing volume,
area, depth of lotic systems described via length, width, and
cross-sectional areas.
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WIND
wiNDspeed (segment, month)
Units: meters/second
Average wind velocity at a reference height of ten
centimeters above the water surface. Parameter is used to
compute a piston velocity for water vapor (Liss 1973,
Deep-Sea Research 20:221) in the 2-resistance treatment of
volatilization losses.
XSA
Cross-sectional (xs) Area (segment)
Units: m
Area of water body in section along advective flowpath.
XSTUR
x Section for TURbulent dispersion (path)
Units: m
XSTUR is cross-sectional area of a dispersive exchange
interface at the boundary between segments JTURB(P) and
ITURB(P). The matching (same "p" subscript) members of
JTURB, ITURB, CHARL, DSP, and XSTUR collectively define a
dispersive transport pathway. The exchange constant E(p)
is computed as:
E(p) (m3/hour) = DSp(p) * XSTUR(P) / CHARL(p)
See also: CHARL, DSP, ITURB, JTURB
YEARl
YEAR 1
Units: n/a
Starting year for mode 3 simulation (e.g., 1985).
YIELD
YIELD of product (path)
Units: mole per mole
YIELD(P) is the product yield from the transformation
pathway "p" with dimensions mole of transformation
product TPROD(P) produced per mole of parent compound
CHPAR(P) reacted (dimensionless).
See also: CHPAR, EAYLD, NPROC, RFORM, TPROD
190
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Appendices
Appendix A
Partitioning to Natural Organic Colloids
EXAMS uses the octanol:water partition coefficient (Kow) to
estimate binding constants (Kpdoc) for "dissolved" (i.e.,
colloidal) organic matter (DOC). The calculation factor is a
simple ratio to Kow (Seth et al. 1999). For developing the
EXAMS factor, values of partitioning on water samples containing
complete DOC (only) were tabulated, i.e., studies on chemically
separated fractions were not utilized. Measurement methods
included, inter alia, dialysis (Carter and Suffet 1982), reversed-
phase separation (Landrum et al. 1984) and solubility
enhancement (Chiou et al. 1986); values developed using
fluorescence quenching methods (Gauthier et al. 1986) were
excluded because this method is subject to interferences that
often lead to over-estimates of Koc ((Laor and Rebhun 1997),
(Danielson et al. 1995), (Tiller and Jones 1997)).
Compound
CAS#
Reference
Log log Kpdoc and Kpdoc/Kow ratios
Kow(1)
limnetic ratio
benthic ratio
1,3,6,8-TCDD
1,3,6,8-TCDD
H7CDD
O8CDD
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
Benzo[a]pyrene
33423-92-6
33423-92-6
37871-00-4
3268-87-9
50-29-3
50-29-3
50-29-3
50-29-3
50-29-3
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
50-32-8
(Servos and Muir 1989)
(Servos et al. 1989)
(Servos et al. 1989)
(Servos et al. 1989)
(Carter and Suffet 1982)
(Landrum et al. 1984)
(Chiou et al. 1987)
(Eadieetal. 1990)
(Kulovaara 1993)
(Landrum et al. 1984)
(Alberts et al. 1994)
(Landrum et al. 1985)
(Morehead et al. 1986)
(Kukkonen et al. 1989)
(McCarthy et al. 1989)
(Kukkonen et al. 1990)
(Eadieetal. 1990)
(Kukkonen and Oikari 1991)
(Kulovaara 1993)
7.13(2)
7.13(2)
8.20(2)
8.60(2)
6.36
6.36
6.36
6.36
6.36
5.97
5.97
5.97
5.97
5.97
5.97
5.97
5.97
5.97
5.97
5.50
5.12
6.85
5.78
4.84
4.52
4.39
4.36
3.81
4.56
4.80
4.80
5.33
5.16
5.18
4.57
5.00
4.36
0.023 6.06 0.085
0.010
0.045
0.002
0.030
0.014
0.011
0.010
0.003
0.039
0.068
5.56 0.389
0.068
0.229
0.155
0.162
0.040
0.107
0.025
191
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Compound
Dehydroabietic acid
Dieldrin
Fluoranthene
Mirex
Naphthalene
Phenanthrene
Phenanthrene
Phenanthrene
4-PCB
2,4,4'-PCB
2,4,2',4'-PCB
2,4,2',4'-PCB
