Modeling of Chemicals in Lakes - Development and Application of User-Friendly Simulation Software (MASAS & CHEMSEE) on Personal Computers

Diss. ETH No. 9632

A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH for the degree of Doctor of Natural Sciences

presented by Markus Ulrich Dipl Natw. ETH

born 8 November 1959 citizen of St. Antoni (FR) and Lucerne (LU)

Prof. D. M. Imboden, examiner Prof. R. P. Schwarzenbach, co-examiner Prof. W. Schaufelberger, co-examiner

Zurich 1991 Sie war fasziniert von der Moglichkeit, diese Vorgange tatsachlich zu registrieren, die Art, wie man der Natur bier auf die Sprunge kam, gefiel ihr. Es waren einfache Gedanken, mit denen man die Wirklichkeit uber- listete, erst die Ausf\ihrung war etwas komplizierter. aus "Der Neue Berg" von Franz Hohler.

Viele munterten mich auf, als die Ausf\ihrung manchmal zu einem un- uberwindlichen Berg anwuchs. Mein Dank gilt ihnen. ACKNOWLEDGEMENTS This work was carried out between 1987 and 1991 at EA WAG in DUbendorf under the supervision of Dieter Imboden and Rene Schwarzenbach. I would like to give my warmest thanks to Dieter for creating a stimulating and familiar atmosphere and for his "open door" at any time, not for scientific matters only. On various occasions, Rene's brief and accurate criticism and suggestions produced valuable additions to my thesis. I am very grateful to him. Despite his full agenda, Werner Schaufelberger agreed at very short notice to be a co-examiner to my thesis. I would like to thank him for his valuable and most appreciated comments. I was working with many colleagues in the process of my thesis. My thanks to Markus Miiller for dealing with the manifold data on Swiss lakes and to Olaf Cirpka for his active and inspiring contribution during the field work in Greifensee. Upon joining EAWAG, Stephan Miiller was faced with the task of converting stacks of "Aproz"-bottles into atrazine data-points. Similarly, Heinz Singer had the dubious pleasure of being first user of MASAS. They were wonderful sportsmen! Mr. Lehnherr and Mr. Jordi (waste water treatment plant Maur), Mr. Kagi and Mr. Grob CWWTP Monchaltorf), Mr. Furrer (WWTP Nieder- uster), Mr. Lang and Mr. Nosari (AGW Kt. Zurich) and Mr. Pfister (AGW Kt. Zurich), as well as my colleagues from EAWAG, Freddy Meier, David Kistler, Ruth Stierli, Erwin Grieder, Richard Illi, Bruno Ribi contributed to the success of the field study. To all of them, my warmest thanks. I was very happy to receive Jean-Philippe Houriet's (BUWAL, Federal Office of Environment, Forest and Landscape) support regarding all issues dealing with NTA and EDTA, including the funding of the analysis of 120 NTAIEDTA samples by BUWAL. Similarly, I am indebted to Mr. A. Weber and Mr. Liechti for their assistance. I would like to mention Andreas Fischlin's and Olivier Roth's support regarding the software-ground work (DialogMachine) of the programs developed. They were very helpful. Special thanks to all the students who stayed up late eagerly awaiting the first version of CHEMSEE. As a happy bunch of guinea-pigs they were thrown into the deep end. I thank them for their generous understanding. I would like to express my sincere appreciation to all the numerous colleagues and friends who contributed in various ways to the success of this thesis. Claudia Pahl was a fabulous group leader - for long periods of time I was the only member of her group and I thank her a lot for her encouraging support. Johny WUest's optimistic and purpose-driven professional- ism was a source of academic inspiration and his encouragement often helped me to keep up the spirits! I would like to express my gratitude to Peter Reichert, Gabriel Piepke, Oskar Wanner and to Mike Sturm for their criticism and valuable additions to my thesis. ii

Jurg Schlatter and Christian Lukasczyk, better known as Luki, have been wonderful friends. Many times, when spirits were low or when my academic inspiration dissolved itself into nothingness leaving me to struggle with the big mental void, they took me along to go for a beer. I will always remember the way they would ask me up to the cafeteria and each of us would dig his teeth into a chocolate stick. I have been enjoying very much their friendship. Many thanks to Vreni Graph for finding a free minute to type a draft and to Heidi Bolliger for completing Fig. 1. Finally, I am indebted to Annette Johnson, Madeleine Biber, Michael Ul- rich, and David Livingstone for their literary help regarding the English language. Without their support, it would have been impossible to complete this work. David was more than only a critic. He shared with me the secrets of hyphens, and introduced me to many idiosyncrasies of the English language. This work was supported by research grants from BUWAL and from COST (European Corporation for Scientific and Technical Research). TABLE OF CONTENTS

1. Introduction ...... 1

2. Concepts ...... 12 2.1. Models for the Description of the Lake ...... 12 2.1.1. Model Variables ...... 12 2.1.2. Processes ...... 14 2.2. Specific MASAS Concepts ...... ZJ 2.2.1. Description of the System...... ZJ 2.2.2. Description of the Compound ...... ZJ 2.2.3. Hierarchical Models for the Physical System ...... 2.4 2.2.4. Description of Transformation and Transport Processes ...... 'lJ 2.2.5. How to Obtain an Optimal Model and Process Description in Lakes ...... 2B 2.2.6. Restrictions ...... 3'.> 2.2.7. Provision of Necessary Data to Build Models ...... 3'.) 2.3. User Interface ...... 31 2.4. Program Language and Structure ...... 35

3. Application of CHEMSEE ...... :Jl 3.1. Mn-Cycle in Greifensee ...... :Jl 3 .1.1. Introduction ...... :Jl 3.1.2. Description of Greifensee ...... 40 3.1.3. Model Set-Up and Illustrative Example ...... 40 3.1.4. Model Results ...... 54 3.1.5. Conclusions ...... 55 3.2. Further Applications ofCHEMSEE ...... 55

4. Application of MASAS ...... fB 4.1. Introduction ...... fB 4.1.1. EDTA ...... fB 4.1.2. NTA ...... 00 4.1.3. PER ...... 62 4.1.4. Atrazine ...... m 4.2. Surmnary of the Field Data ...... 64 iv

4.3. EDTA Models and Illustrative Example ...... 74 4.4. NTA Models ...... 00 4.5. PER Models ...... 101 4.6. Atrazine Models ...... 113

5. Mathematical Models ...... 121 5.1. Continuous 1-Dimensional Vertical Lake Model...... 121 5.2. Chemical Speciation ...... 127 5.3. Equations for MASAS and CHEMSEE ...... 129 5.3.1. MASAS Model without Adsorption to POC ...... 129 5.3.2. MASAS Model with Adsorption to POC ...... 129 5.3.3. CHEMSEE Model...... 130 5.4. Discrete One-Dimensional Vertical Lake Model (n-Box Model) ...... 130 5.5. One-, Two- and Combi-Box Model ...... 136 5.6. Modeling of Processes in MASAS ...... 137 5.6.1. Air-Water Exchange ...... 137 5.6.2. Direct photolysis ...... 144 5.6.2. First Order Reactions ...... 144 5.7. System (Lake) Data ofMASAS ...... 149 5.8. MASAS Compound Data ...... 157

6. Implementation ofCHEMSEE ...... 100 6.1. User Interface ...... 100 6.2. Modular Structure ...... 164

7. Implementation of MASAS ...... 174 7.1. User Interface ...... 174 7 .2. Modular Structure ...... 180 5.2.1. Kernel Modules ...... 180 5.2.2. User-Interface Modules ...... 187 7 .3. Library System ...... 192 7.4. Price of User Friendliness ...... 194

8. Discussion ...... 202

9. Literature ...... a>8

Appendix ...... A-1 v

SUMMARY This dissertation describes the development and use of simulation software designed to model the temporal and spatial behavior of chemical substances in lakes. MASAS is a program specifically designed to investigate the behavior of a given anthropogenic organic trace substance, whereas CHEMSEE is a flexible "model construction kit" for chemical processes in lakes. These two user-friendly programs, with user interfaces consisting of menus, standard dialog boxes and interactive text and graphic windows, were developed on the Apple Macintosh personal computer, and are currently in use both in teaching and research. Underlying both programs is the mathematical description of the behavior of a substance in a lake in terms of a one-dimensional vertical model. Such a model, in which horizontal concentration differences are neglected, is suitable for the description of reactive substances in deep lakes. Physical processes occurring in a lake are not influenced by the presence of trace substances and are therefore represented in the model by corresponding input parameters (e.g. epilimnion depth, temperature etc.). MASAS computes the concentrations of the substance of interest in the water column (and, optionally, in the sediments) as a dynamic model variable. In CHEMSEE, any number of variables may be defined by the user to represent the concentrations of substances and particles in the water column, and of substances in the sediments. In both programs, transport and transformation processes (loading, adsorption, sedimentation, air-water and sediment-water exchange, chemical reactions) can be defined interactively and assigned to the various model variables. The results of simulation runs are output both graphically and on file, and the interactively defined models can be saved for future use. CHEMSEE was employed in the study of metal cycles (Mn, Cr) in Greifensee, of isotopic composition (4He, 3He, 3H) in Lake Van, and of mixing processes in Lago di Cadagno. Problems considered included the identification of relevant processes, the estimation of process parameters, the construction of dynamic models based on known individual processes, and the testing of various hypotheses. Models for HCB, CH4, 222Rn and CCl4 were employed as teaching aids.

In contrast to CHEMSEE, MASAS is a specialized tool for the study of organic substances in lakes, and offers the user correspondingly specialized functions. Library files allow fast access to data on both substances and lakes. Substances are characterized in these files by physico-chemical parameters (vapor pressure, solubility in water, Henry coefficient etc.) and reactivities (hydrolysis rates etc.). Storage of the reactivities of individual species of a substance (dissolved neutral, particulate neutral, dissolved anion/cation) is also possible. The lakes are characterized in terms of morphometric (volume, isobath areas etc.), hydraulic (rate of throughflow, depth of epilimnion, etc.), and physical and chemical (temperature, pH, particle concentration etc.) parameters. vi

MASAS supports an iterative form of model development. Based on initially simple models, models of increasing complexity can be constructed, depending on the type and extent of lake and substance data available. Various types of models (one-box, two-box, "combi-box", n-box) with different degrees of spatial resolution are available to describe the vertical variation in the concentration of the substance being studied. Processes can be described on various hierarchical levels. Information fields show the user which parameters are missing, and which routines are available to approximate these missing parameters. In conjunction with field data from Greifensee (concentration profiles and inflow loading, sampled monthly), MASAS was employed to model the behavior of four test substances with the following results: EDTA (sequestering agent): The probable elimination process was identi- fied as being adsorption on to particulate iron with subsequent transport down to the sediments. The importance of this process has yet to be confirmed in the field. NTA (phosphate substitute in detergents): Comparison of the results of model calculations with field data yielded an in situ microbial degradation rate of 0.035 d-1 (half-life 't112 ~ 20 d). Variations in the NTA concentration in the lake were found not to be the result of a fluctuating temperature- dependent degration rate, but probably of variations in the loading. PER (solvent): The gas-exchange subprogram yielded an estimate of 0.15 mid for the gas-exchange rate, allowing the long-term trend in PER loading to be estimated. This was found to have decreased from about 200 g/d in 1982 to about 20 g/d in 1991. Atrazine (herbicide): A one-box model showed the upper limit for the rate ofhydrolytic (or possibly biological) degradation to be lQ-3 d ('t112 ~ 700 d). The programs were written in Modula-2 (MacMETH development system) and their construction is based on hierarchically structured modules (in the case of CHEMSEE 5 modules, in the case of MASAS 21 modules). The kernel and user interface are implemented in various modules. Employment of a software library (DialogMachine) facilitated the programming of the Macintosh user interface and will also facilitate the planned conversion to an MS-DOS operating system. During the course of this work, it became increasingly obvious that the effort involved in developing programs which are both user-friendly and flexible is often underestimated. Nevertheless, user-friendliness is the decisive factor in making a computer tool available to a large circle of users. The conversion of CHEMSEE to run under MS-DOS and the extension of MASAS to deal with other aquatic systems (shallow lakes, rivers, groundwater) are planned. vii ZUSAMMENFASSUNG Diese Arbeit beschreibt die Entwicklung und die Anwendung von Simula- tionssoftware zur Modellierung des zeitlichen und raumlichen Verhaltens chemischer Substanzen in Seen. MASAS ist ein Spezialprogramm fur anthropogene organische Spurensubstanzen, CHEMSEE ein flexibler "Modellbaukasten" fUr chemische Prozesse in Seen. Die benutzerfreundlichen Programme mit Menus, Standard-Dialogboxen und interaktiven Text- und Graphikfenstem wurden auf dem Apple Macintosh Personal Computer entwickelt. Sie werden in Forschung und Lehre eingesetzt. Fur die mathematische Beschreibung des Sees wurde ein eindimensionales vertikales Modell verwendet, das filr tiefe Seen geeignet ist. Horizontale Kon- zentrationsunterschiede werden vernachliissigt. Spurensubstanzen beein- flussen physikalische Prozesse nicht; diese werden deshalb durch entsprechende Eingabeparameter vorgegeben (Epilimniontiefe, Temperatur, etc.). In MASAS werden als dynamische Modellvariablen die Konzentrationen der zu untersuchenden Substanz in der Wassersaule und, optional, im Seesedi- ment, berechnet. In CHEMSEE kOnnen beliebig viele Variablen fUr Sub- stanz- und Partikelkonzentrationen in der Wassersaule und fur Substanz- konzentrationen im Sediment definiert werden. Transport- und Umwand- lungsprozesse (Eintrag, Adsorption, Sedimentation, Oberflachen- und Sedi- mentgrenzflusse, chemische Reaktionen) konnen in beiden Programmen interaktiv definiert und den Modellvariablen zugeordnet werden. Simula- tionsresultate werden graphisch und auf File ausgegeben; interaktiv defi- nierte Modelle kOnnen gespeichert werden. CHEMSEE diente zur Untersuchung von Metallkreislaufen (Mn und Cr im Greifensee), Isotopenverhaltnissen (4He, 3He, 3H im Vansee) und vertikalen Mischungsraten (Lago di Cadagno). Die Fragestellungen beinhalteten die Identifikation relevanter Prozesse, die Schiitzung von Prozessparametem, den Aufbau dynamischer Modelle aus bekannten Einzelprozessen und die Prtifung verschiedener Hypothesen. Modelle zur Beschreibung von HCB, CH4, 222Rn und CCI4 wurden in der Lehre verwendet. Im Gegensatz zu CHEMSEE ist MASAS ein Spezialinstrument zur Unter- suchung von organischen Substanzen in Seen und bietet entsprechende Spezialfunktionen. Bibliotheksdateien erlauben einen schnellen Zugriff auf Substanz- und Seedaten. Substanzen werden durch physikalisch-chemische Parameter (Dampfdruck, Wasserloslichkeit, Henry-Koeffizient, etc.) und Reaktivitaten (fur Hydrolyse, Photolyse, etc.) charakterisiert. Optional kl>n- nen Reaktivitiiten fUr einzelne Substanzspezies (gelOst neutral, partikuliir neutral, gelostes Anion/Kation) gespeichert werden. Fur Seen werden in der Bibliotheksdatei morphologische (Volumen, Isobathenflache, etc.), hydrau- lische (Durchflussrate, Epilimniontiefe, etc.), physikalische und chemische Parameter (Temperature, pH, Partikelkonzentration, etc.) gespeichert. MASAS untersttitzt eine iterative Modellentwicklung. Ausgehend von ein- fachsten Modellen kann, abhangig von verftigbaren See- und Substanzdaten, die Komplexitat schrittweise erhOht werden. Fur die Beschreibung des Sees stehen Modelle mit verschiedener vertikaler Auflosung zur Verftigung viii

