EPA/600/R!00/081

September, 2000

Revision G (May 2004)

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 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 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
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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 fullfeatured 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 authorities. 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; the complexity of the environmental description and the

number of chemicals is fully user-controlled. This implementation allows for direct access file (UDB) storage

of five interacting chemical compounds and 100 environmental segments; more complex configurations can

be created and subsequently stored using EXAMS’ WRITE command. 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 a sensitivity analysis of 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 PRZM terrestrial model and the FGETS and BASS bioaccumulation models; it is a complete implementation of EXAMS in Fortran 95.

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.
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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. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 3

1.5 EXAMS Process Models. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 3

1.5.1 Ionization and Sorption..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 4

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 Exposure Assessment: Mechanisms and Processes. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 10

2.1 Compartment Models and Conservation of Mass..... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 10

2.2 Equilibrium Processes..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 11

2.2.1 Ionization 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. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 20

2.3.1 Transport..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 20

2.3.1.1 Hydrology and advection. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 21

2.3.1.2 Advected water flows. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 23

2.3.1.3 Advective sediment transport. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 25

2.3.1.4 Dispersive transport. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 27

2.3.1.4.1 Dispersion within the water column (28)

2.3.1.4.2 Dispersion within the bottom sediments (28)

2.3.1.4.3 Exchanges between bed sediments and overlying waters (29)

2.3.1.5 Transport of synthetic organic chemicals. .... .... .... .... .... .... .... .... .... .... .... .... .... ... 32

2.3.2 Volatilization. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 33

2.3.2.1 Chemical Data Entry..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 36

2.3.2.2 Exchange Constants for Water Vapor. .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 36

2.3.2.3 Exchange Constants for Molecular Oxygen. .... .... .... .... .... .... .... .... .... .... .... .... .... . 37
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2.3.2.4 Validation and Uncertainty Studies. .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 38

2.3.2.4.1 Radon in small lakes in the Canadian Shield (38)

2.3.2.4.2 1,4-Dichlorobenzene in Lake Zurich (39)

2.3.3 Direct Photolysis..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 43

2.3.3.1 Direct photolysis in aquatic systems..... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 43

2.3.3.2 Light attenuation in natural waters. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 47

2.3.3.2.1 Distribution functions (D) in natural waters (47)

2.3.3.2.2 Absorption coefficients (a) in natural waters (48)

2.3.3.3 Reaction quantum yields (Ö; Qyield). .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 49

2.3.3.4 Absorption spectra (ABSORG). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 50

2.3.3.5 Effects of sorption on photolysis rates........ .... .... .... .... .... .... .... .... .... .... .... .... . . 52

2.3.3.5.1 Sensitivity analysis of sorption effects on direct photolysis (53)

2.3.3.6 Near-surface solar beam and sky irradiance. .... .... .... .... .... .... .... .... .... .... .... .... .... . 54

2.3.3.7 Input data and computational mechanics – Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.3.4 Specific Acid, Specific Base, and Neutral Hydrolysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.3.4.1 Temperature effects. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 59

2.3.4.2 Ionization effects. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 60

2.3.4.3 Sorption effects..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 61

2.3.5 Indirect Photochemistry, Oxidation and Reduction. .... .... .... .... .... .... .... .... .... .... .... .... .... 63

2.3.5.1 Oxidation. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 63

2.3.5.2 Singlet Oxygen. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 64

2.3.5.3 Reduction. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 65

2.3.6 Microbial Transformations. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 67

2.4 Input Pollutant Loadings..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 71

2.4.1 Product Chemistry. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 73

2.5 Data Assembly and Solution of Equations. . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 74

2.5.1 Exposure. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 74

2.5.2 Fate. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 75

2.5.3 Persistence. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 76

3.0 Tutorials and Case Studies..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 80

3.1 Tutorial 1: Introduction to Exposure Analysis with EXAMS – Laboratory 1..... .... .... .... .... .... .... .... .... ... 80

3.2 Tutorial 1: Introduction to Exposure Analysis with EXAMS – Laboratory 2..... .... .... .... .... .... .... .... .... ... 84

3.3 Command Sequences for Tutorial 1, Lab. 1. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 89

3.3.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1). .... .... .... .... .... .... .... .... .... .... .... .... ... 89

3.3.2 Lab. 1, Exercise 2: Mode 2 Analysis of Initial Value Problems – Sixty Day RUn Time.. . . . . . . . . . . . . . . . . . . . . . . . 91

3.3.3 Lab. 1, Exercise 3: Time-varying seasonal analysis using Mode 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

3.4 Introduction to EXAMS – Lab. 1 Exercises with Complete EXAMS Responses. .... .... .... .... .... .... .... .... .... . 95

3.4.1 Lab. 1, Exercise 1: Steady-State Analysis (Mode 1). .... .... .... .... .... .... .... .... .... .... .... .... ... 95

3.4.2 Lab. 1, Exercise 2: EXAMS in Mode 2..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 104

3.4.3 Lab. 1, Exercise 3: EXAMS in Mode 3..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 111

3.5 Tutorial 2: Chemical & Environmental Data Entry. .... .... .... .... .... .... .... .... .... .... .... .... .... .... 117

3.5.1 Exercise 4: p-DCB in Lake Zurich. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 117

3.5.2 Exercise 4 (Lake Zurich) with illustrative EXAMS Responses. .... .... .... .... .... .... .... .... .... .... ... 120

4.0 EXAMS Command Language Interface (CLI) User’s Guide..... .... .... .... .... .... .... .... .... .... .... .... .... . . 129

4.1 Conventions Used in this Chapter..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 129

4.2 Overview..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 129

4.3 Entering Commands. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 129

4.4 Command Prompting. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 130

4.5 EXAMS Messages..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 131

4.6 The HELP Command. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 131

4.7 Command Procedures. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 131

4.8 Wild Card Characters. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 131

4.9 Truncating Command Names and Keywords. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 132
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4.10 Summary Description of EXAMS’ System Commands. .... .... .... .... .... .... .... .... .... .... .... .... .... . 132

5.0 System Command Descriptions. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 133

AUDIT. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 133

CATALOG. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 135

CHANGE. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 137

CONTINUE. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 139

DESCRIBE..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 143

DO. . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 145

ECHO. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 148

ERASE. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 149

EXIT. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 151

HELP. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 152

LIST. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 154

NAME..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 157

PLOT. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 158

PRINT. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 163

QUIT..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 164

READ. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 165

RECALL. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 167

RUN. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 169

SET.. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 170

SHOW..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 172

STORE. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 175

WRITE. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 177

ZERO. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... 178

6.0 EXAMS Data Dictionary. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . 180

Appendices..... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... . . 196

Appendix A

Partitioning to Natural Organic Colloids. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... 196

Appendix B

EXAMS data entry template for chemical molar absorption spectra (ABSOR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Appendix C

Implementing the microcomputer runtime EXAMS. .... .... .... .... .... .... .... .... .... .... .... .... .... .... 201
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1

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 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 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 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.

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

system loadings consumed by the process. The relative

importance of the transformations indicate which process is
2

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 et al. 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 180)

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

180) 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, although the statistical distributions of chemical

and environmental parameters are not often known in the

requisite detail. Input time series that include ecophysiological

and hydrologic variation directly transfer these variabilities to

the outcomes of simulations. Standard EXAMS time-series and

summary outputs reflect variation in response to meteorology,

and find use in statistical analyses of the expected frequency and

magnitude of toxicologically significant event durations. One

objective of this kind of modeling, in the case of hazard

evaluations, is to estimate the effect of parameter errors on the

overall conclusions to be drawn from the model. This goal can

often be efficiently met by some form of sensitivity analysis.

1.4 Sensitivity Analysis and Error Evaluation

EXAMS does not provide a formal sensitivity analysis among its
3

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 (2 -1, where n is the number (6) of n

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-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 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 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
4

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,” á).

These (á) 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.

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 (QYIELD) 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

10 temperature. When a non-zero value is entered for the Q 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

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
5

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 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.

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

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.

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. 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.

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 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 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
6

(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.

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 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.
7

Daily pesticide export values from PRZM are transferred to

EXAMS as “pulse loading” events at the beginning of the day.

The peak concentration on that day is then reported by EXAMS

as the average of the start-of-day and end-of-day values. This

approximation of PRZM runoff events does not introduce a gross

distortion of the facts: For a 1-ha 2-m deep pond with an initial

concentration of 1 mg/L of a pesticide with a 2-day half-life,

EXAMS reports a first-day peak of 0.854 mg/L. If the same

amount of pesticide is permitted to enter the pond as a steady

load over 24 hours, the peak value is 0.849 mg/L, occurring at

the end of the 24 hour period. If the load were to enter over a

run-off duration of four hours, the pond would achieve a peak

concentration of 0.989 mg/L.

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.

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, or from runoff events that induce

significant hydrologic transients in the receiving water body.

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.
8

! 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

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. 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 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.
9

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, and K.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, 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.

Williams, R. J., M. D. Jürgens, and A> C. Johnson. 1999. Initial

predictions of the concentrations and distribution of 17âoestradiol, oestrone and ethinyl oestradiol in 3 English

rivers. Water Research 33:1663-1671.

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/Part B. 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.
10

2.0 Exposure Assessment: Mechanisms and Processes

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

computer program. 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):

(2-1)

where:

V is 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 that decrease chemical

concentration.

The “canonical environments file” currently supplied with the

EXAMS computer program is intended primarily 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 allocates memory as required by the current description

of the water body.

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. EPA600/7-78-074, U.S. Environmental Protection Agency,

Athens, Georgia.
11

2.2 Equilibrium Processes

The kinetic properties of organic chemicals are often strongly

influenced by the molecular state of the compound. 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 not contain “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) equilibrium partitioning 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 usually justified

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 desorption half-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 (á) are used

as multipliers on the user-specified transformation rate

coefficients for each decomposition process.

2.2.1 Ionization Reactions

According to the Brönsted-Lowry concept of ionization

reactions, acids and bases react with solvent (water) to form a

conjugate acid-base pair (Stumm and Morgan 1970). 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 3 program documentation as “RH .” The potential

acid-base reactions are then:

(1) Basic reactions:

(2-2)

(2-3)

(2-4)

(2) Acidic reactions:

(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 ,

! a singly charged anion ,
12

T(EC

)

Ka2(×1010

)

0 4.45

5 5.20

10 6.03

15 6.92

20 7.85

25 8.85

30 9.89

35 10.9

40 12.0

45 13.1

50 14.2

Tab a2 le 1. K of PhenolSulfonic Acid

! a doubly charged anion , and

! a triply charged anion .

(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 180) 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(1) 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 s 4ingly charged cation (RH ). +

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:

(2-8)

(2-9)

(2-10)

The acidity constants corresponding to Eq. (2-5), (2-6), and (2-7)

are:

(2-11)

(2-12)

(2-13)

These ionization constants are “mixed” constants (Stumm and

Morgan 1970:82) 3 3 , which take pH = -log{H O } ({H O } 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.)

2

In dilute aqueous solutions (unit water activity, {H O}=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(1), PKB(2), and PKB(3) for the

4

first, second and third basicity constants (generating RH ,

+

RH5 6 , RH and respectively) and PKA(1), PKA(2), and 2+ 3+

PKA(3) for the first, second and third acidity constants

2

(generating RH , RH , and R respectively). The equilibrium ! 2! 3!

constants can be entered either as single (temperature-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 and Physics, Ed. 51 p.

D122 (Weast 1971), gives the

a2 second acidity constant (K ) of

phenol-sulfonic acid for 0E to 50EC

(Table 1).

EXAMS could be loaded with a

typical value drawn from this data

a2 set, for example, at 25EC, K =

8.85×10 and PKA(2) = 9.05 (also !10

load EPK(2) = 0.0). The pKa will

then be taken as 9.05 regardless of

the water temperature of the

compartment.

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
13

a

regression of these data on the model K = A exp(-B/RT), where

R is the gas constant (1.9872 cal/deg mol) and T is absolute

temperature, yields the parameters A = 8.2724×10 and (B/R) !7

= 2.0466×10 a and accounts for 99.7% of the variation in K .

3

EXAMS requires the logarithm of A, and B in kcal/mol, as input

data. There a2 fore, the dependence of K on temperature would be

described to EXAMS via

PKA(2) = log A = !6.082

and

EPK(2) = (B/R)×R×0.001(kcal/cal) = 4.067 kcal/mol

EXAMS a2 will then compute a local value of K as a function of

the water temperature (TCEL) in each system compartment,

using the equation:

a

log(K )=PKA(2)-[1000×EPK(2)/{2.303×R×(TCEL+273.15)}]

2.2.2 Sediment Sorption

EXAMS computes a (local) equilibrium value for the partitioning

of each chem 3 4 ical species (RH , RH , 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

coef 3ficient for neutral organics or the unionized molecule (RH ),

KPSG 4(2) applies to the RH 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)):

f

in which X is mole fraction solubility, ÄS is the solute entropy

of fusion, Tm is the melting point in K, T is absolute temperature,

and R f is the gas constant. ÄS is between 12 and 15 eu (entropy

units, cal/mol K), so, for compounds that are solids at the

ambient temperature EXAMS computes the limiting concentration

C l as

in which SOL, MP, and TCEL are EXAMS input parameters; the

f

factor 6.5 results from ÄS /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 NPS loads, as with

calculated EEC ls, may not exceed C 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

general 3ly the RH 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 et al. 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 3 computes the RH partition coefficient (KPSL(1)) for each

system compartment via evaluation of these input data (KPSG(1),

KOCG, KOWG). The KPSG datum is used as a system-wide

partition coefficient whenever it is non-zero, giving precedence

to explicit user entries. KOCG is, however, the preferred datum,

as it provides a system-independent measure of sorption affinity

that can be tailored to the specific properties of each

compartment. When KPSG is zero, and KOCG non-zero, a local

(compartment-specific) KPSL(1) 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
14

non-zero, KOC is computed as 0.35×KOWG, and conversely. KOW

and KOC are also used to estimate KPB (see Chapter 2.2.3) and

KPDOC (Chapter 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:

KIEC(1) for sorption of – KPSL(2) computed from CEC

KIEC(2) for sorption of – KPSL(3) computed from CEC

KIEC(3) for sorption of – KPSL(4) computed from CEC

KIEC(4) for sorption of – KPSL(5) computed from AEC

KIEC(5) for sorption of – KPSL(6) computed from AEC

KIEC(6) for sorption of – KPSL(7) computed from AEC

The units of KIEC are ((mg/kg)/(mg/L))/(meq/100 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:

(2-14)

(2-15)

(2-16)

(2-17)

(2-18)

(2-19)

(2-20)

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” ñ

(kg dry weight/liter of water), and the chemical concentrations

3 3 (e.g., [RH P] and [RH ]) 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 (ñ, 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, ñ (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 × the fresh weight/dry

weight ratio) of the sediment, and the total environmental

volume (VOL, cubic meters) of the compartment. (The

computation takes ñw, the density of water, as 1 g/cm .) The 3

equations (using the internal code variables) are:

(2-21)

where
15

TOTMAS = BUKLD×VOL×1000.

SEDMSL = TOTMAS/(PCTWA/100.), and

WATVOL = (TOTMAS - SEDMSL)×ñw

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)):

(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

3 4 (RH , RH , etc.). The chemical equations and equilibrium +

expressions are analogous to those for sediment sorption:

(2-23)

(2-24)

(2-25)

(2-26)

(2-27)

(2-28)

(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 (μg/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 1981) is

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 (kg/mg) to yield BIOTOL in its proper units.

!6

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):

(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 migrate among

different zones of the ecosystem, or food-chain

biomagnifications. If desired, measured fish bioconcentration

factors or estimates 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 Complexation 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
16

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.

(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. Measurement methods 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 3 3 molecular structures (RH , RH P, etc.). This distribution

is expressed as a vector of concentration ratios (á). Each element

of á is the ratio of the concentration of a particular molecular

species, to the total concentration of chemical in the system

compartment t (S , mg chemical per liter of water in the

compartment). These á 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, subject to the following ionization reactions:

(2-38)

(2-39)

In order to evaluate the effects of ionization on transformation

and transport of the chemical, EXAMS requires three

concentration ratios (CR):

(1) the fraction present as , or

(2-40)

(2) the fraction present as ,or

(2-41)
17

(3) the fraction present as , or

(2-42)

The equilibrium constants for the chemical Eq. (2-38) and (2-39)

are:

(2-43)

(2-44)

As has 3 already been mentioned, EXAMS computes {H O } as

+

10 . The equilibrium expressions, together with a concentration -pH

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

1

the first concentration ratio CR (Eq. (2-40)) combine to give:

(2-46)

The equilibrium expressions can now be solved for

and in terms of 

To wit, Eq. (2-43) can be rearranged to give:

(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. (2-49) into Eq. (2-46), and

cancel 3ing [RH ] from numerator and denominator, yields the

desired result:

(2-50)

The second dis 2tribution coefficient CR can be computed in an

analogous fa 2shion. From the definition of CR in Eq (2-41), and

the concentration condition (Eq. (2-45)),

(2-51)

Solving Eq. (2-43) for , and Eq. (2-44) for 

yields the required expressions in :

(2-52)

(2-53)

Substituting Eqs. (2-52) and (2-53) into Eq. (2-50)2.33, and

canceling from numerator and denominator, then gives

an express 2ion for CR :

(2-54)

This conventional solution for the distribution coefficient is

somewhat unsatisfying for direct implementation in a computer

program a1 , 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:

(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 va a1 lue for K will not cause the computer to attempt a “zero

divide,” 2 and will nonetheless yield the proper value for CR , that

is, zero.

In the same way, substitution of the third term in the

denominator into the numerator of Eq. (2-50) gives:
18

(2-56)

Eq. (2-56) i 3s equivalent to the expression for CR obtained via

substitution of expressions for and (in terms

of 3) into the formula for CR given by:

(2-57)

Rearranging Eq. (2-54) gives in terms of :

(2-58)

and Eq. (2-43) gives:

(2-59)

or, substituting Eq. (2-58) into Eq. (2-59),

(2-60)

Substituting Eqs. (2-58) and (2-60) into Eq.(2-57), and canceling

from numerator and denominator, then yields:

(2-61)

which is equivalent to Eq. (2-56).

This example demonstrates that an algorithm for computing the

distribution coefficients (á) of any multi-species mixture can be

constructed in the following way.

t

(1) Write a concentration condition for [S ] that expresses the

law of conservation of mass (Eq. (2-45)).

(2) Define the distribution coefficient for the parent molecule

3 3 T

(RH ) as [RH ]/[R ] (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 3in terms of [RH ] (Eq. (2-47) and Eq. (2-49)).

3

(4) Cancel [RH ] 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 + these

terms), as in Eq. (2-50) (that is,

DISFCT 3 3 (1) = [RH ]/[RH ] = 1).

i

(6) Compute each distribution coefficient (á ) as

DISFCT(I)/SUMFCT, where DISFCT(1) = 1 and, in this

example,

DISFCT a1 (2) = K /{H } and +

DISFCT a1 a2 (3) = (K )(K )/{H }{H }

+ +

This computational procedure is general for all its subsets: when

any a K = 0, the computed distribution coefficient is zero.

Furthermore, the á 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.
19

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, and T. 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., 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.

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., and K. D. Jones. 1997. Effects of dissolved oxygen

and oc light exposure on determination of K values for PAHs

using fluorescence quenching. Environmental Science and

Technology 31:424-429.

Weast, R. C., editor. 1971. Handbook of 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 of 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

comprehended parameters, 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

to a compartment be balanced by advected flows leaving the
20

compartment. The expression used for dispersive or turbulent

exchanges across compartment boundaries is, as usual, (DSP ×

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 by benthic 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 should be 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 sediment mass to perturb the mass balance for synthetic

organic chemicals.

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
21

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-water inflows), 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 a river system. The NPSFLOs 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 compartment under consideration (that is, if the

current compartment is J, the decision is based on the properties

of compartment (J-1) ). 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-water interface, 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 segment must terminate in at least one bottom

sediment (“B”) compartment.

EXAMS’ computations are somewhat more efficient if one more

rule is observed:

(5) Vertical blocks of compartments are arranged, and

numbered, along the main advective flow paths in the system.

EXAMS’ internal decoding of the (implied) system structure thus

avoids the problem of multiple model-specific parameters, but
22

Figure 1. Improper segmentation and numbering: compartment

number 1 is (B)enthic, benthic sediments are incomplete, and

numbering is horizontally rather than vertically organized.

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”).

CMPT STFLO AREA EVAP VOL DEPTH

1 10 1000 146.1 2000 2

3 10 800 146.1 1600 2

5 200 146.1 400 2

7 1000 146.1 2000 2

Table 2. Hydrologic input data for Noname Slough

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.

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

inappropriate entries, that is, evaporation is allowed only for

compartments with an air-water interface.

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 hydrologic 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:

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.
23

Figure 3. Nine-compartment model of Noname Slough.

CMPT STRMFL EVAPL WATINL

1 10,000 200 9,800

3 10,000 160 9,840

5 0 40 !40

7 0 200 !200

TOTIN 19,400

Table 3. Advective inputs to Noname Slough

Name of environment: Noname Slough - Advection Test Data

Total number of segments (KOUNT) = 9

Segment Number: 1 2 3 4 5 6 7 8 9

Segment "TYPE": L B L B L B L B B

---------------------------------------------------------------

Table 9. Input specifications -- advective transport field.

---------------------------------------------------------------

J FR AD 1 1 3 5 7

I TO AD 3 5 7 7 0

ADV PR 0.750 0.250 1.00 1.00 1.00

 Path No.: 1 2 3 4 5

---------------------------------------------------------------

Exams Output Table 1. Partial advective structure of Noname

Slough.

JFRAD WATFL

TOTIN

1 3 5 7

I

T

O

A

D

1 1.00 0.0 0.0 0.0 0.505155

3 -0.75 1.0 0.0 0.0 0.507216

5 -0.25 0.0 1.0 0.0 -0.002062

7 0.00 -1.0 -1.0 1.0 -0.010309

Table 4. Normalized coefficient matrix, advection equations

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.

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 = SUM OF INPUTS

 X(1) = 9800

 X(3) = 9840 + 0.75 X(1)

 X(5) = -40 + 0.25 X(1)

 X(7) = -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(1) = 9800

X(3) = 9840 + 0.75(9800) = 17190

X(5) = -40 + 0.25(9800) = 2410

X(7) = -200 + 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+1) 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):

The solution of this system of equations is:
24

JFRAD

1 3 5 7

I

T

O

A

D

1 0 0 0 0

3 7350 0 0 0

5 2450 0 0 0

7 0 17190 2410 0

WATOUL 0 0 0 19400

Table 5. Example WATFL matrix and WATOUL vector (L/h)

CHEMICAL: Unspecified Chemical

ECOSYSTEM: Noname Slough - Advection test data

------------------------------------------------------------------------

TABLE . 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

------------------------------------------------------------------------

1L 2.0000E+03 235. 8.50

2B

3L 1.600E+03 413. 3.88

4B

5L 400. 57.8 6.92

6B

7L 2.000E+03 466. 4.30

8B

9B

------------------------------------------------------------------------

* COMP. TYPE: “L”=LITTORAL; “E”=(EPI) AND “H”=(HYPO)LIMNION; “B”=BENTHIC

Exams Output Table 2. Partial transport profile for Noname Slough.

(2-63)

(2-62)

X(1) = 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 for these 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.

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):

and the equation for advected sediment export is

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

water column into a benthic sediment is an infiltration flow

that does not transport sediments.
25

Figure 4. Noname Slough bedload, washload, and

groundwater flows.

(2) Benthic sediment (“B”) compartments can always export

water across system boundaries, and can advect water to

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-1) 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).

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(1) 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)/(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)/(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

to both segments are 0.04 cubic meters of water per hour
26

Name of environment: Noname Slough - Advection Test Data

Total number of segments (KOUNT) = 9

Segment Number: 1 2 3 4 5 6 7 8 9

Segment "TYPE": L B L B L B L B B

---------------------------------------------------------------

Table 9. Input specifications -- advective transport field.

---------------------------------------------------------------

J FR AD 1 1 3 5 7

I TO AD 3 5 7 7 0

ADV PR 0.750 0.250 1.00 1.00 0.980

 Path No.: 1 2 3 4 5

J FR AD 7 2 4 6 9

I TO AD 8 4 8 5 0

ADV PR 2.000E-02 1.00 1.00 1.00 1.00

 Path No.: 6 7 8 9 10

J FR AD 8 8

I TO AD 9 0

ADV PR 0.830 0.170

 Path No.: 11 12

---------------------------------------------------------------

Exams Output Table 3. Full advective structure of Noname

Slough.

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

------------------------------------------------------------------------

1L 2.000E+03 200. 235. 23.5 8.50 8.50

2B 50.0 3.333E+04 0.960 1.200E+03 27.8 27.8

3L 1.600E+03 160. 413. 41.3 3.88 3.88

4B 40.0 2.667E+04 1.92 2.400E+03 11.1 11.1

5L 400. 40.0 62.6 6.26 6.39 6.39

6B 10.0 6.667E+03 4.80 0.000 1.11

7L 2.000E+03 200. 470. 46.1 4.25 4.34

8B 50.0 3.333E+04 11.3 2.407E+03 2.35 13.8

9B 300. 5.087E+05 9.40 0.000 8.12

------------------------------------------------------------------------

* COMP. TYPE: “L”=LITTORAL; “E”=(EPI) AND “H”=(HYPO)LIMNION; “B”=BENTHIC

Exams Output Table 4. Advective transport regime in Noname Slough.

(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.

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

et al. 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 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).

The conventional dispersion equation used in EXAMS describes

the rate of exchange of environmental volume across a boundary

between two compartments. EXAMS’ formula is:

(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).
27

(2-65)

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:

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) 7 SEDFL(I,J) + (FLOW)×(SEDCOL(J)) and

SEDFL(J,I) 7 SEDFL(J,I) + (FLOW)×(SEDCOL(I));

the SEDOUL vector is updated via

SEDOUL(J)7 SEDOUL(J) + (FLOW)×(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 that biogenic 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 5.4×10 (Missouri River near 6

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

diffusion, internal waves, seiches, standing waves, [and]
28

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 1×10 cm /s; this value !4 2

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

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
29

(2-66)

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:

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

sediment:water 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:

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

regarding the solid phase as a simple 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 2 m 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

(1977), working with rhodamine dye in North Sea sediments,

found that physical turbulence induced benthic boundary layer
30

Name of environment: Noname Slough - Dispersion Equation Test Data

Total number of segments (KOUNT) = 9

Segment Number: 1 2 3 4 5 6 7 8 9

Segment "TYPE": L B L B L B L B B

---------------------------------------------------------------

Table 10.13. Mean dispersive transport field.