2,4,2',4'-PCB
2,4,2',4'-PCB
2,5,2',5'-PCB
2,5,2',5'-PCB
2,5,2',5'-PCB
3,4,3',4'-PCB
3,4,3',4'-PCB
2,4,5,2',5'-PCB
2,4,5,2',4,5'-PCB
2,4,5,2',4,5'-PCB
Pyrene
Pyrene
Pyrene
6 PAH
37 Compounds
CAS #
1740-19-8
60-57-1
206-44-0
2385-85-5
91-20-3
85-01-8
85-01-8
85-01-8
2051-62-9
7012-37-5
2437-79-8
2437-79-8
2437-79-8
2437-79-8
35693-99-3
35693-99-3
35693-99-3
32598-13-3
32598-13-3
37680-73-2
35065-27-1
35065-27-1
129-00-0
129-00-0
129-00-0
Reference
(Kukkonen and Oikari 1991)
(Kosian et al. 1995)
(Brannonet al. 1995)
(Yin and Hassett 1989)
(Kukkonen et al. 1990)
(Landrum et al. 1985)
(Landrum et al. 1987)
(Chin and Gschwend 1992)
(Eadieetal. 1990)
(Chiouetal. 1987)
(Landrum et al. 1987)
(Caron and Suffet 1989)
(Kukkonen et al. 1990)
(Hunchak-Kariouk and Suffet 1994)
(Landrum et al. 1984)
(Eadieetal. 1990)
(Kukkonen et al. 1990)
(Kukkonen et al. 1990)
(Kukkonen and Oikari 1991)
(Chiouetal. 1987)
(Kukkonen et al. 1990)
(Eadieet al. 1990)
(Landrum et al. 1987)
(Eadieetal. 1990)
(Chin and Gschwend 1992)
(Liiers and ten Hulscher 1996)
(Ozretichet al. 1995)
Log
Kow(1)
4.80(3)
5.25(3)
4.95
6.89(4)
3.30
4.46
4.46
4.46
4.40
5.62
6.29
6.29
6.29
6.29
6.09
6.09
6.09
5.62(5)
5.62
6.11
7.75(5)
7.75
5.18
5.18
5.18
log Kpdoc and Kpdoc/Kow ratios
limnetic ratio benthic ratio
2.70 0.008
4.43 0.151
3.83 0.076
6.08 0.155
3.00 0.501
4.18 0.525
4.04 0.380
4.19 0.537
4.02 0.417
3.55 0.009
5.49 0.158
5.21 0.083
4.30 0.010
4.68 0.025
3.88 0.006
3.88 0.006
4.60 0.032
4.90 0.191
4.00 0.024
4.05 0.009
5.54 0.006
4.42 0.0005
4.71 0.339
3.76 0.038
4.73 0.355
3.326
0.072
Averages 0.074 0.46
1. Kow from ChemFate database (http://esc.syrres.com/efdb.htm) except as otherwise indicated.
192
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2. (Covers and Krop 1998)
3. Kow cited by author
4. (Devillers et al. 1996)
5. (Rapaport and Eisenreich 1984)
References for Appendix A
Alberts, J. J., C. Griffin, K. Gwynne, and G. J. Leversee. 1994.
Binding of natural humic matter to polycyclic aromatic
hydrocarbons in rivers of the southeastern United States.
Water Science and Technology 30:199-205.
Brannon, J. M., J. C. Pennington, W. D. Davis, and C. Hayes.
1995. Fluoranthene KDOC in sediment pore waters.
Chemosphere 30:419-428.
Caron, G., and I. H. Suffet. 1989. Binding of no npolar pollutants
to dissolved organic carbon: Environmental fate modeling.
Pages 117-130 in I. H. Suffet and P. MacCarthy, editors.
Aquatic Humic Substances: Influence on Fate and
Treatment of Pollutants. American Chemical Society,
Washington, D.C.
Carter, C. W., and I. H. Suffet. 1982. Binding of DDT to
dissolved humic materials. Environmental Science and
Technology 16:735-740.
Chin, Y.-P., and P. M. Gschwend. 1992. Partitioning of
polycyclic aromatic hydrocarbons to marine porewater
organic colloids. Environmental Science and Technology
26:1621-1626.
Chiou, C. T., D. E. Kile, T. I. Brinton, R. L. Malcolm, J. A.
Leenheer, and P. MacCarthy. 1987. A comparison of water
solubility enhancements of organic solutes by aquatic humic
materials and commercial humic acids. Environmental
Science and Technology 21:1231-1234.
Chiou, C.T.,R.L. Malcolm, T. I. Brinton, and D. E. Kile. 1986.
Water solubility enhancement of some organic pollutants
and pesticides by dissolved humic and fulvic acids.
Environmental Science and Technology 20:502-508.
Danielson, K. M., Y.-P. Chin, J. S. Buterbaugh, T. L. Gustafson,
and S. J. Traina. 1995. Solubility enhancement and
fluorescence quenching ofpyrene by humic substances: The
effect of dissolved oygen on quenching processes.
Environmental Science and Technology 29:2162-2165.