(Ein-, Zwei-, Combi- und n-Box-Modell}. Prozesse konnen auf verschiedenen hierarchischen Stufen beschrieben werden. Informationsfelder zeigen feh- lende Parameter und verfugbare Approximationsroutinen an. Mit MASAS wurde das dynamische Verhalten von Testsubstanzen model- liert. In Kombination mit Felddaten, die fur den Greifensee erhoben wurden (monatliche Bestimmung von Konzentrationsprofilen im See und von abflussproportionalen Tagesproben in den Zuflilssen), wurden folgende Er- gebnisse erarbeitet: - EDTA (Komplexierungsmittel): Die Modellrechnung ergab als wahr- scheinlichen Eliminationsprozess eine Adsorption an partikulares Eisen mit anschliessendem Transport ins Seesediment; dieser Prozess ist durch Felduntersuchungen noch zu bestiitigen. - NTA (Phosphatersatz in Waschmitteln): Der Vergleich von Modellrech- nungen und Felddaten ergab eine mikrobielle in situ Abbaurate von 0.035 d-1 (Halbwertszeit t112 .. 20 d). Die variable Konzentration im See wird nicht durch einen variablen, temperaturabhangigen Abbau, sondern wahrscheinlich durch einen schwankenden Eintrag verursacht. - PER (Losungsmittel): Mit Hilfe des Unterprogramms fur Gasaustausch wurde die Gasaustauschgeschwindigkeit abgeschatzt (0.15 mid) und der Langzeittrend des Eintrags ermittelt. Dieser sank von 1982 bis 1991 von ca. 200 g/d auf ca. 20 g/d. - Atrazin (Herbizid): Ein Einbox-Modell lieferte die obere Grenze von l0-3 d-1 (t112 :<: 700 d) ftir die Rate des hydrolytischen, evt. biologischen Abbauss. Die Programme wurden in Modula-2 geschrieben (Entwicklungssystem MacMETH) und sind aus hierarchisch geordneten Modulen aufgebaut (CHEMSEE 5 Module, MASAS 21). Kernel und Benutzerschnittstelle sind in verschiedenen Modulen implementiert. Die Benutzung einer Softwarebiblio- thek (DialogMachine) erleichterte die Programmierung der Macintosh- Benutzerschnittstelle und wird eine Portierung auf MS-DOS wesentlich ver- einfachen. Deutlich zeigte sich wahrend dieser Arbeit, dass die Entwicklung von benutzerfreundlichen und flexiblen Programmen aufwendig ist und oft unterschatzt wird, und dass andererseits Benutzerfreundlichkeit das ent- scheidende Kriterium ist, um ein Computerwerkzeug einem breiteren An- wenderkreis zugii.nglich zu machen. Eine Version von CHEMSEE fiir MS-DOS sowie die Erweiterung von MASAS filr weitere aquatische Systeme (flache Seen, Flilsse, Grundwasser) sind geplant. 1. INTRODUCTION

All through the ages, man has attempted to improve his understanding of the world around him by the use of models. In their simplest form, these are scaled-down physical versions of an original in which only essential features are reproduced. Examples of such physical models are architec- tural models and the test watercourses employed by hydraulic engineers. Should the realization of physical models be impossible or impractical, abstract models, which often use the language of mathematics, are employed. Such mathematical models utilize a set of equations to describe an aspect of the real world in a simplified fashion. The solution of these equations using computers is referred to as computer simulation. Computer simulation is nowadays indispensable in many fields: it allows the acting out by proxy of scenarios which might take too long, involve too much effort, or have catastrophic results if acted out in reality. A few examples illustrate the diversity of use to which computer simulation is put today: flight simulators allow pilots to practise for emergencies, such as engine failure, under realistic conditions; learning programs allow students to construct models according to the current state of their knowledge of a particular subject and to obtain immediate feedback; ocean and climate models describe the greenhouse effect and can be used to develop strate- gies to counteract it; traffic simulation games can aid in city planning; and lake simulation programs are used to model eutrophication and lake restoration. In all these examples, regardless of whether the field concerned is education, research or planning, there exists a common goal: the development of a better understanding of reality.

In order to deal with the acute problems now threatening nature, new interdisciplinary approaches are required. The rapid growth and development of the environmental sciences during the past few years is a direct result of the employment of such new approaches. One of these relatively new interdisciplinary sciences, environmental chemistry, is concerned with the fate of man-made substances released into the environment. Approximately 70,000 man-made substances now exist, and this number is increasing at a rate of about 1,000 a year (OECD, 1981). To avoid or min- imize risks to the environment, extensive and time-consuming experiments would need to be carried out on all of these compounds before their release. In view of the many different types of substances, their numerous possible uses, and the complexity of the various environmental systems, 2 this task is hopelessly immense. In addition to this, many substances which have been used in large quantities have already left traces in the environment.

Concepts for the analysis of anthropogenic compounds in environmental systems

The current unsatisfactory situation can only be alleviated by systemati- cally taking into account "chemodynamic concepts - physico-chemical laws coupled with co~pound-specific data" (Stumm et al., 1983). In doing so, it is possible to determine, for a given substance in a given environmental system, which of the many transformation and transport processes are expected to occur and how fast. With the help of such concepts it is also possible to estimate unknown physico-chemical parameters, reactivities and the environmental behavior of new substances. These estimates are based on related substances with known properties. For this type of analysis, computers are needed to estimate substance parameters, to ascertain relevant processes, and, in particular, to simulate the dynamic behavior of a substance in the environment.

We now turn to the environmental system that is the focus of this work: the lake. Concepts for the analysis of chemical substances in lakes have been developed and illustrated by various examples in Schwarzenbach & Imboden (1984) and Imboden & Schwarzenbach (1985). Fig. 1 gives an overview of the transport and transformation processes to which a substance in a lake is subjected. The coupling of all these processes deter- mines the development of the substance concentration in the lake over time. This can be summarized as the dynamic behavior of the system. Depending on the model chosen, the dynamic behavior also includes the spatial distribution of the substance. EDTA in lake Greifensee is a con- crete example which illustrates this (numbers refer to Fig. 1).

EDTA, a sequestering agent used, among other things, in electroplating, in cleaning products and in the photographic industry, is transported from wastewater treatment plants (WWTP) into lakes by inflowing water. This loading process brings EDTA into surface water layers (7a) and into or below the thermocline (7b). Physical mixing processes distribute EDTA throughout the lake. Horizontal mixihg rapidly eliminates horizontal concentration gradients (indicated in Fig. 1 by horizontal lines). Vertical EDTA concentration gradients are reduced more slowly by diffusive water Mass transfer at lake surface

Air/water exchange (7) (7) Input by precipitation Inflow of water (1) (volatilization/absorption) (dry or wet deposition) (2) Outflow of water

~ One-dimensional vertical lake model of MASAS and CHEMSEE. The figure illustrates the transport and transformation processes which determine the temporal and spatial behavior of chemicals in lakes. Numbers refer to the model equations in Tabs. 19 and 21. 4

exchange (vertical eddy diffusion, 5). Forces acting at the lake surface (wind, solar radiation) produce a well mixed surface layer of temporally variable thickness, the epilimnion (3). Within this layer, all concentration gradients of EDTA are rapidly eliminated. Outflow of surface water re- moves EDTA from the lake (2).

EDTA is adsorbed on particulate iron, resulting in two EDTA species: dissolved EDTA (speciation Sl) and particulate EDTA (82). The particulate EDTA fraction is transported with settling particles (6) into deeper water layers and into the sediments (magnification at bottom left of Fig. 1). Particulate sediment matter is transported into the permanent sediment layer (12), thus removing EDTA from the system. Resuspension of sediment particles brings EDTA back into the water column (lla), closing the water/sediment cycle.

All other processes, such as surface fluxes (7), photolysis (8), chemical reactions in the water column (8, 9) or in the sediment (13, 14) are oflittle or no importance and can be neglected.

Existing software for the analysis of anthropogenic compounds in aquatic systems

A few representative examples of the large number of existing programs (Ambrose and Barnwell, 1989; Cohen et al., 1990; Jury et al., 1987; Reichert and Wanner, 1990; TOXFATE, described in Halfon and Brendon, 1990) will be briefly presented. EXAMS (Burns, 1985; EXAMS, 1990) was developed to determine the dynamic behavior of organic substances based on simple, measurable physico-chemical parameters. Today this program is available on personal computers.

E4CHEM (Matthies and Trapp, 1988) is a package comprising various mathematical models for calculating the concentration of chemical substances in various environmental systems and the consequent risk to the system. The subprogram EXWAT contains a compartment model for the transport and transformation of substances in surface waters. This model, however, only allows equilibrium calculations. Other modules treat air, (EXAIR), soil (EXSOL), organisms (ETTOX, ETSYS), and the calculation of emission rates based on user data (RLTEC). 5

Programs for particular applications are numerous (Wolfe et al, 1980; Nirmalakhandan and Speece, 1988a; Chiou et al., 1977; Nirmalakhandan and Speece, 1988b; ECOTOX, described in J0rgensen, 1990). With tools of this type and with the help of physico-chemical or empirical relationships, unknown physico-chemical parameters or reaction rates can be estimated. GCSOLAR is a nice example of this (GCSOLAR, 1988). The program calculates photolytic degradation rates for different seasons and latitudes based on the absorption and quantum yield of a substance.

User friendly software in environmental science

In the last few years, computer technology has improved enormously. Today, affordable personal computers have a capacity as large as that of an entire computer center a few years ago. Users must no longer worry about technical details or incomprehensible computer commands. User- friendly programs offer comfortable, consistent user interfaces with menus, windows, help commands, etc. Thus the average user can reap the benefits of computer technology. These improvements are being increasingly integrated into the area of environmental computer science. General concepts for the use of computers in environmental sciences are discussed in Page & Gaeschke (1988).

Two recently developed modeling and simulation tools employing the improvements mentioned are SAM-Set (Vancso-Polacsek, 1990) and ModelWorks (Ulrich, 1987; Fischlin et al. 1990). With both tools, models can easily be formulated by short definition programs written in a common programming language (Modula-2). SAM-Set further allows interactive alteration of model structure. SAM-Set, based on concepts of modelling and simulation theory, is very versatile and allows hierarchical system definition with different model types (discrete time, continuous time, etc.). Both tools provide user-friendly interfaces including pull-down menus, graphic display of simulation results, etc. They were not used for this work, however, because many specific features of the lake system required the development of specialized software. 6

Common characteristics of CHEMSEE and MASAS

This dissertation is concerned with two computer programs: MASAS and CHEMSEE. Both can he used to set up mathematical models which simulate the temporal and spatial behavior of trace substances in lakes. To do this, the real system must he translated into a model system, a task which requires abstraction and simplification. The previous example of EDTA in Greifensee illustrates how this is typically done.

Computer tools are often used in combination with field studies to investigate scientific problems inaccessible using only field or laboratory experiments. They are also used for teaching purposes in the Department of Environmental Sciel).ces at ETH Zurich. Both programs were implemented on personal computers, putting an emphasis on user-friendliness.

Fig. 2 illustrates the specific characteristics of MASAS and CHEMSEE, respectively. In the following, specific applications and problem formula- tions for both programs shall be outlined.

CHEMSEE

CHEMSEE is a flexible model construction kit for use in teaching and research. Computer models can be set up with ease and may contain multiple variables. These variables can describe, for example, the concentration of chemical substances in the water column and in the lake sediments. Temporal and spatial changes in concentration, i.e. the dynamic behavior of the model, are determined by transformation and transport processes.

The built-in mathematical models have already been used in an earlier program (Imboden & Schwarzenbach, 1985). The purpose of CHEMSEE, however, was to generalize the model for multiple substances while creating a user-friendly program.

The following problems can be addressed with CHEMSEE:

Setup of a dynamic model using information on the system of interest: In cases when individual processes involved in a system of interest (e.g. ~ Comparison of the lake modeling tools MASAS and CHEMSEE.

MAS AS Tool for the evaluation of the behavior of anthropogenic organic chemicals in aquatic systems In-depth analysis of one selected organic compound in an aquatic CHEMSEE system (lake), using Flexible tool for the modeling of variables of different type in a lake Quick setup of dynamic lake models, using • specific compound and system data stored in libraries • one or more model variables (chemicals, physical parameters, organisms)

• dynamic models of different • a predefined set of transport and transformation processes which can be complexity assigned to the model variables in a flexible way

• a set of transport and • automatic modular composition of model equations using user-defined transformation processes, variables and processes activated by the user with support of the program

• hierarchical model and process description depending on available data and problems to be solved 8

metal cycles) are well known, this information can be assembled to give a dynamic model simulating the entire system. Examples: Mn cycle (section 3.1) and Cr cycle (Tab. 6) in Greifensee.

- Discrimination between different hypotheses which explain measured concentrations of substances of interest. Example: Isotopic compositions in Lake Van (3He/4He/3H system, Tab. 6).

- Identification of relevant transport or transformation processes Example: Identification of major mixing processes in Lago di Cadagno, using model calculations in combination with field data on lake temperature and conductivity (Tab. 6).

- Development of teaching models: Simple models can be easily developed for teaching purposes to illustrate the behavior of chemicals in lakes or the characteristics of the model equations (Tab. 6).

MASAS

MASAS is a tool specifically designed for the investigation of anthropogenic organic substances in lakes. It is the focal point of this work. MASAS allows one chosen substance to be modelled at a time, and it contains various features intended to reduce the user's workload; e.g. access to libraries containing information on lakes and chemical compounds, models of various degrees of complexity, hierarchical description of processes, and user support for modeling of processes.