---------------------------------------------------------------

J TURB 7 8

I TURB 8 9

XS TUR m2 1.000E+03 1.000E+03

CHARL m 1.02 0.175

DSP m2/hr* 1.500E-04 3.600E-05

 Path No.: 1 2

---------------------------------------------------------------

* Average of 12 monthly mean values.

Exams Output Table 5. Dispersive interconnections – test data.

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 **** TOXICANT CONCENTRATIONS ****

 2 TOTAL DISSOLVED SEDIMENTS BIOTA

 G/M KILOS % MG/* MG/L MG/KG UG/G

--- ------ ------ ------ --------- --------- --------- ---------

 1 2.000E-03 2.0001E-03 100.00 1.000E-03 6.250E-04 3.75

 SUBTOTAL: 2.0001E-03 1.21

 AND IN THE BOTTOM SEDIMENTS:

 2 0.125 0.1250 76.61 3.75 2.500E-04 3.75

 3 3.818E-02 3.8176E-02 23.39 7.505E-02 2.500E-04 7.501E-02

 SUBTOTAL: 0.1632 98.79

TOTAL MASS (KILOGRAMS) = 0.1652

------------------------------------------------------------------------

* TOTAL CONCENTRATION AS MG/L IN WATER COLUMN, AS MG/KG IN SEDIMENTS.

Exams Output Table 6. Test of dispersive exchange equation.

exchange coefficients of 2.9×10 to 6.2× cm /s. The test DSP

!6 !4 2

for Noname Slough were selected from this range of values.

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 3×10 .

5

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/L of 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 μg/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 expectations, this result for the washload and the mud

layer is exactly opposite the expected outcome of the test.

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, an organic 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 (á) and

sediment:water ratios (SEDCOL) of the water-column and
31

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 **** TOXICANT CONCENTRATIONS ****

 2 TOTAL DISSOLVED SEDIMENTS BIOTA

 G/M KILOS % MG/* MG/L MG/KG UG/G

--- ------ ------ ------ --------- --------- --------- ---------

 7 2.000E-03 2.0000E-03 100.00 1.000E-03 6.250E-04 3.75

 SUBTOTAL: 2.0000E-03 0.49

 AND IN THE BOTTOM SEDIMENTS:

 8 0.313 0.3125 76.61 9.38 6.250E-04 9.38

 9 9.543E-02 9.5428E-02 23.39 0.188 6.250E-04 0.188

 SUBTOTAL: 0.4079 99.51

TOTAL MASS (KILOGRAMS) = 0.4099

------------------------------------------------------------------------

* TOTAL CONCENTRATION AS MG/L IN WATER COLUMN, AS MG/KG IN SEDIMENTS.

Exams Output Table 7. Test of modified dispersive exchange

procedure.

benthic compartments specified for an exchange pathway. Given

á(d,w) and á(d,b) as the total dissolved fraction in the water

column (w) and benthic (b) compartments, respectively, and

á(s,w) and á(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:

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 transport of 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 = (á(29)+á(31)+á(32))×WATOUL +

á(30)×SEDOUL/SEDCOL

where á(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

INTOUL =(á(29)+á(31)+á( 3 2 ))×SUMWAT +

á(30)×SUMSED/SEDCOL

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

contribution of transport processes to the overall loss constant

“K” of Eq. (2-1).
32

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:

(á(29)+á(31)+á(32))×WATFL + á(30)×SEDFL/SEDCOL

using values of á, 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

element of INTINL by the volume of the receiving compartment:

INTINL7 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. LakesChemistry, 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

River between 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,

Mackay and Leinonen 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.
33

Figure 6. Whitman (1923) two-resistance or two-film model of a

gas-liquid interface (after Liss and Slater 1974).

(2-67)

(2-68)

(2-69)

(2-70)

(2-71)

Spec lifically, in Figure 6, C is the concentration of contaminant

g

(mol@m ) in the bulk water phase; P is its partial pressure !3

sl (atmospheres) in the bulk air above the interface; C is its aqueous concentra sg tion at the interface; and P is its partial pressure

on the atmospheric side of the interface. The flux of compound

F (mol@m @h ) through the aqueous film can be described using !2 !1 

Fick’s first law

where D is the aqueous diffusion constant of the chemical in the

film (m @h ), C is the concentration of unionized, unsorbed

2 !1 

compound (mol@m ), and dC/dZ is the concentration gradient in !3

the film.

Similarly, the flux of chemical through the stagnant atmospheric

layer is

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 (8.206×10 m -atm/mol-K), and T is -5 3

Kelvin temperature.

Environmental gas exchange processes are often formulated in

terms of an exchange constant “k”, that is, as a conductivity (the

inverse of the film transport resistance). The exchange constant

has dimensions of velocity (m @h ); it is also known as the “mass !1

transfer coefficient,” “permeability coefficient,” “adsorption/exit

coefficient,” and “piston velocity.” The flux of gas F through

the stagnant layers is then

in the liquid phase, and

in the gas phase, where dC is the concentration difference across

the film, dP is the partial pressure difference across the film, and

k(@) (@) (@) (@) = D /z , z the film thicknesses. The reciprocals ® = k ) of -1

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 interface ,

1

the mass fluxes through the stagnant layers of air and water (see

Figure 6 l g ) must be the same. Therefore, F /F , and we can set (2-

69) equal to (2-70) and substitute the concentration differences

(Figure 6), obtaining

where kl g is the liquid phase, and k the gas phase, exchange

coefficient. The partitioning of the exchanging (unionized)

substance across the air-water interface is given by Henry’s

Law sg sl : P = H C , where H is the Henry’s Law constant

sl sg (atm-m /mol). Using Henry’s Law, C and P can be eliminated

3

from (2-71), yielding an equation relating the transport flux F to

the bulk phase concentrations only:

(2-72)

where Kl, the transport conductance, is defined by

(2-73)

The total resistance to transfer of a gas across the air-water

l l interface (R = K ) is thus the sum of the series of resistances in -1

l g the liquid (k ) and gas (RT/(H k )) phases of the interface. The -1

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 (Bird et al. 1960:652)).

The “two-film” picture of the air-water interface (Figure 6) is

physically inexact, although events at molecular scales

Note that this is a microscopic, rather than a macroscopic, assumption--that 1

is, these extremely thin interfacial films are merely assumed to maintain a rapid

equilibrium with the adjacent, themselves fully dynamic, bulk layers.
34

(2-75)

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 (Dilling et al. 1975, Dilling 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 app glication requires a knowledge of P , the bulk

atmospheric part gial pressure. Lacking a measured value of P , 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 organics volatilized from aquatic systems rapidly

g

removes them from the vicinity of the interface, so that P can be

neglected (Mackay and Leinonen 1975, Mackay 1978). This

approach was adopted for EXAMS, resulting in a simplification

of (2-71), yielding

(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 V is the

volume (m ) of the compartment, gives 3

The factor á1 is the fraction of the total pollutant concentration

[C] present as a volatilizable (unionized, unsorbed) chemical

l 1 species. The group K Aá /V is a pseudo-first-order rate constant

with units h . This rate constant is computed by EXAMS in a

-1

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 chem lical pollutants in the liquid (k ) and atmospheric (RT/(H -1

k )g) 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 

 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

g

transport resistance R from the equation

(2-76)
35

(2-77)

(2-78)

(2-79)

(2-82)

where T w is Kelvin temperature, V is the water vapor exchange

constant (piston velocity, m @h ), H the Henry’s Law constant, !1

the molecular weight of water is taken as 18 g/mol, and MWT is

the molecular weight of the 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 of the 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 of the 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 of the

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 l liquid-phase transport resistance R is then simply

whe O2 re K is the oxygen (molecular weight 32) exchange

constant.

The total transport resistance of the pollutant is the simple sum

of the individual l g phase transport resistances, R + R . The

exchange con lstant of the contaminant (K , the conductivity) is

the reciprocal of that sum:

Thus, EXAMS completes computation of the pseudo-first-order

vola l 1 tilization rate constant as K á A/V.

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 of the

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-m /mol), or as a function of temperature. When input 3

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

When no data for the Henry’s Law constant is available at run

time, but EXAMS detects the presence of a non-zero value of the

vapor pressure of the 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.

(2-80)

(2-81)

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 !1

exchange constant for water vapor (VW) 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

!1 !1

vapor exchange constant

in which changes in wind velocity at 10 cm above the water

surface (WIND) account for 98.3% of the variance in the
36

(2-83)

exchange constant for water vapor (VW, m @h ) over a range of !1

wind speeds (at 10 cm height) from 1.6 to 8.2 m @s . !1

Wind speeds observed at other heights can be converted to wind

speed at 10 cm via the usual assumption of a logarithmic wind

profile 1 2 (Israelsen and Hansen 1962). Wind speeds U and U at

heights Z1 2 and Z are related by

U1 2 1 2/ U = 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 !1

constant measured at 20 EC, 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 ).

!1 !1

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 !1

cm@h , and as large as 100 cm@h , have been observed. Liss and !1 !1

Slater (1974) estimated an average exchange constant for the

open sea of 20 cm@h . !1

In the more quiescent waters of 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 !1

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 , !1

(2-84)

and, for 5.5 m @s # U < 30 m @s , !1 !1

(2-85)

where KL is the oxygen exchange constant (in m @s ) and U is !1

wind speed (m @s ) at 10 m above the water surface. Over a !1

range of wind speeds from 1 to 30 m @s , the oxygen exchange !1

constant thus would change from 1.5 to 104 cm@h . When the !1

oxygen piston velocity is not entered, EXAMS uses Eq. (2-84) or

Eq. (2-85) to calculate its value. Banks’ equations are not

temperature-specific; they were derived as an amalgam of

studies at many temperatures. Exams takes the result of Banks’

equations and adjusts for the ambient water temperature using

(2-83)

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.

2.3.2.4.1 Radon in small lakes in the Canadian Shield

Emerson (1975) conducted an experimental investigation of the

222 loss of radon gas (Rn ) from small lakes in Canada’s

Experimental Lakes Area (ELA). He reported his results in terms
37

TEC 10 X 10 H

4 2

0 4.24 4.25

5 3.40 5.31

10 2.77 6.50

15 2.31 7.81

20 1.95 9.24

25 1.68 10.8

30 1.46 12.3

35 1.29 14.0

40 1.16 15.6

45 1.05 17.2

50 0.96 18.8

Table 6. Rn Solubility

(2-88)

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 !1

temperatures in these lakes are about 20 EC (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.

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-m mol ) of Rn gas between 3 !1

0 and 50 EC can be computed

from these data.

Regression of these data on the

model H = A exp(-B/RT) 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 E H E N =

(B/R)×R×0.001 (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 EC will be taken as 0.09239 atm-m /mol. 3

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 !1

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 WIND=1.5 (log 100/log 1000)=1.0 m @s . !1

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 !1

g

resistance (R in Eq. (2-76)) is

(2-86)

= 0.079 h@m

!1

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

!1

is less than 5.5 m @s ; Eq.(2-84) therefore applies and !1

 = 5.93×10 m @s . -6 !1

EXAMS’ input datum (KO2) has dimensions of cm@h ; the units !1

conversion yields KO2 = 2.13 cm@h for direct entry. (Recall, !1

however, that EXAMS will use Banks’ equations to generate KO2

when only the wind speed is entered (1.0 m @s at 10 cm).) !1

EXAMS’ estimates the liquid-phase transport resistance using the

molecular weight of the pollutant as an indexing factor. The

temperature of the epilimnion (20EC) 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 t lransport resistance (R in Eq. (2-77)) can be computed as

(2-87)

 = 123.66 h@m . !1

Resistance in the liquid phase thus amounts to 99.9% of the total

Rn transport resistance (Rt = Rg + Rl = 123.74 h@m ). The !1

estimated exchange constant for Rn gas is therefore Rt = -1

8.08×10 m @h = 0.19 m/d, which value can be compared to -3 !1

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 36). Under these models, Eq.

(2-77) for the liquid phase transport resistance becomes

Where K VO 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

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
38

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 1.37×10 cm @s at 25 EC. The -5 2 !1

diffusion constant of molecular oxygen in water at 25EC is

2.41×10 cm @s (Vivian and King -5 2 !1(1964), cited from Reid,

Prausnitz, and Sherwood (1977:576)). The diffusivity ratio

2

(D(Rn)/D(O )) is thus 1.37/2.41 = 0.568. Application of Eq. (2-

88) then yields a liquid-phase transport resistance in these small

l

lakes of R = 1/(0.0213×0.568) = 82.6 h@m and a Rn exchange !1

constant l g (1/(R +R )) of 0.29 m/d. Application of surface renewal

l

theory would give R = 1/(0.0213×%0.568) = 62.3 h@m and a Rn !1

exchange constant of 0.38 m@d . !1

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 Rl = 1/(.0213×0.70) = 67.1 h@m , and a Rn exchange !1

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 ) !1 !1

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

g

 R = (293.15×8.206×10 )/(30.0×0.09239×%[18/222]) -5

 = 0.0305 h@m

!1

l

 R = 1/(0.2×%[32/222]) = 13.17 h@m

!1

tgl R = R + R = 13.2 h@m

!1

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

6 4 2

(C H Cl ) is 147.0. The aqueous solubility and octanol-water

partition coefficient of DCB have been measured by Banerjee et

al. (1980). DCB is soluble to 0.502 mM in water at 25 EC (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 EC, 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 EC

(Weast 1971). The vapor pressure (Pv) of solid 1,4-DCB at 10,

30, and 50 EC 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 = 9.63×10 , B = 8223, and accounts for 11

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, and EVPR=(B)(R)(0.001)=16.34 kcal/mol.

Alternatively, the Henry’s Law constant can be estimated via

interpolation of the observed vapor pressure to 25EC. The latter

procedure was used for this analysis of DCB in Lake Zurich.

The interpolated value of Pv is 1.011 torr at 25 EC. The Henry’s

Law constant is therefore (1.011/760)/0.502 = 2.66×10-3

atm-m /mol. 3

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

DCB-contaminated waste- water effluents. The central basin 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).
39

Parameter Compartment

Number 1 2 3

Type E H B

Area, m 6.8×10 6.8×10 6.8×10 2 7 7 7

Depth, m 10 40 0.02

Vol, m 6.8×10 2.72×10 1.36×10 3 8 9 6

Sused (mg/L) 5 5

Bulkd (g/cm ) 1.5 -3

Pctwa, % 150

Froc 0.02 0.02 0.02

Wind,

m @s @10cm !1

1.38

Table 7. Environmental Data for Central Basin of Lake Zurich

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 for the period 1955-63, 1965-69 was 2.6 m @s

!1

(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- !1

84) then gives 2.5 cm@h via !1

Average wind speed at 10 cm height (EXAMS’ input parameter

WIND) would be 2.6(log 100/log 6000) = 1.38 m @s . !1

The mean depth of the central basin is 50 m, and the surface area

is 68 km , giving a total volume of 3.4×10 m . The lake 2 9 3

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 6.8×10 m . For the epilimnion segment (compartment 1, 7 2

TYPE(1) = “E”), DEPTH(1) = 10 (m), and VOL(1) = 6.8×10 (m ). 8 3

The hypolimnion ( compartment 2, TYPE(2) = “H”) then has

DEPTH(2) = 40, and VOL(2) = 6.8×10 ×40 = 2.72×10 m .

7 9 3

Assuming a 2 cm depth of active benthic sediments (

compartment 3, TYPE(3) = “B”) gives DEPTH(3) = 0.02, and

VOL(3) = 1.36×10 m .

6 3

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(1)) was 11EC; the mean hypolimnion temperature was

5.6EC (TCEL(2)). The eddy dispersion coefficient at 10 m depth

averaged 0.058 cm @s during the stratified period and was 2 !1

about 1 cm @s during the balance of the year. The annual mean 2 !1

value (DSP for parameterizing average transport between the

epilimnion and hypolimnion) was 0.6 cm @s ; EXAMS’ input 2 !1

value of DSP = 0.2 m @h . The dispersion coefficient for 2 !1

exchange between the hypolimnion and benthic sediments was

taken as 10 m @h . -4 2 !1

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 sediment:water 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.

The combined flow from the upper basin (2,500), small creeks

draining into the central basin (100), and treatment plant

effluents (28) was 2,628×10 m /yr, giving STFLO(1) = 3×10 m 6 3 5 3

@h . The total load of DCB on the central basin was 88 kg/yr, of !1

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(1) to the epilimnion of

the central basin of 0.010 kg @h . (Chapter 3.4 demonstrates the !1

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 37 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 38 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

(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 1.5×10 kg @h . Proportionate -3 !1

reduction of the DCB load on the epilimnion gives STRLD(1) =

8.5×10 kg @h . Given this modification, EXAMS predicted a -3 !1

larger concentration of DCB in the hypolimnion (13.5 ng/L), and
40

Method Conc

ng/L

Mass

kg

Volatile

kg/yr

Export

kg/yr

Measured 11.2 38. 60. 27+1

10.7 37. 59.4 28.2

Partial load

hypolimnion

10.7(E)

13.5(H)

44. 59.4 28.2

DDCB O 2 /D 12.9 44.2 53.7 33.9

9.1 31.3 63.7 24.0

KO 2=20cm/h

V W =30m/h

1.95 6.69 82.5 5.13

Table 8. Results of EXAMS simulations of the behavior of DCB (1,4-

dichlorobenzene) in Lake Zurich, Switzerland

 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:

 1 7.3 20.00 1.074E-05 1.074E-05 1.759E-04 0.00

 2 29. 80.00 1.074E-05 1.074E-05 1.759E-04 0.00

 ======== ======

 37. 99.33

 and in the Benthic Sediments:

 3 0.24 100.00 1.798E-04 1.065E-05 1.744E-04 0.00

 ======== ======

 0.29 0.78

 Total Mass (kilograms) = 36.75

 ---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

 ** Excludes complexes with "dissolved" organics.

Exams Output Table 8. Predicted concentration and resident

mass of DCB.

 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: 1.065E-05 mg/L dissolved in pore water;

 maximum total concentration = 1.798E-04 mg/kg (dry weight).

 Biota (ug/g dry weight): Plankton: Benthos:

 Fate:

 Total steady-state accumulation: 36.8 kg, with 99.33%

 in the water column and 0.67% in the benthic sediments.

 Total chemical load: 0.24 kg/ day. Disposition: 0.00%

 chemically transformed, 0.00% biotransformed, 67.79%

 volatilized, and 32.21% exported via other pathways.

 Persistence:

 After 216. days of recovery time, the water column had

 lost 50.52% of its initial chemical burden; the benthic zone

 had lost 19.55%; system-wide total loss of chemical = 50.3%.

 Five half-lives (>95% cleanup) thus require ca. 35. months.

Exams Output Table 9. EXAMS summary of DCB in Lake Zurich.

a resident mass of 44 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

b b from molar volume at the normal boiling point (V ), and V can

c

itself be estimated from V , molar volume at the critical

temperature c(Reid et al. 1977). V for p-DCB is 372 cc/g-mole;

Vb c = 0.285(V ) (Tyn and Calus method) = 140.9 cc/g-mole.

1.048

The aqueous diffusivity of p-DCB at 25 EC, computed via the

Hayduk-Laudie revision of the Othmer-Thakar relationship, is

(13.26×10 ) (0.8904 )(140.9 ) = 8.46×10 cm @s , !5 !1.4 !0.0589 !6 2 !1

taking the viscosity of water at 25 EC as 0.8904 cp (Weast

1971:F-36) 2. The diffusivity ratio D(DCB)/D(O ), given the

diffusivity of molecular oxygen D(O2) as 2.41×10 cm @s , is !5 2 !1

0.35; its square root is 0.59.

Substituting these values for EXAMS’ molecular weight transport

index

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.

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.

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.

Banks, 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

Engineering Division, Proc. ASCE 103(EE3):489-504.

Bird, R. B., W. E. Stewart, and E. N. Lightfoot. 1960. Transport

Phenomena. John Wiley and Sons, Inc., New York.

Brunskill, G. J., and D. W. Schindler. 1971. Geography and

bathymetry of selected lake basins, Experimental Lakes
41

Area, northwestern Ontario. Journal of the Fisheries

Research Board of Canada 28:139-155.

Burns, L. A. 1982. Identification and evaluation of fundamental

transport and transformation process models. Pages 101-126

in K. L. Dickson, A. W. Maki, and J. Cairns, Jr., editors.

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,

editor. 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.

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. pDichloro-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 (EE1):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 airsea 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 of the Fisheries Research

Board of Canada 28:157-169.

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, D.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
42

and Estuaries. U.S. Environmental Protection Agency,

Washington, D.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 “PHOTO1,” 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

3

species (RH 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 energy by a pollutant 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” Ö; 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

po ëllutant’s absorption spectrum å (EXAMS’ input ABSORG), and

its quantum efficiency for photochemical transformations Ö

(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.

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:

(2-89)

where Eo = photon scalar irradiance, photons cm s

!2 !1

z = depth, m (EXAMS variable DEPTHG)

K = diffuse attenuation coefficient for irradiance, /m, and

(2-90)

(Smith and Tyler 1976), where

D is the mean optical path per unit z (dimensionless),
43

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, and over 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:

(2-91)

where E(0) is the irradiance at the top of the layer. The rate of

light absorption Ew (photons cm s ) in the layer is !2 !1

(E(0)!E(z)), or

(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: !1 !1

(2-93)

where A = 6.023×10 photons/mole (Avogadro’s number). 23

Denoting E(0)/A as Io (Einsteins cm s ), the volumetric !2 !1

absorption rate Iw (in E L s ) is thus !1 !1

(2-94)

The electronic absorption spectra of synthetic organic chemicals

are usually reported as (decadic) molar absorptivities or

extinction coefficients å, with units (cm (mole/liter) ) or !1 !1

(cm M ) (EXAMS’ input variable ABSORG). The defining !1 !1

equation is:

(2-95)

where Ab is the absorbance measured in a spectrophotometer, l

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 ( 

!1

total rate of light absorption in the water body then becomes, by

substitution into Eq. (2-94),

(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:

and the rate of light absorption by the pollutant Ia, in E L s ,

!1 !1

is:

(2-97)

At trace levels of the pollutant (EXAMS’ operating range), by

definition the quantity (230.3å[P]) << a, and (a + 230.3å[P]) can

be approximated by the natural absorption coefficient of the

water body, a. Equation (2-97) in these circumstances reduces

to:

(2-98)

The quantity Ia/[P] is called the “specific sunlight absorption

rate” of the pollutant, Ka (Zepp 1980).
44

ë center

(nm)

Ä ë

(nm)

çw çp çdoc ë center

(nm)

Ä ë

(nm)

çw çp çdoc

280.0 2.5 0.288 145 8.35 380.0 10.0 0.0220 46 1.96

282.5 2.5 0.268 138 8.05 390.0 10.0 0.0191 42 1.69

285.0 2.5 0.249 132 7.77 400.0 10.0 0.0171 41 1.47

287.5 2.5 0.231 126 7.49 410.0 10.0 0.0162 39 1.27

290.0 2.5 0.215 120 7.22 420.0 10.0 0.0153 38 1.10

292.5 2.5 0.194 115 6.97 430.0 10.0 0.0144 35 0.95

295.0 2.5 0.174 109 6.72 440.0 10.0 0.0145 32 0.82

297.5 2.5 0.157 106 6.48 450.0 10.0 0.0145 31 0.71

300.0 2.5 0.141 101 6.25 460.0 10.0 0.01566 28 0.61

302.5 2.5 0.133 95 6.03 470.0 10.0 0.0156 26 0.53

305.0 2.5 0.126 90 5.81 480.0 10.0 0.0176 24 0.46

307.5 2.5 0.119 85 5.61 490.0 10.0 0.0196 22 0.40

310.0 2.5 0.105 80 5.41 503.75 17.5 0.0295 19 0.33

312.5 2.5 0.0994 78 5.21 525.0 25.0 0.0492 14 0.24

315.0 2.5 0.0952 75 5.03 550.0 25.0 0.0638 10 0.17

317.5 2.5 0.0903 72 4.85 575.0 25.0 0.0940 8 0.12

320.0 2.5 0.0844 70 4.68 600.0 25.0 0.244 6 0.08

323.1 3.8 0.0793 68 4.47 625.0 25.0 0.314 5

330.0 10.0 0.0678 64 4.05 650.0 25.0 0.349 8

340.0 10.0 0.0561 59 3.50 675.0 25.0 0.440 13

350.0 10.0 0.0463 55 3.03 706.25 37.5 0.768 3

360.0 10.0 0.0379 55 2.62 750.0 50.0 2.47 2

370.0 10.0 0.0300 51 2.26 800.0 50.0 2.07 0

Table 9. w p Spectral intervals used in EXAMS, and spectral absorption coefficients of water ç (m ), chlorophylls + pheophytins ç (m (mg/L) ), !1 !1 !1

doc s

(humic) dissolved organic carbon ç (m (mg/L) ). Suspended sediments ç taken as uniformly 0.34 m (mg/L) . See section 2.3.3.2 !1 !1 !1 !1

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 s ) is !2 !1

found by integration of Eq. (2-91) over the depth of the layer z,

followed by division of the resulting integral by z:

(2-99)

where E(0) is the intensity at the top of the layer. Em (photons

cm s ) can be converted to molar units (Im, E cm s ) via !2 !1 !2 !1

division by Avogadro’s number A:

(2-100)
45

which, as Io = E(0)/A,

The term Io(1-exp(Daz))/az in this equation is embedded in Eq.