Devillers, J., S. Bintein, and D. Domine. 1996. Comparison of
BCF models based on log P. Chemosphere 33:1047-1065.
Eadie, B. J., N. R. Morehead, and P. F. Landrum. 1990. Three-
phase partitioning of hydrophobic organic compounds in
Great Lakes waters. Chemosphere 20:161-178.
Gauthier, T. D., E. C. Shane, W. F. Guerin, W. R. Sietz, and C.
L. Grant. 1986. Fluorescence quenching method for
determining equilibrium constants for polycyclic aromatic
hydrocarbons binding to dissolved humic materials.
Environmental Science and Technology 20:162-1166.
Covers, H. A. J., and H. B. Krop. 1998. Partition constants of
chlorinated dibenzofurans and dibenzo-p-dioxins.
Chemosphere 37:2139-2152.
Hunchak-Kariouk, K., and I. H. Suffet. 1994. Binding of organic
pollutants to dissolved organic matter in anoxic pore waters.
Pages 1031-1036 in N. Senesi and T. M. Miano, editors.
Humic Substances in the Global Environment and
Implications on Human Health. Elsevier Science B.V.,
Amsterdam.
Kosian, P. A., R. A. Hoke, G. T. Ankley, and F. M.
Vandermeiden. 1995. Determination of dieldrin binding to
dissolved organic material in sediment pore water using a
reverse-phase separation technique. Environmental
Toxicology and Chemistry 14:445-450.
Kukkonen, J., J. F. McCarthy, and A. Oikari. 1990. Effects of
XAD-8 fractions of dissolved organic carbon on the
sorption and bioavailability of organic micropollutants.
Archives of Environmental Contamination and Toxicology
19:551-557.
Kukkonen, J., and A. Oikari. 1991. Bioavailability of organic
pollutants in boreal waters with varying levels of dissolved
organic matter. Water Research 25:455-463.
Kukkonen, J., A. Oikari, S. Johnsen, and E. Gjessing. 1989.
Effects of humus concentrations on benzo[a]pyrene
accumulation from water to Daphnia magna: Comparison
of natural waters and standard preparations. Science of the
Total Environment 79:197-207.
Kulovaara, M. 1993. Distribution of DDT and benzo[a]pyrene
between water and dissolved organic matter in natural
humic water. Chemosphere 27:2333-2340.
Landrum, P. F., S. R. Nihart, B. J. Eadie, and W. S. Gardner.
1984. Reverse-phase separation method for determining
pollutant binding to Aldrich humic acid and dissolved
organic carbon of natural waters. Environmental Science
and Technology 18:187-192.
Landrum, P. F., S. R. Nihart, B. J. Eadie, and L. R. Herche.
1987. Reduction in bioavailability of organic contaminants
to the amphipod Pontoporeia hoyi by dissolved organic
matter of sediment interstitial waters. Environmental
Toxicology and Chemistry 6:11-20.
Landrum, P. F., M. D. Reinhold, S. R. Nihart, and B. J. Eadie.
1985. Predicting the bioavailability of organic xenobiotics
to Pontoporeia hoyi in the presence of humic and fulvic
materials and natural dissolved organic matter.
Environmental Toxicology and Chemistry 4:459-467.
Laor, Y., and M. Rebhun. 1997. Complexation-flocculation: A
new method to determine binding coefficients of organic
contaminants to dissolved humic substances. Environmental
Science and Technology 31:3558-3564.
Liiers, F., and T. E. M. ten Hulscher. 1996. Temperature effect
on the partitioning of polycyclic aromatic hydrocarbons
between natural organic carbon and water. Chemosphere
193
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33:643-657.
McCarthy, J. F., L. E. Roberson, and L. W. Burrus. 1989.
Association of benzo(a)pyrene with dissolved organic
matter: Prediction of Kdom from structural and chemical
properties of the organic matter. Chemosphere 19:1911-
1920.
Morehead, N. R., B. J. Eadie, B. Lake, P. F. Landrum, and D.
Berner. 1986. The sorption of PAH onto dissolved organic
matter in Lake Michigan waters. Chemosphere 15:403-412.
Ozretich, R. J., L. M. Smith, and F. A. Roberts. 1995. Reversed-
phase separation of estuarine interstitial water fractions and
the consequences of C18 retention of organic matter.
Environmental Toxicology and Chemistry 14:1261-1272.
Rapaport, R. A., and S. J. Eisenreich. 1984. Chromatographic
determination of octanol-water partition coefficients (Kow's)
for 58 poly chlorinated bipheny congeners. Environmental
Science and Technology 18:163-170.
Servos, M. R., and D. C. G. Muir. 1989. Effect of dissolved
organic matter from Canadian Shield lakes on the
bioavailability of 1,3,6,8-tetrachlorodibenzo-p-dioxinto the
amphipod Crangonyx laurentianus. Environmental
Toxicology and Chemistry 8:141-150.