The MASAS project was created in response to the need of public environmental institutions such as BUWAL (Bundesamt fiir Umwelt, Wald und Landschaft/Federal Office of Environment, Forest and Landscape, Berne, Switzerland) for an instrument to investigate the behavior of anthropogenic substances in aquatic systems. In Switzerland, according to the "Ordinance Relating to Environmentally Hazardous Substances" (Stoffverordnung; BUWAL, 1986), every new substance to be used in pure or mixed form must be registered with BUWAL. An applicant must provide information on physico-chemical parameters, degradation pathways (reactivity), as well as ecotoxicity. A verbal interpretation of the test results predicting the behavior of the substance in the natural environment, is also required (BUWAL, 1989). Field experiments investigating the be- 9 havior of the substance in the environment, however, are only required in special cases.

MASAS is a computer program which combines available information on the substance to be tested with the aquatic system of interest, thereby allowing the investigation of environmental behavior by means of computer simulation. The first version of MASAS was realized for lakes. Expansion to other aquatic systems (rivers, aquifers), however, is planned and the program has been structured accordingly.

The lake is described in terms of morphological, hydraulic, physical and chemical parameters, while the substance is characterized by physico- chemical parameters and specific reactivities. Preparation of the data is possible through library files, where all available data on any substance or lake can be stored.

MASAS allows the iterative development of a computer model suited to the data available and to the problem to be solved. One usually starts with a simple model which already describes most relevant aspects of the situation. However, if unsatisfactory, this model can be improved step by step, either by increasing its spatial resolution or by introducing a more elabo- rate description of the transport and transformation processes involved. In order to accomplish this, compound and lake data are combined. This allows, for example, a reaction rate at a particular lake temperature to be calculated from the reaction rate at a standard temperature (contained in the compound library), and the lake temperature (contained in the system library).

Should the program need more precise information on the substance or the lake, it notifies the user. Thus at each step, the user can decide whether or not the extra effort required to provide appropriate data is worth the expected improvement in the model. Often important substance parameters are missing. In such a case, the program provides functions for estimating the missing values. Since the user himself/herself imple- ments each expansion of the model, it is possible to maintain an overview and still follow exactly what the model is calculating. 10

MASAS can be used to solve problems of the following type, possibly in combination with field studies:

- Model validation: development of a reasonable model in cases when little knowledge about the behavior of a given compound is available. Field data may be required for validation. Example: development of the EDTA model (section 4.3).

- Characterization of unknown processes: the relevance of unknown processes and the optimal way of their description in the model can be evaluated by means of the hierarchical process implementation, described in Section 2.2.4. Examples: - Comparison of different descriptions of atrazine hydrolysis (section 4.6). - Development of a valid description of air/water exchange for PER (section 4.5). - Comparison of different descriptions of biological NTA degradation (section 4.4).

- Estimation of input to a lake and of unknown process rates Examples: - Estimation ofloading and temporal development for PER (section 4.5) - Determination of in situ rate of biological degradation of NTA (section 4.4). - Estimation of atrazine hydrolysis rate (section 4.6).

- Defining the optimal spatial resolution to be used in the model Example: different spatial resolutions for PER models, depending on whether long-term behavior or a particular event is to be modeled (section 4.5).

- Predictions: Using a valid model, future concentrations of a substance in the water column and in the sediment can be predicted. A model validated for a given compound in a given lake may be employed either to predict the concentration of similar compound in the same lake or the same compound in another lake. 11

Overview of this work

In Section 2 the. concepts underlying MASAS and CHEMSEE are presented. Section 2.1 contains a short presentation of the physical and chemical processes occurring in lakes and the input parameters required for their description. The next two sections deal with the application of CHEMSEE and MASAS, respectively, to scientific questions. Each section includes an example with many screen-copies illustrating the use of these tools in assembling computer models. Section 4.2 summarizes a field study performed in Greifensee to collect data on four organic compounds (EDTA, NTA, tetrachloroethylene, atrazine) for which computer models were set up.

The next three sections constitute a detailed description of the programs. They are specifically intended for the interested reader and may be skipped. Section 5 presents all model equations and process descriptions and includes the system, compound, and process parameters and formulas used in MASAS. Sections 6 and 7 describe the implementation of CHEMSEE and MASAS, respectively. They include a brief description of all menu commands, and may thus serve as a short user's manual. In Section 7.5 certain aspects of the development of user-friendly software are briefly discussed.

The last section contains the discussion. 12

2. CONCEPTS

This chapter presents the concepts which were formulated to achieve the previously formulated goals. The discussion is grouped into four sections: models (2.1), specific MASAS concepts (2.2), user interface (2.3), and programming (2.4).

2.1. MODELS FOR THE DESCRIPTION OF THE LAKE

The model developed by Imboden and Schwarzenbach (1985) is suited for the description of reactive substances in deep lakes. The selected model is a one- dimensional model which can describe vertical concentration variations of model substances. Such variations are frequent, due to the density stratification of lakes. Horizontal variations are not significant in most cases. Therefore, it is assumed that the lake is horizontally completely mixed. The topography of the lake basin is included for the description of the relative importance of the lake sediment at different depths. This is relevant for all sedimentJwater exchange processes.

Mixing characteristics, density and temperatures, typical variables of physical lake models, are not influenced by trace substances. Therefore, these variables are defined as input parameters, and do not form a part of the dynamic model.

2.1.1.MODEL VARIABLES

Three types of variables, which will be designed by the following short names, can be used in the model (Fig. 3):

1) Dissolved variable

Total concentration of a compound (or a biological or physical property) in the water column.

A compound may be present in different forms, called species. It is assumed that the species are interrelated by reactions which are significantly faster than other transformations. Note that one species may refer to the chemical adsorbed on suspended solids. The concentration of the species can be l3

Types of model variables:

Dissolved variable Sediment variable Solid variable

Compound (or a Compound in the Suspended solid in biological or sediment, adsorbed on the water column physical property) in particles/POC (particu- (MASAS: only POC) the water column late organic carbon)

Compound species (for dissolved variables): (9) Dissolved neutral species

Dissociation reaction Dissolved cation (+ 1) G ... Fast equilibrium • E) (MASAS only)

Dissociation reaction .... Dissolved anion (-1) G Fast equilibrium • 0 (MASAS only)

Adsorption (neutral, 4-.--~~__;_~~~~·~ Particulate species G Fast equilibrium • adsorbed on particles/POC

~ Model variables and compound species in MASAS and CHEMSEE. 14 calculated from the total concentration by partitioning equations. Therefore, only the total concentration appears as a model variable.

2) Solid variable (or Particulate variable)

Concentration of a suspended solid in the water column (e.g. a solid metal oxide or particulate organic carbon, POC).

Note the difference between solid variables and the adsorbed species of a dissolved variable (particulate species): Solid variables are independent model variables, whereas particulate species are directly related to the concentration of the corresponding dissolved variable. However, both, solid variables and particulate species, are subjected to settling (removal to the sediments).

3) Sediment variab/.e

Concentration of a compound in the sediment, adsorbed on particulate matter. A sediment variable is always coupled to a dissolved variable which describes a compound with a species adsorbed on suspended solids.

In MASAS, three fixed variables are included: the total concentration of the compound in the water column (dissolved variable, with speciation), the total concentration of the compound in the sediment, and POC in the water column (solid variable). In CHEMSEE, an arbitrary number of variables of any type can be defined.

2.1.2. PROCES3l!.S

MASAS and CHEMSEE are process oriented simulation programs, organized in the following way: Different types of transport and transformation processes are available like in a "process provision". When a model is set up, an arbitrary number of processes (of different or equal type) are taken from these stores, and assigned to model variable(s). Each process is treated as a unit, and the differential equation of the model is composed of the contributions of all defined processes.

Now, we shall have a closer look at all these processes which determine the fate of a substance in a lake. The required input parameters are given and references to section 5 help to find quickly the exact mathematical formulation. 15

Boundary fluxes

All processes which transport a substance across a system boundary are boundary fluxes. They include, loading, output by lake outflow, air/water exchange, and, depending on the model type, sediment/water exchange.

Loading - A substance can be transported into the lake by the inflows, either into the surface layer (7a in Fig. l)t or into deeper layers (7b). For the sake of simplicity, it may be convenient to define loading processes independent of water flow, as a mere substance input at a given lake depth (7). Input can also occur at the lake surface as dry or wet deposition (7).

Input by precipitation Inflow of water

Input at given (7) depth (7b

The following parameters are required: amount of substance entering the system per unit time and the depth of intrusion (#14, 15, 16, 11 in Tab. 24).

Outflow - The lake volume is constant in the model (Eq. 31): the same quantity of water as flows into the lake leaves it by the outflow (2) located at the lake surface (2a) or, possibly, at deeper water layers (2b). Each inflow and outflow is defined by water flow and depth level (Tab. 30, sections General system parameters/ Flow parameters). The formulas used are given in Tab. 31 (#1-4).

t Remark: The numbers refer to the differential equations (Tabs. 19 and 21). Therefore, identical numbers are used if different processes are described by the same term of the equations, e.g. by the term (7) which summarizes all net fluxes into the water column. 16

The outflowing water has the same substance concentration as the corresponding lake water. In this way, substance is constantly removed from the system (#4 in Tab. 24; Eq. 30).

Air I water exchange - Volatile organic chemicals may be transported from the lake across the air/water interface into the atmosphere (volatilization) or vice versa (absorption) (7).

The process is described by Eq. 48; the most important process parameter used in the equation is the mass transfer coefficient (#12, 13 in Tab. 24). It defines the velocity at which the concentration difference between water and air is equilibrated.

This parameter is very often unknown. An air/water exchange subprogram in MASAS can approximate the mass transfer coefficient (see p. 137). It uses various physico-chemical relations based on the two-film model for gas exchange. It employs various compound and system parameters, such as Henry coefficient, water solubility, water temperature, wind speed, etc. The procedure is depicted on p. 28 and the formulas used are given in Tab. 26.

Sediment I water exchange - As long as the sediment does not constitute an integral part of the model - the opposite will be discussed below - all sediment/water exchange processes (7 and 10) have to be considered as boundary fluxes. 17

Constant fluxes are described as zero order sediment flux (#9 in Tab. 24); fluxes proportional to the substance concentration in the water are modelled as first order sediment flux process (#10 in Tab. 24).

Internal transport processes

Directed and nondirected water flow within the lake affects all substances independent of their nature. In the epilimnion, both horizontal and vertical diffusive and advective mixing processes are fast.

Epilimnion

-----u----·------

The epilimnion is therefore considered as one completely mixed box. This is a reasonable assumption for most cases. The temporally variable epilimnion thickness (seep. 150) and Eqs. 32-34 are used for the calculations.

Vertical advective (i.e. directed) water flow (4) results in all these cases when water enters or leaves the lake at different depths (Eqs. 35-40).

Vertical adveclive flow (4)

All small-scale nondirected water flows together constitute an apparently diffusive transport, called eddy diffusion. This transport process has similar characteristics as molecular diffusion. It transports substances against their concentration gradients, and, in the long run, it would equilibrate all

Vertical eddy diffusion (5) 18 concentration differences. Vertical eddy diffusion is calculated by means of one model parameter, the vertical eddy diffusion coefficient (p. 151), and Eqs. 41-43.

Transformation processes

Now, we shall discuss what happens to a substance within the system. A whole range of different transformation processes (chemical reactions, radioactive decay) may remove: produce:

or transform: a substance. The transformations can be described as zero order (7), first order (8) or second order (9) reactions. Other reaction types (Michaelis- Menten kinetics, etc.) are currently not included in the models. The required process parameters are the corresponding reaction rates (#6, 7, 8 in Tab. 24).

In CHEMSEE different substances may be coupled by chemical reactions using stoichiometric factors (Fig. 12). MASAS, on the other hand, offers more support in modeling common degradation pathways for one substance.

Hydrolytic degradation is one of these pathways. It can either be described with one overall reaction rate or acid, neutral and alkaline hydrolysis can be distinguished. In the latter case, rates for the partial reaction with H20, fI:+, and Off, and the lake pH are required. The typical pathways from a simple to a more complex process description is depicted on p. 27; the formulas used for the calculation of the final reaction rate are summarized in Tab. 28.

Other degradation reactions supported by MASAS are biological degradation (Tab. 29) and various pseudo-first-order reactions (Tab. 27), including sensitized photolysis. Direct photolysis is not implemented in the current version.

At this point, a remark about pseudo-first-order reactions is necessary. We shall take alkaline hydrolysis of a compound A as example: 19

A + OH" -----+ transformation products

The transformation rate r of this second order reaction is given as:

r =k · [A] · [Off]

However, A is typically a micropollutant, and thus, the concentration of OH" is not influenced by the reaction. It can be treated as a first order reaction with the following pseudo-first-order rate k*:

r = k* ·[Al with k* = k · [Off}

Proton transfer reactions (MASAS only)

Compounds may have functional groups which undergo proton transfer reactions (acids/bases). If the corresponding acidity constant pKa is close to the lake pH-value, both neutral and charged species can be found. The species may have different characteristics (reaction rates, no air/water exchange for charged species, etc.). MASAS allows the inclusion of a charged species with specific reaction rates.

8 1 Dissolved 8 2,3 Dissolved neutral compcund charged compcund (anion or cation) 0 0 QOO ©fB ee 0 000° - ©e e(.:I;;.. o o oo - ee e~

Proton transfer is a very fast reaction, so that equilibrium conditions can be assumed. The concentration of the species can be directly calculated with the corresponding pKa of the compound (Tab. 32) and the lake pH (Tab. 30) as input parameters from the total concentration, using the partitioning equations given in Tab. 20.

Adsorption on particles

Substances dissolved in the water column may adsorb on particles.

81 Dissolved 84 Neutral compcund neutral compcund adsorbed to particles A common mechanism is the adsorption of neutral organic compounds on particulate organic carbon (POC), a process which is driven by hydrophobic interactions. Other adsorption mechanisms such as adsorption of charged compounds on particles (e.g. charged surfaces of metal oxides) have not been included in the model because they are of minor importance for organic chemicalst.

The adsorption reaction is sufficiently fast in comparison with other reactions in most cases, that equilibrium conditions can be assumed. The concentration of the neutral and particulate species is calculated from the total concentration using the partitioning equations (Tab. 20). A linear adsorption isotherm is assumed, because saturation effects are not relevant at concentration levels encountered in lakes. As input parameter for the equations, one needs the organic carbon/water partition coefficient of the compound (Tab. 32) and the concentration of the particles in the water. Latter may be described by means of an additional model variable, or entered as input parameter to the model (Tab. 30).

The particulate species may have different characteristics (reaction rates, etc.). Most important, the particulate species is transported with the particles (6) into deeper water layers and into the sediments.