(2-98); Ia 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)

Light absorption is a one-quantum process, so Ia (E L s ) also !1 !1

gives the rate of electronic activation of the pollutant (M/s). Ka,

the specific sunlight absorption rate, thus has units s . If each !1

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 transformation

process is called the “reaction quantum yield” Ö (EXAMS input

parameter QYIELD), with (“dimensionless”) units of

moles/Einstein. Zepp (1978) has described procedures for

measuring Ö of organic chemicals in dilute air-saturated aqueous

solutions; the measurement of Ö 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:

(2-103)

The quantity (Ö)(Ka) 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,

PHOTO1, 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 !1 !1

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 a pseudo-first-order rate constant of 3.7×10 d . (A -3 -1

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

0 DP

the ambient light (I.I ), this observed rate constant (K (0), i.e.,

K at z=0) is equivalent to:

KDP(0) = ÖKa = 2303 Ö å Io D

integrated across the solar spectrum. The average photolysis rate

con DP stant K (z) in a layer of appreciable depth z is then

(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 !1

annual average pseudo-first-order photolysis rate constant under

cloudless conditions at a specified latitude RFLAT. RFLAT is

expressed in degrees + tenths. For example, Menlo Park,

California, is at 37E27' 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 !1

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 D is the mean optical path per unit vertical

depth in the system; D may be 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

term “inverted value of an average cosine” originated from a
46

generalization of the fact that the path length of the solar beam

is given by the secant (1/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 a 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 44E. The total incident photochemically active

irradiance (wavelengths #370 nm) is dominated by skylight at

solar altitudes less than 45E, and irradiance at wavelengths #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 coefficient (a, units /m) for

each spectral interval in each water column compartment via Eq.

(2-105):

(2-105)

The spectral specific absorption coefficients supplied with the

p wpdoc s rogram (ç , ç , ç , ç ) 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, Chl a can attain 2 mg/L

((Talling 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

coef pficients (ç 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 doc ’ specific absorption coefficients for DOC (ç ) (Table 9),

representative of freshwater aquatic humus, were calculated

from the expression

çdoc = 0.71 exp (0.0145(450-ë))

where ë 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 331 nm light,

via actinometer experiments conducted on a series of 6 natural

sediment suspensions. The specific absorbance coefficient

((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 !1 !1
47

vector of speci sfic sediment absorption coefficients ç ; 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 PHOTO1 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 spectrum of 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 (Ö; 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” Ö, 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 daughter products per mole of photons

absorbed.

The (secondary) transformation 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 Ö 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 Ö. 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).

Ö 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 Ö on the pH of the medium. EXAMS allows for

the entry of a separate value of Ö (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 å(R) and

quantum yield Ö(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)

The apparent Ö of an organic acid or base will depend on the pH

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

3 2 its uncharged RH molecule and its anion RH (see Chapter !
48

2.2.4) as:

(2-107)

and

(2-108)

where á 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 2 and the RH anion. At pH 4.5, the compound !

would di 3 2stribute as 76% RH and 24% RH . Given, for !

example 3 2, å of RH as 5000, and å of RH 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: 3 2 (0.76)(5000)(Ö(RH )) + (0.24)(10000)(Ö(RH )) = 10 !

2: 3 2 (0.50)(5000)(Ö(RH )) + (0.50)(10000)(Ö(RH )) = 20 !

giving a reaction quantum yield Ö for the uncharged molecule

RH3 2 of 1.5×10 , and Ö(RH ) = 3.9×10 .

!4 ! !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” phototransformations 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

á (Chapter 2.2.4). Once in possession of 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))

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 i, EXAMS computes the total contribution of that species

to the overall direct photolysis rate constant by fixing the value

of i, and summing the expression

{á(i,k) × QYIELD(i,k) × Ka(i)}

over the k=1 (dissolved), k=2 (sediment sorbed), plus k=3 (DOCcomplexed) 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.

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’
49

Center Spectral Absorption Coefficients å, cm-1M-1

ë, nm pH 5.1 pH 7.0 pH 8.9 RH3 2 RH -

pH 7.0

297.5 14 18 193 14 3767 16

300.0 3.8 7.2 173 3.8 3551 6.0

302.5 2.4 3.8 150 2.4 3097 4.4

307.5 0 2 92.6 0 1941 1.2

310.0 2 63.5 1331 0.8

312.5 1 40.3 845 0.5

315.0 0 24.1 505 0.3

317.5 13.0 272 0.2

320.0 6.2 130 0.08

323.1 3.8 80 0.05

330.0 0

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

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 (å, 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); at pH 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.

The observed absorption coefficient at any fixed wavelength is

3

the sum of absorbance attributable to the uncharged (RH )

molecule, plus 2 that attributable to the anionic (RH ) species. !

Denoting the observed absorption coefficient as , this sum

can be expressed algebraically:

(2-110)

This equation can be used to estimate the separate absorbances

of the RH3 2 and RH molecules. For example, at 297.5 nm, for !

pH 7.0:

3 2 åo= 18 = (0.999369) å(RH ) + (6.31×10 ) å(RH )

!4 !

and at pH 8.9,

3 2 åo = 193 = (0.952) å(RH ) + (0.0477) å(RH )

!

Simultaneous solution of these equations yields

3

å(RH ) = 15.6 and

2

å(RH ) = 3734. !

The uncharged molecule is present as 99.9992% of the total

concentration at pH 5.1, so the pH 5.1 spectrum can be taken as

3

a direct experimental measurement of å(RH ). Given this, the

2

absorption spectrum of the (RH ) anion can be calculated using !

the pH 8.9 spectrum and Eq. (2-110). For example, at 297.5 nm,

193 2 = (0.952)(14) + (0.0477) å (RH )

!

gives 2å (RH ) = 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) + (6.31×10 )(3767) = 16 (cm M )

!4 !1 !1

as compared to the experimental value of 18 cm M . The !1 !1

3

computed absorption spectra of the uncharged RH molecule and

2

the RH anion of p-Cresol are also shown in Table 10, along !

with the projected absorption coefficients at pH 7.0. The

absorption spectra of the individual molecules are the appropriate

data for entry into EXAM S’ chemical data base (input vectors

ABSORG).

2.3.3.5 Effects of sorption on photolysis rates

The effects on direct photolysis of sorption to suspended material

are incorporated into EXAM S via separate disappearance quantum

yields (QYIELD) for the sorbed forms of each dissolved species

3 2 (RH , RH , 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 EXAM S

allows for alternatives to this assumption via the QYIELD

chemical input vector. (This capability is only available for

compounds for which the absorption spectrum is known.)

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
50

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 EXAM S’ 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 EXAM S 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

microenvironment. 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 per unit frequency range) !3

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 and Zepp 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

(Oliver et al. 1979).

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 et al. 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 EXAM S.

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(1,1) = 1.0, QYIELD(2,1) =

10.0). The distribution function (DFACG) was taken as 1.20;

EXAM S 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

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
51

Kp

L/kg

% of Water

Column in

Dissolved

State

% of Total

Resident in

Benthic

Zone

Pseudo-First-Order Halflives, h

Water

Column

Whole

System

0 100 0.29 8.50 8.52

1 100 0.87 8.50 8.57

10 100 5.8 8.49 9.01

100 99.9 36.9 8.42 13.4

1000 99.0 85.2 7.80 52.9

10000 90.9 98.2 4.68 253

100000 50.0 99.7 1.55 452

1000000 9.09 99.8 0.93 492

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)

[S]

mg/L

% of Water

Column in

Dissolved

State

% of Total

Resident in

Benthic

Zone

Mean

Iz/Io

(% )

Pseudo-First-Order

Halflives, h

Water

Column

Whole

System

0 100 36.96 84.8 1.18 1.87

1 100 36.96 59.3 1.68 2.67

10 99.9 36.93 11.8 8.42 13.4

100 99.0 36.73 1.22 75.2 119

1000 90.9 34.77 0.12 449 688

10000 50.0 22.67 0.012 1484 1918

Table 12. Effect of suspended sediment concentration [S] on net

photoreactivity when partition coefficient Kp = 100 L/kg

Kp

L/kg Suspended Sediment Concentration, mg/L

0 1 10 100 1000 10000

0 1.18 1.69 8.52 82.2 819 8182

1 1.19 1.70 8.57 81.8 749 4157

10 1.25 1.79 8.93 79.1 437 861

100 1.87 2.65 12.3 65.4 125 209

1000 8.06 10.5 29.1 51.7 63.3 137

10000 69.9 50.0 45.9 49.0 56.6 130

100000 689 89.6 49.2 48.6 55.9 129

1000000 6876 97.4 49.6 48.6 55.8 129

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

reactive than dissolved species

(0–1×10 L/kg) are given in Table 12. These results are given in 6

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 captured by the (dark) benthic subsystem. The

system-level half-life of the compound increases steadily with

increasing Kp, despite the greater photoreactivity 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).

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).
52

The effects of sorption to organic slicks at the air-water interface

are not included in EXAM S 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

and Baughman 1978).

EXAM S 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 EXAM S as a vector

ë ë (“WLAM” for W ) of 46 photon irradiances. W 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).

EXAM S corrects the input irradiance for effects of cloud cover

using the empirical relationship:

(2-111)

(Büttner 1938), cited from (Zepp 1980); see also (Schultz and

Gräfe 1969).

The cloudiness (EXAM S’ 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:

WMO

Code

Oktas Tenths

0 (clear) 0 0

1 1 okta or less,

but not zero

1/10 or less, but not zero

2 2 oktas 2/10 - 3/10

3 3 oktas 4/10

4 4 oktas 5/10

5 5 oktas 6/10

6 6 oktas 7/10 - 8/10

7 7 oktas or more,

but not 8 oktas

9/10 or more, but not

10/10

8 8 oktas 10/10

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

W ëat 39 of the wavelength intervals adopted for EXAM S. (They

reported “midseason,” “midday” spectral irradiance at 40E N

latitude. For EXAM S, such values must be reduced by a day

length factor (daylight hours/24), and sinusoid averaging over the

photoperiod (i.e., multiplied by 2/ð).

Procedure PHOTO1 begins from near-surface pre-computed

photolysis rate constants (KDP), and thus does not use the

ë

spectral irradiance vector W in its computations. PHOTO1 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 1.038×10 cal cm yr at the pole, to

5 !2 !1

2.744×10 langley yr at the equator. Regression of the (8) 5 !1

estimates on:

Y = a + b cos(2L)

where L is the latitude in radians, yielded a = 1.917×10 , b = 5

8.705×10 , and accounted for 99.7% of the variation in total 4

irradiance (Y) with latitude. In procedure PHOTO1, the correction

term for KDP is computed as:

For example, for a rate constant measured at the equator and a

polar ecosystem,
53

Figure 7. Improper segmentation: incomplete vertical definition

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 PHOTO1 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

exi 3 4 sting ionic species (RH , RH , 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 PHOTO1 are inherently

less accurate than wavelength-specific computations based on

the chemical’s absorption spectrum (PHOTO2), because PHOTO1

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 (Wë). 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 PHOTO1, the incident light field has a nominal

value of 1.0, because KDP has built into it the clear-sky

irradiance at latitude RFLAT.) PHOTO1 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. EXAM S 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-1)

compartment is another E or H segment, irradiance at the bottom

of the (J-1) 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.

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

compartment 3, EXAMS checks the compartment type of the (J-1)

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-1) compartment 4, finds it to be (B)enthic, and starts

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.

Figure 8. Proper segmentation: complete vertical definition.
54

In this case, upon initiation of computations for the hypolimnion

(now numbered 6), EXAMS finds that the (J-1) 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 PHOTO1, 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 a percentage 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 for monitoring 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.

Büttner, 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 dissolved pollutants. 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

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., and K. Gräfe. 1969. Consideration of sky ultraviolet

radiation in the measurement of solar ultraviolet radiation.

Pages 359-373 in F. Urbach, editor. The Biologic Effects of

Ultraviolet 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. EPA600/7-78-074, 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.
55

Journal of Chemical Physics 37:814-822.

Talling, J. F., R. B. Wood, M. V. Prosser, and R. M. Baxter.

1973. The upper limit of photosynthetic 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 3 of hydrolytic pathways: specific-acid (H O ) 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:

(2-112)

where R and R’ can be any alkyl or acyl moiety. Although this

equation accurately depicts the stoichiometry of ester hydrolysis,

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.

Elect 3ron-deficient chemical species (e.g., hydronium ions, H O )

+

are called “electrophiles.” Electrophiles are particularly attracted

to atoms with negative charge, to a lone 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

atom of this “polar” molecule has a lone pair of electrons.

The mechanisms of the formation and hydrolysis of organic

esters have been reviewed by Kirby ). 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 protonates the

carbonyl (C=O) oxygen. The protonated ester then undergoes a

nucleophilic addition of water to give a tetrahedral 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
56

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 á, 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 hydrolyzes 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

similar to 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 hydrolytic 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 hydrolytic transformation is thus the sum of

obs three competing reactions, and the observed rate constant (K ,

units /h) can be computed as the sum of contributions from

acid-catalyzed (KAH), neutral (KNH), and base-catalyzed (KBH)

reactions (Wolfe 1980):

(2-113)

Kobs is a pseudo-first-order (h ) rate constant under the specified

!1

environmental conditions (pH, pOH) in pure water. After

incorporation of the effects of ionization and sorption on

obs reactivity, K becomes the hydrolytic contribution to the overall

pseudo-first-order rate constant K of Eq. (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 !1 !1

constant, has units h . The environmental data for this !1

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

nucleophi 1 2 1lic substitution (SN and SN ) 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 obs solution can be used to determine K 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

obs significant. The pH-rate profile, a plot of log K vs. pH, then

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).
57

Figure 9. Hydrolysis pH-rate profile of phenyl acetate at 25EC.

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 s ( Figure -8 !1

9). EXAMS’ chemical input parameter (KNH, units /h) would be

6.6×10 (s )×3600 (s/h)=2.38×10 h .

-8 !1 -4 !1

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) “preexponential” 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 (Ea) 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 = 2.38×10 h at -4 !1

25E C (Figure 9), A=2.38×10 /exp(-20000./(1.99×298.15)) = -4

1.04×10 h , and log A, the EXAMS input (KNH) = 11.02 h .

11 !1 !1

In this instance, if EXAMS were loaded with KNH=2.38×10 and !4

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!((1000×ENH)/4.58(TCEL+273.15))

or, at TCEL = 25EC,

log KNH = 11.02!(1000×20)/(4.58×298.15) = !3.626

and

KNH = 2.36×10 h .

-4 !1

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):

(2-114)

and

(2-115)

in which Ea has units of calories/mole, and A is in h .

!1

(Because the RT term is only about 600 cal/mol at room

temperature, however, in many cases reported values of H are in

fact uncorrected Arrhenius activation energies (Ea).)

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 15EC 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 A (Eq.(2-115)) would be:

log A = !4.1/4.58 + log(288.15) + 14.319, yielding log A =

15.88 h = KAH.

!1

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 a 2lkaline carboxyl ester hydrolysis plus an E elimination

reaction kinetically dependent on hydroxide ion concentration

[OH ] !(Wolfe et al. 1977). These authors computed entropies

and (uncorrected) 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 ) = (7.5018×10 )(T)×exp(-H/RT)×exp(S/R), !1 13

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 0E

and at 27E C; it was 0.067 and 5.5 M s , respectively. The !1 !1

activation energy and frequency factor can be easily computed

from these data, giving KBH = 23.67 h and EBH = 26.6

!1

kcal/mol.
58

Figure 10. Hydrolysis pH-rate profile for a substituted

2-Phenyl-1,3-Dioxane. Data from Bender and Silver 1963.

2.3.4.2 Ionization 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 3×7 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 studied by Bender and Silver (1963). All

the acetals in the series showed the usual acid-catalyzed

hydrolysis, but those members containing an o- or p-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 o obs f 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.

From a comparison of the kinetics of o- and p-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 obs species. The uniformity of K 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 hydrolysis (KAH) of the uncharged and the

anionic species. The existence of the unionized species and the

phenolate anion is signaled by setting SPFLG(1) 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 !1 !1

(Figure 10). This information is loaded to EXAMS by setting

KAH(1,1) to 540, and KAH(1,5)=4.68E5. The first index (Chapter

2.3.4.3) simply denotes the form of the compound (1=dissolved).

The second index specifies the ionic species to be transformed.

Subscript (1,1) thus specifies the unionized dissolved HND as

3

reacting at rate (540)[H O ] h , and subscript (1,5) specifies the + !1

pheno 3late anion as reacting at rate (4.68×10 )[H O ] h .

5 + !1

2.3.4.3 Sorption effects

Sorption of a compound with sediment phases or complexation

with DOC removes the compound to a microenvironment 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’-di chl orobenz o p h e none t oward DDMU

(1,1-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

microenvironment 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

1 1 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

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 (Chapter 2.3.4.2). EXAMS therefore provides for the entry

of rate constants (or Arrhenius functions) that specify the
59

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, and DOC-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)

where [CW W ] is aqueous concentration (mg/L) and K is a

first-order (or pseudo-first-order) rate constant (units h ). !1

Similarly, the rate of transformation of the sorbed phase of the

compound can be taken as:

(2-117)

where S[C ] is the concentration of the sorbed phase of the

compound (mg/kg dry sediment S) and K is a first-order rate

constant.

Under EXAMS’ assumption that a rapid (i.e., faster than the

W S reaction rates) sorption equilibrium is maintained, [C ] and [C ]

are T T in constant proportion to [C ], where [C ] 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:

(2-118)

where ñ 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,

(2-119)

and

(2-120)

where á1 p =1/ 2 p p p(1+ñK ), and á =ñK /(1+ñK ),K the partition

coefficient as described in Chapter 2.2.4. Substitution of Eq. (2-

119) and Eq. (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

Kw=-(ln0.5)/33=0.021 h .

!1

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(1) 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 100 mg/L suspension

of the sediment. (So long as local sorption equilibrium is

maintained, the half-life can be ascertained by following either

CW S T or C over time.) The fraction of C present as dissolved and

as psorbed NOC can be computed using ñ and K . The

sediment/water ratio ñ is (100 mg/L)×(10 kg/ml) = 10 kg/L;

-6 -4

1 p the dissolved fraction is thus á =1/(1+ñK ) = 1/(1+(90000×10 )) -4

= 0.10. 2 p p Similarly, the fraction sorbed is á =ñK /(1+ñK ) =

(90000×10 )/(1+(90000×10 )) = 0.90. -4 -4

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

proport 1ion with á , 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 S on the rate of transformation of the compound, K

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.

When residence in a sorbed state inhibits the transformation

reaction, the observed rate constant Kobs will decrease in

proportion with the residual water concentration of the

compound. From 1 2 Eq. (2-121), Kobs = á Kw + á Ks. In this

example 1, á = 0.10, and, if Ks = 0, then Kobs = (0.10)(0.021) =

0.0021 h .

!1

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 ô is given

by:

ô = -(ln 0.5)/(0.0021) = 330 hours.
60

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 may be 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 = -(ln 0.5)/(150) = 4.621×10 .

-3

Given K = obs á1 W 2 S K + á K , then

4.621×10 = (0.1)(0.021) + (0.9) Ks -3

and Ks = 2.80×10 . The rate of reaction of NOC in the sorbed -3

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(2,1)=2.8E-3.

Second-order pH-mediated reactions occurring on sediments are

accommodated in EXAMS in a wholly analogous way. EXAMS

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

3

sorption and DOC complexation on first-order, [H O ] 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. Photoreactivity 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-BrisbaneToronto.

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 Science and Technology

11:88-93.

2.3.5 Indirect Photochemistry, Oxidation and

Reduction

Direct photolysis is not the sole pollutant transformation process

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).

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.

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.
61

Source Singlet Oxygen

 (M×10 )

13

Peroxy Radical

(M×10 )

9

Hydroxyl Radical

(M×10 )

17

Aucilla

River, FL

18 2.8 1.8

Okefenokee

Swamp, GA

22

Mississippi

River, LA

5

Coyote

Creek, CA

9.1, 5.0 1.6

Boronda

Lake, CA

9.5, 0.45 0.15

Table 14. Steady-state concentration (moles/liter) of oxidants in

some natural waters

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

(OHC) and peroxy (ROOC) radicals (Mill et al. 1978), singlet

oxygen O2) (1 (Zepp et al. 1977b). 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 et al. 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,

!1 !1

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 × OXRADL(J) !1

The input parameter KOX is a 3×7 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 Chapter 2.3.4.

Effects of temperature are entered via a parallel matrix of

Arrhenius activation energies EOX (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. (1977b) 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 et al. 1977b, Mill et al. 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 for singlet !13

oxygen, 10 for peroxy radicals, and 10 for hydroxyl radicals. !9 !17

More recent investigations have found surface concentrations of

singlet oxygen from about 9×10 M down to 4×10 M !13 !15 (Haag

and Hoigné 1986). Even at these low concentrations, 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 to 10 M, with reaction with !15 !18

hydroxyl radical potentially accounting for as much as 15% of

total carbaryl dissipation in some shallow water systems

(Armbrust 2000).

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 5×10 to 10 M !15 !13 (Huang and

Mabury 2000); in the Greifensee (Switzerland) summer

carbonate radical concentrations are about 3×10 M !14 (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:

I/Io = (1-exp(-kZ))/kZ

where I/Io is the average light intensity expressed as a fraction

of its surface value (Io), 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 × (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
62

loaded in the data bases.

2.3.5.2 Singlet Oxygen

Sing 2let oxygen ( O ), an electronically excited state of the 1

dioxygen molecule (O2), is formed in natural waters by the

transfer of solar energy from illuminated DOM. EXAMS calculates

2

the concentration of O in each surface water segment, using 1

methods described by Zepp and coworkers (Zepp et al. 1985,

Zepp 1988b), corrects for depth and couples the result to a 3×7

matrix of second-order reaction rate constants K1O2 for

appl 2ication in the mass balance Eq. (2-1). Generation of O

1

begins with the production of electronically excited triplet states

(S*) via absorption of sunlight energy by dissolved organic

matter (S). The wavelength-specific quantum yields ö used in

EXAMS for this process were computed from the relationship

ö=0.015 exp(0.01(366-ë), where ë is the wavelength in nm. The

pre-exponential factor in this relationship was derived from the

average value of ö (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 ö 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 å of Table 9.

ë (nm) ö ë (nm) ö

280 0.035 400 0.011

300 0.029 450 0.006

312.5 0.026 490 0.004

350 0.016 650 0.0009

Table 15. Example quantum yields for production of triplet

sensitizer S* from DOC

S* decays back to its ground state by spontaneous decay (with

3

first-order rate constant KdecS , with units s ) and self- !1

quenching interactions with other components of the DOM

(second-order process rate constant Kqnch with units M s ). !1 !1

The second-order rate constant (Kox) for the production of

singlet oxygen by energy transfer to dissolved oxygen from S*

is between 1×10 and 3×10 (R. G. Zepp 2000, oral comm.), for 9 9

EXAMS it is taken as 2×10 M s 9 !1 !1 (Zepp et al. 1985). Singlet

oxygen undergoes rapid physical quenching by water to ground

2

state dioxygen with a pseudo-first-order rate constant KdecO of

2.5×10 s ( 5 !1(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 Iave is the

average light intensity. The quantity (ö å Iave) is integrated

across the sunlight spectrum during EXAMS’ computations.

(2-122)

(2-123)

(2-124)

The 2 steady-state concentration of O in sunlit surface waters is 1

given by

(2-125)

where

(2-126)

Increasing the oxygen concentration from 2.6×10 M (air !4 

saturated) to 1.2×10 M (oxygen saturated) decreases [S*(ss)] !3 

by some 4× (Zepp et al. 1985). Given Kox[O2], the sum of the

othe 3r terms in the denominator of Eq. (2-126) (KdecS and

Kqnch[DOC]) is 1.6×10 . These experiments were conducted 5

using [DOC] of about 20 mg/L (R. G. Zepp 2000, oral comm.).

For EXAMS, the terms were equally partitioned, thus giving

KdecS3 = 8.0×10 (s ) and Kqnch = 4,000 (s (mg/L) ) for 4 !1 !1 !1

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 2 concentrations of O

1

(Zepp et al. 1977b, Haag and

Hoigné 2 1986). The calculated O concentrations (Table 16) are

1

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 noncolored DOC (R. G. Zepp 2000, oral com.).
63

Water 2[DOC] [ O ]ss ×10 M 1 14

mg/L Lit Val EXAMS

Lentic

Türlersee 8.3 2.8 9.7 1

Greifensee 3.5 3.3 4.5 1

Lützelsee 7.9 5.4 9.3 1

Etang de la

Gruère1

13 11.6 14.

Okefenokee 24 17.8 6.9 2

Lotic

Rhine 3.2 2.4 4.1 1

Kleine Emme 3.2 4.1 4.1 1

Glatt 4.1 4.6 5.2 1

Aucilla 24 14.6 6.9 2

Ecofina 41 15.4 9.0 2

Fenholloway 77 13.8 11. 2

Wakulla 6 0.16 2.4 2

St. Marks 9 0.49 3.3 2

Mississippi 14 0.41 4.8 2

1. June Swiss values from Haag and Hoigné 1986, corrected to

full day values by day length and factor of 2/ð

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

Table 16. Comparison of EXAMS’ singlet oxygen estimates

with literature values

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

transformation half-lives of nitroaromatics 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 3×7 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. Hoigné. 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. Hoigné, 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. Hoigné, 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., and R. G. Zepp. 1988. Reactivity of the carbonate

radical with aniline derivatives. Environmental Toxicology

and Chemistry 7:265-274.

Macalady, D. L., P. G. Tratnyek, and T. J. Grundl. 1986. Abiotic

reduction reactions of anthropogenic chemicals in anaerobic

systems: A critical review. Journal of Contaminant
64

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, and D. 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. Colón, C. L. Tebes-Stevens, and E. J. Weber.

2000. Effect of redox zonation on the reductive

transformation of p-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. Schüürmann 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 DDD. 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

skepticism is 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)
65

where μ is called the “specific growth rate” and N 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

(2-128)

in which [S] is the concentration of the growth limiting substrate,

μmax is the “maximum specific growth rate” obtaining when [S]

S

is present in excess (i.e., non-limiting), and K , the “halfsaturation constant” is that value of [S] allowing the population

max to grow at rate μ /2. 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 = -Y dS, where Y is the

“yield coefficient” in cells or biomass produced per unit S

metabolized. Taking

(2-129)

gives dS/dt = -(μ/Y) N, or, substituting Eq. (2-128) for μ,

(2-130)

The observed microbial growth yield Y is 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 Y can 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 transformation rates then

gave max Sthe Monod parameters μ = 0.37 /h, K = 2.17 ìmol/L

(0.716 mg/L), and Y = 4.1×10 cells/ìmol (1.2×10 cells/mg). 10 11

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

max their results in terms of Monod parameters, finding μ = 0.62

S

/h, Y = 1.8×10 cells/mg, and K = 0.84 mg/L. 9

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 trace concentrations of pollutants (i.e.,

S S [S] << K ), however, the term (K + [S]) in the denominator of

Eq. S(2-130) can be approximated by K , giving the linear

approximation

(2-131)

This formulation is similar to the “second-order” equations used

to describe the kinetics of chemical reactions, and the term

μmax S/(Y K ) can by analogy be termed a second-order biolysis rate

con B2 stant K , with units /h/(cells/L) when population sizes N are

expressed in cells/L.

