EXPOSURE ANALYSIS MODELING SYSTEM (EXAMS II) User's Guide for Version 2.97 by Lawrence A. Burns, Ph.D. Research Ecologist Ecosystems Research Division National Exposure Research Laboratory U.S. Environmental Protection Agency Athens, Georgia 30605-2700 Ecosystems Research Division National Exposure Research Laboratory U.S. Environmental Protection Agency Athens, Georgia 30605-2700 Disclaimer The information in this document has been funded wholly or in part by the United States Environmental Protection Agency. It has been subject to the Agency's peer and administrative review, and it has been approved for publication as an EPA document. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Foreword Environmental protection efforts are increasingly directed toward 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 laboratory 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 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 (cli), 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 2.97 includes file-transfer interfaces to the PRZM3 terrestrial model and the FGETS bio accumulation model; it is a complete implementation of Exams in Fortran 90. This report covers a period from October 1, 1995 to March 31, 1997 and work was completed as of April 7, 1997. T A B L E O F C O N T E N T S Disclaimer ii Foreword iii Abstract iv Introduction to the Exposure Analysis Modeling System (exams) 1 Exposure Analysis in Aquatic Systems 1 The exams program 2 Sensitivity Analysis and Error Evaluation 4 Exams Process Models 5 Ecosystems Analysis and Mathematical Systems Models 7 Further Reading 12 Exams command language interface (cli) user's guide 15 Conventions Used in this Section 15 Overview 15 Entering Commands 16 Command Prompting 17 Exams Messages 19 The help Command 19 Command Procedures 20 Wild Card Characters 20 Truncating Command Names and Keywords 21 Summary Description of exams' System Commands 21 System Command Descriptions 22 audit 22 catalog 24 change 26 continue 28 describe 32 do 34 erase 37 exit 39 help 40 list 42 name 45 plot 47 print 53 quit 54 read 55 recall 57 run 59 set 60 show 62 store 66 write 68 zero 70 Exams II Data Dictionary 72 Exams data entry template for chemical molar absorption spectra (ABSOR). 104 Implementing the microcomputer ms-dos Runtime exams 2.97 105 User's Guide for exams ii Version 2.97 Introduction to the Exposure Analysis Modeling System (exams) Industrial production of agricultural chemicals, plastics, and pharmaceuticals has increased steadily over the past four 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 toxicological 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 a responsive, general, and controllable tool for readily deriving and evaluating the behavior of synthetic chemicals in the environment. The research effort has focused on the development 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. 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. 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. Residuals can be compared to the concentrations 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. 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 pollutant to 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. 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 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. 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 examsprogram 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 et al. 1978) 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) 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 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. 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 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 sediment phase, 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? examsallows 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. 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- independent"models: 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 characteris tics 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. Ionization and Sorption Ionization of organic acids and bases, complexation with dissolved organic carbon (doc), and sorption of the compound with sediments and biota, are treated as thermodynamic 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 a dissolved, 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," alpha). These (alpha) values enter the 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 decomposition may 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. 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, ebhis 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 (quant) 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, Steen and Burns 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 an 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 qtbawand 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 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. 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 transport pathways. 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 specification of 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. 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 a rainfall loading (pcpld) entered for the hypolimnion or benthic sediments of a lake ecosystem. When this type of corrective action is executed, the change is reported to the user via an error message. 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. System resources 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 concentrations. 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 process models, 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. 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 decision-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 examsbegins 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 repeat itself indefinitely, and constitutes a positive 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 forecast the prevailing climate of chemical exposures, rather than to give detailed local forecasts of eecs in specific locations. 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 equivalently 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 via partial 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. 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 low concentrations of pollutants; its validity at high concentrations is less certain. Exams controls its computational range to ensure that the assumption of trace-level concentrations is not grossly violated. This control is keyed to aqueous-phase (dissolved) residual concentrations of the compound: exams aborts any analysis generating eecs that exceed (the lesser of) 50% of the compound's aqueous solubility or 10 micromolar (10-5 M) concentrations of a dissolved unionized molecular species. This restraint incidentally allows the program to ignore precipitation of the compound from solution and precludes inputs of solid particles of the chemical. 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. 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 Republic of Germany. 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 assessment models. pp. 148-172 In: Gesellschaft für Strahlen- und Umwelt forschung mbH München, Projektgruppe "Umwelt gefahrdungspotentiale von Chemikalien" (Eds.) Environmental Modelling for Priority Setting among Existing Chemicals. Ecomed, München-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, New York. Burns, L.A., and D.M. Cline. 1985. Exposure Analysis Modeling System: Reference Manual for exams ii. Epa/600/3-85/038, U.S. Environmental Protection Agency, Athens, Georgia. 83 pp. Burns, L.A., D.M. Cline, and R.R. Lassiter. 1982. Exposure Analysis Modeling System (exams): User Manual and System Documentation. Epa-600/3-82-023, U.S. Environmental Protection Agency, Athens, Georgia. 443 pp. 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 assessment models. 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. 372-386 In: G. Angeletti and A. Bjørseth (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.H., 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. 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, D.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 models to the long-term fate of dieldrin in a reservoir. Water Research 25:621-631. Schramm, K.-W., M. Hirsch, R. Twele, and O. 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, astm stp 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 hexa chlorocylcopentadiene in the aquatic environment. Chemosphere 11:91-101. Exams command language interface (cli) user's guide Introduction This section 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. Conventions Used in this Section Convention Meaning ctrl/x The phrase ctrl/x indicates that you must press the key labeled ctrlwhile simultaneously pressing another key, for example, ctrl/q. exams-> 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.