This process is characterized with the settling velocity of the particles (Tab. 30) and Eqs. 44 and 45.

t Remark: The adsorption of charged EDTA to particulate iron is of the latter type. Although not explicitly included in the model, this adsorption can be modelled, too, because it can be described with the same equations. 21

Sediment

In order to follow the fate of a substance transported into the sediment, the model has to be extended. We define a sediment layer of constant thickness, which stays on one side in contact with the water column and on the other side with the permanent sediment layer. The layer is composed of particles and is assumed to be completely mixed. This implies that no vertical profiles within the sediment can be modelled. However, different substance concentration within the sediment at different lake d~pths are considered. By this, the increasing importance of the sediment with increasing lake depth can be taken into consideration. As a new model variable, the compound in the sediment adsorbed on particles, is introduced:

Processes in the sediment

Within the sediment, the compound may be transformed by first (13) or second (14) order reactions.

For the process description, the corresponding reaction rate (#7, 8 in Tab. 24) is required.

The particle mass in the sediment is assumed to be constant. Particle input from the water column is balanced by two processes: particle degradation within the mixed sediment (defined by the preservation factor p, section Sediment parameters in Tab. 30), and the transport of particles into the permanent sediment layer. As a consequence, the compound is passively removed with the particles from the mixed sediment layer (12):

Processes at the sediment/water interface are responsible for the coupling of the sediment to the water column. They are either described as

particle resuspension (lla) or pore water diffusion (llb).

By these processes a substance which has been accumulated in the sediment may be transported back into the water column. The processes are calculated using Eqs. 5, 6 and 7 and one model parameter, the resuspension rate (Tab. 30). 2.2. SPECIFIC MASAS CONCEPI'S

The concept of the MASAS system is summarized in Fig. 4 and explained in detail below.

2.2.1. DESCRJPI10N OF 'l1IE SYSTEM

The lake is characterized by a set of parameters (Tab. 30):

- Geometry of the lake basin (volume, depth, isobath area,. .. ) - Hydraulics (inflow, outflow, vertical exchange flow, eddy diffusion coefficient,. .. )

With these characteristic lake data, it is already possible to set up simple dynamic models, which allow estimating the reaction of the lake to external changes, e.g. increasing substance input. The behavior of a substance is not only determined by its specific properties. The physical and chemical conditions within the lake are of equal importance:

- Temperature, pH, conductivity, oxygen, etc. - Particles and corresponding sedimentation velocity - Sediment characteristics (mass of mixed sediment layer, preservation factor, resuspension rate)

These parameters may be a function of time and lake depth; for each parameter, a comment can optionally be stored. General lake parameters, such as lake volume, are independent of the particular computer model used, whereas model dependent lake parameters are compiled especially for a particular model type.

2.2.2. DESCRWTION OF THE COMPOUND

Compound data include physico-chemical parameters, reactivities and auxiliary parameters (Tab. 32).

- Physico-chemical data: boiling point, vapor pressure, aqueous solubility, octanol/water partition constant, Henry coefficient, etc.

These data are available for many parameters and already allow rapid estimations of expected transport processes and environmental sinks. Further data, which allow creating more precise models, can be included if available: - Compound specific reactivities (e.g. hydrolysis rate constants, photolysis rate constants) - Reactivities can be defined for different species of the compound (dissolved neutral, dissolved charged, particulate) - Additional parameters describing temperature dependence of physico- chemical parameters and reactivities

Identification data of the compound (name, synonyms, etc.) and optional comments on parameters, typically used to record the reference, can also be stored.

2.2.3. HIEBARCIDCAL MODELS FOR THE PHYSICAL SYSTEM

MASAS includes a hierarchically ordered class of mathematical models allowing the construction of simulation models of successively increasing complexity with respect to both, the description of the lake and the description of the transport and transformation processes affecting the substance. In this way an optimal model can be developed, i. e., a model

- whose input data requirements can be satisfied with the available data; - which corresponds to the nature of the problem to be solved; - which avoids any unjustified complexity.

For the description of the lake, MASAS offers a set of different box models, which are all based on the previously described one-dimensional vertical model. The characteristics of the included models, from the simple one-box model to the most complex, the n-box model, are explained in Tab. 1.

Fig. 4 (to the riflht): Concepts of the MASAS-system. The figure shows the major elements of the MASAS-system. Left: provision of necessary data; Top: models of different complexity; Bottom: Process "stores"; Center: program which sets up the computer model and calculates the simulation results; Right: output data produced during simulation runs. Current settings: Two- box model for EDTA in Greifensee with two user-defined processes, Loading and Adsorption I Sedimentation. Models for the Aquatic System One-box Two-box Combl-box n-box &Jmmer.,._... Wmtet a LI Streif.,,; fake (op> "1dh)ll0imnion) System Library Comtina:ion of one~ and =ont mixing> ruction two-box model Vertica1 m~ < readion r.

Simulation Results Concentration of Compound CNer Tllllll I Depth

Lak8 Lucerne

Compound Dominant Processes Unknown Process Parameters Library Description of Processes Cl Residence lime in Aquatic System (HCB)He----~: ""'b Optimal Model Description

/CH,-Co; EDTA yH;-N~ C";N~.: Other degrad. Further compounds ... processes..,,, Tuh...l.;, Models of successively increasing complexity available in MASAS.

Model Short Description Basic model assumptions Typical applications T e One box Lake described as one com- overall reaction rate < horizontal - Conservative (or quasi-conservative) pletely mixed compartment and vertical mixing rates. substances Lake topography can be ignored - No fine resolution within lake needed - Coarse evaluation without spatial structure Two box Lake described by two vertical epi-/hypolimnion mixing < Substance with significant boundary completely mixed reaction< processes (air-water exchange, adsorption compartments. The upper box vertical mixing in boxes < to particles and sediment/water interactions) represents the epilimnion and horizontal mixing rates. in a stratified lake the lower box the hypolimnion. Lake topography included for Simplest model to introduce a calculation of epilimnion, structure. h olimnion and sediment area. Combi ation of one-box and winter: assumptions of one-box same as two box, for holomictic lakes (i.e. box two-box model. Includes model; lakes which mix completely every year) seasonal changes: Two-box summer: assumptions of two-box during summer stratification, model. one-box during winter circulation. n-box One-dimensional vertical lake vertical mixing rate < reaction rate Reactive substances with vertical gradients. model with a given number of < horizontal mixing rates. Significant processes which vary along adjacent boxes. Each box is Lake topography included in the depth axis (e. g. effects of variable considered to be completely model epilimnion thickness, subsurface inflows, mixed surface reactions or fluxes such as photolysis or air-water exchange, sediment interactions). 2.2.4. DESCRIPTION OF TRANSFORMATION AND TRANSPORT PRO- CESSES

In MASAS, all processes are defined by the user (except standard processes, Tab. 24). In this way, he/she has control over the model. The program only performs the laborious background calculations related to process definitions.

Each process (loading, air/water exchange, reactions, etc.) can be modelled as an independent unit. Certain processes (e.g. loading processes) lie en- tirely in the user's responsibility and are defined without support from MASAS. The program allows different descriptions, such as constant or time variable load.

Other processes are defined with support of MASAS. In this case, the program offers to the user different options, which take into account the available system and compound data. Descriptions of different complexity and approximations of missing parameters are included in this procedure. Information windows display available or missing parameters.

A typical sequence of a process description with increasing complexity is illustrated for a hydrolysis reaction:

on lake tem erature

Separate mode ing of acid, neutral and alka ine hydro ys1s, using specific reaction rates for each partial process and taking into account the pH-values in the lake Temperature dependence of reaction rates and pKw ignored

Same as a ove, plus: reaction rates and pKw depending on lake tem erature

Separate mo e ing o the y rolysis reaction for the species o the compound (dissolved neutral, dissolved charged, particulate) S ecific h drol sis rates ma ecies The approximation of unknown process parameters shall be illustrated with the mass transfer coefficient for the air/water exchange:

Constant mass transfer coefficient

Tem orall variable mass transfer coefficient

If mass transfer coefficient not available: built-in two-film model for air/water exchan e activated:

Henry coefficient required to deci e w e er gas or iqui m resistance is dominant. It can be rovided b various o tions:

eratu re

erature

Step by step, one can proceed to approximations which need less data information. The accompanying loss of accuracy is compensated by the possibility to use the model as a first estimate even in situations when little data are available.

2.2.5. BOW TO OBTAIN AN OPTIMAL MODEL AND PROCESS DESCRIPTION IN LAKES

Before we describe the rather straightforward user support provided by MASAS to find the optimal model and process description, a short introduction to the concept of mixing time and space scales will be given. As described by Imboden and Schwarzenbach (1985), characteristic mixing and transformation times can be employed to find the adequate spatial resolution for a given situation. The procedure can be summarized as follows: The mixing time measures, as an order of magnitude, the time needed to de- stroy possible concentration inhomogeneities by mixing. It is inversely related to mixing rate. Mixing times have to be compared to the transformation time, a measure for the time needed to transform the compound into another chemical species or to remove it from the system. Whenever mixing is much faster than transformation, the corresponding spatial variation can be neglected.

Generally, horizontal mixing rates are much larger than vertical mixing rates. The assumption of horizontal homogenity is justified when horizontal mixing is faster than reactions. Below the epilimnion, characteristic vertical mixing rates are much lower, and thus, a vertical space coordinate describes concentration gradients created by reactions. Other factors which influence the optimal spatial resolution of the model are the spatial distribution of sources, sinks, and reaction processes (e.g. surface vs. bulk reaction) and the purpose for which the model shall be used (e.g. long-term vs. short-term behavior of compound).

The determination of the model with optimal spatial resolution is assisted by the program which allows a quick switch between different model types. Simulation results can be compared in multiple graph windows to evaluate the optimal model.

The choice of dominant transport and transformation processes, and of the optimal process description is supported with the following features: - Processes can be selectively activated (switched on and om and processes of the same type can be multiply defined, with different parameter settings. These features allow to identify the contribution of an individual process to the simulation result and to compare different scenarios. - Process approximations (e.g. constant/time variable/temperature dependent) can be easily changed and compared. - The contribution of each process to the differential equation can be calculated and displayed. 2.2.6. RESTRICTIONS

The assumptions that are inherent in all models imply some restrictions:

Assumption of horizontal homogenity:

Highly reactive compounds may show significant horizontal concentration gradients because horizontal mixing may be too slow to smooth gradients which occur due to the reaction. Such compounds violate the assumption of horizontal homogenity inherent in all MASAS models.

MASAS is restricted to deep lakes with one major basin. In large and shallow, or in branched lakes, horizontal concentration gradients, which cannot be described with the vertical model, may play an important role. However, the replacement of the vertical lake model by a horizontal model which would allow horizontal subdivision of the lake basin, is prepared in the program and could easily be implemented.

Simulation of local phenomena:

The spatial resolution of MASAS is adapted to phenomena which affect the whole lake. MASAS cannot provide the necessary spatial resolution for local processes, such as local pollution in harbors or around inlets of sewage efilu- ents.

Simulation of transformation products:

MASAS is designed for the simulation of one compound. Reaction products cannot be included in the same model. It is possible, however, to develop for the reaction products independent models, which use the parent compound as an input function.

2.2. 7. PROVISION OF NECESSARY DATA 'IO BUILD MODEl.S

A library system is included in MASAS to facilitate the provision of the necessary data. It can create and read files which contain complete data sets of lakes or compounds. System and compound libraries can be assembled and supplied with MASAS. User's can extend this set with own files. 31

Auxiliary calculation procedures are included in the program to compute parameter values. Automatic procedures calculate parameters which always depend in the same way on others (example: calculation of geometry of the two-box model from general lake geometric data). Explicit procedures, activated by the user, calculate or approximate unknown parameters (example: calculation of lake temperature for one-box model as volume weighted average from temperature profiles).

2.3. USERINTERFACE

The guidelines which have been formulated for the design of software for the Apple Macintosh personal computer are also relevant for the MASAS and CHEMSEE user interface, and are briefly summarized here:

- Real-world metaphors: Instead of abstract concepts, analogues to the real world are used. Example: a document (file) is displayed on the screen with an icon:

Chapt•r 2 When the file should be deleted, its icon is simply dragged into the trash displayed on the screen:

Trashmm - User control: The user initiates and controls all actions; he/she decides what to do, not the program. - Feedback and dialog: Immediate feedback (alerts, beeps, busy pointers, etc. ) confirms all the user actions. In this way the user is always kept informed about the initiated actions and about the current program status. - See-and-point instead of remember-and-type: All options which are available in the program are always visible to the user (by menus). - Consistency across applications and within applications: Standard menus (S, File, etc.) and standard functions (Open, Save, Print, Quit) enhance transfer of skills. - Forgiveness: Whenever possible user actions are reversible and the user is informed when an irreversible actions is initiated. As a consequence, the user can safely explore the program. For MASAS and CHEMSEE the following concepts were formulated (Fig. 5):

- Pull-down menus are used to control the program. Their commands are always visible; disabled commands are shown in grey. - The user controls the flow of the actions; restrictions are limited to the minimum required by the program logic. - Several defined program statuses with a specific set of available commands provide orientation within the program. Two examples:

(i) Simulating: numerical solution of the defined model equations; all menu commands, except Quit program and Interrupt/Abort simulation disabled.

(ii) Simulation Pause: The simulation run is interrupted, and program actions which do not interfere with data consistency are allowed (e.g. editing of certain parameter values). - Use of one interactive text window for each dataset used (system, compound, model/simulation) (MASAS only). These windows constitute together with associated standard operations (file access, printing, window specific menu, etc.) clearly structured elements of the user interface. - Interactive text windows for process approximation (MASAS only). - An arbitrary number of graph windows to display the simulation results and model parameters. Simulation results are plotted during the simulation to give an immediate feedback to the user. A basic set of functions allows to modify the graphs (rescale, lock windows, save data on file, etc.). - Standardized entry forms for data input (parameters, program settings, etc.). The user dialog can always be terminated in two ways: OK accepts the input, CANCEL ignores the entire dialog. - Standardized entry forms for the process description (MASAS only): Different options available for the description of a process, and for each option, available and missing system and compound parameters are shown.

The Apple Macintosh computer with its user friendly and well developed interface concept was considered to be the most suited available personal computer for the realization of these concepts, and was therefore selected for the implementation. The most important characteristics of both programs are compared in Tab. 2. 33

Consistency across appllcatlons save ~s I Pulldown menus Sime Rs ••• .. I Modify... 31:!M r-o;i;·-···-...... ;;1 Configure ... Feedback and dialog Show All Doto Lock/Unlock Siil Q Clenr

User Control Forgiveness It Delete selected process?

Cone el « OK J

multiple text standardized entry forms and graph windows for data Input ® one-boH model O Two-boH model

Cancel c OK » Gro h: Profiles Run2 . =Diiii Graph: Total Moss

Slmulotlon Start limo [day I: 78.0 Clearly defined program statuses siop time: 394.o Simulation limo: 279.0 Noxt oulput: 2Sll.O }1,:~~:~=]I ]£~~=~--~ rn;;~~=~l [~~~-:=~

.Eig._Q;, User interface concepts of user friendly, interactive programs, which were applied to MASAS and CHEMSEE. ~ Major features of MASAS and CHEMSEE.