The propriety of this substitution can be readily evaluated. For

malathion, μmax S/(Y K ) = 0.37/(4.1×10 ×2.17) =4.16×10 10 -12

/h/(cells/L). Paris et al. B2 (1975) also computed values of K from

microbial population sizes and malathion transformation rates in

B2 their experimental studies, finding a mean value for K of

(2.6±0.7)×10 (95% confidence interval) /h/(cells/L) for a -12

series of 8 experimental determinations spanning a concentration

range from 0.0273 to 0.33 ìmol/L in malathion. The measured

KB2 max S differed from μ /(Y K ) 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

elabora max Stion of μ , Y, and K , when the region of concern is

S

restricted to levels of compound << K .

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

effluent Ss, K for p-cresol (0.84 mg/L) is uncomfortably close to

the highest measured concentrations.

The need for a thorough evaluation of the propriety of Eq. (2-
66

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]/dt) is first-order in

compound concentration [S] and in microbial activity, biomass,

or populat B2 ion size B, and in which the identification of K with

μmax S/(Y K ) 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 d[S]/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 1E to 29E 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

22E C, ranged from 4×10 to 9×10 cells/ml 2 5 (Paris et al. 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

B2 intervals (based on among-site variation) for K (/h/(cell/L)) and

the number of sites for these compounds were (5.42±0.97)×10-10

(2,4-DBE, 31 sites), (4.54±0.74)×10 (malathion, 14 sites), and -11

(2.63±0.72)×10 (chlorpropham, 11 sites). -14

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,

B2 the second-order biolysis rate constant (K ) for chlorpropham

is unaffected when population densities are augmented via

nu B2 trient broth supplementation, but the measured K 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

B2 rate of degradation by water-column populations, at K of

2.3×10 and 4.0×10 /h/(cell/L) respectively. Aerobic biolysis -10 -11

of chlorpropham by the sediment-derived populations was,

however, B2 almost an order of magnitude faster (K = 1.42×10 )

-13

B2 than biolysis by the water-column populations (K = 2.4×10-14

/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 s or mass -3 -1

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 true that, at

elevated pollutant concentrations, both a reduction in the

apparent first-order rate constant (Tinsley 1979:149ff), and

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
67

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 BNBAC 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

observed bacterial population densities in aquatic systems can be

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) in both cases. -1 -1

EXAMS internally converts benthic microbial population densities

to units (cfu/mL of water) commensurate with the units of the

rate constant KBACS. This conversion is executed via the

expression (BNBAC × 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)/10(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 4×7 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

10 single values, or a Q function can be invoked to depict the

effects of temperature on biolysis rates.

The Q10 values (i.e., increase in biolysis rate per 10E 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 25E C (in accordance with Subpart N guidelines), and

10 recalculates the second-order rate constant via the Q equation:

(2-133)

Because microbial communities frequently adapt their metabolic

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 important that 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 = á × KBAC × B, where

á 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 10 temperatures using the Q 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).

Biolysis rate data can alternatively be supplied as Aquatic

Aerobic and Anaerobic Metabolism half-lives (in days). When

no KBAC value is non-zero, these variables (AerMet and

AnaerM) respectively) are converted to KBACW and KBACS. 

AerMet and AnaerM are available for each chemical under
68

study. At RUN time they are converted to internal second-order

biolysis rate constants using properties of the water body. For

aerobic metabolism, the average bacterioplankton population

density is calculated and AerMet is converted to KBACW. For

anaerobic metabolism, the average surficial benthic bacterial

population density is calculated and AnaerM is converted to

KBACS. In both cases, the resulting KBAC is applied to all

dissolved forms (i.e., pH independence is presumed);

DOC-complexes and sorbed forms are presumed to be unavailable

for biolysis. In MODE 1 and MODE 2, the particular temporal

element of the water body data selected for the RUN is used in the

conversion (i.e., one of MONTHs 1-12 or 13 (average values)). In

MODE 3, the bacterial populations of June, July, and August are

considered. Aquatic metabolism inputs are only considered when

user-entered second-order biolysis rates are not already present

in the chemical data.

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 microorganisms. Pages 246-253 in J. M. Lynch and N. 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

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 Technology 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.

Pirt, 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. EPA600/7-78-074, 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-BrisbaneToronto.

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.

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
69

(2-134)

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.

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

run 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 rules discussed in

Chapter 2.3.1.1 are followed. For all other water-column

compartments, EXAMS simply looks back at the (J-1)

compartment. If this (J-1) 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 á,

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,

where Load is in kg/h, Inflow in L/h, Limit (kg/L) is one-half the

solubility of the least soluble chemical species, and â 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 sediment partitioning

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 (á) 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.
70

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 run 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

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 l g l l) is given (see Eq. (2-71)) by F = K (P /H - C ) where K is !1

g l the exchange constant (m h ), and both (P /H) and have C units !1

of (moles m ). By assuming the bulk atmosphere to be !3

uncontam g ginated (P / 0.0), the term (P /H) was discarded in the

development of EXAMS’ algorithm for computing volatilization

losses of pollutants from aquatic systems. In much the same

way, the gross pollutant loadings imposed by a contaminated

atmosphere l can be computed by taking C / 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 × [C] ×10 (kg/mg) !6

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 arise 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 á 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, the process

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 generating process is specified using the following 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-eight reactive 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 transformation 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-
71

mole formation, would be specified to EXAMS as follows:

CHPAR 1 1

TPROD 2 2

NPROC 4 6

RFORM 3 1

YIELD 0.5 1

EAYLD 0 0

Pathway: 1 2

Given these specifications, EXAMS calculates the resultant

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:

(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

(2-135)

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 m (10 L). The 3 7

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 m ; its aqueous volume is 250,000 liters of 3

water (Eq. (2-21)). Defining exchange between the benthic

subsystem and the water column via XSTUR = 10,000 m ,

2

CHARL = (1.05/2) = 0.525 m, and DSP = 10 m /h, the rate of !4 2

exchange of fluid volume between the water column and the

interstitial pore water (Chapter 2.3.1.4) is

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) = 952.4/10 = 9.524×10 /h, and 7 !5

Kt(b) = 952.4/250,000 = 3.810×10 /h !3

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/V by 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) 7 (952.4 × 1.00)/250,000 = 3.810×10 /h !3
72

 Ecosystem: Static 1-hectare pond, 1 meter deep

 Chemical: Unsorbed chemical subject to neutral hydrolysis

 ---------------------------------------------------------------

 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 2.0 100.00 0.199 0.199 0.00 0.00

 ======== ======

 2.0 99.32

 and in the Benthic Sediments:

 2 1.37E-02 100.00 2.740E-02 5.479E-02 0.00 0.00

 ======== ======

 1.37E-02 0.68

 Total Mass (kilograms) = 2.000

 ---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

 ** Excludes complexes with "dissolved" organics.

Exams Output Table 10. EXAMS output tabulation of steady-state

concentrations.

and the load factor on the water column resulting from

contamination of the benthic interstitial water is

INTINL(w,b) 7 (952.4 × 1.00)/10 = 9.524×10 /h 7 !5

(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

constant of 0.01 /h, the behavior of the chemical in the system is

given by:

d[Cw]/dt=(0.02×10 /10 )+9.524×10 (Cb)-(0.01 + 9.524×10 )Cw 6 7 !5 !5

d[Cb]/dt= 3.810×10 (Cw) - (0.01 + 3.810×10 )Cb !3 !3

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[Cb]/dt = 0.0, resulting in

2 equations in 2 unknowns:

- 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.

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 + 9.524×10 (Cb)) / 0.01009524 !5

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(Cw)/0.01381

= (0.00381×0.19811)/0.01381 = 0.05467

From the initial estimates, the second iteration proceeds to

compute a refined estimate of Cw and Cb:

Cw=(0.002+(9.524×10 ×0.05467))/0.01009524=0.19863

!5

Cb=(0.00381×0.19863)/0.01381 = 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 ), EXAMS continues with a third iteration of the !6

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 “TINIT” to time “TFINAL”
73

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

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 × Cw × WATVOL(w) × 10 (kg/mg) -6

= (0.01)(0.1986)(10 )(10 ) = 0.01986 kg/h 7 -6

in the water column, and

Fb = (0.01)(0.05479)(2.5×10 )(10 ) = 0.000137 kg/h 5 -6

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,

followed by conversion of this result to a percentage 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% of the 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 = - ln(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 × 2.00)/(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,
74

 Ecosystem: Static 1-hectare pond, 1 meter deep

 Chemical: Unsorbed chemical subject to neutral hydrolysis

 -----------------------------------------------------------------

 Table 19. Summary time-trace of dissipation of steady-state

 chemical mass following termination of allochthonous loadings.

 -----------------------------------------------------------------

 Time Average Chemical Concentrations Total Chemical Mass

 ----- ------------------------------- -------------------

 Hours Water Column Benthic Sediments Water Col Benthic

 -------------------- ------------------- --------- --------

 Free-mg/L Sorb-mg/kg Pore-mg/L Sed-mg/kg Total kg Total kg

 -----------------------------------------------------------------

 0 0.20 0.0 5.48E-02 0.0 2.0 1.37E-02

 12 0.18 0.0 5.43E-02 0.0 1.8 1.36E-02

 24 0.16 0.0 5.30E-02 0.0 1.6 1.32E-02

 36 0.14 0.0 5.11E-02 0.0 1.4 1.28E-02

 48 0.12 0.0 4.87E-02 0.0 1.2 1.22E-02

 60 0.11 0.0 4.62E-02 0.0 1.1 1.15E-02

 72 9.63E-02 0.0 4.34E-02 0.0 0.96 1.09E-02

 84 8.53E-02 0.0 4.06E-02 0.0 0.85 1.01E-02

 96 7.56E-02 0.0 3.78E-02 0.0 0.76 9.44E-03

 108 6.70E-02 0.0 3.50E-02 0.0 0.67 8.75E-03

 120 5.94E-02 0.0 3.23E-02 0.0 0.59 8.08E-03

 132 5.27E-02 0.0 2.97E-02 0.0 0.53 7.43E-03

 144 4.67E-02 0.0 2.73E-02 0.0 0.47 6.82E-03

Exams Output Table 11. Sample EXAMS output for dissipation of

chemical after removal of external loadings.

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:

In Mode 1, EXAMS computes the dissipation of the compound

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 step size, variable

order (1-12) Adams PECE method. In the event that the 2

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 step size

code employing multi-step 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 dissipation of

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 and relative 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 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 ?. At the expiration of 144 hours, the

 So called from their successive operations of prediction, resident pollutant mass had fallen from 1.986 to 0.467 kg in the 2

derivative evaluation, correction, and final derivative evaluation.
75

 Ecosystem: Static 1-hectare pond, 1 meter deep

 Chemical: Unsorbed chemical subject to neutral hydrolysis

 ---------------------------------------------------------------

 Table 20.01. Exposure analysis summary.

 ---------------------------------------------------------------

 Exposure (maximum steady-state concentrations):

 Water column: 0.199 mg/L dissolved; total = 0.199 mg/L

 Benthic sediments: 5.479E-02 mg/L dissolved in pore water;

 maximum total concentration = 2.740E-02 mg/kg (dry weight).

 Biota (ug/g dry weight): Plankton: Benthos:

 Fate:

 Total steady-state accumulation: 2.00 kg, with 99.32%

 in the water column and 0.68% in the benthic sediments.

 Total chemical load: 2.00E-02 kg/ hour. Disposition: 100.00%

 chemically transformed, 0.00% biotransformed, 0.00%

 volatilized, and 0.00% exported via other pathways.

 Persistence:

 After 144. hours of recovery time, the water column had

 lost 76.49% of its initial chemical burden; the benthic zone

 had lost 50.21%; system-wide total loss of chemical = 76.3%.

 Five half-lives (>95% cleanup) thus require ca. 14. days.

Exams Output Table 12. Example summary output table (Mode 1).

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

(Li, Eq. 10) of this subsystem from the water column.)

In a first-order system, the decrease of an initial mass of material

Qo over time is given by

Q(t) = Q(0) exp(-k × t), or

ln(Q(t)/Q(0)) = -k×t

where k is the first-order rate constant and t 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, that is,

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.

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

estimate of decontamination time.

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 (?). 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:

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 day

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 ( ln 0.5 / Hb) )

or 1.4% of the original benthic pollutant mass. The system-wide
76

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. 1971a. 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

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.
 I thank Dr. Frieda B. Taub of the University of Washington School of Aquatic and Fishery Science for 3

her material contributions to the development of these introductory EXAMS laboratories.

EXAMS Laboratory Exercises. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 77

3.0 Tutorials and Case Studies3

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 89) 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 82 and 83 , and prepare for Lab. 2 by

entering the Lab. 1 data for methyl parathion chemistry and its behavior in the standard pond on pages 86 and 88.

Ia. 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 78

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. The control variable “OUTFIL” is used to specify which output files are to be produced.

These may include both ecotoxicological exposure files (EcoToxC.xms or EcoToxR.xms) and ordered sets of annual maximum series of

standard, or user-specified (see variable “EventD”) exposure event durations (files EcoRiskC.xms or EcoRiskR.xms.)

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 180 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 paper copy of all the input and output tables (Tables 1-20, which may in some instances 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 82 and 83 , you may return to the introductory exercises to explore Mode 2 (initial

value approaches) and Mode 3 (seasonal effects).
EXAMS Laboratory Exercises. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 79

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 80

Diagram of EXAMS’ Constructed Farm Pond

Your questions:

(1)

(2)

(3)
EXAMS Laboratory Exercises. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 81

3.2 Tutorial 1: Introduction to Exposure Analysis with EXAMS – Laboratory 2

EXAMS’ second tutorial (beginning on page 117) 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 Lab1. Note that the only required EXAMS commands were

SET MODE=1

RECALL CHEM 11

RECALL ENV 2

SET STRL(1,1,13)=.01

SET OUTFIL(1)=Y

SET OUTFIL(2)=Y

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

set mwt(1)=147.0

change sol(1,1) to 73.8

set kow(1)=2340

set henry(1)=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

SET STRL(1,1,13)=.01

SET OUTFIL(1)=Y

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 86 and 88.

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.

SET FROC(1,13)=0.4

SET FROC(2,13)=0.4
EXAMS Laboratory Exercises. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 82

SHOW FROC(1,13) (Write down what you see) 

SHOW FROC(2,13) (Write down what you see) 

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 86 and 88)

LIST 6

LIST 1

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 hyper-eutrophic pond.

REC CHEM 11

REC ENV 6

SET STRL(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 83

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 Units Methyl Parathion 1,4-Dichlorobenzene

Molecular Weight

Solubility

Kow (n-Octanol:water solubility ratio)

Henry’s Law Constant

KBACW

KBACS

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.

II.

III.

2. Compare the standard and the hyper-eutrophic ponds.

a. Define FROC 

( , ) units 

Variable Standard Pond Hyper-Eutrophic Pond

FROC (1,13)

FROC (2,13)
EXAMS Laboratory Exercises. .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 84

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)

Conc in plankton (μg/g)

Conc in benthos (μg/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 Introductory Laboratory (Mode 1). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 85

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 SET MODE to 1

8 CATALOG CHEMICAL

9 CATALOG ENVIRONMENT

10 CAT

11 LO

12 HELP RECALL

13 REC CHEM 11

14 RECALL ENV 2

15 HELP LOAD

16 SHOW LOAD

17 SET STRL(1,1,13)=.01

18 SET OUTFIL(1)=Y

19 SET OUTFIL(2)=Y

20 RUN

21 LIST

22 HELP

23 20

24 LIST 18

25 LIST 15

26 HELP PLOT

27 PLOT

28 KI

29 PL

30 3

31 6

32 0

33 0
EXAMS Introductory Laboratory (Mode 2). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 86

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 SET STRLD(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 SET OUTFIL(1)=Y

12 SET OUTFIL(2)=Y

13 RUN

14 PLOT KI PL

15 3

16 6

17 0

18 0

19 SHOW LOAD

20 ZERO LOAD

21 SHOW LOAD

22 SET ICHEM(1)=1

23 SET ISEG(1)=1

24 SET IMASS(1)=.1

25 SHO PU

26 SET TEND=14

27 RUN

28 PL KI PL

29 3

30 6

31 0

32 0

33 CONTINUE

34 28
EXAMS Introductory Laboratory (Mode 2). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 87

35 CONTINUE

36 60

37 PL KI PL

38 3

39 6

40 0

41 0

42 PL KI PL

43 6

44 0

45 0
EXAMS Introductory Laboratory (Mode 3). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 88

3.3.3 Lab. 1, Exercise 3: Time-varying seasonal analysis using Mode 3

1 REC CHEM 11

2 SET MODE=3

3 REC ENV 2

4 SET ICHEM(1)=1

5 SET ISEG(1)=1

6 SET IMASS(1)=.1

7 SET IMON(1)=5

8 SET IDAY(1)=15

9 SET ICHEM(2)=1

10 SET ICHEM(3)=1

11 SET ISEG(2)=1

12 SET ISEG(3)=1

13 SET IMASS(2)=.1

14 SET IMASS(3)=.1

15 SET IMON(2)=6

16 SET IMON(3)=6

17 SET IDAY(2)=1

18 SET IDAY(3)=15

19 SHO PU LO

20 SET OUTFIL(1)=Y

21 SET OUTFIL(2)=Y

22 RUN

23 PL KI PL

24 3

25 6

26 0

27 0

28 PL KI PL

29 3

30 0

31 0

32 CONTINUE

33 PLOT KI PL

34 3

35 0
EXAMS Introductory Laboratory (Mode 3). .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 89

36 0

37 LIST 20

38 Y

39 QUIT
Introduction to EXAMS .................................... Page 90

3.4 Introduction to EXAMS – Lab. 1 Exercises with Complete EXAMS Responses

These three exercises illustrate the EXAM S command line interface, data entry, and operations “modes.” The commands in the summary

listings are given here in BOLD/ITALIC. Note that EXAM S 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 <current release number>

 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 <date of 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 CATALOG CHANGE CONTINUE DESCRIBE

DO (@) ERASE HELP LIST NAME

PASSWORD PLOT PRINT QUIT READ

RECALL RUN SET SHOW (?) STORE

WRITE ZERO

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 <topic>, where <topic>

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.

TABNUM describes EXAMS’ table numbering scheme.

EXAMS-> HELP HELP

The HELP command invokes EXAMS' HELP facility to display
Introduction to EXAMS .................................... Page 91

information about a particular EXAMS topic. For a tutorial

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 92

CHEMNA ECONAM LOADNM PRODNM TYPE AIRTY FIXFIL IUNIT MCHEM

KCHEM MODE PRSW MONTH NYEAR YEAR1 TCODE CINT TEND

TINIT ABSER RELER SPFLG MWT SOL ESOL PK EPK

KOC KOW KPB KPDOC KPS KIEC MP HENRY EHEN

VAPR EVPR QYield KDP RFLAT ABSOR LAMAX KAH EAH

KNH ENH KBH EBH KOX EOX K1O2 EK1O2 KRED

ERED KBACW QTBAW KBACS QTBAS KOUNT JFRAD ITOAD ADVPR

JTURB ITURB XSTUR CHARL DSP SUSED BULKD FROC CEC

AEC PCTWA TCEL PH POH OXRAD REDAG BACPL BNBAC

PLMAS BNMAS KO2 DOC CHL CLOUD DFAC DISO2 OZONE

VOL AREA DEPTH XSA LENG WIDTH RAIN EVAP LAT

LONG WIND ELEV RHUM ATURB STFLO STSED NPSFL NPSED

SEEPS STRLD NPSLD PCPLD DRFLD PRBEN SEELD IMASS ISEG

ICHEM IMON IDAY IYEAR SPRAY CHPAR TPROD NPROC RFORM

YIELD EAYLD

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 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.

EXAMS-> SET MODE to 1

EXAMS-> CATALOG CHEMICAL

UDB # Name of Chemistry Dataset

===== ==================================================

 1 Chemical Data Entry Template

 2 p-Cresol [CAS# 106-44-5]

 3 Benz[a]anthracene [CAS# 56-55-3]

 4 Benzo[a]pyrene [CAS# 50-32-8]

 5 Quinoline [CAS# 91-22-5]

 6 Benzo[f]quinoline [CAS# 85-02-9]

 7 9H-Carbazole [CAS# 86-74-8]

 8 7H-Dibenzo[c,g]carbazole [CAS# 194-59-2]

 9 Benzo[b]thiophene [CAS# 95-15-8]

 10 Dibenzothiophene [CAS# 132-65-0]

 11 Methyl Parathion [CAS# 298-00-0]

 12 Mirex [CAS# 2385-85-5]

 13 Heptachlor [CAS# 76-44-8]

EXAMS-> CATALOG ENVIRONMENT

UDB # Name of Environmental Dataset

===== ==================================================

 1 Environmental Data Entry Template

 2 Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

 3 Georgia OPP Farm Pond (MLRA P136, WBAN 13873)

 4 Mississippi OPP Farm Pond (MLRA P134, WBAN 03940)

 5 Index Reservoir, IL (MLRA M115, WBAN 13994)

 6 Lake Zurich (Untersee), Switzerland: annual means
Introduction to EXAMS .................................... Page 93

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 <version number>

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 <datatype> <UDB#> [AS ADB#]

 where <datatype> can be CHEM, ENV, LOAD, or PRODUCT

EXAMS uses these four kinds of datasets:

 1. CHEMICAL reactivity and partitioning (up to MCHEM sectors),

 2. ENVIRONMENTal physical and chemical parameters,

 3. allochthonous chemical LOADings, and

 4. PRODUCT chemistry for generating interconversions

 among multiple chemicals in an analysis.

For more information: ADB, AS, UDB

EXAMS-> REC CHEM 11

Selected compound is "Methyl Parathion [CAS# 298-00-0]"

EXAMS-> RECALL ENV 2

Selected environment is "Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)"

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

target segment, and the mass (kg) of the pulse.

If, during a RUN, a loading violates assumptions of the model
Introduction to EXAMS .................................... Page 94

(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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Total number of segments (KOUNT) = 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 1

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: 1) Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Mean of year 2000: allochthonous chemical loads (kg/hr).

---------------------------------------------------------------

Load data--

Seg Streams Rainfall Seeps NPS Loads Drift

--- --------- --------- --------- --------- ---------

 1

 2

 3

 4

 5

 6

---------------------------------------------------------------

EXAMS-> SET STRL(1,1,13)=.01

Command processing complete; ready for input.

EXAMS-> SET OUTFIL(1)=Y 

Command processing complete; ready for input.

EXAMS-> SET OUTFIL(2)=Y 

Command processing complete; ready for input.

EXAMS-> RUN

Simulation beginning for:

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

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

 1 Chemical inputs: FATE Data

 2 Chemical inputs: PRODUCT Chemistry

 3 PULSE Chemical Loadings

 4 Environmental Input Data: BIOLOGICAL Parameters
Introduction to EXAMS .................................... Page 95

 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

 14 Allochthonous Chemical LOADS and Pulses

 15 DISTRIBUTION of Chemical in Environment

 16 Chemical SPECIATION of Dissolved Concentrations

 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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Table 20.01. Exposure analysis summary.

---------------------------------------------------------------

Exposure (maximum steady-state concentrations):

 Water column: 7.393E-02 mg/L dissolved; total = 7.405E-02 mg/L

 Benthic sediments: 6.586E-02 mg/L dissolved in pore water;

 maximum total concentration = 3.32 mg/kg (dry weight).

 Biota (ug/g dry weight): Plankton: 27. Benthos: 24.

Fate:

 Total steady-state accumulation: 2.44 kg, with 62.72%

 in the water column and 37.28% in the benthic sediments.

 Total chemical load: 0.24 kg/ day. Disposition: 2.30%

 chemically transformed, 81.86% biotransformed, 0.06%

 volatilized, and 15.78% exported via other pathways.

 Persistence:

 After 12.0 days of recovery time, the water column had

 lost 85.24% of its initial chemical burden; the benthic zone

 had lost 29.47%; system-wide total loss of chemical = 64.4%.

 Five half-lives (>95% cleanup) thus require ca. 58. days.

EXAMS-> LIST 18
Introduction to EXAMS .................................... Page 96

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

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 4.0378E-03 1.68 418.6

Reduction

Radical oxidation

Direct photolysis 1.4853E-03 0.62 1138.

Singlet oxygen oxidation

Bacterioplankton 0.1686 70.23 10.03

Benthic Bacteria 2.7911E-02 11.63 60.56

Surface Water-borne Export 3.7872E-02 15.78 44.63

Seepage export

Volatilization 1.3355E-04 0.06 1.2656E+04

---------------------------------------------------------------

Chemical Mass Balance:

 Sum of fluxes = 0.2400

 Sum of loadings = 0.2400

 Allochthonous load: 100.0

 Autochthonous load: 0.0

 Residual Accumulation = 0.0 0.0

---------------------------------------------------------------

* Pseudo-first-order estimates based on flux/resident mass.

EXAMS-> LIST 15

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

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 1.5 96.84 7.405E-02 7.393E-02 3.70 27.5

 3 1.07E-02 0.70 7.152E-02 7.140E-02 3.57 26.5

 5 3.76E-02 2.46 6.263E-02 6.253E-02 3.13 23.2

 ======== ======

 1.5 62.72

 and in the Benthic Sediments:

 2 0.61 66.71 0.898 1.783E-02 0.892 6.62

 4 6.72E-02 7.39 3.32 6.586E-02 3.29 24.5

 6 0.24 25.90 2.91 5.768E-02 2.88 21.4

 ======== ======

 0.91 37.28

Total Mass (kilograms) = 2.438

---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

** 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
Introduction to EXAMS .................................... Page 97

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.