Servos, M. R., D. C. G. Muir, and G. R. B. Webster. 1989. The
effect of dissolved organic matter on the bioavailability of
polychlorinated dibenzo-/>-dioxins. Aquatic Toxicology
14:169-184.
Seth, R., D. Mackay, and J. Muncke. 1999. Estimating the
organic carbon partition coefficient and its variability for
hydrophobic chemicals. Environmental Science and
Technology 33:2390-2394.
Tiller, C. L., andK. D. Jones. 1997. Effects of dissolved oxygen
and light exposure on determination of Koc values for PAHs
using fluorescence quenching. Environmental Science and
Technology 31:424-429.
Yin, C., and J. P. Hassett. 1989. Fugacity and phase distribution
of mirex in Oswego River and Lake Ontario waters.
Chemosphere 19:1289-1296.
194
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Appendix C
Implementing the microcomputer runtime EXAMS
EXAMS program files are installed from a self-unpacking archival compressed format
(Instxms.exe) . The files require a total of about 3 Mb of mass storage for transfer to your
hard disk, plus an additional 20 megabytes for storage of the files as they are retrieved
from the Archives, plus additional working space for the files produced while EXAMS runs.
The files include, besides the file README.XMS you are now reading,
o The file for installing the EXAMS program, in file INSTXMS.EXE, within
which is contained:
o The task image in file EXAMS.EXE, which allows space for five simul-
taneous chemicals (or one chemical and two degradation products,
etc.), and environmental models of up to one hundred segments.
o The unformatted direct access data- and help-file EXAMS.DAF,
with space for 25 chemical datasets, 10 environmental datasets,
5 external chemical load series, and 5 product chemistries.
o A global database of total column ozone (ozone.daf) from the TOMS
(Total Ozone Mapping Spectrometer) flown on the Nimbus-7 spacecraft.
o An EXAMS command file for testing the installation, in TEST.EXA.
o TESTOUT.XMS, a sample output for comparison with the results of the
installation test run.
o the User's Guide for EXAMS (file EXAMS.pdf) in Adobe PDF (Portable
Document Format). This file can be read and printed using the Adobe
Acrobat reader, available gratis from
http://www.adobe.com/products/acrobat/readstep.html
EXAMS makes use of the Phar Lap DOS-extender to access extended memory
EXAMS will require time to set up its virtual memory system when
loaded from DOS; load time can usually be reduced by running as a
Windows task. When running in a DOS session under MS-Windows set all
memory properties to "Auto" except DPMI memory. Set DPMI memory to 65535.
First, make sure that your IBM PC/AT 386/486/Pentium or "Compatible"
measures up to the following minimum hardware and software speci:
o appropriate diskette drive (for installation from diskette only)
o 20 megabyte available mass storage (hard disk)
o 80x87 math co-processor
o MS-DOS version 5.0 or higher
If your machine does not conform to these minimum specifications, the
EXAMS program WILL NOT execute properly.
- more -
196
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Then, to i:
the program
Transfer the file ' instxms.exe' to your hard disk in some suitable
subdirectory or partition and install EXAMS.
a. Set the default drive to the mass
storage device (e.g. , hard disk "C") :
b. Create an EXAMS directory: (BUT, if
installing as part of PIRANHA, the
PIRANHA\EXAMS directory already exists.
Do NOT create another EXAMS directory)
c. Request verification of copy results:
d. Change default directory to EXAMS,
or to PIRANHA EXAMS subdirectory
C :
MKDIR EXAMS
VERIFY ON
CD\EXAMS
CD\PIRANHA\EXAMS
e. If necessary, transfer the files from
diskette (e.g., "A") to the hard disk: COPY A:*.*
f. Execute the file INSTXMS.EXE to
recover files from the archives:
Start the EXAMS program from the EXAMS
a. Start the EXAMS program:
b. When you reach the EXAMS system
prompt, start the test command file:
INSTXMS
EXAMS
EXAMS-> DO TEST
When the test run finishes compare the outcome (in file REPORT)
with the file TESTOUT supplied with the program:
FC
REPORT.XMS TESTOUT.XMS
Files TESTOUT.XMS and TEST.EXA are not needed for routine
operation of EXAMS and can be deleted, as can file INSTXMS.EXE.
EXAMS uses Logical Unit Number (LUN) Seven, writing to device
PRN, for its Print command. As part of starting the program under DOS,
you may wish to make the DOS print routine memory-resident (i.e., set up
a print spooler) before starting EXAMS.
a. Load the DOS print routine (optional) PRINT
b. Start the EXAMS program: EXAMS
197