DESCRIPTION CS MS Program Implementation for Apple Macintosh personal computer x x Modular program structure. Hierarchically organized. Separation of kernel and x x interface modules Implementation in Modula·2 programming language x x Use of DialogMachine for provision of basic user Interface elements (menus, x x windows, entry forms, event loop)

Data structures General data structure for variables and processes created interactively or by x x program commands Differential equations dynamically created with variable and process definition x x List manager for the management of parameters, variables, processes, windows, x graphs, etc. Lists of arbitrary size Decoupling of Input and model data (Input: any spatial and temporal resolution; x x model: interpolation to box depth and simulation limes)

Provision of data Library files for system and compound data x Files for storage of model setup x x

Variables Dissolved, solid and sediment variables x x Adsorption to POC/particles x x Charged species (acid/base reaction) x Arbitrary number of model variables x

Processes Arbitrary number of processes from predefined set of process types x )( Process description based on system and compound data x Process description of different complexity based on available data x Approximation of unknown process parameters x Display of process contribution to differential equation )( Option to switch processes on and off for evaluation of models (x) x

Models Models of different complexity Arbitrary number of boxes in n-box model x x Variable box thickness x

Output Display of system, compound and model data in interactive text windows x Storage of simulation results in computer memory; later modification of graphs (x) x Storage of simulation runs in computer memory x Graphical output during simulation run in multiple windows x x Time series graphs x Profile graphs x x Variable output interval x Display of variables, parameters and auxiliary variables In graph x 35

2.4. PROGRAM LANGUAGE AND STRUCTURE

The program language Modula-2 (WIRTH, 1985), closely related to Pascal, and the programming environment MacMETH (1988) were used. Modula-2 optimally supports the organization of a program with different units (modules), and provides structured data types (records, etc.), dynamic data structures (pointers, dynamic lists), and procedure variables. These elements are, together with the strict type checking ofModula-2, indispensable elements to develop a structured, expandable and safe program.

Both programs were organized in a modular way with a number of distinct functional units dedicated to specific tasks. The concept of units, or modules, is well developed in Modula-2, and has been described by Wirth (1985):

- A program can be partitioned into several modules, each module containing constants, variables, procedures and types. - Objects introduced in other modules can be used in a module X, if they are imported into X. - Every subsidiary module may again import objects from other modules; a program therefore constitutes an entire hierarchy of modules. The main program is said to be at the highest level. - Subsidiary modules are divided into a definition part and an implementation part. The definition part contains the declarations of the exported objects; the implementation part the actual implementation of the objects. Each module can be compiled separately; low level implementation modules can be modified and recompiled without implications for higher modules.

Modules were structured according to two criteria. First, the programs were hierarchically organized with low level modules for basic data structures and procedures, intermediate level modules for functions, such as MASAS- system data, or model equations, and one high level master module, for program control. Second, on each level, distinct modules exist for kernel functions (data structures, calculations) and for user interface functions (windows, entry forms, file input/output). The master module assembles kernel and interface elements into one program, and in this way defines the overall behavior of the program. This program structure facilitates development, maintenance and extension of the program, and modules may be reused in other programs. The programming effort for the user interface (including event loop, menus, entry forms, windows and graphs, file input/output, etc.) was minimized as much as possible by using an appropriate program library, the DialogMachine (Fischlin, 1986). 3. APPLICATION OF CHEMSEE

In this section, it will be shown how CHEMSEE has been used in research and in student courses. We focus on the Mn-cycle in Greifensee, other applications will be presented in summarized form. This section will further make you familiar with the program CHEMSEE. The illustrative example in section 3.1.3 presents the program from a user's point of view, and can also serve as brief ~ntroduction to CHEMSEE for people interested in working with the tool.

3.1. MN.CYCLE IN GREIFENSEE

3.1.1. INTRODUCTION

The model of the Mn-cycle was fully described by Johnson et al. (1991). It was developed in combination with field data of dissolved Mn(II), particulate Mn, and oxygen (Fig. 6), determined by Sigg et al. (1991). Here, the model will be described in summarized form and with only the most important references.

In lakes and sediments with variable redox conditions, manganese occurs in two oxidation states: as reduced Mn(II) and as particulate Mn(III,IV) oxides. The Mn concentrations are governed by a number of physical, chemical and microbiological processes which interact to create a highly dynamic and sensitive system. These processes will be briefly discussed.

Mn oxide reduction - It has been well established by laboratory studies that microorganisms are capable of reducing Mn oxides. Field studies have focussed on Mn oxide reduction within sediments, which can be a major source of Mn(II) within eutrophic lacustrine and estuarine systems. Measured Mn(II) fluxes into the water column are within the range of0.07 to 2.3 mmol·m·2.d-1.

Mn([!) oxidation - Mn(II) oxidation in natural waters occurs homoge- neously, in heterogenous systems where surfaces act as catalysts, or through microbial mediation. Pseudo-first-order rate constants for Mn(II) oxidation in homogeneous solution, calculated for Greifensee, are in the range of 7·10·7 d·l to 9·10·6 d·l (5°C). Reported values of microbial oxidation are many orders of magnitude faster (0.04 - 0.74 d·l). Emerson (1980) deter- 38 mined, in the waters ofSaanich Inlet, overall Mn(II) oxidation rates of0.15 - 0.62 d-1, which compare favorably to those describing microbial oxidation.

Particulate Mn settling - Mn oxide particles in natural waters have a variety of morphotypes and undergo coagulation processes. Since settling characteristics depend strongly on morphology, it is difficult to predict exact sedimentation rates.

MnC03 precipitation - Solubility of Mn(II) is limited by precipitation of MnC03. The maximum concentration of Mn(II) in equilibrium with MnCOa is calculated to be 10 µM (Greifensee, 5°C). This process was neglected because, with only two exceptions, all measured Mn(II) concentrations were well below this level.

Mn(Il) sorption on colloidal particles - An assessment of the importance of Mn(II) sorption to colloidal particles proved that sorption does not play an important role in the regulation of the Mn(II) content of Greifensee waters.

Aims of the model study

The following aims were addressed with the model calculations: - To bring together all the separate pieces of information about the physical and chemical processes controlling the Mn cycle in one dynamic model. - To validate the model in combination with field data and to identify the dominant processes. - To estimate the rates and parameters of the processes and to compare the results with values reported in the literature: - Mn(II) flux from the sediment into the water column • Rate constant of Mn(II) oxidation - Rate constant of removal of particulate Mn - Oxygen threshold concentration for Mn(II) flux and Mn(II) oxidation - To identify gaps of knowledge as a stimulus for further research.

CHEMSEE was used for the model study because it allows working with several model variables, which was necessary for the Mn cycle (two Mn species and oxygen). The n-box model included allowed an adequate spatial resolution, other spatial resolutions were not needed. All the necessary process 00 0.25 0.5 0 0.25 0.5 0 0.25 0.5 0 0.25 0.5 i I I I I I I ,.I ) I .... , ... 10 l 8 Jun ( •....-;;Jun "" 20 Jul ,.. 3Aug I \ \ ) \ l I 20 I ~· I l I: I • I I la! I 30 •

o....._...... _.__._..._._...... ,....,,....._...... __.~~...... i....+~,+-+-H-1...... i I ) ,....- 27 Oct E 10 .... .c ...., 20 a. Cl> c 30

O·..._..._....lo-M...... i..-+-1-....&..;~..;...,...... _H-1...... i.~..-...++.-.-1-+-J-....J'-'-'-...... ,....._.-...... J I I I I 10 ) 16 Nov 14 Dec j 12 Jan --·Oxygen , I 89 I I - Mn(ll) 20 I I ··•·· Mnp I I I 30 )

0 4 8 0 4 8 0 4 8 12 [Mn], µM

~ Depth profiles of Mn(II), particulate Mn and oxygen in Greifensee determined in 1988 (Sigg et al. 1991). The profiles were taken at the deepest point in the lake (#6 in Fig. 19). 0.25 mM 02 = 8 mg/I. Oxygen content was measured at 1 m intervals. 40 types, including chemical reactions with stoichiometry and processes influ· enced by external variables (oxygen), were available in CHEMSEE.

3.1.2. DESCR.WI'ION OF GREIFENSEE

Greifensee (Fig. 19, p. 65) is a eutrophic, holomictic lake with regular overturn in winter (December - March). Characteristic parameters of the lake are given in Tab. 3. Both field studies on Mn (June 88 - November 89) and on the anthropogenic chemicals (March 90 - February 91, described in Section 4) were undertaken in Greifensee. Regular successions of oxic and anoxic conditions in the hypolimnion cause significant temporal variations of the redox conditions. A high population density and intensive agricultural activities cause a significant input of anthropogenic chemicals into the lake. The lake was intensively monitored by other groups (Ambuhl, 1990), and therefore, data for chemical and physical characterization of the lake were available.

'.rah....a;. Characteristic parameters of Greifensee.

Morphology and hydraulics: Volume 0.151 km3 Maximum depth 32 m Lake isobath area, A o m (lake surface) 8.49 km2 10 m 6.55 km2 20 m 3.51 km2 30 m 1.02 km2 Average flow, Q 4.28 m3/s Catchment area: (Liechti, 1988) Area (excl. lake} 160 km2 Inhabitants (excl. Pfaffikersee) 85'000 Inhabitants, catchment of Pfaffikersee 15'000

3.1.3. MODEL SET·UP AND ILLUSI'RATIVE EXAMPLE

The model with the variables and processes included in the Mn cycle is depicted in Fig. 7. All variables and processes with the values used in the model are given in Tabs. 4 and 5, respectively. The model includes two Mn species, the reduced Mn(II) and the oxidized, particulate Mn. We start the description of the relevant processes at the sediment/water interface. Mn(II) is released from the sediment when oxygen is depleted in the water column and is then accumulated in anoxic waters (#3 in Fig. 7). At the onset of lake 41 circulation in autumn, when oxygen again reaches deeper waters, Mn(II) is rapidly oxidized to particulate Mn (4), probably catalyzed by microorganisms. Particulate Mn is removed from the water column by coagulation (5) and subsequent rapid settling to the sediments. Reduction in the sediment com- pletes the Mn-cycle.

(2) l(6) --,:>------v -.------(1) ,:, Mn(ll)~ Mn part·:":"•(~ ti11"- .._...___ ,~ •• • .J, lllillllt-'--,)~~~~~~~-- _____ ,:, ~~~~~--.---

Fig. 7: One-dimensional vertical lake model of the Mn-cycle realized with CHEMSEE. The figure shows the included processes and variables (numbers refer to Tab. 5) and the separation of the lake into adjacent horizontal boxes. The variable sediment area per box is highlighted by the black bars at the bottom of the boxes.

~ Variables of the model of the Mn-cycle in Greifensee. * It is assumed that freshly formed Mnpart has no apparent settling (colloidal or very fine particles). The removal of Mnpart is summarized in one process which includes coagulation and rapid settling of the newly formed larger particles. The latter are not included in the model (no field data available). t Variables added to the model for comparison of field data to model results. Not included in illustrative example and in Fig. 55. 0 The expression Static Variables is explained in Fig. 10. VARIABLE TYPE CALCULATION 1 Oxygen (field data 1988) dissolved static0 (input parameter) 2 Mn (II) (simulated data) dissolved dynamic (initial value: field data 8 June 1988) 3 Mnpart (simulated data) dissolved* dynamic (initial value: field data 8 June 1988) 4 Mn (11) (field data 1988)t dissolved static" 5 Mnpart (field data 1988)t dissolved* static0 '..IJili.Ji;. Processes and parameter values used in the model of the Mn-cycle in Greifensee. The numbers refer to Figs. 7 and 55. Column INFO shows the number of the involved variables (Tab. 4), and the definition of the process: implicit: implicitly with variable definition; explicit: explicitly by user. PROCESS INFO PROCESS PARAMETERS 1 Vertical eddy diffusion 2,3 Numerical values determined with heat budget method {VERDI, implicit Section 5.7) using 1988 temperature profiles. Typical values [cm2/s]: ~ dali! Jal:! lli .fill 7m 0.01 0.01 0.04 22 m 0.1 0.04 0.25 27m 0.03 0.02 0.16 2 Lake outflow 2,3 Hydraulic rate for epilimnion calcuated automatically from variable implicit epilimnion volume and flow (4.28 m3/s}. Typical values: Epi-d~'2lb Hydraulic rate fepi)· 2m 0.022 d-1 10 m 0.0048 d-1 32 m 0.0025 d-1 3 (P21) Mn (II} release from 1 (Trigger}, Time variable, decreasing rate constant: sediment 2 Day Zero order sediment flow [mmol·m·2.d-1] (Zero order sediment flux) explicit 160 - 189 1.0 209 0.2 378 0.0 Oxygen threshold concentration: process active if [02] < 5.10-s M 4 (P22} Oxidation of Mn (II) to Mnpart 1 (Trigger), First order reaction rate constant: 0.2 d-1 in the water column 2,3 Oxygen threshold concentration: process active if [02] > 3.10-s M (First order reaction~ exe/icit 5 (P31) Coagulation and subse- 3 First order reaction rate constant: 0.035 d-1 quent rapid sedimentation explicit of Mnpart (Mn part sink} (First order reaction~ 6 Mixing of the epilimnion 2,3 Values obtained from 1988 temperature profiles; typical values: implicit ~ 1fill 2.22 m m Thickness 2m 2m 10 m 30 m ~ Setup of a lake model with CHEMSEE. The figure shows the typical actions which are performed when a model is interactively created. CHEMSEE is an event driven program: the user, not the program, decides about the flow of actions. Restrictions are limited to cases when erroneous program states would result. The typical order of user actions is indicated by the numbers. In the lower left corner, it is shown how the differential equations for the model are created step by step by variable and process definitions. 44

Now we shall start the illustrative example and show how the Mn model was developed. In this way the characteristics of the user interface and the typical actions performed when working with CHEMSEE will be demonstrated. An overview of the interactive user actions executed to build the model is given in Fig. 8. The construction of the Mn model is described step by step in the captions of Figs. 9-14, and the corresponding figures illustrate the events on the computer screen. The internal data structure which is created by the interactive user actions and which represents the Mn model within CHEMSEE is depicted in Fig. 55 (p. 171).

Figs. 9-14 given on the following pages consitute the illustrative example for CHEMSEE. The text is continued on p. 54.