Related topics: LIST, PRINT

Syntax: PLOT <option1 option2 option3>

For more information on option1: 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 [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B 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
Introduction to EXAMS .................................... Page 98

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 1.53 I*

 I

 I *

 I

 I *

 1.02 I

 I+ + + +

 I + + + +

 I * + + + +

 I * +

 0.510 I *

 I *

 I * * *

 I *

 I

 0.00 I

 +----+----+----+----+----+----+----+----+----+----+

 0.00 2.40 4.80 7.20 9.60 12.0

 1.20 3.60 6.00 8.40 10.8

 Time, Days
Introduction to EXAMS .................................... Page 99

3.4.2 Lab. 1, Exercise 2: EXAMS in Mode 2

Using EXAM S 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”, “SET

OUTFIL(1)=Y”, SET OUTFIL(2)=Y”, and “RUN” to begin this exercise.

EXAMS-> SET MODE=2

Command processing complete; ready for input.

EXAMS-> SHOW TI FR

A RUN will integrate from 0 to 1 Days

with output at intervals of 1 Days.

CINT = 1, TINIT = 0

TEND = 1, TCODE = 2

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 halflife 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 processing complete; ready for input.

EXAMS-> SET TEND=60

Command processing complete; ready for input.

EXAMS-> SET CINT=1

Command processing complete; ready for input.

EXAMS-> SHOW TI FR

A RUN will integrate from 0 to 60 Days

with output at intervals of 1 Days.

CINT = 1, TINIT = 0

TEND = 60, TCODE = 2

EXAMS-> RUN

Simulation beginning for:

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

RUN command completed.

EXAMS-> PLOT KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B
Introduction to EXAMS .................................... Page 100

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

System: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 1.52 I *******************************

 I *******

 I ***

 I **

 I *

 1.02 I *

 I *

 I * +++++++++++++++

 I * ++++++++

 I * ++++++

 0.508 I +++++

 I * ++++

 I ++++

 I * ++

 I ++++

 0.00 I++++

 +----+----+----+----+----+----+----+----+----+----+

 0.00 12.0 24.0 36.0 48.0 60.0

 6.00 18.0 30.0 42.0 54.0

 Time, Days
Introduction to EXAMS .................................... Page 101

EXAMS-> SHOW LOAD

Name of environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Total number of segments (KOUNT) = 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 2

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: 1) Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Mean of year 2000: allochthonous chemical loads (kg/hr).

---------------------------------------------------------------

Load data--

Seg Streams Rainfall Seeps NPS Loads Drift

--- --------- --------- --------- --------- ---------

 1 1.000E-02

 2

 3

 4

 5

 6

---------------------------------------------------------------

EXAMS-> ZERO LOAD

All allochthonous loads removed.

EXAMS-> SHOW LOAD

Name of environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Total number of segments (KOUNT) = 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 2

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: 1) Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Mean of year 2000: allochthonous chemical loads (kg/hr).

---------------------------------------------------------------

Load data--

Seg Streams Rainfall Seeps NPS Loads Drift

--- --------- --------- --------- --------- ---------

 1

 2

 3

 4

 5

 6

---------------------------------------------------------------

EXAMS-> SET ICHEM(1)=1

Command processing complete; ready for input.

EXAMS-> SET ISEG(1)=1

Command processing complete; ready for input.

EXAMS-> SET IMASS(1)=.1

Command processing complete; ready for input.
Introduction to EXAMS .................................... Page 102

EXAMS-> SHO PU

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 2

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: 1) Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Table 3. Chemical input data: pulse loadings.*

---------------------------------------------------------------

Load Entry--

ICHEM-ADB# 1

ISEGment 1

IMASS (kg) 0.100

Event Number 1

---------------------------------------------------------------

* N.B.: Input data only; may be revised during simulation.

EXAMS-> SET TEND=14

Command processing complete; ready for input.

EXAMS-> RUN

Simulation beginning for:

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

RUN command completed.

EXAMS-> PL KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B 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 103

System: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 0.100 I*

 I

 I *

 I

 I *

 6.667E-02 I

 I *

 I *

 I *

 I *

 3.333E-02 I *

 I *

 I * * *

 I + + + + + + + + +

 I + + + +

 0.00 I+ +

 +----+----+----+----+----+----+----+----+----+----+

 0.00 2.80 5.60 8.40 11.2 14.0

 1.40 4.20 7.00 9.80 12.6

 Time, Days

EXAMS-> CONTINUE

Initial time for integration will be 14 Days

Enter ending time of integration, Help, or Quit-> 28

Simulation continuing for:

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

CONTINUE command completed.

EXAMS-> PL KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B 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 104

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 0.112 I * *

 I

 I* * *

 I *

 I * *

 7.467E-02 I *

 I * *

 I * * *

 I * * *

 I * * *

 3.733E-02 I * * *

 I ** * **

 I * +++++ ++++++++++++++

 I +++* ++++ ++ *** ++++++++++

 I ++++++ ++ ******* +++

 0.00 I++ ********

 +----+----+----+----+----+----+----+----+----+----+

 0.00 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 [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B

The following parameters are available for time-trace plotting

of values averaged over the ecosystem space:

Notice how the pulse is

repeated on each “continue”

if it is not explicitly removed.
Introduction to EXAMS .................................... Page 105

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-> 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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 2.395E-02 I *****

 I * **

 I * **

 I ****** * ***

 I * ** **

 1.597E-02 I * **

 I * **

 I * **

 I ******* ***

 I ** ***

 7.983E-03 I *

 I *

 I *

 I *

 I

 0.00 I*

 +----+----+----+----+----+----+----+----+----+----+

 0.00 12.0 24.0 36.0 48.0 60.0

 6.00 18.0 30.0 42.0 54.0

 Time, Days
Introduction to EXAMS .................................... Page 106

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 [CAS# 298-00-0]

EXAMS-> SET MODE=3

Command processing complete; ready for input.

EXAMS-> REC ENV 2

Selected environment is "Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)"

EXAMS-> SET OUTFIL(1)=Y

Command processing complete; ready for input.

EXAMS-> SET OUTFIL(2)=Y

Command processing complete; ready for input.

EXAMS-> SET ICHEM(1)=1

Command processing complete; ready for input.

EXAMS-> SET ISEG(1)=1

Command processing complete; ready for input.

EXAMS-> SET IMASS(1)=.1

Command processing complete; ready for input.

EXAMS-> SET IMON(1)=5

Command processing complete; ready for input.

EXAMS-> SET IDAY(1)=15

Command processing complete; ready for input.

EXAMS-> SET ICHEM(2)=1

Command processing complete; ready for input.

EXAMS-> SET ICHEM(3)=1

Command processing complete; ready for input.

EXAMS-> SET ISEG(2)=1

Command processing complete; ready for input.

EXAMS-> SET ISEG(3)=1

Command processing complete; ready for input.

EXAMS-> SET IMASS(2)=.1

Command processing complete; ready for input.

EXAMS-> SET IMASS(3)=.1

Command processing complete; ready for input.

EXAMS-> SET IMON(2)=6
Introduction to EXAMS .................................... Page 107

Command processing complete; ready for input.

EXAMS-> SET IMON(3)= 6

Command processing complete; ready for input.

EXAMS-> SET IDAY(2)=1

Command processing complete; ready for input.

EXAMS-> SET IDAY(3)=15

Command processing complete; ready for input.

EXAMS-> SHO PU LO

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 3

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: 1) Methyl Parathion [CAS# 298-00-0]

---------------------------------------------------------------

Table 3. Chemical input data: pulse loadings.*

---------------------------------------------------------------

Load Entry--

IMONth 5 6 6

IDAY 15 1 15

ICHEM-ADB# 1 1 1

ISEGment 1 1 1

IMASS (kg) 0.100 0.100 0.100

Event Number 1 2 3

---------------------------------------------------------------

* N.B.: Input data only; may be revised during simulation.

EXAMS-> RUN

Simulation beginning for:

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

RUN command completed.

EXAMS-> PL KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B 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.
Introduction to EXAMS .................................... Page 108

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 0.111 I *

 I * *

 I *

 I * *

 I *

 7.384E-02 I * *

 I * *

 I * *

 I * * *

 I * * *

 3.692E-02 I * * *

 I ** * *

 I * *+++

 I *+++*++

 I +++ * *++++

 0.00 I++++++++++++++++++++ ****+++++++++++++++++++++

 +----+----+----+----+----+----+----+----+----+----+

 0.00 73.2 146. 220. 293. 366.

 36.6 110. 183. 256. 329.

 Time, Days

EXAMS-> PL KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B 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;
Introduction to EXAMS .................................... Page 109

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 0.111 I *

 I * *

 I *

 I * *

 I *

 7.384E-02 I * *

 I * *

 I * *

 I * * *

 I * * *

 3.692E-02 I * * *

 I ** * *

 I * * *

 I * ****

 I ** * **

 0.00 I******************* *************************

 +----+----+----+----+----+----+----+----+----+----+

 0.00 73.2 146. 220. 293. 366.

 36.6 110. 183. 256. 329.

 Time, Days

EXAMS-> CONTINUE

CONTinuing integration through 31 December 2001.

Simulation continuing for:

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical 1: Methyl Parathion [CAS# 298-00-0]

CONTINUE command completed.

EXAMS-> PLOT KI PL

Chemical: Methyl Parathion [CAS# 298-00-0]

Environment: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Simulation units: Days

Number of segments: 6

Segment Number: 1 2 3 4 5 6

Segment "TYPE": L B L B L B

The following parameters are available for time-trace plotting

of values averaged over the ecosystem space:
Introduction to EXAMS .................................... Page 110

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:

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: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

 0.111 I * *

 I ** **

 I * *

 I ** **

 I * *

 7.384E-02 I * * **

 I ** **

 I * * * *

 I *** * **

 I *** ***

 3.692E-02 I *** ***

 I *** ***

 I *** ***

 I *** ***

 I   ** **

 0.00 I********** ********************** *************

 +----+----+----+----+----+----+----+----+----+----+

 0.00 146. 292. 439. 585. 731.

 73.1 219. 366. 512. 658.

 Time, Days

EXAMS-> LIST 20
Introduction to EXAMS .................................... Page 111

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 3

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

Table 20.01. Exposure analysis summary: Maximum Events of 2000.

-----------------------------------------------------------------------------

Event Duration 96-hour 21-day 60-day 90-day 2000

==============----==== --------- --------- --------- --------- ---------

 ***** Ecotoxicological Direct Exposure Concentrations ******

Water Column Min. 2.781E-03 5.128E-04 7.040E-05 7.664E-06 0.00

 dissolved mg/L Mean 3.995E-03 2.423E-03 1.487E-03 9.994E-04 2.581E-04

 Peak 5.505E-03 5.505E-03 5.505E-03 5.505E-03 5.505E-03

Benthic Sediment Min. 1.617E-03 1.165E-03 5.834E-04 1.578E-04 0.00

 mg/L dissolved Mean 1.640E-03 1.446E-03 1.108E-03 8.522E-04 7.716E-05

 in pore water Peak 1.650E-03 1.650E-03 1.650E-03 1.650E-03 1.650E-03

 ***** Ecotoxicological Trophic Exposure Concentrations *****

Water Column Min. 1.03 0.191 2.615E-02 2.847E-03 0.00

 ug/g dry weight Mean 1.48 0.900 0.552 0.371 9.588E-02

 of plankton Peak 2.04 2.04 2.04 2.04 2.04

Benthic Sediment Min. 0.601 0.433 0.217 5.864E-02 0.00

 ug/g dry weight Mean 0.609 0.537 0.411 0.317 2.867E-02

 of benthos Peak 0.613 0.613 0.613 0.613 0.613

 ***** Total Media Concentrations ***************************

Water Column Min. 2.785E-03 5.137E-04 7.051E-05 7.677E-06 0.00

 total mg/L Mean 4.002E-03 2.427E-03 1.489E-03 1.001E-03 2.585E-04

 Peak 5.514E-03 5.514E-03 5.514E-03 5.514E-03 5.514E-03

Benthic Sediment Min. 8.145E-02 5.867E-02 2.938E-02 7.951E-03 0.00

 total mg/kg Mean 8.262E-02 7.284E-02 5.579E-02 4.293E-02 3.887E-03

 dry weight Peak 8.313E-02 8.313E-02 8.313E-02 8.313E-02 8.313E-02

More? (Yes/No/Quit)-> Y

Exposure Analysis Modeling System -- EXAMS Version <ver>, Mode 3

Ecosystem: Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

Chemical: Methyl Parathion [CAS# 298-00-0]

Table 20.01. Exposure analysis summary: Maximum Events of 2001.

-----------------------------------------------------------------------------

Event Duration 96-hour 21-day 60-day 90-day 2001

==============----==== --------- --------- --------- --------- ---------

 ***** Ecotoxicological Direct Exposure Concentrations ******

Water Column Min. 2.781E-03 5.128E-04 7.040E-05 7.664E-06 2.018E-12

 dissolved mg/L Mean 3.995E-03 2.423E-03 1.487E-03 9.994E-04 2.588E-04

 Peak 5.505E-03 5.505E-03 5.505E-03 5.505E-03 5.505E-03

Benthic Sediment Min. 1.617E-03 1.165E-03 5.834E-04 1.578E-04 9.304E-12

 mg/L dissolved Mean 1.640E-03 1.446E-03 1.108E-03 8.522E-04 7.737E-05

 in pore water Peak 1.650E-03 1.650E-03 1.650E-03 1.650E-03 1.650E-03

 ***** Ecotoxicological Trophic Exposure Concentrations *****

Water Column Min. 1.03 0.191 2.615E-02 2.847E-03 7.499E-10

 ug/g dry weight Mean 1.48 0.900 0.552 0.371 9.613E-02

 of plankton Peak 2.04 2.04 2.04 2.04 2.04

Benthic Sediment Min. 0.601 0.433 0.217 5.864E-02 3.456E-09

 ug/g dry weight Mean 0.609 0.537 0.411 0.317 2.874E-02

 of benthos Peak 0.613 0.613 0.613 0.613 0.613

 ***** Total Media Concentrations ***************************

Water Column Min. 2.785E-03 5.137E-04 7.051E-05 7.677E-06 2.022E-12

 total mg/L Mean 4.002E-03 2.427E-03 1.489E-03 1.001E-03 2.592E-04

 Peak 5.514E-03 5.514E-03 5.514E-03 5.514E-03 5.514E-03

EXAMS-> QUIT
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 112

3.5 Tutorial 2: Chemical & Environmental Data Entry

This tutorial example illustrates the entry of EXAMS chemical

and environmental data and the exploration of alternative

process models and parameters. The EXAM S session duplicates

the problem analysis in the text beginning on page 39; it

should be executed using that text as a guide. Note that the

environmental data are not a complete characterization of Lake

Zurich; they include only a simple geometry plus the

parameters needed to construct a volatilization model for

EXAM S. The data entry sequence contains some errors; these

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 SET MODE=1

3 CHEM NAME IS 1,4-Dichlorobenzene

4 set mwt(1)=147.0

5 change sol(1,1) to 73.8

6 set kow(1)=2340

7 set henry(1)=2.66e-3

8 cat chem

9 stor chem 20

10 rec env 1

11 env name is Lake Zurich - central basin (Untersee)

12 set elev=431

13 set airty(*)=r

14 set lat=47.48

15 set lon=8.53

16 SET KO2(1,13)=2.5

17 SET WIND(13)=1.38

18 HELP WIND

19 SET WIND(1,13)=1.38

20 SHOW KOUNT

21 SET KOUNT=3

22 SET TYPE(1)=E

23 SET TYPE(2)=H

24 SET TYPE(3)=B

25 SET DEPTH(1)=10

26 SET VOL(1)=6.8E8

27 SET DEPTH(2)=40

28 SET VOL(2)=2.72E9
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 113

29 SET DEPTH(3)=0.02

30 SET VOL(3)=1.36E6

31 SET AREA(*)=6.8E7

32 SET TCEL(1,13)=11

33 SET TCEL(2,13)=5.6

34 SET TCEL(3,13)=5.6

35 SET DSP(1,13)=0.2

36 SET DSP(2,13)=1.E-4

37 SET XST(*)=6.8E7

38 SET JTUR(1)=1

39 SET ITUR(1) TO 2

40 CHA JTUR(2)=2

41 SET ITUR(2)=3

42 SHO DISP

43 HELP CHARL

44 SET CHARL(1)=25

45 SET CHARL(2)=20.01

46 SHO DISP

47 SET SUSED(*,13)=5

48 SET BULKD(3,13)=1.5

49 SET PCTWA(3,13)=150

50 SET FROC(*,13)=.02

51 SET STRFL(1,13)=3.E5

52 HELP STFL

53 SET STF(1,13)=3.E5

54 SET STSED(1,13)=1500

55 SET STRLD(1,1,13)=0.01

56 SET OUTFIL(1)=Y

57 Show Ad

58 RUN

59 LI 15

60 list 18

61 list 20

62 CAT ENV

63 STOR ENV 5

64 SET DRFL(2,1,13)=1.5E-3

65 SET STRL(1,1,13)=8.5E-3

66 ENV NAME IS Lake Zurich with partial load to hypolimnion

67 run

68 li 15

69 list 18
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 114

70 li 20

71 zero load

72 cat chem

73 rec chem 20

74 rec env 5

75 env name is Lake Zurich with oceanic wind and reaeration

76 show ko2(1,13)

77 set ko2(1,13)=20

78 sho wind(1,13)

79 set wind(1,13)=2.6245

80 set strl(1,1,13)=0.01

81 run

82 list 15

83 list 18

84 era chem 20

85 quit
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 115

3.5.2 Exercise 4 (Lake Zurich) with illustrative EXAMS Responses

 Welcome to EXAMS Release <current release number>

 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 <date of 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-> REC CHEM 1

Selected compound is "Chemical Data Entry Template"

EXAMS-> SET MODE=1

Command processing complete; ready for input.

EXAMS-> CHEM NAME IS 1,4-Dichlorobenzene

EXAMS-> set mwt(1)=147.0

EXAMS-> change sol(1,1) to 73.8

EXAMS-> set kow(1)=2340

EXAMS-> set henry(1)=2.66e-3

EXAMS-> cat chem

UDB # Name of Chemistry Dataset

===== ==================================================

 1 Chemical Data Entry Template

 2 p-Cresol [CAS# 106-44-5]

 3 Benz[a]anthracene [CAS# 56-55-3]

 4 Benzo[a]pyrene [CAS# 50-32-8]

 5 Quinoline [CAS# 91-22-5]

 6 Benzo[f]quinoline [CAS# 85-02-9]

 7 9H-Carbazole [CAS# 86-74-8]

 8 7H-Dibenzo[c,g]carbazole [CAS# 194-59-2]

 9 Benzo[b]thiophene [CAS# 95-15-8]

 10 Dibenzothiophene [CAS# 132-65-0]

 11 Methyl Parathion [CAS# 298-00-0]

 12 Mirex [CAS# 2385-85-5]

 13 Heptachlor [CAS# 76-44-8]

EXAMS-> store chem 20

Chemical stored: "1,4-Dichlorobenzene"

EXAMS-> rec env 1

Selected environment is "Environmental Data Entry Template"
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 116

EXAMS-> env name is Lake Zurich - central basin (Untersee)

EXAMS-> set elev=431

EXAMS-> set airty(*)=r

EXAMS-> set lat=47.48

EXAMS-> set lon=8.53

EXAMS-> SET KO2(1,13)=2.5

EXAMS-> SET WIND(13)=1.38

Invalid number of subscripts.

EXAMS-> HELP WIND

WIND is a Real Matrix with 100 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)=1.E-4

EXAMS-> SET XST(*)=6.8E7

EXAMS-> SET JTUR(1)=1

EXAMS-> SET ITUR(1) TO 2

EXAMS-> CHA JTUR(2)=2

EXAMS-> SET ITUR(2)=3

EXAMS-> SHO DISP
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 117

Name of environment: Lake Zurich - central basin (Untersee)

Total number of segments (KOUNT) = 3

Segment Number: 1 2 3

Segment "TYPE": E H B

---------------------------------------------------------------

Table 10.13. Mean dispersive transport field.

---------------------------------------------------------------

J TURB 1 2

I TURB 2 3

XS TUR m2 6.800E+07 6.800E+07

CHARL m 1.00 0.000

DSP m2/h* 0.200 1.000E-04

 Path No.: 1 2

---------------------------------------------------------------

* Average of 12 monthly mean values.

EXAMS-> HELP CHARL

CHARL is a real vector with 600 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: 1 2 3

Segment "TYPE": E H B

---------------------------------------------------------------

Table 10.13. Mean dispersive transport field.

---------------------------------------------------------------

J TURB 1 2

I TURB 2 3

XS TUR m2 6.800E+07 6.800E+07

CHARL m 25.0 20.0

DSP m2/h* 0.200 1.000E-04

 Path No.: 1 2

---------------------------------------------------------------

* Average of 12 monthly mean values.

EXAMS-> SET SUSED(*,13)=5

EXAMS-> SET BULKD(3,13)=1.5

EXAMS-> SET PCTWA(3,13)=150

EXAMS-> SET FROC(*,13)=.02

EXAMS-> SET STRFL(1,13)=3.E5

Option not identified.

EXAMS-> HELP STFL
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 118

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

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 STSED(1,13)=1500

EXAMS-> SET STRLD(1,1,13)=0.01

EXAMS-> SET OUTFIL(1)=Y

EXAMS-> Show ad

Name of environment: Lake Zurich - central basin (Untersee)

Total number of segments (KOUNT) = 3

Segment Number: 1 2 3

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
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 119

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:

 1 7.3 20.00 1.074E-05 1.074E-05 1.759E-04 0.00

 2 29. 80.00 1.074E-05 1.074E-05 1.759E-04 0.00

 ======== ======

 37. 99.33

 and in the Benthic Sediments:

 3 0.24 100.00 1.798E-04 1.065E-05 1.745E-04 0.00

 ======== ======

 0.24 0.67

Total Mass (kilograms) = 36.76

---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

** 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 7.7323E-02 32.22 329.5

Seepage export

Volatilization 0.1627 67.78 156.6

---------------------------------------------------------------

Chemical Mass Balance:

 Sum of fluxes = 0.2400

 Sum of loadings = 0.2400

 Allochthonous load: 100.0

 Autochthonous load: 0.0

 Residual Accumulation = 6.82E-09 0.0

---------------------------------------------------------------

* Pseudo-first-order estimates based on flux/resident mass.

EXAMS-> list 20
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 120

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: 1.065E-05 mg/L dissolved in pore water;

 maximum total concentration = 1.798E-04 mg/kg (dry weight).

 Biota (ug/g dry weight): Plankton: Benthos:

Fate:

 Total steady-state accumulation: 36.8 kg, with 99.33%

 in the water column and 0.67% in the benthic sediments.

 Total chemical load: 0.24 kg/ day. Disposition: 0.00%

 chemically transformed, 0.00% biotransformed, 67.78%

 volatilized, and 32.22% exported via other pathways.

 Persistence:

 After 216. days of recovery time, the water column had

 lost 50.52% of its initial chemical burden; the benthic zone

 had lost 19.55%; system-wide total loss of chemical = 50.3%.

 Five half-lives (>95% cleanup) thus require ca. 35. months.

EXAMS-> CAT ENV

UDB # Name of Environmental Dataset

===== ==================================================

 1 Environmental Data Entry Template

 2 Georgia Piedmont Farm Pond (WBAN 13873, MLRA P136)

EXAMS-> STORE ENV 5

Environment stored: "Lake Zurich - central basin (Untersee)"

EXAMS-> SET DRFL(2,1,13)=1.5E-3

EXAMS-> SET STRL(1,1,13)=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
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 121

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 1.759E-04 0.00

 2 37. 83.41 1.350E-05 1.349E-05 2.210E-04 0.00

 ======== ======

 44. 99.31

 and in the Benthic Sediments:

 3 0.31 100.00 2.260E-04 1.339E-05 2.193E-04 0.00

 ======== ======

 0.31 0.69

Total Mass (kilograms) = 44.32

---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

** 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.

---------------------------------------------------------------

 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 7.7323E-02 32.22 397.3

Seepage export

Volatilization 0.1627 67.78 188.8

---------------------------------------------------------------

Chemical Mass Balance:

 Sum of fluxes = 0.2400

 Sum of loadings = 0.2400

 Allochthonous load: 100.0

 Autochthonous load: 0.0

 Residual Accumulation = 6.82E-09 0.0

---------------------------------------------------------------

* Pseudo-first-order estimates based on flux/resident mass.

EXAMS-> li 20
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 122

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: 1.339E-05 mg/L dissolved in pore water;

 maximum total concentration = 2.260E-04 mg/kg (dry weight).

 Biota (ug/g dry weight): Plankton: Benthos:

Fate:

 Total steady-state accumulation: 44.3 kg, with 99.31%

 in the water column and 0.69% in the benthic sediments.

 Total chemical load: 0.24 kg/ day. Disposition: 0.00%

 chemically transformed, 0.00% biotransformed, 67.78%

 volatilized, and 32.22% exported via other pathways.

 Persistence:

 After 252. days of recovery time, the water column had

 lost 54.37% of its initial chemical burden; the benthic zone

 had lost 25.71%; system-wide total loss of chemical = 54.2%.

 Five half-lives (>95% cleanup) thus require ca. 37. months.

EXAMS-> zero load

All allochthonous loads removed.

EXAMS-> cat chem

UDB # Name of Chemistry Dataset

===== ==================================================

 1 Chemical Data Entry Template

 2 p-Cresol [CAS# 106-44-5]

 3 Benz[a]anthracene [CAS# 56-55-3]

 4 Benzo[a]pyrene [CAS# 50-32-8]

 5 Quinoline [CAS# 91-22-5]

 6 Benzo[f]quinoline [CAS# 85-02-9]

 7 9H-Carbazole [CAS# 86-74-8]

 8 7H-Dibenzo[c,g]carbazole [CAS# 194-59-2]

 9 Benzo[b]thiophene [CAS# 95-15-8]

 10 Dibenzothiophene [CAS# 132-65-0]

 11 Methyl Parathion [CAS# 298-00-0]

 12 Mirex [CAS# 2385-85-5]

 13 Heptachlor [CAS# 76-44-8]

 20 1,4-Dichlorobenzene

EXAMS-> rec chem 20

Selected compound is "1,4-Dichlorobenzene"

EXAMS-> rec env 5

Selected environment is "Lake Zurich - central basin (Untersee)"

EXAMS-> env name is Lake Zurich with oceanic wind and reaeration

EXAMS-> show ko2(1,13)

KO2(1,13) is 2.500

EXAMS-> set ko2(1,13)=20

EXAMS-> sho wind(1,13)

WIND(1,13) is 1.380

In this study, we wish to set Vw to 3000 cm/h. Use Eq. (2-82) (On page 37) 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 on <date> at <time> for:
EXAM S Lake Zurich Data Entry Laboratory . .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Page 123

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 Mass ******** Chemical Concentrations *********

 # Total Dissolved Sediments Biota

 Kilos % mg/* mg/L ** mg/kg ug/g

--- -------- ------ --------- --------- --------- ---------

In the Water Column:

 1 1.3 20.00 1.953E-06 1.952E-06 3.198E-05 0.00

 2 5.3 80.00 1.953E-06 1.952E-06 3.198E-05 0.00

 ======== ======

 6.6 99.33

 and in the Benthic Sediments:

 3 4.45E-02 100.00 3.270E-05 1.937E-06 3.172E-05 0.00

 ======== ======

 4.45E-02 0.67

Total Mass (kilograms) = 6.684

---------------------------------------------------------------

 * Units: mg/L in Water Column; mg/kg in Benthic Zone.

** Excludes complexes with "dissolved" organics.

EXAMS-> list 18

Ecosystem: Lake Zurich with oceanic wind and reaeration

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 1.4060E-02 5.86 329.5

Seepage export

Volatilization 0.2259 94.14 20.50

---------------------------------------------------------------

Chemical Mass Balance:

 Sum of fluxes = 0.2400

 Sum of loadings = 0.2400

 Allochthonous load: 100.0

 Autochthonous load: 0.0

 Residual Accumulation = 8.33E-09 0.0

---------------------------------------------------------------

* Pseudo-first-order estimates based on flux/resident mass.

EXAMS-> quit
124

4.0 EXAMS Command Language Interface (CLI) User’s Guide

Introduction This Chapter describes the EXAM S 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 EXAM S 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 EXAM S

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.

<option> Angle brackets indicate that a command requires a

choice among two or more options.

4.2 Overview

The EXAM S command language provides a set of commands for

! Entering, storing, and manipulating data describing the

reaction chemistry of synthetic compounds, the

environmental parameters governing their transport and

transformation in aquatic systems, patterns of allochthonous

loadings, and product chemistry.

! Studying the results of an analysis by listing tabular output

on a terminal, plotting the concentration data (“Expected

Environmental Concentrations” or EECs) computed during

simulations, and printing paper copy.

! Choosing among analytical frameworks for investigating

exposure to chemicals in a particular case study. EXAM S

includes three operational modalities or “MODEs”

MODE Analytical Methodology

1 Long-term consequences of continued releases of

chemicals; steady-state analysis.

2 Detailed examination of immediate consequences of

chemical releases; initial-value problems.

3 Intermediate-scale resolution of events over several

years, including effects of seasonal environmental

variability; analysis of time-series data.

4.3 Entering Commands

EXAM S commands are composed of English-language words

(mostly verbs) that describe what you want EXAM S to do. Some

commands require qualifiers and parameters. These give EXAM S

more information on how to execute the command. Command

parameters describe the object to be acted upon by the command.

In some cases, the object is a keyword (as in the HELP

command); in others, it is an EXAM S data element (SET

command) or a section of a file of input data or analysis results

to manipulate (STORE and LIST commands).

Throughout this Chapter, EXAM S commands are printed in

uppercase letters for the sake of clarity. However, EXAM S will

accept commands entered in uppercase, lowercase, or a mixture

of uppercase and lowercase letters. Most EXAM S commands and

keywords can be abbreviated to the least number of characters

needed to uniquely distinguish them from other options

available. For example, to end EXAM S you can enter “QUIT”,

“QUI”, “QU”, or “Q”. The least number of required characters

depends on the context, however, but is never more than three.

For example, the SHOW command includes among its options

both <QUALITY> and <QUIT>; in this case you must enter three

characters for EXAM S to distinguish between them. In EXAM S’

“help fields” and prompts, capitalization is used to show you

how many characters are required for uniqueness.

The following example shows an AUDIT command and EXAM S’

response, as they would appear on a terminal.

EXAM S-> AUDIT ON

All input will now be copied into the file

named “AUDOUT” on Fortran Unit Number 4

EXAM S-> ! This Command File should be renamed file.EXA
125

EXAM S->

EXAM S analyzes the parts of the above example as follows.

EXAM S-> The EXAM S system prompt for command input; a

greater-than (->) means that EXAM S’ command

interpreter is ready for a command to be entered.

AUDIT The command name, requesting that EXAM S

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

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.

EXAM S-> 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 EXAM S

is ready for additional input.

! This Command File should be renamed file.EXA

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 EXAM S

analysis sessions or command procedures.

EXAM S-> The next EXAM S system command prompt,

confirming that the comment has been recorded in

the Notepad/Command file and EXAM S 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, EXAM S’

command interpreter prompts you for all missing information.

For example:

EXAM S-> 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 EXAM S prompt.

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 EXAM S prompts

for a more complete specification of the intended action. The line

ending with a -> indicates that EXAM S is waiting for the

additional input.

In many cases, EXAM S’ 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.

EXAM S-> 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

.

.

.

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. EXAM S prompts for the missing

information; typing HELP in response to the LIST prompt displays

a catalog of EXAM S output tables.

4.5 EXAMS Messages

When a command is entered incorrectly, EXAM S displays a

descriptive error message indicating what is wrong. For example,
126

if a data subscript larger that the maximum available is entered,

EXAM S 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.

EXAM S 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, EXAM S’ 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. EXAM S’ HELP command provides this information

in EXAM S’ interactive environment. For example, you can type

the command:

EXAM S-> HELP LIST

EXAM S responds by displaying a description of the LIST

command, its syntax, and the options needed to specify the range

of the command.

The HELP facility also provides on-line assistance for EXAM S’

input data, e.g.,

EXAM S-> HELP QYIELD

will display the subscript ranges, their meanings, the physical

dimensions, and the English definition of EXAM S chemical input

datum “QYIELD”. This information is available online for all

EXAM S’ input data and control parameters. The names of all of

EXAM S’ input variables were selected as mnemonics for their

English-language names. (For example, QYIELD is the

photochemical quantum yield.) These mnemonics are used in

EXAM S’ output tables; definitions are given in the Data

Dictionary (pages 180 ff.) as well as in the on-line HELP.

EXAM S’ 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

EXAM S-> HELP TUTOR

4.7 Command Procedures

A command procedure is a file that contains a sequence of

EXAM S 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

SET KCHEM TO 4

SET NYEAR TO 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

EXAM S-> DO START

or EXAM S-> @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 EXAM S’ @ command. You can

use another file suffix, if you so inform EXAM S when you enter

the command request. For example, to execute commands in a

file named START.UP

EXAM S-> @START.UP

4.8 Wild Card Characters

Some EXAM S commands accept a “wild card” character in the

input command specifications. The asterisk (*) is the only

symbol having this function in EXAM S. 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

EXAM S vary with the individual commands. The command

descriptions later in this Chapter indicate where wild cards are

allowed and describe their effects.

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).
127

4.10 Summary Description of EXAMS’ System

Commands

EXAM S 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 or @ Execute file of EXAM S commands (file.EXA)

ECHO Control command echoing

ERASE Clear section of stored database (UDB)

HELP Describes access to EXAM S on-line HELP facility

LIST Show tabular results on the screen

NAM E 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-EXAM S 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

5.0 System Command Descriptions

A U D I T

Creates a copy of user input commands and responses in an external file.

Related: Control variables: AUDOUT

Commands: DO

Syntax: AUDIT <option>

Options

ON

OFf

Prompt: 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 EXAM S prompt.

AUDIT->

Options: OFf

Ends copying of EXAM S commands to the external file.

ON

Begins copying of EXAM S commands to an external file.

Description: The AUDIT command starts copying inputs typed at the terminal, into an external file. These inputs include EXAM S commands,

and user responses to EXAM S prompts and option selections. The output terminus for the copy is a file named “AUDOUT.” The

resulting output file can be used to record an analysis procedure, or as a general notepad. The output file can be renamed

“file.EXA” and used as an EXAM S command (DO) file.

Examples:

1. EXAM S-> 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 EXAM S prompt.

AUDIT-> ON

All input will now be copied into file “AUDOUT.XMS”.

This command begins recording of input from the terminal into an external file. The output will go to a disk file named

“AUDOUT.XMS”. After leaving EXAM S, this file can be printed to give a permanent record of the analysis.
129

2. EXAM S-> AUDIT OFF

The AUDIT option has been terminated.

This command ends copying of EXAM S commands and responses to the external medium (usually a disk file).

3. EXAM S-> AUDIT ON

All input will now be copied into the

file named “AUDOUT” on Fortran Unit Number 4

EXAM S-> RECALL ENV 2

Selected environment is: Phantom Inlet

EXAM S-> RECALL CHEM 2

Selected compound is: Dichloroexample

EXAM S-> RECALL CHEM 4 AS 2

Selected compound is: Tetrabromoexample

EXAM S-> AUDIT OFF

These commands build a file (AUDOUT) that can later be used as a command file upon entering the EXAM S 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--

EXAM S-> DO MYCOMAND
130

C A T A L O G

Lists, by accession number, the title of all current entries in the specified User Database (UDB).

Related: Control variables: none

Commands: ERASE, NAM E, RECALL, STORE

Syntax: CATALOG <option>

Options: CHEMICAL, ENVIRONMENT, LOAD, PRODUCT

Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->

Options: CHEMICAL

Lists the titles, by access number, of chemical databases currently in the User Database. Each entry corresponds to a single

chemical, and contains the laboratory data describing ionization and (species-specific) partitioning and reaction kinetics.

ENVIRONMENT

List the titles, by access number, of environmental databases currently in the User Database. Each entry contains a “canonical”

physical and chemical model of an aquatic system, including the environmental data needed to compute reactivity and transport

of synthetic chemicals in the system.

LOAD

Lists the titles, by access number, of allochthonous chemical loading patterns stored in the User Database. These data include

monthly values (kg/hour) for stream-loads, non-point-source loads, groundwater seepage loads, precipitation loads, and drift loads

of chemicals entering the aquatic environment, plus specification of pulse loadings. The pulse load data include the magnitude

(kg), target environmental segment, and scheduling (month and day) of pulses of synthetic chemicals entering the system.

PRODUCT

Lists the titles, by access number, of reaction or transformation product chemistries stored in the User Database. These data

include the Activity Database numbers of chemical parent and product compounds, the number of the process responsible for the

transformation, and the yield efficiency (mole/mole) as an (optional) function of temperature.

Description: The CATALOG command inventories the contents of the specified User Database (UDB) and lists the titles of active entries

on the terminal screen. Four types of UDBs are available, corresponding to the four options available to the CATALOG

command. The titles are listed by accession number; this number is used to STORE, RECALL, or ERASE database entries.

Examples:

1. EXAM S-> CATALOG HELP

The CATALOG 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-> CHEMICAL

Catalog of CHEMICAL parameter sets

UDB No. Name of Entry Volume
131

1 Chemical Data Entry Template

2 p-Cresol

3 Benz[a]anthracene

.

.

.

EXAM S->

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. EXAM S-> CATALOG ENVIRON

Catalog of ENVIRONMENTal 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

C H A N G E

Use to enter data into the activity database (synonymous with SET).

Related: Commands: DESCRIBE, HELP, SET

Syntax: CHANGE <name of variable> TO <new value>

or SET <name of variable> = <new value>

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. EXAM S-> CHANGE VOL(153) TO 7E5

Subscript out-of-range.

EXAM S-> DESCRIBE VOL

VOL is a Real Vector with 100 elements.

EXAM S> CHANGE VOL(2) TO E

Invalid numeric quantity after TO or =.

EXAM S-> 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 EXAM S 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 accidental entry of an alphabetic character

(“E”) for the volume was trapped by the CHANGE command; VOL(2) was not altered.

2. EXAM S-> 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.

EXAM S-> CHANGE TCEL(2,7) TO 24
133

This command changes the July temperature in segment 2 to 24E 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. EXAM S-> 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.

EXAM S-> 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.
134

C O N T I N U E

The CONTINUE command resumes EXAM S’ simulation analysis of chemical dynamics beginning from the current state of the system.

Related: Control variables: CINT, TINIT, TEND, TCODE, NYEAR

Commands: RUN, SHOW_TIM E_FRAM E

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 EXAM S’ simulation analysis of chemical dynamics, beginning from the current state of the

system. Chemical loadings and other input data can be altered (CHANGEd or SET) between simulation time segments; EXAM S

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 TIM E FRAM E (abbreviate to SH T F) command can be used to

examine the current state of the integrator timer controls. In Mode 2, the Communications INTerval CINT can be used to vary

the temporal resolution in different segments of the analysis (see Example 1). In Mode 3, NYEAR, the number of years in a

simulation time segment, can similarly be altered.

Examples:

1. EXAM S-> SET MODE=2

EXAM S-> SHOW TIM E FRAM E

A RUN will integrate from 0. to 24. Hours

with output at intervals of 2.00 Hours

EXAM S-> SET TCODE=2

EXAM S-> SET TEND=10

EXAM S-> SET CINT=1

EXAM S-> SH TI F

A RUN will integrate from 0 to 10 Days

with output at intervals of 1 Days

EXAM S-> RUN

Simulation beginning for:

Environment: Pond -- code test data

Chemical 1: Dichloroexample

Run complete

EXAM S-> PLOT KIN PL (3,0,0 -- see PLOT command)

 System: Pond -- code test data
135

 Chemical: Dichloroexample

 2.00 I** ***

 I * ****  

 I ** **   ** *

 I * ****   ** 

 I ** ***

 1.33 I

 I

 I

 I

 I

 0.667 I

 I

 I

 I

 I

 0.000 I

 +----+----+----+----+----+----+----+----+----+----+

 0.000 2.00 4.00 6.00 8.00 10.0

 1.00 3.00 5.00 7.00 9.00

 Time, Days

EXAM S-> 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.

EXAM S-> SET CINT=10

EXAM S-> ZERO PULSE LOAD

EXAM S-> 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.

EXAM S-> PLOT KINETIC PLOT (3,0,0)
136

 System: Pond -- code test data

 Chemical: Dichloroexample

 3.49 I *

 I **

 I **

 I *

 I **

 2.33 I **

 I* **

 I*** ***

 I ***

 I ** *

 1.16 I *

 I *

 I * * *

 I

 I

 0.000 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, EXAM S would introduce an additional pulse on day 30.

2. EXAM S-> SET MODE=3

EXAM S-> SHO TI FR

A RUN will integrate from 1 January 1989

through 31 December 1989.

(YEAR1 = 1989, and NYEAR = 1.)

EXAM S-> RUN

Simulation beginning for:

Environment: Pond -- code test data

Chemical 1: Dichloroexample

Run complete.

EXAM S-> SHO TI FR

A RUN will integrate from 1 January 1989

through 31 December 1989.

(YEAR1 = 1989, and NYEAR = 1.)

CONTinuation will proceed through 31 December 1990

(NYEAR = 1.)

EXAM S-> SET NYEAR=3

EXAM S-> SH TI F
137

A RUN will integrate from 1 January 1989

through 31 December 1991.

(YEAR1 = 1989, and NYEAR = 3.)

CONTinuation will proceed through 31 December 1992

(NYEAR = 3.)

EXAM S-> CONTINUE

CONTinuing integration through 31 December 1992.

Simulation beginning for:

Environment: Pond -- code test data

Chemical 1: Dichloroexample

Run complete.

EXAM S->

These commands illustrate the use of the CONTINUE command in Mode 3. “SHOW TIM E FRAM E” is used to check the state of the

integrator timer controls.
138

D E S C R I B E

Reports the data type, dimensionality, and maximum UDB storage size of parameters.

Related: Control variables:

Commands: HELP

Syntax: DESCRIBE <parameter>

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.

Description: The DESCRIBE command returns information about EXAM S’ 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 the maximum size that can be STOREd in the UDB (file “EXAMS.DAF”) of the version of

EXAM S in use. When a SET or CHANGE command fails, the DESCRIBE command can help diagnose the problem by indicating

the structure and dimensionality of the variable.

Examples:

1. EXAM S-> DESR MODE

Command not recognized. Type HELP for command information.

EXAM S-> 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 EXAM S prompt.

2. EXAM S-> CHANGE VOL(133) TO 7E5

Subscript out-of-range.

EXAM S-> DESCRIBE VOL

VOL is a Real Vector with 100 elements.

This command reports that VOL is a real variable, a vector of 100 elements. In this example, the number of segments (NPX) of an

aquatic system model to be STOREd in the UDB of the version of EXAM S currently in use is set for 100 at most. The number of

computational elements of the current model in the ADB is controlled by the user’s setting of “KOUNT.” Any (intentional or
139

accidental) attempt to enter a value for the VOLume of a segment > the current value of KOUNT (e.g., VOL(133)) will fail, as

illustrated above. Similarly, an attempt to STORE an environment with >100 segments will be rejected, with the suggestion that

the WRITE command be used to store the data in a separate file. DESCRIBE can be used to check the reason for a failure of the

STORE command when a problem with dimension sizes is suspected.

3. EXAM S-> DESCRIBE QYIELD

QYIELD is a Real Table with dimensions (3,7,5)

EXAM S-> HELP QYIELD

QYIELD is a Real Table with dimensions (3,7,5)

Quantum_YIELD (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 (3×7=21) for each potential molecular type of each chemical allow the effects of speciation

and sorption on reactivity to be specified in detail. 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). (QYIELD is an efficiency, hence dimensionless.)

These commands report the data type and UDB storage capacity of EXAM S’ input “QYIELD” (result of “DESCRIBE QYIELD”) and

then report the meaning of the dimensions and the physical units of the variable (result of “HELP QYIELD”). The local

implementation of EXAM S used in this example has the capacity to store load patterns and product chemistries of no more than

five chemicals simultaneously. Thus, QYIELD was DESCRIBEd as consisting of a set of five matrices, each of (fixed) size (3,7).

Note, however, that EXAM S can accommodate an unlimited number of chemicals in any given study (e.g., 25 separate

organophosphorus pesticides, a suite of 12 PCB congeners, etc.) by SETting the value of KCHEM. Loading patterns or product

chemistries of dimensionalities >5 cannot be stored in the UDB, however, but must be written to external files (see documentation

for the WRITE command).
140

D O

Executes a command procedure; requests that EXAM S read subsequent input from a specific file.

Related: Control variables:

Commands: AUDIT

Syntax: DO <name of command file>

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 EXAM S commands. If you do not specify a file type suffix, EXAM S 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 EXAM S command procedure can contain

! Any valid EXAM S 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 EXAM S’ 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; EXAM S’ 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 EXAM S 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. EXAM S-> AUDIT ON

All input will now be copied into the

file named “AUDOUT” on Fortran Unit Number 4

EXAM S-> RECALL

Enter Environment, Chemical, Load, Product, Help or Quit-> ENV

Enter environment UDB catalog number, Help, or Quit-> 2

Selected environment is: Phantom Inlet

EXAM S-> RECALL CHEM 2

Selected compound is: Dichloroexample

EXAM S-> RECALL LOAD 2

Selected load is: Aedes control spray drift
141

EXAM S-> ! Load 2 is the Phantom Inlet salt marsh study

EXAM S-> SET KCHEM TO 2

EXAM S-> RECALL CHEM 4 AS 2

Selected compound is: Tetrabromoexample

EXAM S-> AUDIT OFF

These commands build a file (AUDOUT.DAT) that can later be used as a command file upon entering the EXAM S 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:

EXAM S-> DO SETUP

or, EXAM S-> @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

SET KCHEM 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. EXAM S-> DO

Enter name of file (no more than nn characters), Help, or Quit-> HELP

The “DO” or “@” command provides a means of executing stored EXAM S commands. In response to the prompt, enter the 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

EXAM S/DO-> ! Audit trail of input sequence from EXAM S.

EXAM S/DO-> RECALL

Enter Environment, Chemical, Load, Product, Help, or Quit->

EXAM S/DO-> ENV

Enter environment UDB catalog number, Help, or Quit->

EXAM S/DO-> 2

Selected environment is: Phantom Inlet

EXAM S/DO-> RECALL CHEM 2

Selected compound is: Dichloroexample

EXAM S/DO-> RECALL LOAD 2

Selected load is: Aedes control spray drift

EXAM S/DO-> ! Load 2 is the Phantom Inlet salt marsh study

EXAM S/DO-> SET KCHEM TO 2
142

EXAM S/DO-> RECALL CHEM 4 AS 2

Selected compound is: Tetrabromoexample

EXAM S/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.
143

E C H O

The ECHO command turns echoing of command inputs on and off.

Related: Control variables: None

Commands: None

Syntax: ECHO ON/OFF

Prompt: The following Echo options are available

ON – begins command echo,

Off – turns off echo of input commands,

Help – this message

Quit – return to the EXAM S prompt

Options: OFF

End echoing of commands

ON

Begins echoing of commands

Description: If ECHO is entered from the EXAM S prompt command level, EXAM S stops echoing commands on the interactive terminal. This

feature is primarily useful for batch processing, in which case it serves to limit the size of resulting output files.

Examples:

1. EXAM S-> ECHO OFF

This command terminates echoing of EXAM S commands at the terminal or in the log file.

2. EXAM S-> ECHO ON

This command resumes echoing of EXAM S commands at the terminal or in the log file.
144

E R A S 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 <option> <accession number>

Options

CHEMICAL

ENVIRONMENT

LOAD

PRODUCT

Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->

Options: CHEMICAL

Deletes the contents, by entry access number, of chemical databases currently in the User Database. Each entry corresponds to

a single chemical, and contains the laboratory data describing ionization and (species-specific) partitioning and reaction kinetics.

ENVIRONMENT

Deletes the contents, by entry access number, of environmental databases currently in the User Database. Each entry contains a

canonical physical and chemical model of an aquatic system, including the environmental data needed to compute the reactivity

and transport of synthetic chemicals in the system.

LOAD

Deletes the contents, by entry access number, of chemical loading patterns stored in the User Database. These data include

monthly values (kg/hour) for stream loads, non-point-source loads, groundwater seepage loadings, precipitation loads, and drift

loads of chemicals entering the aquatic environment, plus the magnitude (kg), target environmental sector, and scheduling (month

and day) of chemical pulse loads.

PRODUCT

Deletes the contents, by entry access number, of chemical product data stored in the User Database (UDB). These data include the

Activity Database numbers of reactants and products, the number code of the chemical process, and yield efficiencies (mole/mole)

as an (optional) function of temperature.

Description: ERASE deletes the contents of a single sector of the specified User Database (UDB) library (chemical, environmental, loads,

or product chemistry). The data to be deleted are selected by choosing the appropriate accession number. (If you work in a

multi-user environment, be sure to avoid erasing others’ data.)

Examples:

1. EXAM S-> ERASE ENV 20

Environment 20 erased.
145

This command erases the data stored at Environmental UDB sector number twenty. The space is now available for storing another

dataset.

2. EXAM S-> 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.
146

E X I T

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 EXAM S prompt command level, EXAM S stops and returns control to the computer operating system.

Examples:

1. EXAM S-> EXIT

This command terminates an interactive EXAM S session.
147

H E L P

Displays, on the terminal, information available in EXAM S’ help files. EXAM S 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 EXAM S input data) that tells EXAM S what information to display.

! None--if HELP is typed with no keyword, EXAM S lists the keywords that can be specified to obtain information about other

topics.

! Topic-name--describes either a basic EXAM S 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 EXAM S’ collection of on-line user aids and information texts. This material includes

! Brief discussions of the syntax and function of each of EXAM S’ command words (RECALL, RUN, etc.)

! Definitions, physical dimensions, and meanings of subscripts for EXAM S’ chemical and environmental input data and control

parameters.

! A series of information pages providing orientation to the concepts implemented in the EXAM S 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. EXAM S-> HELP

EXAM S 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 EXAM S main command library. When

responding to one of the topics on the list, EXAM S displays a HELP message on that topic, and a list of subtopics (if any).

2. EXAM S-> HELP QUOIT

No information available for this request.
148

EXAM S->

When you request information for a topic not on file, EXAM S displays a message to that effect and returns you to the EXAM S->

prompt.

3. EXAM S-> HELP QYIELD

QYIELD is a Real Table with dimensions(3,7,4)

Quantum_YIELD (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 (3×7=21) for each potential molecular type of each chemical allow the effects of speciation

and sorption on reactivity to be specified in detail. 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). (QYIELD is an efficiency.)

You can request information about any input datum (chemical, environmental, control parameters, analysis parameters) accessible

to the CHANGE and SET commands. EXAM S 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 180).
149

L I S T

Displays an EXAM S output table on the terminal screen.

Related: Control variables: FIXFIL

Commands: PLOT, PRINT

Syntax: LIST <option>

Options

table-#

ALL

HELP

Prompt: Enter Table Number -> option

Options: table-# specifies the number of an EXAM S output table to be displayed.

ALL Sequential display of all current output tables.

HELP Displays a list of titles of EXAM S output tables.

Description: The LIST command displays EXAM S’ output tables at the terminal. To temporarily halt the output and resume it at the line

where it was interrupted, use CTRL/S followed by CTRL/Q.

When you request a primary table number (that is, an integer from 1 to 20) EXAM S displays the first table of that number present

in the analysis file. If additional tables of that type are present in the file, EXAM S will display the first, and then search for more

tables of that type and, if any are found, ask if you want to see them.

Examples:

1. EXAM S-> LIST

A PRINT, LIST, or PLOT command was issued before executing a RUN. If results exist from a previous simulation, these can be

accessed after issuing the command: SET FIXFIL TO 1

EXAM S-> SET FIXFIL TO 1

EXAM S-> LIST

Enter Table Number -> HELP

1 Chemical inputs: FATE Data

2 Chemical inputs: PRODUCT Chemistry

3 PULSE Chemical Loadings

.

.

.
150

18 Sensitivity Analysis of Chemical FATE

19 Summary TIM E-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 EXAM S results file be displayed on the terminal. For illustrative purposes,

it was assumed that the user had left EXAM S and then returned to inspect Table 18 generated in the previous session.