Fiir. 9 (p. 48): Illustrative example for CHEMSEE: Definition oflake parameters. This figure shows the interactive definition of parameters for lake geometry and hydraulics for the Mn-model. Cll Definition of lake depth: the user selects command Depth from menu System, which activates the entry form Maximal depth of the lake. OK is pressed to enter the new value, CANCEL to abort the dialogue without changes. ® Definition of lake isobath area (cross section): after the corresponding menu has been selected with the mouse, the standard entry form Define the following parameter is shown. In our example, the options Variable over depth and From file were chosen, and subsequently, the entry form to locate the data file Greifensee.Crossection is shown. After clicking Open, the file is accessed and the data read into the program. @ Definition of epilimnion depth: The menu command activates the same standard entry form for parameters (not shown), giving the options Constant and Variable over time. We select the second option together with User defined, and enter the number of data pairs (Number of values, up to 1024 data pairs). Then, the entry form Enter time series for parameter is shown. Any time distance between data points is possible, intermediate values are calculated by linear interpolation. The definition of In- and Outffow, Vertical Diffusion Coefficient and Sediment, is not shown. Fig. 10 (p. 49): Illustrative example for CHEMSEE: Definition of variables. The figure depicts the definition of two of the three variables used in the Mn- model. (!)After the menu command New Variable (keyboard shortcut, indicated on the right of the menu text: Command (K)-N) has been selected, the entry form Define new variable is shown. The user types in the variable's name and unit. Oxygen is defined as a Static variable, i.e. a predefined input variable which is not calculated by the model. Later, oxygen can be used as gov- erning variable for processes. Static variables are also utilized to include field data for comparison with simulation results. Three types of variables are available: Dissolved (used for oxygen), Particulate (sedimentation implicitly defined), and Sediment variables. The last option is not available in our example, and the user is told why in the entry form. After clicking OK, the entry form to define the static value for oxygen is shown (standard entry form for parameters). This time, all five options for time/depth functions are available. After clicking OK and selecting the file (not shown), the oxygen variable is ready to be used in the model. @ The same command is activated again to define the dynamic Mn2-variable. After selecting Variable over depth, Number of values: 10 (not shown), the initial value profile can be entered in the entry form Enter profile for parameter. The definition of the third model variable, Mn part (simulated data) is not shown.

Fig. 11 {p. 50): Illustrative example for CHEMSEE: Definition of processes. This figure shows the definition of one of the three explicitly defined processes of the Mn-model. The command New Process activates the entry form, where the name and the type of the new process can be entered. Reactions of different order, different loading processes, surface and sediment boundary fluxes, including gas exchange, and particle resuspension for sediment variables are available. In our example, First order reaction is selected. After clicking OK, further definitions are given in the entry form Define process. The variable included in the process is selected: Mn2. The option Edit /view process constant is checked, OK is clicked, and the rate constant can be subsequently entered. Fi2'.12 (p. 51); Illustrative example for CHEMSEE: Modification of pro· cesses (stoichiometry, trigger variables). To modify the previously defined process, the command Change Process and the process to be changed (Mn2 oxidation) is selected by the user. The options Define trigger variable and Define stoichiometry are checked. In the entry form Define trigger variable for process, Oxygen is selected as trigger variable for Mn2 oxidation. The process will be active only, if oxygen is in the interval 0.03 • 20.0 moVm3. In the entry form Define stoichiometry for process, the chemical reaction k=0.2 Mn(II) Mnpart

-k[Mn(II)] k[Mn(II)] is entered into the model. The included variables (up to four educts and products) and their stoichiometry factors (·l, 1) are defined.

Fig-. 13 (p. 52): Illustrative example for CHEMSEE: Simulation and graph setup & simulation run. All variables and processes are now defined, the number of boxes is set to 32 (not shown), and the following definitions can be made: © The simulation parameters are defined with the command Simulation Set Up. Start and stop time are set to 8 June 1988 (day 160), respectively 27 Sept (day 270), with output to be created every 55 days. Precision of the integration algorithm: the time step is adjusted, within the limits 0.1-2.0, that the rela· tive change does not exceed 0.01 for all model variables in all boxes. @ Show Graph Set opens for each variable one graph to display profiles. The scaling is manually adjusted with the command Modify Graph (not shown). ® Now, we are ready for a simulation: Start Run, and our model is calculated for the requested output times, day 160, 215 and 270. In the window Oxygen (field data 1988), the predefined values, interpolated to corresponding dates and depths, are displayed, in the other windows, the simulation results are shown. Date labels have been added manually for clarity. 47

Fie. 14 (p. 53): Illustrative example for CHEMSEE: Modification of model & new simulation run. The figure illustrates the iterative procedure: a modified version of the model is compared with the original model presented in the previous figure. © Modification of a process: Change Process is selected and in the entry form, the process Mn2 oxidation is chosen. We want to test the influence of oxygen with a modified threshold level, therefore, it is decreased to 0.005 mo1Jm3 in the entry form Define trigger variable for process. @ Previous simulation results should be conserved for later comparison. With the command Lock Graph, applied to all windows, the graphs are locked (x in top left comer) and no more used for further output. @ With Show Graph Set, a new set of windows is opened. All windows are automatically arranged on the computer screen. © A new simulation run is performed, and a comparison of tho two models is easily possible: oxygen profiles are unchanged. Due to the lower threshold level, Mn2 is more readily oxidized, resulting in significantly lower values for Mn2 and higher values for Mnpart on 3 Aug. The influence is less pronounced on 27 Sept, because at this date, oxygen concentrations already fell below the new threshold level. 1. Definition of lake parameters

Depth ••. @ Depth ••• In- and Outflow •.• In- and Outflow •.. In- and Outnow •.• Cron Section R(z) ••. Cross SecUon ll(z) ••• Eplllmnlon Depth ••• Eplllmnlon Depth ••• I I uert1c111 Diffusion Coeffieien •• uertical Diffusion Coefficient. uertlcal Diffusion co Sediment ••• Sediment ••. Sediment •••

Temperature ••• Temperature ••• Temperature ••• UEROI UERDI llERDI

Define the following paramete Mnnimal depth of the lolce fm): l11ke cross section Rl:zl lm2 IFtii O const11nt, llalue: Epi-/Hy ~1·~u~1i1eg1l!l!!ill I1 OD.O I OK • ® uarlt1ble ouer depth (Profile) Entry options: ®From file rr======::ii1'11!!!5=ii::;JO User defined ber of ualues: ouer time: Number of ualues: ouer depth: [:] le!i 6reifensee 6rundd11ten I ~ D Gre1fensee.C10>se£t10n O saue p11rameter on me = UP-Pilatus 11--C-RN-C""'E=-L'"' OK Cl Greifensee.Diffuslon t , Cl Greifensee.Epidepth Enter time series for parameter: Depth of epilimnion !ml time ldayl l!lllUe EQiiji J16o.o [ Ct1ncel J j202.o 12.0 J321.0 110.0 J37B.O J3o.o .., 2. Definition of variables @ Define new u11ri11ble: Unit: Imol/m3 Define Porticulat~··········-~;;;..- Orgonit Carbon •.. ...,. N11me:jMn2 lsimul11ted d11tol Cll11nge Darieble... Type: ®Dynamic ® Oissollled Cllange Initial/Static l!alue ... O Static 0 Particulate Delete Uariable ... Sediment: only if POC 11nd dissolued u11riable Delete RH Uariables ... witll adsorption defined!

Define new uariable: Name: lo11ygen I field date 1908) Unit: !mol/m::'ij Type: O Dynamic ® Dlssolued ®Static O Particulate Sediment: only If POC ond dlssolued unrinble with adsorption defined! I OK I Enter profile ror parameter: Initial llOIUe [Mol/m3) depth lml uolue Define the following parameter: llalue of static uariable lmol/m3) O Constant, llalue: Epi-/Hypolimnlon: O Uartable ouer depth (Profile) 0 llarioble ouer time mmeseries) O IJartable ouer time, different for epl-/hypolimnlon ® U11riable ouer lime and depth Entry options: llR:From me 0 User defined Number of ualues: ouer depth: ouerlime: ~ O S3ue p3rameter on flle OD - 3. Definition of processes Define protess First order re8ttion Mn2 oHidation llarlallle(s) of the process: O OHggen (field data 1988) @ Mn2 (slmul8ted dntnl O Mnp8rt (simulated data) Delete 1111 Processes .•• i Editluiew process constant Options: O Define trigger uoriable (Current setting: NO trigger 1.111rinllle) O Define stoichiometry (l:urrent setting: ND stoichiometry) O Zeroth order reaction (kl ® First order reaction (k•lll) [ l:ANCEL ) I OK I O Second order reaction (k•u1•112J Special processes:

O Loading from inflow (concentrntionl Define the following parameter: O Loading from inflow (moss/dog) First order rate constant 11/dagl O Input in top boH (mess/day) f&: i::onstant, U1!1ue: Epi-/Hypolimnion: O Surface Input lmass/m2/doyl O Uorlable 011er depth (Profile) O 6as eHthange with atmosphere O Uarloble ouer time (Timeserles) O Resuspension of ndsorlled substonce O Uarioble 011er time, different for epi-/hypolimnion O Sediment fluH, zeroth order O Uariable ouer time and depth O Sediment flUH, first order Entry options: QFrom me ®User defined Number of ualues: ouer depth: [G ouertime: [G O Soue 1)<1rameter on file - I OK I 4. Modification of processes (stoichiometry, trigger variables)

New Process... 3£P

Edit Process Constant ••. Delete Process ••• Define trigger uftrlable for process Delete RH Processes ... Mn2 011ldatlon

Select process to Ile changed: O No 11arlable O Mn2 releHe from ndlment Sediment flu11, zeroth \ 011ygen (field data 1988) Mol/m3 'tMn2 011ldntlon Firs! order reaction O Mn2 (simulated data) Mol/m3 O Mn part sink Finl order reaction O Mnpert (s1mu111ted data) mol/m3 Process 11cti11e, if trigger 11ariable in the inler11al: Min: fnMJ MeH: ~j2_0_.o ____ ~

Define process first order reaction Mn2 oHldation llariable(s) of the process: O 011ygen (field date I 988l Define stoichiometry for process @ Mn2 (simulated data) Mn2 011idalion 0 Mnpart (simulated datnl jil•MilTI::J~~ No uariable 0 0 @ @ O Edlt/uiew process constant OHygen (field date 1988) 0 0 0 0 Op lions: Mn2 (simulaled data) ® 0 0 0 !8l Define trigger variable (Current selling: NO trigger uarlable) Mnpart (simulated data) 0 0 0 !8l Define stoichiometry (Current setting: NO stoichiometry)- ' OD ~ 5. Simulation and graph setup & simulation run

Define Output File ... @ Simulation Set Up •••

New Graph... 11€6 Pause Modify Graph ••. Pouse • •• Sl

-20.0 -20.0 I -25.0 . / -25.0

-30.0 . .j -30.0

0.000 0.200 0.400 0.000 0.001 o. O)(\lgen, field dote !Mol/"3 Mnpor-t, simulated data (11101 /m3 6. Modification of model & new simulation run

. . . ® I• olelfimmleloLu_t_p_u_t -Fi-le-.-•• -~ @ Define Output file ••• New Process ••• New Graph... 11!6 New Graph... KG Simulation Set Up ••• Edit Process tonstont.:! Modify Graph ••• Modify Graph ••• ------·-·--- Delete Process ••• Lock/Unloct Graph 31JL tlenr Graph Clear Graph '\'PnUS(l I :.;r Delete RU Processes •.• Sl

Select pn>ceH to ba changed: leor All Graphs KB Clear RU Graphs O Mn2 release from sediment Sediment flUH, zeroth 'Mn2 oHldotlon First order reaction Mn2 (simulated datol Mnpart tslmulotedJ• 0 Mn port sint First order reaction 0.0 0.0 I

.. ·•. I I .I.

Mn2 oHidatlon Trigger ~•noble:

Mot/m3 o.o Mol/m3 mol/m3 -10.0

-20.0 · · · a June·aa. · -20.0

-30.0 -30.0

0.200 0.40il 0.t)(X) 0.005 0. field dai.a lt1ol /r!l3 11n2, simulated data fMol/m3 3.1.4. MODEL RESULTS

The model calculations proved that it is possible to construct a simple dynamic model of the Mn cycle using the known processes involved in the transport and transformation of Mn. Three processes were found to be relevant: Mn (II) release from sediment, oxidation of Mn (II) to particulate Mn in the water column, and coagulation and subsequent rapid sedimentation of particulate Mn. The measured Mn profiles could be simulated by the model, using parameter values in the range of those reported in the literature. The results for both Mn species will be briefly discussed.

Mn(ll) profiles - The most important features of the measured Mn(II) profiles have been simulated by the model (Fig. 13). They include rapid Mn(II) flux from bottom sediments at the beginning of the stagnation period, fol- lowed by the diffusion of Mn(Il) from the lateral sediments later in the season, and the decline in Mn(II) concentrations in bottom waters with time.

The flux of Mn(II) from the sediments is the most important process for the determination of Mn(II) profiles. Mn(Il) profiles could only be reproduced if the Mn(II) was allowed to vary temporally (Tab. 5). At the beginning of oxygen depletion in bottom waters (June 88), the flux of Mn(II) was 1 mmol·m·2.d-1, but after 30 days the flux decreased almost exponentially. In Greifensee the Mn(II) flux is thus probably a function of organic detritus originating in surface waters and of the amount of reducible Mn oxides in the surface layers of the sediments.

The oxygen threshold concentration used in these calculations was 5· 10-5 M. Mn(II) actually begins to diffuse from the sediments when oxygen concentrations are below 3 to 15· 10·5 M (8 June 1988, Fig. 6).

Particulate Mn profiles - The particulate Mn profiles are generated by a combination of the Mn(II) flux, Mn(Il) oxidation and particulate Mn settling processes. The performance of the model with the particulate Mn was not as good as for dissolved Mn. Nevertheless, the simulated and measured profiles are in general agreement (Fig. 13).

The particulate Mn profiles in the Greifensee can only be reproduced with oxidation rate constants akin to those reported for microbial oxidation. The oxidation rate constant chosen was 0.2 d-1. The oxygen threshold concentration for Mn oxidation was a critical parameter and was chosen on the basis of the overlap of the oxygen and Mn(II) profiles. The optimal value was 3·10·5 M.

The coagulation rate of particulate Mn with other particles in the lake water was determined on the basis of the removal of the particulate Mn concentra· tion peak from 3 to 23 August. Linear interpolation between the data sets gave a coagulation rate of0.035 d-1 with respect to particulate Mn.

3.1.5. CONCLUSIONS

The calculations have shown that it is possible to represent the Mn cycle in Greifensee using a time-dependent model. The model included processes known to be relevant in the Mn cycle. The dominant processes could be iden- tified; the estimated constants were in the range of those cited in the literature. The critical role of oxygen in major processes was highlighted. Values for oxygen threshold concentrations could be determined, both for release of Mn(II) from the sediments and for Mn(II) oxidation. The values of constants determined should be treated with caution, however, keeping the assumptions of the model in mind. More important is the demonstration that such models can provide us with a greater understanding and highlight the gaps in our knowledge of the Mn cycle in lacustrine systems.