2. EXAM S-> 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, EXAM S 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, and NYEAR 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 CONTINUEd, 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 EXAM S’ output tables identify the ADB number of the chemical, the indexes of any ions (see SPFLG in the

Data Dictionary on page 180), and the month of the year, as follows.

Table Sub-tables Examples Sub-table Meaning
151

1 1.cc.i 1.01.1 Table.chemical.ion

4-6, 8, NN.mm 4.01 Table.month

10,11,13 10.13 (13 = annual mean)

12 12.cc.mm 12.01.12 Table.chemical.month

14 (Mode 1⁄2) 14.cc 14.01 Table.chemical

14 (Mode 3) 14.cc.mm 14.01.12 Table.chemical.month

15-18,20 NN.cc 18.01 Table.chemical

20.01
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N A M E

Use the NAM E command to attach unique names to datasets.

Related: Control Variables: MCHEM

Commands: CATALOG, ERASE, STORE, RECALL

Syntax: <datatype> NAME IS a[aa...] (up to 50 characters), where <datatype> can be CHEmical, ENvironment, LOad, or PROduct

Prompt: Options available are:

Help - this message.

Quit - return to EXAM S command mode.

<carriage return> = Help

<any other response> - accepted as the new name.

Enter new name->

<datatype>: EXAM S uses these four kinds of datasets:

1. CHEMICAL reactivity and partitioning,

2. ENVIRONMENTal physico/chemical parameters,

3. allochthonous chemical LOADings, and

4. PRODUCT chemistry for generating interconversions among multiple chemicals in an analysis

Description: The NAM E 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 EXAM S’ 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. EXAM S-> CHEM NAME IS Tetrachloroexample

The NAM E 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. EXAM S-> SET MCHEM = 2

EXAM S-> 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. EXAM S-> 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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P L O T

Used to plot character graphics for the chemical state of the ecosystem.

Related: Control Variables: MCHEM

Commands: LIST, PRINT

Syntax: PLOT <Option1, Option2, Option3>

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 EXAM S program prompt

Option->

Plot options: POINT

“POINT” plots are generalized profiles 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 upstream-downstream

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. The “LIST” option produces numerical output which, when re-directed to an external file, can

be used as a data source for any graphics package that can assimilate ASCII files.

Description: Use the PLOT command to display results of the current analysis. Three kinds of character graphic PLOTs are available on-line

from EXAM S: 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 EXAM S prompts. The available second- and third-level options 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
154

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. EXAM S-> 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 EXAM S 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 EXAM S prompt

Option-> AVERAGE

 9.00E-04 -I

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 A C I A||||I||||A I

 V E I A||||S||||A S

 E N I A||||S||||A S

 R T 7.00E-04 -I A||||O||||A O

 A R I A||||L||||A L

 G A I A||||V||||A V

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 I I A||||D||||A D

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 N I A||||M||||A M

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 I A||||L||||A A||||L||||A

 5.00E-04 -+_AAAAAAAAAAA_AAAAAAAAAAA

 Water Col Benthic

EXAMS-> SET MCHEM=2

EXAMS-> PL PO DI AV
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 A C I I I

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 E N I S AAAAASAAAAA

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 O 2.50E-03 -I A|||| ||||A A|||| ||||A

 N I A||||M||||A A||||M||||A

 I A||||G||||A A||||G||||A

 I A||||/||||A A||||/||||A

 I A||||L||||A A||||L||||A

 2.00E-03 -+_AAAAAAAAAAA_AAAAAAAAAAA

 Water Col Benthic

This example illustrates EXAM S’ 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. EXAM S-> 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 EXAM S prompt

Option-> TOTAL

The following options are available:

WATER - Water Column concentrations

SEDIMENTS - Benthic Sediment concentrations

Help - This message

Quit - Return to the EXAM S prompt

Option-> WATER
156

 8.00E-01 -I

 I

 I

 I

 C I

T O 6.00E-01 -I

O N I

T C I 015

A E I H|H

L N I 014 H|H

 T 4.00E-01 -I H|H H|H

M R I H|H H|H

G A I 013 H|H H|H

/ T I E|E H|H H|H

L I I 012 E|E H|H H|H

 O 2.00E-01 -I 010 011 E|E E|E H|H H|H

 N I 009 E|E E|E E|E E|E H|H H|H

 I 008 E|E E|E E|E E|E E|E H|H H|H

 I 005 006 007 E|E E|E E|E E|E E|E E|E H|H H|H

 I 002 003 004 E|E E|E E|E E|E E|E E|E E|E E|E E|E H|H H|H

 0.00E+00 -+_001_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_EEE_HHH_HHH

 WATER COLUMN

The above example illustrates EXAM S’ internal prompts for a PROFILE plot. As with the POINT option, this entire command could be

entered on a single line:

EXAM S-> PLOT PROF TOT WAT

3. EXAM S-> 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 EXAM S prompt

Option-> PLOT

Chemical: Methyl Parathion

Environment: Pond -- code test data

Simulation units: Days

Number of segments: 2

1 2

Type of segment (TYPE): L B

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)

4 - Benthic: average “dissolved” (mg/L)

5 average sorbed (mg/kg)
157

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 I **

 I *

 I ** *

 I *

 I * *

 0.106 I * *

 I * *

 I * *

 I * +++

 I * ++* +++

 5.322E-02 I * ++ * +++

 I ** * + ** +++

 I * ++ * ++++

 I * * *+ ** +++++

 I *+++++++ **** ++++++++++

 0.000 I++++++++++++++++****** *****************+

 +----+----+----+----+----+----+----+----+----+----+

 0.000 73.0 146. 219. 292. 365

 Time, Days

This example illustrates EXAM S’ prompting in KINETIC plots. The numerical options cannot be entered on the command line, but must

be entered in response to the prompts.
158

P R I N T

Use the PRINT command to export an output table to a file for hardcopy printing or incorporation into a document.

Related: Control variables: FIXFIL

Commands: LIST

Syntax: PRINT <option>

Options:

table-#

ALL

Help

Quit

Prompt: Enter Table Number->

Options: table-# specifies the number of an EXAM S output table to be displayed.

ALL Sequential printing of all current output tables.

HELP Displays a list of titles of EXAM S output tables.

Description: The PRINT command transfers EXAM S results tables to an output file for subsequent printing. The command functions much

like the LIST command, except that output is saved for later hardcopy printing or incorporation into a report, rather than being

routed to your interactive terminal. Should the PRINT command result in output at your terminal, you may need to consult

with site ADP personnel to properly direct the print stream to a file.

Examples: See the documentation for the LIST command.
159

Q U I T

Use QUIT to abort a command in progress or to end an interactive EXAM S session.

Related: Control variables:

Commands: EXIT

Syntax: QUIT

Prompt: None

Options: None

Description: Entering QUIT at the EXAM S prompt command level will terminate an interactive session, returning control to the computer’s

operating system. QUIT is included as an option of many EXAM S commands to allow the command to be aborted.

Examples:

1. EXAM S-> 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 EXAM S prompt.

AUDIT-> QUIT

EXAM S->

This command terminates processing of the AUDIT command and returns control to the EXAM S prompt command level. The current

status of AUDIT is not altered.

2. EXAM S-> QUIT

This command terminates an interactive EXAM S session.
160

R E A D

Use the READ command to transfer data from a properly organized non-EXAM S file into the Activity Data Base (ADB).

Related: Control variables: MODE, MCHEM, PRBEN

Commands: WRITE

Syntax: READ <datatype> <name of file>

Prompt: Enter Environment, Chemical, PRZM, Meteorology, Help, or Quit->

Description: The READ command provides a facility for up-loading EXAM S datasets from external ASCII sequential files. These non-EXAM S

files can be stored entirely separately from the main EXAM S User Data Base (UDB), which is contained in a direct access file

named “EXAM S.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 EXAM S.

Under the ENVIRONMENT option of READ, the entire ADB dataset (“months” 1 through 13) will be uploaded from the external file

called <name of file>.

Under the CHEMICAL option of READ, the chemical dataset to be uploaded from <name of file> is put into the MCHEM sector of

the Activity Data Base (ADB).

In the PRZM option of the READ command, EXAM S 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 EXAM S’ 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 (and only if) an EXAM S environment has been selected, the period-of-record monthly averages

are transferred to the corresponding EXAM S 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 EXAM S.
161

Examples:

1. Transfer an environmental dataset from a file called “INLET.DAT” on the default directory; the dataset can then be then STOREd in

EXAM S’ direct access UDB file.

EXAM S-> 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 <name of file> option; the default suffix for meteorology files is “.met”.

EXAM S-> SET MODE=3

EXAM S-> READ MET C:\EXAM S\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 EXAM S

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.

EXAM S-> READ PRZM PRZM2EXA.D89
162

R E C A L L

Use RECALL to upload data from the permanent database (UDB) into current foreground memory (ADB).

Related: Control Variables: MCHEM

Commands: CATALOG, ERASE, NAM E, STORE

Syntax: RECALL <datatype> <UDB#> [AS ADB#]

Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->

Command parameters:

<datatype> can be Chemical, Environment, Load, or Product

(EXAM S 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 EXAM S are held

in a foreground memory bank (Activity DataBase or ADB) with four sectors, one for each datatype required by EXAM S--

<C>hemical reactivity and partitioning,

<E>nvironmental physical and chemical parameters,

allochthonous chemical <L>oadings, and

<P>roduct chemistry for generating interconversions among multiple chemicals in an analysis.

When EXAM S 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 EXAM S 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 dataset number 9

into ADB sector 1, and UDB #14 into sector 2:

Either:

EXAM S-> SET MCHEM TO 1

EXAM S-> RECALL CHEMICAL 9

EXAM S-> SET MCHEM TO 2
163

EXAM S-> RECALL CHEMICAL 14

or, equivalently:

EXAM S-> RECALL CHEMICAL 9 AS 1

EXAM S-> RECALL CHEMICAL 14 AS 2

2. Long-term retention of data required by EXAM S 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:

EXAM S-> RECALL ENVIR 2

Selected environment is: Phantom Inlet, Bogue Sound

EXAM S->
164

R U N

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. EXAM S-> RECALL CHEMICAL 22

Selected compound is: Dibromoexample

EXAM S-> RECALL ENVIRON 17

Selected environment is: Albemarle Sound--Bogue Bank

EXAM S-> SET STRL(1,1,13)=.01

EXAM S-> RUN

Simulation beginning for:

Environment: Albemarle Sound--Bogue Bank

Chemical 1: Dibromoexample

Run complete.

EXAM S->

In this example, a steady-state (MODE=1) 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, EXAM S initial default value) and invoking EXAM S’

simulation algorithms with the RUN command.
165

S E T

Use SET to specify the values of data in the activity database.

Related: Commands: CHANGE (synonym), DESCRIBE, HELP

Syntax: SET <name of variable> TO <new value>

or

SET <name of variable> = <new value>

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. EXAM S-> SET VOL(167) TO 7E5

Subscript out-of-range.

EXAM S-> DESCRIBE VOL

VOL is a Real Vector with 100 elements.

EXAM S> SET VOL(2) TO E

Invalid numeric quantity after TO.

EXAM S-> 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 EXAM S 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. EXAM S-> 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.

EXAM S-> SET TCEL(2,7)=24
166

This command changes the July temperature in segment 2 to 24EC. 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. EXAM S-> 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.

EXAM S-> SET 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.
167

S H O W

Use SHOW to display current data values or control settings.

Related: Control Variables: MCHEM, MONTH

Commands: CHANGE, SET

Syntax: SHOW <option> [range]

Prompt: The following options are available:

Advection, Chemistry, Dispersion, GEometry,

GLobals, Loads, PLot, PUlse Loads,

PRoducts, QUAlity, Time Frame, Variables,

Help, or QUIt->

Command parameters:

Range: Some options of the SHOW command accept the specification of a range of values to define the scope of the data to be

displayed (see Example 1). Use MCHEM to delimit the range of SHOW Chemistry, and MONTH for GEometry, QUAlity, etc.

Options:

ADVECTION

SHOW ADVECTION gives the advective hydrologic flow structure of the current aquatic system. A single element in a dataset might

typically look like the following example.

J FR AD 1 J FRom ADvection: Source Segment

I TO AD 3 I TO ADvection: Terminus

ADV PR 1.00 ADVection Proportion: Percent

of total JFRAD flow on path

Path No.: 1 Vector index for SETting data

No more than NCON hydrologic pathways can be specified. If more are needed, special versions of EXAM S can be produced. Specify

export pathways by entering a zero (0) for the number of the segment to receive the flow (ITOAD). Do not specify a hydrologic source

term by entering zero in the JFRAD vector; instead use stream flows, non-point-source flows, etc.

DISPERSION

SHOW DISPERSION displays the input data describing transport in the active (loaded in the ADB) Environmental dataset. The index

vectors (JTURB, ITURB) define the existence of inter-segment dispersive transport paths. A zero in either vector, when paired with a

non- zero value at the corresponding position in the other index vector, is taken as a boundary condition with an uncontaminated body

of water. A single element in a dataset might typically be displayed like the following example.

J TURB 1 Segment number for dispersion

I TURB 2 Segment number for dispersion

XS TUR m 5.000E+04 Cross-sectional area of path 2

CHARL m 2.53 CHARacteristic_Length of path

DSP m /h 4.676E-05 Eddy DiSPersion coefficient 2

 Path No.: 1 Vector index for data entry
168

No more than NCON hydrologic pathways can be specified. If more are needed, this number can be increased and EXAM S 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

SHOW GLOBALS 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:

when PRSW 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 EXAM S 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

N PROC 7 Number of transforming PROCess

R FORM 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 180), 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

EXAM S can be created.

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.
169

PULSE LOADS

SHOW PULSE LOADS displays the specifications for allochthonous pulses of chemicals entering the system. This display may not

represent the final values used in the analysis, because EXAM S will modify loads that result in violation of the linearizing assumptions

used to construct the program. Although faulty pulse loads are discarded, EXAM S 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 EXAM S’ 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 subscript 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)”.

TIM E FRAM E

SHOW TIM E FRAM E 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 EXAM S 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:

EXAM S-> 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:

EXAM S-> SHOW PH(*,9)

and the pH of segment 7 through the year could be displayed by:

EXAM S-> SHOW PH(7,*)
170

S T O R E

Use STORE to download current (ADB) data into the permanent database (UDB).

Related: Control Variables: MCHEM

Commands: CATALOG, ERASE, NAM E, RECALL

Syntax: STORE <datatype> [ADB# IN] <UDB#>

Prompt: Enter Environment, Chemical, Load, Product, Help, or Quit->

Command parameters:

<datatype> can be Chemical, Environment, Load, or Product

(EXAM S 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

EXAM S are held in a foreground memory bank (Activity DataBase or ADB) with four sectors, one for each datatype required

by EXAM S:

CHEMICAL reactivity and partitioning,

ENVIRONMENTal 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 EXAM S 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 data types.) When the ADB# (an integer from 1 to KCHEM) 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:

EXAM S-> SET MCHEM TO 1

EXAM S-> STORE CHEMICAL 9

EXAM S-> SET MCHEM TO 2

EXAM S-> STORE CHEMICAL 14

or, equivalently:

EXAM S-> STORE CHEMICAL 1 IN 9
171

EXAM S-> STORE CHEMICAL 2 IN 14

2. Long-term retention of data required by EXAM S 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:

EXAM S-> STORE ENVIR 2

Environment record 2 is in use with

Pond -- code test data

Replace?-> no

Nothing changed.

EXAM S-> STORE ENVIR 14

Environment stored: Phantom Inlet-Bogue Sound Study Data

EXAM S->

Note that EXAM S provides a measure of protection against accidental overwriting of existing datasets, an important courtesy in

a multi-user environment.
172

W R I T E

Use the WRITE command to transfer data from the Activity Data Base (ADB) to an external (outside EXAM S’ UDB direct access file

EXAM S.DAF) sequential file.

Related: Control variables: MODE, MCHEM

Commands: READ

Syntax: WRITE <datatype> <name of file>

Prompt: Enter Environment, Chemical, Load, Help, or Quit->

Description: The WRITE command provides a facility for off-loading EXAM S datasets into external ASCII sequential files. These files are

stored separately from the main EXAM S 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 prior to invoking WRITE, when the intention is to transfer data from the UDB to an external file. The

write command must be used to store problem scenarios (load patterns, product chemistries) or datasets in which the number

of environmental segments exceeds 100 or the number of chemicals being studied exceeds five.

Under the CHEMICAL option of WRITE, the chemical dataset to be downloaded to <name of file> 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. The setting of MODE affects the

amount of data written to the file. In Mode 1, only monthly data and mean values are written from the ADB, pulse loadings are

deleted as they are of no relevance to a steady-state analysis. 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. Using EXAM S’ WRITE facility to manage chemical

loading scenarios thus requires some care when moving across operational modes. For example, a MODE 3 pulse load scenario

can be read by EXAM S operating in MODE 2, but when the scenario is RUN, the daily values are accumulated into a single set of

initial mass loadings for each segment.

In the PRODUCT option of WRITE, product chemistry specifications are written to an external file; the file contains internal character

fields documenting the data structure. 

Examples:

1. In this example, the environmental data is RECALLed from the UDB, and the data are downloaded to a file called “INLET.DAT”

on the default directory.

EXAM S-> RECALL ENVIRONMENT 12

Selected environment is: Chinquoteague Inlet

EXAM S-> WRITE

Enter Environment, Chemical, Load, Product, Help, or Quit-> EN

Enter name of file, Help, or Quit-> INLET.DAT

2. To continue the above example, an entire loadings dataset could be stored in another file by changing the MODE to 3. Note that

a directory other than the default can be specified as part of the WRITE command <name of file> option.

EXAM S-> SET MODE=3

EXAM S-> WRITE LOAD C:\EXAM S\PROJECTX\INLET.LOD
173

Z E R O

Use the ZERO command to initialize (set to zero) loadings databases or the concentration of pollutant chemicals throughout the ecosystem.

Related: Control variables: MODE

Commands: CONTINUE, RUN

Syntax: ZERO <option>

Options:

PULSE LOADS

LOADS

RESIDUALS

Prompt: The following options are available:

Pulse Loads - zero all pulse loads,

Loads - zero all other loads,

Residuals - zero all pollutant concentrations,

Help - this message, or

Quit - return to command mode with no action.

ZERO->

Description: The ZERO command initializes (sets to zero) the entire suite of allochthonous chemical pulse loadings (IMASS), longer term

loadings (stream loads, drift loads, etc.), or the current values of pollutant chemical concentrations throughout the ecosystem.

The ZERO command is designed primarily for use during the course of temporally segmented simulation studies. The same

effect can be achieved with multiple applications of the CHANGE/SET command; ZERO is a block-mode implementation that

reduces the work needed to remove loadings datasets. (See Example 1 in the documentation of the CONTINUE command.)

Examples:

1. EXAM S-> SET MODE=2

EXAM S-> RECALL CHEMICAL 22

Selected compound is: Dibromoexample

EXAM S-> RECALL ENVIRON 17

Selected environment is: Albemarle Sound--Bogue Bank

EXAM S-> SET STRL(1,1,13)=.01

EXAM S-> SET IMASS(1)=2.0

EXAM S-> SET ISEG(1)=14

EXAM S-> SET ICHEM(1)=1
174

EXAM S-> RUN

Simulation beginning for:

Environment: Albemarle Sound--Bogue Bank

Chemical 1: Dibromoexample

Run complete.

.

.

.

EXAM S-> ZERO PULSE LOADS

EXAM S-> 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, EXAM S’ 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, EXAM S 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.
175

6.0 EXAMS Data Dictionary

ABSER

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

EXAM S 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 EXAM S

simulates the behavior of several (MCHEM) chemicals

simultaneously, the ADB for chemicals has MCHEM separate

sectors. These data are lost when you EXIT from EXAM S, so

be sure to STORE any new or corrected datasets before

leaving EXAM S.

ABSOR

ABSORption spectra (wavelength, ion, chemical)

Units: cm (mole/L) (N.B.: When entering data, “w” is the -1 -1

index number for ë, not the wavelength per se. See page 200

for the mapping between ABSOR entries and ë.)

Mean decadic molar light extinction coefficients in 46

wavelength intervals over 280 – 825 nm. For wavelength

“w” and chemical “c”:

AB 3SOR (w,1,c) is molar absorption coefficient of RH

(neutral molecule)

AB 4SOR (w,2,c) is molar absorption coefficient of RH (+1 +

cation)

AB 5SOR (w,3,c) is molar absorption coefficient of RH (+2 2+

cation)

AB 6SOR (w,4,c) is molar absorption coefficient of RH (+3 3+

cation)

AB 2SOR (w,5,c) is molar absorption coefficient of RH (!1

!

anion)

ABSOR (w,6,c) is molar absorption coefficient of RH (!2

2!

anion)

ABSOR (w,7,c) is molar absorption coefficient of R (!3

3!

anion)

ADVPR

ADVection PRoportion (path)

Units: n/a Range: > 0 – 1.0

PRoportion of flow ADVected 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 ADVPRs for each segment

must sum to either 0 or 1, failing which, EXAM S 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.

AerMet

Aquatic Aerobic Metabolism Half-Life (chemical)

Units: days

Aquatic aerobic metabolism half-life of each study

chemical.

When biolysis rate constants are zero, AerMet is used to

calculate biolysis rate constants for water-column bacterioplankton. In making this computation, the planktonic

populations are averaged for the entire water body. In Mode

1 and Mode 2, the single temporal value in use at RUN time

is used. For other Modes, the mid-Summer populations are

used.

AIRTY

AIR mass TYpe (month)

Units: letter codes

Select: Rural (default), Urban, Maritime, or Tropospheric

AnaerM

Aquatic Anaerobic Metabolism half-life (chemical)

Units: Days

Aquatic anaerobic metabolism half-life of each study

chemical. When biolysis rate constants are zero, AnaerM is

used to calculate biolysis rate constants for the benthic

microbiota. This computation uses a population number for

benthic bacteria calculated as the average for all surficial
176

sediments. In Mode 1 and Mode 2, the single temporal value

in use at RUN time is used. For other Modes, the

mid-Summer populations are used.

AREA

AREA (segment)

Units: m2

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 TURBidity (month)

Units: km

Equivalent aerosol layer thickness.

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/100g dry sediment

Population density of benthic bacteria that degrade

xenobiotics. The abbreviation “cfu” stands for a “colony

forming unit.”

BNMAS

BeNthic bioMASs (segment, month)

Units: g(dry)/m2

Biomass of small benthos – infauna subject to biosorption.

BULKD

BULK Density (segment, month)

Units: g/cm3

Fresh weight per unit volume of benthic sediments.

CEC

Cation Exchange Capacity (segment, month)

Units: meq/100g (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

CHEMNA

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:

EXAM S-> 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

CHLorophylls + pheophytins (segment, month)

Units: mg/L

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.

CHPAR

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”.
177

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, EXAM S (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 EXAM S itself sets the value of CINT according to the

needs of the analysis.

CLOUD

CLOUDiness (month)

Units: dimensionless Range: 0 – 10

Mean monthly opaque 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; EXAM S 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

Concent 2ration of dissolved oxygen (O ) 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

DRiFt 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 2

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

a

E for Acid Hydrolysis (form, ion, chemical)

Units: kcal/mol

A arrhenius activation energy E 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: DOC-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 ) is -1 -1

calculated from:

EAYLD

E A YieLD (path)

Units: kcal

a

EAYLD(p) is activation energy E to 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)):

See also: CHPAR, NPROC, RFORM, TPROD, YIELD

EBH
178

a

E for Base Hydrolysis (form, ion, chemical)

Units: kcal/mol

A arrhenius activation energy E 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: DOC-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 -1 -1

calculated from:

EHEN

Enthalpy term for HENry’s law (chemical)

Units: kcal/mol

Used to compute Henry’s law constants as a function of

TCEL (environmental temperature). When EHEN is non-zero,

the Henry’s law constant (H) affecting volatilization at a

particular (segment, month) is computed from TCEL:

EK1O2

a

E K1O2 (singlet oxygen) (form, ion, chemical)

Units: kcal/mol

Arrhenius activation energy for singlet oxygen

photo-oxygenation of chemicals. Matrix indices match those

of K1O2, 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

EK1O2 is non-zero, the second-order rate constant K (M h ) -1 -1

is calculated as:

ELEV

ELEVation

Units: meters above mean sea level

Ground station elevation.

ENH

a

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 ) is -1

calculated from:

EOX

a

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 EOX is

non-zero, the second-order rate constant K (M h ) is -1 -1

calculated from:

EPK

Enthalpy term for pK (ion, chemical)

Units: kcal/mol

When EPK is non-zero, pK is computed as a function of

temperature via:

The vector indices for EPK (“c” denotes the chemical) are

4 3 EPK(1,c) contains datum for generation of RH from RH +

5 4 EPK(2,c) contains datum for generation of RH from RH 2

+ +

6 5 EPK(3,c) contains datum for generation of RH from RH 3+ 2+

2 3 EPK(4,c) contains datum for generation of RH from RH -

2

EPK(5,c) contains datum for generation of RH from RH =

-

EPK(6,c) contains datum for generation of R from RH 3- =

ERED

a

E REDuction (form, ion, chemical)

Units: kcal/mol

Arrhenius activation energy for reductive 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 ) is -1 -1

calculated as:

ESOL

Enthalpy term for SOLubility (ion, chemical)
179

Units: kcal/mol

ESOL describes chemical solubility as a function of

temperature (TCEL). The matrix indices (“c” denotes the

chemical) denote:

3

ESOL(1,c) is datum for solubility of neutral molecules RH

ESOL(2,c) is datum for solubility of singly charged cations

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 R3-

EVAP

EVAPoration (segment, month)

Units: mm/month

(Monthly) evaporative water losses from ecosystem

segments.

EVPR

Molar hEat 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:

EVENTD

Event Duration (5)

Units: days Range: 1–364

In Mode 3, Exams reports period average, minimum, and

peak concentrations, and the minimum, average, and peak

concentration of the largest (as defined by their mean

values) 96-hour, 21-day, 60-day, and 90-day events during

each time segment (one or more years) of the simulation. Up

to five additional averaging periods can be specified; these

are reported in an addendum to Table 20.

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 EXAM S 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.