The open questions include: the relationship between oxygen concentrations and the Mn(II) flux, and the relationship between oxygen concentrations and the Mn(II) oxidation reaction, and the dynamics of Mn oxide production, co· agulation, and settling which result in the formation of particulate Mn peaks.

3.2. FURTHER APPLICATIONS OF CHEMSEE

CHEMSEE has been successfully applied both in teaching and research; the most important models which have been developed with CHEMSEE and their purpose are summarized in Tab. 6. In general, users had few difficulties with the program and were able to set up models in a comparatively short time. The model calculations were employed for the evaluation of relevant processes, for testing of different hypotheses, and for the illustration of the characteristics of a given aquatic system (diffusive processes, numerical effects). The major limitation was that CHEMSEE offers a fixed set of ~ Applications of CHEMSEE. Explanation of the abbreviations used in column AI (area of interest): MF: Computers modelling in combination with field study. Research. T: University teaching (course in systems analysis for students of environmental natural sciences at ETH Zurich). F: Field data available for comparison. MODEL Al VARIABLES DESCRIPTION REFERENCE Mn-cycle in Greifen- MF 02 (input variable) During summer stratification with anoxic conditions Johnson et al., see. Mn (II) (reduced, below"' 1 O m, Mn (II) is released from the sedi- 1991 a Identification of major dissolved) ment. Decline of the thermocline in fall and subse- transport and transfor- Mn part (oxidized, quent reoxygenation induces oxidation of Mn (II) to mation processes colloidal) colloidal Mn, which is removed from the water col- (metal cycle) Mn part* (oxidized, umn due to coagulation and subsequent settling. articulate Heliumffritium in Lake MF 4He (natural) To evaluate different hypothesis, the model was Kipfer, 1991 Van, East Turkey. 3He (natural) used in combination with measured 3He/4He-ra- Determination of the 3H (atomic bomb tests) tios, information about the helium isotopic com- water age and mixing position in geochemical reservoirs, such as earth characteristics (natural crust and earth mantel, and the influence of bomb and man-made iso- produced tritium. to s Cr-cycle in Greifensee. MF CrO~- (Chromate) The chromate cycle in Greifensee is dominated by Johnson et al., Determination of input two processes examined in this model: the input 1991 b and removal rates of through the lake inflow and the elimination by ad- chromate (metal cycle). sorption and subsequent settling. Temperature and con- MF Temperature Measured temperature and conductivity profiles Uhde, 1991. ductivity in Lago di Conductivity and information about the characteristics of the Cadagno, Ticino, underwater sources (temperature and conductivity) Switzerland. were used in combination with the model calcula- Determination of mix- tions to determine the flow of the sources and the ing characteristics and vertical mixing of the lake. underwater sources (physical variables in a lake. Tab. 6: Applications of CHEMSEE (continued). Methane-cycle in TF CH4 (dissolved) Methane is formed in the sediment and diffuses Bossard and Greifensee (naturally 02 (dissolved) into the water column. When oxygen is depleted in Gachter, 1981. formed organic com- summer due to oxidation of methane and organic pound) matter, methane is accumulated in the bottom waters. At the onset of lake circulation in fall/winter, oxygen reaches these waters, and oxidizes methane. Hexachlorobenzene T HCB in water column HCB, reaching the lake from anthropogenic Imboden and (HCB) in Greifensee (dissolved, adsorption sources, is eliminated from the water column by Schwarzen- (organic to POC) air/water-exchange and by adsorption to particu- bach, 1985. micropollutant) POC in water column late organic matter (POC) and subsequent settling. (particulate) The second process leads to an accumulation in HCB in sediment the sediment from which HCB is released even after several years input has been banned. Radon in a large test TF 222Rn in water column 222Rn is transported into the lake by the inflows Imboden and lake and in Greifensee and by diffusion from the sediment where it is pro- Emerson, (radioisotope) duced by decay of 226Ra. The elimination occurs 1978. by air/water-exchange into the atmosphere and radioactive decay. Analytical solutions of the problem are compared to model calculations; the influence of lake topography is examined. Tetrachloroethylene TF CCl4 (dissolved) The eliminations processes of PER, air/water ex- Imboden and (PER) in Greifensee change and lake outflow, are compared using Schwarzen- (organic simple hand-calculations and the computer model. bach, 1985. micropollutant) Tab. 6: Applications of CHEMSEE (continued). Test models to T Test variable - Broadening of a standard peak caused by verti- Piepke, 1991. demonstrate basic cal eddy diffusion features of the model - Pseudo-advection: Influence of depth dependent equations and possi- lake topography and Kz on diffusion (resulting in ble numerical errors pseudo-advective term) - Numerical diffusion (for cases with vertical advection; caused by the local truncation error introduced by discretization of the model equations) - Demonstration of stability conditions for numerical integration 59 4. APPLICATION OF MASAS

4.1. INTRODU.CTION

In this section, we will show how MASAS has been applied to investigate the behavior of anthropogenic compounds in lakes. For four selected compounds, computer models were developed in combination with a field study in Grei- fensee to answer specific questions. An introductory example to the MASAS system is included in the Section on EDTA (Figs. 25-32). This example illustrates a typical sequence of events involved when developing models with MASAS. In the following, we shall briefly characterize the model compounds and the specific problems addressed.

4.1.1. EDTA

The chemical structure of ethylenediamine-tetraacetic acid (EDTA) is shown in Fig. 15. EDTA is the most widely used organic sequestering agent used for deactivating metal ions which cause problems in technical processes. It is mainly used in industries (cleaning, electroplating, water softening, poly- merization; 30 %), in detergents and cleaning products (20 %) and in photographic laboratories (20 %) (AIS 1987). In Switzerland, the EDTA content of detergents is limited to 0.5/1% (BUWAL, 1986).

EDTA is a critical substance in the environment because it has been found to be rather persistent. The only degradation process which might be relevant for EDTA is photolytic degradation. In the laboratory, fast photodegradation of the Fe(III)-EDTA-complex, which absorbs strongly at 260 nm, has been observed (Lockhart et al., 1975). The pseudo-first order rate was in the range of 2.5 d- 1 (4000 ft-candles, pH 6.9). Degradation rates of the same order of ....0, lfS H0-8 't-OH 'c- H H..,c! H' \ ~ ~ I ... H IN-C-C-NI H.., / ~ ~ \c, H I "'H H' \ HO-C,, ,,C.,. OH ,o, \~ ~ Chemical structure of ethylenediamine-tetraacetic acid (EDTA). 00 magnitude were found in River Glatt water exposed to sunlight (Ch. Schaff- ner, pers. comm.). However, it is not known under which conditions this process might play a significant role in aquatic systems. Other EDTA com- plexes, e.g. with calcium, do not absorb light and are thus not degraded. The fraction of EDTA which is present as photodegradable Fe(IIl)-EDTA has been calculated for natural conditions (River Glatt), assuming equilibrium conditions. This fraction was found to be very low (6·10-5: B. Sulzberger, pers. comm.). The degradation rate calculated using this fraction, however, is significantly lower (approximately lQ-4 d-1) than the degradation rate observed in the River Glatt. This discrepancy may be due to non-equilibrium conditions (e.g. anthropogenic Fe(Ill)-EDTA not in equilibrium with river water).

Elimination ofEDTA by volatilization can be excluded, because it occurs pre- dominantly as charged species under natural conditions.

Adsorption on humic acid, silica, kaolin, river sediment and humus solids was found to be negligible within up to 48 hours (Gardiner, 1976). For soils and aquifers, Hering (1991) suggested that EDTA may sorb to naturally- occurring iron oxides, which might be relevant for lakes, too.

The following aspects were addressed with the EDTA model: - Setup of a simple and reasonable model which is in accordance with field data and current knowledge. - To ascertain whether EDTA is eliminated in lakes by significant transport or degradation processes - Prediction of parameters for the proposed processes.

4.1.2. NTA

The structure of nitrilotriacetic acid (NTA) is shown in Fig. 16. NTA is a complexing agent which functions as a phosphate substitute in detergents (ffilmanns, 1983). NTA is degraded aerobically, and also anaerobically in the presence of nitrate. The degradation pathway is known. Degradation rates reported in the literature are summarized in Tab. 7. The concentrations of NTA in Swiss lakes, rivers and aquifers have not changed significantly since the use of phosphates was prohibited by law in 1986 (Houriet, 1988). 61

Tuh....1;. First order rate constants reported for the biological degradation of NTA including information on experimental conditions .

Q11si;dglillD gf m11lblld u:wd .Eillld..m Ielnl!. liIA .BaleJm Bllnwl:lss ~ [°CJ [µg/I] .slall1 [d-1] Laboratory studies with natu- Ruhr River, 50 - 0.31±0.20 Mean of different Larson et al. ral waters. NT A analysis with Germany 1000 0.37±0.34 NT A concentra- (1981) gas chromatography after lions. No significant derivatization dependence on concentration. Results of 2 experiments 50 0.11±0.05 influence of temperature 2 500 0.07±0.02 010"'2 23 500 0.30±0.10 Activation energy 46kJ·mo1-1 influence of oxygen 500 0.05±0.03 0.3 mg 02·~ 1 500 0.24±0.Q4 13.2 mg 02.1-1 Laboratory studies with ra- Grand 5 1.99±0.18 Larson & diolabelled NT A in natural wa- river, 50 1.43±0.13 Davidson ters Canada (1982) White river, 5 0.83±0.03 lndana, 50 2.48±1.00 USA. Abukuma 5 0.15±0.01 river 50 0.61±0.13 Ohio river 5 0.12±0.02 50 0.24±0.05 Laboratory studies with natu- upstream 12 0.22 Calculated from val- Bartholomew ral waters (heterotrophic up- 7 0.022 ues given in paper as & Pfaender take technique with radiola- VmaxAlm (1983) belled NTA). Newport river estuary, North Carolina USA. estuarine 14 0.044 water 6 0.07 marine 8 0.031 Ohio river 0.34 Cited in Larson et Thompson& al. (1981) Duthie (1968) Grinestone 0.53 Cited in Larson et Shannon et al. Creek, Can. al. (1981) (1974) Grand 0.79 Cited in Larson et Allen, Thomp- River, al. (1981) son, unpub- Canada lished data Laboratory column experi- 10. 3.8· 1 Apparent first Kuhn et al. ments with aquifer material 25 9.5 order rate constant (1987) from natural river water/ after first day. groundwater infiltration site. NTA analysis with gas chroma- 15 after adaptation tography after derivatization or use of radiolabelled NT A. H H HQ. 1 I .,OH c-c-N-c-c 1'5"' I I ~01 - H I H - H-C-H I ;.:C, IS/' OH fil.Ll& Chemical structure of nitrilotriacetic acid (NTA).

In waste water treatment plants, NTA is largely eliminated. Alder et al. (1988) found a degradation rate in moderately loaded plants of~ 98 %; in two overloaded plants, the fraction of NTA eliminated was found to be 41% and 79%, respectively. Alder et al. (1990) determined a specific load of 0.5 ± 0.3 g NTA·person-1.d-1 in the Zurich-Glatt WWTP in the primary efiluent, corresponding to 0.015 g NTAperson·l.d-1 discharged into the receiving water at 97% degradation.

The principal degradation pathway of NTA, in contrast to that of EDTA, is known: microbial degradation. Other degradation processes (adsorption/settling, photolysis) may occur, but these were not included in the model calculations, because their corresponding rate constants are significantly lower. Volatilization can be excluded because NTA, like EDTA, occurs in ionic form under natural conditions.

The in situ rate of microbial degradation of NTA is not known. It can neither be directly measured nor extrapolated from laboratory data. The specific problem addressed here, therefore, was to estimate this using the MASAS system. The procedure used was to compare the field data with the results of various model calculations in order to identify the type of degradation which could best explain the field data. A reliable estimate of the NTA load entering the lake was essential for this procedure (see below).

4.1.8. PER

Tetrachloroethylene (PER, Fig. 17) is a widely used solvent. 60-70 % are used for degreasing of metals, 20-30 % for dry-cleaning. A small fraction is used as an extraction solvent and for chemical synthesis (DVGW 1985). In lakes, particularly under oxic conditions, PER does not undergo any significant transformations. It is neither adsorbed significantly on particles nor de- 63

graded by chemical or biological reactions. Elimination in lakes occurs by volatilization and lake outflow only.

The following problems were addressed with the PER-model:

- Characterization of air/water-exchange using the relevant specific MA- SAS subprogram. Determination of a reliable air/water-mass transfer coefficient Vtot for PER using the subprogram and the model calculations. Identification of the influence of temperature and wind speed variations on Vtot·

- Estimation of the PER load, for which usually only scanty data exist. Identification of long term trends of PER input into Greifensee.

- Development of a plausible model to explain PER concentration profiles found after an accidental PER input in 1985.

Cl, ,...... Cl C=C Cl.....- 'Cl Fig. 17: Chemical structure of tetrachloroethylene (PER).

4.1.4. ATRAZINE

Atrazine (Fig. 18) belongs to the class of triazines and is used as a selective herbicide in cultures of corn, asparagus, vines and sugar-cane. In combination with other herbicides, it is used as a total herbicide on roads and noncul- tivated land. Its use on railroads was prohibited in Switzerland in 1990 because of groundwater contamination (Bundesamt filr Verkehr, 1991). In Switzerland, most of the atrazine is used in corn cultures in May and June (Ciba-Geigy, 1987). After 30 June, its use is prohibited. The dosage allowed Cl NAN l II /CH3 C2 H 5-NH~N~NH-CH "'-cH3 fu.....18.;. Chemical structure of atrazine. 64

in corn cultures is 1-1.5 kg/ha. Couch-grass in corn is treated with 5-7 kg/ha (Atrazin und Simazin, 1987).

Various degradation processes in aquatic systems have been reported (Grover, 1989): hydrolysis, biodegradation, sorption to particles, volatilization, and photolytic degradation. Degradation products are hydroxyatrazine and desethylatrazine.

The following questions were addressed: - Which are the relevant degradation processes? - What are the in situ rates of the relevant processes? (Determination using field data in combination with the computer model.) - How does the atrazine input into the lake vary with time?

4.2. SUMMARY OF THE FIELD DATA

A sampling program was undertaken in Greifensee during the period March 1990 - February 1991 in collaboration with analytical chemists in order to obtain data sets which were adequate for the development of the computer models. The concentrations of all compounds of interest in the lake and their rates of input were determined. Morphometric and hydrological parameters characterizing Greifensee are listed in Tab. 3.