FROC

FRaction Organic Carbon (segment, month)

Units: dimensionless

Organic carbon content of solids as fraction of dry weight.

FROC is coupled to KOC to generate the sediment partition

3

coefficient for neutral chemicals (R-H ) as a function of a

property (organic carbon content) of the sediment.

HENRY

HENRY’s law constant (chemical)

Units: atmosphere-m /mole 3

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):

ICHEM

I CHEMical (event)

Units: n/a Range: 1--KCHEM

Event “e” is a pulse of chemical number ICHEM(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(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 ICHEM(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
180

IMASS

Initial MASS (event)

Units: kg

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 CONTINUation 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

CONTINUed 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.

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 ICHEM(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: 1--KOUNT

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 M (or half its aqueous solubility, -5

whichever is less), the size of the event is reduced, to avoid

violating the linearizing assumptions used to create EXAM S.

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

ITOAD

I TO ADvection (path)

Units: n/a Range: 0--KOUNT (0 = export)

Chemicals are advected to 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

ITURB

I TURBulent dispersion (path)

Units: n/a Range: 0--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.

JFRAD

J FRom ADvection (path)

Units: n/a Range: 1– KOUNT

Chemicals are advected from segment JFRAD(p) to segment

ITOAD(p). The matching (same subscript) members of

JFRAD, ITOAD, and ADVPR define an advective hydrologic

flow pathway. EXAM S 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.

See also: ITOAD, ADVPR

JTURB

J TURBulent dispersion (path)

Units: n/a Range: 0--KOUNT
181

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.

See also: CHARL, DSP, ITURB, XSTUR

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--1: aqueous, 2:

solids-sorbed, and 3: DOC-complexed; by seven ions--1:

neutral, 2-4: cations, and 5-7: anions.

KBACS

K BACteria benthoS (form, ion, chemical)

Units: (cfu/mL) hour -1 -1

Second-order rate constants--benthic sediment bacterial

biolysis of chemicals normalized by “colony forming units”

(cfu) per mL of test medium. When the matching (same

10 subscripts) Q (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 25EC, and local

values of the rate constant are computed as a function of

temperature (TCEL) in each ecosystem segment. Indices refer

to four forms--1: aqueous, 2: solids-sorbed, 3:

DOC-complexed, and 4: bio-sorbed; by seven ions--1:

neutral, 2-4: cations, and 5-7: anions.

KBACW

K BACterioplankton Water (form, ion, chemical)

Units: (cfu/mL) hour -1 -1

Second-order rate constants K for water column bacterial

biolysis of chemicals normalized by “colony forming units”

(cfu) per mL of test medium. When the matching (same

10 subscripts) Q (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 25EC, and local

values of the rate constant are computed as a function of

temperature (TCEL) in each ecosystem segment. Indices refer

to four forms--1: aqueous, 2: solids-sorbed, 3:

DOC-complexed, and 4:bio-sorbed; by seven ions--1: neutral,

2-4: cations, and 5-7: anions.

KBH

K Base Hydrolysis (form, ion, chemical)

Units: per mole [OH ] per hour -

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--1:

neutral, 2-4: cations, and 5-7: anions.

KCHEM

Number of chemicals under review in current study.

Units: n/a

KDP

K Direct Photolysis (ion, chemical)

Units: hour-1

Estimated photolysis rates--use only when ABSOR, the actual

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(1,c) are pseudo-first-order photolysis rate constants of

n 3eutral molecules RH

KDP(2,c) are pseudo-first-order photolysis rate constants of

4

singly charged cations RH +

KDP(3,c) are pseudo-first-order photolysis rate constants of

doub 5ly charged cations RH 2

+

KDP(4,c) are pseudo-first-order photolysis rate constants of

6

triply charged cations RH 3

+

KDP(5,c) are pseudo-first-order photolysis rate constants of

2

singly charged anions RH -

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 R3-

KIEC

Kp for Ion Exchange Capacity (ion, chemical)

Units: Kp (meq/100g dry)-1
182

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 for anionic 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 EXAM S using

KIEC.

KIEC(1,c) is datum for relating CEC and sorption of singly

4

charged cation RH +

KIEC(2,c) is datum for relating CEC and sorption of doubly

5

charged cation RH 2

+

KIEC(3,c) is datum for relating CEC and sorption of triply

6

charged cation RH 3

+

KIEC(4,c) is datum for relating AEC and sorption of singly

2

charged anion RH -

KIEC(5,c) is datum for relating AEC and sorption of doubly

charged anion RH=

KIEC(6,c) is datum for relating AEC and sorption of triply

charged anion R3-

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

1st-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--1: 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

behavior in the 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) × FROC(segment, month)

KOUNT

Number of segments used to define current ecosystem.

Units: n/a

KOW

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 (EOX) is zero, KOX

is interpreted as the second-order rate constant. When the

matching entry in EOX 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--1: aqueous, 2:

solids-sorbed, and 3: DOC-complexed; by seven ions--1:

neutral, 2-4: cations, and 5-7: anions.

KO2

KO2 (segment, month)

Units: cm/hour

Oxygen exchange constant or piston velocity at 20 degrees

C in each ecosystem segment. When explicit values of KO2

are not present as environmental descriptors, EXAM S

calculates KO2 from windspeed.

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:

K 3PB(1,c) datum for biosorption of neutral molecules RH

KPB(2,c) datum for biosorption of singly charged cations

RH4+

KPB(3,c) datum for biosorption of doubly charged cations

RH52

+

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 R3-
183

KPDOC

KP Dissolved Organic Carbon (ion, chemical)

Units: (ug/g)/(mg/L)

Partition coefficient (Kp) for equilibrium complexation with

DOC. The “c” subscript denotes the chemical; the “ion”

subscripts identify:

KPDOC(1,c) datum for complexation of neutral molecules

RH3

KPDOC(2,c) datum for complexation of singly charged

4

cations RH +

KPDOC(3,c) datum for complexation of doubly charged

5

cations RH 2

+

KPDOC(4,c) datum for complexation of triply charged

6

cations RH 3

+

KPDOC(5,c) datum for complexation of singly charged

2

anions RH -

KPDOC(6,c) datum for complexation of doubly charged

anions RH=

KPDOC(7,c) datum for complexation of triply charged anions

R

3-

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:

K 3PS(1,c) datum for sorption of neutral molecules RH

K 4PS(2,c) datum for sorption of singly charged cations RH +

K 5PS(3,c) datum for sorption of doubly charged cations RH 2

+

K 6PS(4,c) datum for sorption of triply charged cations RH 3

+

K 2PS(5,c) datum for sorption of singly charged anions RH -

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

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-order rate 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--1: aqueous, 2:

solids-sorbed, and 3: DOC-complexed; by seven ions--1:

neutral, 2-4: cations, and 5-7: anions.

K1O2

K1O2 (singlet oxygen) (form, ion, chemical)

Un 2its: per M [ O ] per hour 1

Second-order rate constants for singlet oxygen

photo-oxygenation of chemicals. When the matching (same

subscripts) Arrhenius activation energy (EK1O2) is zero,

K1O2 is interpreted as the second-order rate constant. When

the matching entry in EK1O2 is non-zero, K1O2 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--1:

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. EXAM S 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.

LENG

LENGth (segment)

Units: m

Length of a reach – used to compute volume, area, depth.

LOADNM

LOADings database NaMe (50 characters)

Units: n/a

Do not use “CHANGE” or “SET” to enter names! The NaMe

for a LOADings database is entered via the command

sequence:

EXAM S-> LOAD NAME IS nnn...
184

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. EXAM S 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 CHEMical

Units: n/a

Number of chemical in activity data base.

MODE

MODE sets the operating “mode” of EXAM S.

Three operating modes are available; these are selected by

SETting MODE to 1, 2, or 3:

MODE Operational characteristics of EXAM S

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.

M P

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 of fusion is

taken as 13 eu.

MW T

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

Non-Point-Source FLow (segment, month)

Units: m /hour 3

Non-point-source water flow entering ecosystem segments.

NPSLD

Non-Point-Source LoaD (segment, chemical, month)

Units: kg/hour

Chemical loadings entering segments via non-point sources.

NYEAR

Number 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
185

Concentration of environmental oxidants in near-surface

waters (e.g., peroxy radicals). EXAM S computes

segment-specific oxidant concentrations using ultra-violet

light extinction in the system.

OUTFIL

OUTput FILe controls (10)

Units: “Y” or “N”

Control the production of output files. Set the corresponding

member to “Y” to produce the file(s); set it to “N” to

suppress file production. 

1: standard report file (report.xms)

2: standard plotting files (ssout.plt, kinout.plt)

3: BASS data transfer file (bassexp.xms)

4: FGETS data transfer files (fgetscmd.xms and

fgetsexp.xms)

5: HWIR data transfer file (HWIRExp.xms)

6: compartment-oriented EcoTox exposure data file

(EcoToxC.xms)

7: compartment-oriented event analysis and report file

(EcoRiskC.xms)

8: reach-oriented EcoTox exposure data file

(EcoToxR.xms)

9: reach-oriented event analysis and report file

(EcoRiskR.xms)

10: full Exams compartment file of time-varying

concentrations (FullOut.xms)

OZONE

OZONE (month)

Units: centimeters NTP Typically 0.2--0.4 cm

M 3ean (monthly) ozone (O ) content of atmosphere.

EXAM S includes a database (ozone.daf) of total column

ozone data summarized from the Total Ozone Mapping

Spectrometer (TOMS) that flew on the Nimbus7 spacecraft

from 11/Nov/78 through 06/May/93. From the latitude and

longitude of the environment, EXAM S 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. In this grid,

south latitude is negative, north latitude positive; west

longitude is negative, east longitude positive.

PCPLD

PreCiPitation LoaD (segment, chemical, month)

Units: kg/hour

Chemical loadings entering each segment via rainfall.

PCTW A

PerCenT W Ater (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). PCTW A 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 PCTW A 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

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

hyd 3ronium ions [H O ] in gram-equivalents per liter. +

PK

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 subscript values.) When EPK(I, c) is non-zero,

PK is taken as the base-10 logarithm of the pre-exponential

factor in the equation for pK as a function of environmental

temperature TCEL.

The vector indices for PK (“c” denotes the chemical) are

4 3 PK(1,c) contains datum for generation of R-H from RH +

5 4 PK(2,c) contains datum for generation of R-H from RH 2

+ +

6 5 PK(3,c) contains datum for generation of R-H from RH 3+ 2+

2 3 PK(4,c) contains datum for generation of R-H from RH -1

2

PK(5,c) contains datum for generation of R-H from RH =

-

PK(6,c) contains datum for generation of R from RH 3- =

PLMAS

PLanktonic bioMASs (segment, month)

Units: mg (dry weight)/L

Total plankton subject to biosorption of synthetic chemicals.

POH

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. -

PRBEN

PRoportion of sorbed chemical delivered to BENthic zone

Unitless

The PRZM model generates an output file that can be read by
186

the READ 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.

PRODNM

PRODuct 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:

EXAM S-> PRODUCT 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 ... .”

PRSW

PRint SWitch

Units: n/a

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.).

QTBAS

Q Ten BActeria benthoS (form, ion, chemical)

Units: dimensionless

Q10 values for benthic bacterial biolysis (see KBACS) of

10 chemical. “Q " is the increase in the second-order rate

constant due to a 10EC increase in temperature. Indices refer

to 28 molecular spp: 4 forms--1:aqueous, 2:solids-sorbed,

3:DOC-complexed, and 4: bio-sorbed; by 7 ions--1:neutral,

2-4:cations, and 5-7:anions. When QTBAS is non-zero, the

matching (same subscripts) rate constant is computed as:

KBACS(f,i,c)=QTBAS(f,i,c) × KBACS(f,i,c) (TCEL(seg,month)-QTBTS)/10

QTBAW

Q Ten BActeria Water (form, ion, chemical)

Units: dimensionless

Q10 values for bacterioplankton biolysis (see KBACW) of

10 chemical. “Q " is the increase in the second-order rate

constant due to a 10EC increase in temperature. Indices refer

to 28 molecular spp: 4 forms--1:aqueous, 2:solids-sorbed,

3:DOC-complexed, and 4: bio-sorbed; by 7 ions--1:neutral,

2-4:cations, and 5-7:anions. When QTBAW is non-zero, the

matching (same subscripts) rate constant is computed as:

KBACW(f,i,c)=QTBAW(f,i,c) × KBACW(f,i,c) (TCEL(seg,month)-QTBTW/10

QTBTW

Q_Ten_Base_Temperature_Water (form,ion,chemical)

Units: EC

Temperature of aerobic limnetic metabolism study.

QTBTS

Q_Ten_Base_Temperature_Sediment (form,ion,chemical)

Units: EC

Temperature of anaerobic benthic metabolism study.

QYIELD

Quantum_YIELD (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

(3×7=21) for each potential molecular type of each chemical

allow the effects of speciation and sorption on reactivity to

be specified in detail. 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). (QYIELD is an efficiency.)

RAIN

RAINfall (month)

Units: mm/month

Average (monthly) rainfall in geographic area of system.

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,
187

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)

(RFLAT - LAT) corrects for North or South displacement of

the ecosystem LATitude from the location (RFLAT) of a

photochemical study used to develop a matched (same

subscript) KDP pseudo-first-order rate constant.

RF 3LAT(1,c) refer to photolysis of neutral molecules RH

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

+

R 2FLAT (5,c) refer to photolysis of singly charged anions RH -

RFLAT (6,c) refer to photolysis of doubly charged anions RH=

RFLAT (7,c) refer to photolysis of triply charged anions R3-

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 Neutral Cations Anions Total

Valence 0 1+ 2+ 3+ 1- 2- 3- (all)

Forms: Dissolved 1 5 9 13 17 21 25 29

Solids-sorbed 2 6 10 14 18 22 26 30

DOC-complexed 3 7 11 15 19 23 27 31

Biosorbed 4 8 12 16 20 24 28 32

RHUM

Relative HUMidity (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.

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: m /hour 3

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

SOLubility (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 have the same subscript values.) 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(I, c)=1”) when entering chemical

data in order to show EXAM S the ionic structure of the

chemical. When EXAM S starts, only SPFLG(1,*) are set, i.e.,

the default chemical structure is a neutral (non-ionizing)

molecule. As additional SPFLG are set, EXAM S displays the

additional chemical data tables needed to display the
188

properties of the ionic species.

set SPFLG(1,c)=1 to signal existence of a neutral molecule

RH3

set SPFLG(2,c)=1 to signal existence of a singly charged

4

cation RH +

set SPFLG(3,c)=1 to signal existence of a doubly charged

5

cation RH 2

+

set SPFLG(4,c)=1 to signal existence of a triply charged

6

cation RH 3

+

set SPFLG(5,c)=1 to signal existence of a singly charged

2

anion RH -

set SPFLG(6,c)=1 to signal existence of a doubly charged

anion RH=

set SPFLG(7,c)=1 to signal existence of a triply charged

anion R3-

SPRAY

SPRAY drift from agricultural chemicals

Unitless percentage

The PRZM model generates an output file that can be read by

the READ command in EXAM S. 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. EXAM S defaults

SPRAY to 10%. Note that values of SPRAY are entered as

percentages rather than as fractions.

STFLO

STream FLOws (segment, month)

Units: m /hour 3

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. EXAM S 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!

STRLD

STReam LoaD (segment, chemical, month)

Units: kg/hour

Chemical loadings entering ecosystem segments via stream

flow.

STSED

STream-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

Name of aquatic ecoSYStem TYPe (50 characters)

Units: n/a

Do not use “CHANGE” or “SET” to enter names! The name of

a water body is entered into the database via the command

sequence:

EXAM S-> 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,

EXAM S-> CHAN SYSTYP(7) TO ...

TCEL

Temperature in 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.

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

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, EXAM S 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
189

TEND. At the end of each integration, TINIT is reset to TEND.

The simulation can be extended by invoking the

“CONTINUE” command; EXAM S 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 INITial 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. EXAM S 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.

TPROD

Transformation PRODuct (path)

Units: n/a Range: 1-KCHEM

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

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 EXAM S 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

Used to compute Henry’s law constant when HENRY datum

is zero (0) but VAPR is non-zero:

HENRY = (VAPR/760) / (SOL/MW T)

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

VOLume (segment)

Units: m3

Total environmental volume of ecosystem segments.

WIDTH

WIDTH (segment)

Units: m

Average bank-to-bank distance–for computing volume, area,

depth of lotic systems described via length, width, and

cross-sectional areas.

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: m2

Area of water body in section along advective flowpath.

XSTUR

X Section for TURbulent dispersion (path)

Units: m2

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
190

computed as:

E(p) (m /hour) = DSP(p) × XSTUR(p) / CHARL(p) 3

See also: CHARL, DSP, ITURB, JTURB

YEAR1

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
191

Appendices

Appendix A

Partitioning to Natural Organic Colloids

EXAM S 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 EXAM S

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), reversedphase 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

Kow(1)

log Kpdoc and Kpdoc/Kow ratios

limnetic ratio benthic ratio

1,3,6,8-TCDD 33423-92-6 (Servos and Muir 1989) 7.13 5.50 0.023 6.06 0.085 (2)

1,3,6,8-TCDD 33423-92-6 (Servos et al. 1989) 7.13 5.12 0.010 (2)

H7CDD 37871-00-4 (Servos et al. 1989) 8.20 6.85 0.045 (2)

O8CDD 3268-87-9 (Servos et al. 1989) 8.60 5.78 0.002 (2)

p,p'-DDT 50-29-3 (Carter and Suffet 1982) 6.36 4.84 0.030

p,p'-DDT 50-29-3 (Landrum et al. 1984) 6.36 4.52 0.014

p,p'-DDT 50-29-3 (Chiou et al. 1987) 6.36 4.39 0.011

p,p'-DDT 50-29-3 (Eadie et al. 1990) 6.36 4.36 0.010

p,p'-DDT 50-29-3 (Kulovaara 1993) 6.36 3.81 0.003

Benzo[a]pyrene 50-32-8 (Landrum et al. 1984) 5.97 4.56 0.039

Benzo[a]pyrene 50-32-8 (Alberts et al. 1994) 5.97 4.80 0.068

Benzo[a]pyrene 50-32-8 (Landrum et al. 1985) 5.97 5.56 0.389

Benzo[a]pyrene 50-32-8 (Morehead et al. 1986) 5.97 4.80 0.068

Benzo[a]pyrene 50-32-8 (Kukkonen et al. 1989) 5.97 5.33 0.229

Benzo[a]pyrene 50-32-8 (McCarthy et al. 1989) 5.97 5.16 0.155

Benzo[a]pyrene 50-32-8 (Kukkonen et al. 1990) 5.97 5.18 0.162

Benzo[a]pyrene 50-32-8 (Eadie et al. 1990) 5.97 4.57 0.040

Benzo[a]pyrene 50-32-8 (Kukkonen and Oikari 1991) 5.97 5.00 0.107

Benzo[a]pyrene 50-32-8 (Kulovaara 1993) 5.97 4.36 0.025

Dehydroabietic acid 1740-19-8 (Kukkonen and Oikari 1991) 4.80 2.70 0.008 (3)
Compound CAS # Reference Log

Kow(1)

log Kpdoc and Kpdoc/Kow ratios

limnetic ratio benthic ratio

192

Dieldrin 60-57-1 (Kosian et al. 1995) 5.25 4.43 0.151 (3)

Fluoranthene 206-44-0 (Brannon et al. 1995) 4.95 3.83 0.076

Mirex 2385-85-5 (Yin and Hassett 1989) 6.89 6.08 0.155 (4)

Naphthalene 91-20-3 (Kukkonen et al. 1990) 3.30 3.00 0.501

Phenanthrene 85-01-8 (Landrum et al. 1985) 4.46 4.18 0.525

Phenanthrene 85-01-8 (Landrum et al. 1987) 4.46 4.04 0.380

Phenanthrene 85-01-8 (Chin and Gschwend 1992) 4.46 4.19 0.537

4-PCB 2051-62-9 (Eadie et al. 1990) 4.40 4.02 0.417

2,4,4'-PCB 7012-37-5 (Chiou et al. 1987) 5.62 3.55 0.009

2,4,2',4'-PCB 2437-79-8 (Landrum et al. 1987) 6.29 5.49 0.158

2,4,2',4'-PCB 2437-79-8 (Caron and Suffet 1989) 6.29 5.21 0.083

2,4,2',4'-PCB 2437-79-8 (Kukkonen et al. 1990) 6.29 4.30 0.010

2,4,2',4'-PCB 2437-79-8 (Hunchak-Kariouk and Suffet 1994) 6.29 4.68 0.025

2,5,2',5'-PCB 35693-99-3 (Landrum et al. 1984) 6.09 3.88 0.006

2,5,2',5'-PCB 35693-99-3 (Eadie et al. 1990) 6.09 3.88 0.006

2,5,2',5'-PCB 35693-99-3 (Kukkonen et al. 1990) 6.09 4.60 0.032

3,4,3',4'-PCB 32598-13-3 (Kukkonen et al. 1990) 5.62 4.90 0.191 (5)

3,4,3',4'-PCB 32598-13-3 (Kukkonen and Oikari 1991) 5.62 4.00 0.024

2,4,5,2',5'-PCB 37680-73-2 (Chiou et al. 1987) 6.11 4.05 0.009

2,4,5,2',4,5'-PCB 35065-27-1 (Kukkonen et al. 1990) 7.75 5.54 0.006 (5)

2,4,5,2',4,5'-PCB 35065-27-1 (Eadie et al. 1990) 7.75 4.42 0.0005

Pyrene 129-00-0 (Landrum et al. 1987) 5.18 4.71 0.339

Pyrene 129-00-0 (Eadie et al. 1990) 5.18 3.76 0.038

Pyrene 129-00-0 (Chin and Gschwend 1992) 5.18 4.73 0.355

6 PAH (Lüers and ten Hulscher 1996) 3.326

37 Compounds (Ozretich et al. 1995) 0.072

Averages 0.074 0.46

1. Kow from ChemFate database (http://esc.syrres.com/efdb.htm) except as otherwise indicated.

2. (Govers and Krop 1998)

3. Kow cited by author

4. (Devillers et al. 1996)
193

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 DOC . Fluoranthene K in sediment pore waters.

Chemosphere 30:419-428.

Caron, G., and I. H. Suffet. 1989. Binding of nonpolar 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 of pyrene by humic substances: The

effect of dissolved oxygen 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. Threephase 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.

Govers, 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.

Lüers, F., and T. E. M. ten Hulscher. 1996. Temperature effect

on the partitioning of polycyclic aromatic hydrocarbons

between natural organic carbon and water. Chemosphere

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. Reversedphase separation of estuarine interstitial water fractions and
194

18 the consequences of C 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 polychlorinated biphenyl 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-dioxin to 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-p-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., and K. D. Jones. 1997. Effects of dissolved oxygen

oc and light exposure on determination of K 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.
195

Appendix B

EXAMS data entry template for chemical molar absorption spectra (ABSOR)

Waveband Waveband

No. Center

nm

Bandwidth

nm

ABSOR

cm-1 M-1

No. Center

nm

Bandwidth

nm

ABSOR

cm-1 M-1

1 280.0 2.5 24 380.0 10.0

2 282.5 2.5 25 390.0 10.0

3 285.0 2.5 26 400.0 10.0

4 287.5 2.5 27 410.0 10.0

5 290.0 2.5 28 420.0 10.0

6 292.5 2.5 29 430.0 10.0

7 295.0 2.5 30 440.0 10.0

8 297.5 2.5 31 450.0 10.0

9 300.0 2.5 32 460.0 10.0

10 302.5 2.5 33 470.0 10.0

11 305.0 2.5 34 480.0 10.0

12 307.5 2.5 35 490.0 10.0

13 310.0 2.5 36 503.75 17.5

14 312.5 2.5 37 525.0 25.0

15 315.0 2.5 38 550.0 25.0

16 317.5 2.5 39 575.0 25.0

17 320.0 2.5 40 600.0 25.0

18 323.1 3.75 41 625.0 25.0

19 330.0 10.0 42 650.0 25.0

20 340.0 10.0 43 675.0 25.0

21 350.0 10.0 44 706.25 37.5

22 360.0 10.0 45 750.0 50.0

23 370.0 10.0 46 800.0 50.0
196

Appendix C

Implementing the microcomputer runtime EXAMS

Exams program files are installed from a self-unpacking archival compressed

format (Install_EXAMS.exe). The files require a total of about 1.5 Mb of mass

storage for transfer to a 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

The file for installing the EXAMS program, in file Install_EXAMS.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.

This User's Guide for EXAMS (file ExamsRevG.pdf) is available separately in

 Adobe PDF (Portable Document Format); it can be read and printed with

 the Adobe Acrobat Reader, available gratis from http://www.adobe.com/

EXAMS accesses extended memory through Windows services; under the

 Windows console all virtual memory is available to the program.

 Under Windows 95/98, best performance can be achieved by setting

 memory properties of the DOS console to "none" for Expanded (EMS)

 and all others 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 specifications.

o 20 megabyte available mass storage (hard disk)

o 80x87 math co-processor

o Windows 95/98/NT/2000 operating system

If your machine does not conform to these minimum specifications, the

EXAMS program WILL NOT execute properly.

 - more -
197

Then, to install the program

1. Transfer the file "Install_EXAMS.EXE" to your hard disk in some suitable

 subdirectory or partition and install Exams. From a Windows console:

 a. Set the default drive to the mass

 storage device (e.g., hard disk "C"): C:

 b. Create an EXAMS directory: (BUT, if MKDIR EXAMS

 installing as part of PIRANHA, the

 PIRANHA\EXAMS directory already exists.

 Do NOT create another EXAMS directory)

 c. Request verification of copy results: VERIFY ON

 d. Change default directory to EXAMS, CD\EXAMS

 or to PIRANHA EXAMS subdirectory CD\PIRANHA\EXAMS

 e. Execute the file Install_EXAMS.EXE to 

 recover files from the archives: Install_EXAMS

 and follow the directions provided.

2. Start the EXAMS program from the EXAMS directory.

 a. Start the EXAMS program: EXAMS

 b. When you reach the EXAMS system

 prompt, start the test command file: EXAMS-> DO TEST

3. When the test run finishes compare the outcome (in file REPORT.XMS)

 with the file TESTOUT.XMS 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 Install_EXAMS.EXE.