An overview of the sampling program is given in Tab. 8. The lake and all sampling sites are shown in Fig. 19. Profiles in the lake were taken at monthly intervals at 7-10 different depths. Five inflows, which were found to be relevant for the determination of the load, were monitored at monthly intervals with flow-proportional 24-hour samples. The resources available allowed the input rates of the various compounds of interest to be estimated fairly accurately; precise values, however, could not be obtained and neither could Q-c relationships be determined. For the development of the models, however, the estimates obtained were sufficient. In the week of July 20 to July 26, the input of NTA, EDTA and borate into Greifensee was determined daily in order to estimate the variation of the load occurring during the course of the week. 65

Okm lkm 2km

Fig 19: Greifensee with major inflows, residential areas, and sampling sites (modified, from Cirpka, 1990). 1: WWTP Niederuster (discharge into the lake). 2: River Aa, at water gauge Niederuster; outflow of Pfiiffikersee, receives the discharge from WWTP Wetzikon, WWTP Hinwil, and WWTP Aathal. 3: WWTP Monchaltorf (discharge into River Aabach below water gauge). 4: River Aabach, at water gauge Monchaltorf, receives the discharge from WWTP Monchaltorf, WWTP Gossau and WWTP Egg-Oetwil. 5: WWTP Maur (discharge into the lake). 6: Sampling site in the lake (deepest point). 66

~ Overview of parameters measured in Greifensee 1990/91. Lake: Profiles at deepest point of the lake. Inflow: Flow-proportional 24-hour samples at five major inflows (River Aa, water gauge Niederuster; River Aabach, water gauge Monchaltorf; WWTP Niederuster, WWTP Monchaltorf, and WWTP Maur). Sampling devices for rivers: battery-powered S-4500 portable discrete samplers (Manning Tech- nologies, Scotts Valley, California, USA). Sampling in WWTPs: fixed sampling devices with sample cooling down to 5°C. (continued to the right)

Lake I Inflows IHlahw.Ma. IHlahw.MO. McJ1lh Day Q TINIEIAIPIB IOIT NIEIAIPIB IQ NIEIAIB IQ NIEIAIB Jan. Mon March Mon 5. x Mon 19. ·X X x x April Mon 2. x Mon 30. x x x x x x May Thu 10. x x x x Mon 14. x Mon 28. x x x x x x June Tue 12. x x x x Mon 25. x x x x x x July Tue 10. x x x x Wed 18. x x x x x x Fri 20. x x x Sat 21. x x x Sun 22. x x x Mon 23. x x x Tue 24. x x x x Wed 25. x x x Thu 26. x x x Aua. Mon 20. x x x x x x Sept. Tue 4. x x x x Tue 18. x x x x x x Sun 30. Oct. Wed 3. x x Thu 4. x x x x Mon 15. x x x x x x Mon 29. x x x Tue 30. x x x x Wed 31. x x x x x x x x x x x Nov. Mon 12. x x x x x x x x x Fri 30. Dec. Tue 11. x x x x Jan. Mon 7. x Mon 28. x x x x x Thu 31. x x x u T INIEIAIPIB IUI NIEIAIPIB IQ INIEIAIB IQ NIEIAIB Highwater Ma/Mo: Samples taken during high-water discharge events at the surplusing works in WWTP Maur and WWTP Monchaltorf. Abbreviations: Q Daily flow rates [m3/day] of lake outflow/river/WWTP. T Lake/river temperature. N NTA. E EDTA. A Atrazine. P PER. B Bor.

The measured concentration profiles of NTA, EDTA, PER and atrazine in Greifensee are given in Figs. 20-23. For PER, additional data from 1982-1985 were available. The results of the input measurements are summarized in Fig. 24 and Tab. 9. Characteristic parameters of waste water treatment plants (WWTP) discharging directly or indirectly into Greifensee are given later in Tab. 14.

Captions of figures given on the following pages:

~ EDTA concentration (-+-) and water temperature (······-) in Greifensee 1990/91. NTA was analyzed using gas chromatography after esterification (Schaffner and Giger, 1984) by Institut Bachema, Zurich. Error bars mark the analytical error for NTA (~ 1.0 mg/m3: ± 0.2 mg/m3; > 1.0 mg/m3: ± 20%, Gloor, 1990). Detection limit: 0.2 mg!m3. Lower left corner: sampling date and total NTA in lake.

~ NTA concentration (-+-) and water temperature (·······) in Greifensee 1990/91. Analytical procedure and error: same as EDTA (Fig. 20).

~ Tetrachloroethylene (PER) concentration (-+-) in Greifensee. Typical profiles from 1982-1985 and from 1990 are illustrated. Lower left comer: sampling date, Julian day number (day 1 = 1 Jan 82), and total PER in lake. The analysis was performed by means of gas chromatography after a closed-loop gaseous stripping/adsorption/elution concentration procedure (Schwarzenbach et al., 1979) by R. Stierli, EAWAG, Kastanienbaum.

~ Atrazine concentration (-+-) and water temperature (·······)in Greifensee 1990/91. Detection limit: 20 µg/m3. Lower left corner: sampling date, day number (day 1=1 Jan 90), and total atrazine in lake. The analytical procedure was carried out by S. Muller, EAWAG, Dubendorf, and employed GC-MSD (gas chromatography with mass selective detector) as described by Singer (1991). 68

EDTA concentration in Greifensee [mg/m3]

24 'E :;; 0 0 Cl. -Q) 0

1 2 12

7 13 19 1 7 13 19 Temperature [0 C] 69 3 NTA concentration in Greifensee [mg/m ]

0__ ~ ___2__ 3~0.-d...----~-----2~-3~0.--,---;--==-"2--.~3,-----, 0 0 -- : .fi~\-!"------;------;------;------11----~---J------<------•:!______,______,______,______11------<-----~----·~------<'_t--i_"____ ,______,____ ;i___ -·------< ; .. 't 0 >--~·----··· .L.f..1- r.:y.:)------t---.·r-"-+ ------+------+------+------11------i------+""'-"""'~"·---·+------;------;------1 12 ·-ii•--< ""!~b•_, ______,,v______, ______,______,______,______, ~Coo-~-.. ,~••_-_· __ •__ , ______,______, ______,______1 Li : 12

11 [/ '''' '''''' ~ :. ' -----+------i------+------i------1 ---Hl~---.,~__,_/ ... ;------i------+------i------+------j 24 24 19 Mar 90 30 Apr 90 28 May 90 rfl~ l 1.94 kg _ HI._.-----•---•----<------•---- 49 kg - ~f._--'.---• -----•------;--- 145 kg

0 ,-----,----,-rl_IJ!o-;;;;;;;;;~-~,--;.. .---, ,___ ,, __ _, ! ; :::+l--- 0 ;-; Zc___, '--~ .______,,, r-::-7:-----0 ,------, '. ! .. ~< 1-1'2'.. ,,_ ...... ~~ ·~ I + !_,;;.·---.---:-~~-·''-5--·.-P 1 I ,)·~ • .~~

1 2 ---~ ." ------•• ··••••••• ·f:4·---~-----·~-·"___ -_ ...._/ ______.... ______,______,______+------1 l------'--·"">-411'r_.______,______, ______,______,______j 1 2 i /' ···H -f··· :...... ··-······~·-···· ·······-· .. ······~·-·· ...... ~--······ ...... ·----<------i-•------<------;------;------;------j

24 ------24 25 Jun 90 i 18Jul90 20 Aug 90 201 kg 172 kg 280 kg E ~·. • i • : _c 0 H 0 a. ~ ~,, - .-1'.:;- ~ Q) ------H H '--

12 t-· '-·:·-···· ········i········· ······t ·····+·l---+------1------·------j ····· ~ ~ :······--· ·······-:·-······· ···· ·-!i···· .. r_,H___ +-----+------+------•------1 , _____ ,_, ______,, ______, ______, _____ , ______, ------~----- __ ,,______,______, ______,______, ______<

24 - _, ____ ------·------<------·------11 Dec 90 i-t r ------8.8 kg • lrJ.~ ~~9J~~ 91 1i 7 13 19 1 7 13 19 Temperature [°C] F.ii.22 70 PER concentration in Greifensee [mgtm3] 0 .. 0 2 04 0.2_ 04 0 2 04 0 6 0 0 I· ...... t ...... :, ...... !...... •...... ~ ~- ~~ ·········· i I ...... •. ; ...... •l[ ...... :...... : ...... :...... J 12 l 112 ...... •. ····· ; ...... ; ...... ,.,..; ...... \ ...... ;...... lf ...... ;...... ; ...... ;.... . f...... ~.... :...... ;...... ;...... i...... : ...... l i 24 • · · "ti' 17 Jan 83 ...... 4 21 Nov 83 24 ...... ' ~········ ··········; ..... \l ..... ~~8k~ lJ .. ·~ ~~9k~ ~=·k=g======~=~=='.~~~~---~---.._,~~-~---~~~ 0 j ~ ; • j ' ' +Al;;;;;;~-!-'..- I tit..... 0 L. .. J' ; -~ ..? I~ • ~ 12 ...... , ... ,,,, ...... , ...... " •• L...... : ...... ; ...... ; ...... ,;...... - 12 i I : ...... i ...... ,; ...... c...... ; ...... 1 ..... r.t...... ;...... ;...... ; ...... ; ...... J

...... 6 May 85 • i' ···'·············3 Jun 85 t 1 Jul 85 24 #1221 : #1249 #1277

13kg ! 34kg ' 34 kg L--'--~~~-~-...... J . 0

•...... ···'"··~ , 4~ l··-'-····G-'1!1..... ~ ...... : ... J :f •...... IL ...... ; ...... : ...... ;...... i ...... ,; ...... ; ...... J ...... :i l,.-1"...... ;...... I' 12 I'

-· .... ;...... ; ...... ·": ..... ' . . 1 • i ...... ,...... !

24 ...... ,..li ...... i...... : 5 Aug 85 I.... ··· .. ,...... 3 Sep 85 ···; ' .. .. 23 Sep 85 24 '. ., ...... :.... #1312 .. •· .... #1341 \...... j.;...... , ...... #1361 l·--~~2_2_k_g_ _.L_,',._•_J___L_1_9_k-=g_ _j I 14 kg 0 [I• ! i~L ...... •...... ,...... :...... :...... :...... ;...... «

...... : ...... ~ I ;...... ; ...... 12 '

>......

,. .... ) ......

24 ! ...... ···············18 Nov 85

l #1417 1......

\ j 9 kg t kg 7l Fi&L 23 Atrazine concentration in Greifensee [µg/m3] 100 200 300

24 ...... §. £0a. 0 (!) 0

0 6 6 12 18 24 Temperature [0 C] '.I.ah..Jt;, Flow rates of major inflows into Greifensee during 1990/91, along with corresponding NTA, EDTA, PER and atrazine concentrations and load- ings. The values listed are means (bold) and minima/maxima. Loading due to minor inflows (Tiifenbach Riedikon, Dorfbach Maur, Dorfbach Greifensee) estima1:ed with spot samples was found to be negligible. NTA: River Aa and River Aabach account for 71 % and 25%, respectively, of the load. Loading depends significantly on the day of the week (20-26 July 90): Mon, Tue 25%; Wed, Thu 10%; Fri 15%; Sat 10%; Sun 5%. High-water discharge events (WWTP Maur): 194 µg/l (19/602 µg/l), roughly four times higher than concentrations in the efiluent. EDTA: Less pronounced dependence of the load on the day of the week: Mon- Thu 15%; Fri 20%; Sat 13%; Sun 7%. High-wa1:er discharge events (WWTP Maur): 388 µg/l (141658 µgll), roughly 50% higher than concentrations at the efiluent. The analysis was performed by the following persons: NTA and EDTA: Cirpka (1990); PER: R. Stierli, EAWAG, Kastanienbaum; Atrazine: S. Millier and H. Singer, EAWAG, Diibendorf.

AaN'uster Aabach Ma NWTP Nust. WWTP MO. WWTP Maur Total Water flow (1000 m3/d) 189 157 17.2 1.53 1.96 367 43/347 17/608 10.2/44.9 0.60/4.0 0.80/4.2 Concentrations NTA [µg/I) (n=12, Maur: 7) 7.7 7.3 2.5 7 49 2.3/32 1.9/26 0.8/8.3 3.6/12 3.2/510 EDTA [µg/I] (n=12) 6.8 36 47 32 256 3.3/11 4.5/100 13/95 7.1/67 64/410 PER [!!!Jll] !n=2) 71 44 32 54 Atrazine (ng/I) (n•6) 96 380 66 190 240 <10/300 130/960 <10/160 <10/910 43/400 Load [g/daxJ NTA (n=7) 2100 740 56 1 3 75 3000 250/4900 94/2300 19/130 5/ 25 4/230 490/5900 EDTA (n=7) 1100 2100 670 34 446 4300 460/1425 720/5200 330/970 29/45 191/632 2100/7600 PER (n=2) 6.6 0.9 0.4 0.1 7.8 4.4/11 Atrazine (n=6) 27 55 1.1 0.31 0.52 84 1.7/110 5.6/150 <0.16/2.3 <0.01/1.5 0.07/0.86 8.61260 73

[g/d] EDTA (n=7) NTA (n=7) 2500 2500 2000 total: 4300 g/d 2000 total: 3000 g/d 1500 1500 1000 1000 500 500 0 0 (2) (4) (1) (3) (5) (2) (4) (1) (3) (5)

7 PER (n=2) 60 Atrazine (n=6) 6 50 5 total: 8 g/d 40 total: 84 g/d 4 30 3 2 20 1 10 0 0 Aa Aabach WNTP WWTP WWTP Aa Aabach WWTP WWTP WWTP N'uster tv'Q. N'uster tv'(i, Maur N'uster Ml. N'uster M:i. Maur (2) (4) (1) (3) (5) (2) (4) (1) (3) (5)

Fie. 24: Measured input of investigated compounds into Greifensee in 1990/91 due to five major inflows. Numbers in parentheses refer to Fig. 19. Further details given in Tab. 9. 74

4.3. EDTA MODELS

SETUP OF THE MODELS

The EDTA models were set up in three stages: preparation of input data, selection of inadequate spatial model resolution, and determination of relevant processes and the corresponding parameters.

First, the system data were compiled for 1990/91. This included the preparation of the following parameters according to the description given in Section 5.7: temperature, epilimnion depth and vertical eddy diffusion coefficient. The results for the period 19 March 1990 (day 78) to 28 January 1991 (day 394) were stored on a system library file (illustrative example, Fig. 25). The epilimnion depth for the two-box model was set to 7.5 m, and a value of 6 m3/s was used for the vertical exchange flow between epilimnion and hypolimnion. Compound data were stored on a library file for EDTA (Figs. 26 and 27).

An adequate spatial resolution for the model was then selected. Even in summer, measured concentrations were often very similar over the entire water column (Fig. 20, 18 July 90, 18 Sept). However, in certain profiles (e.g. 18 Sept, 11 Dec) concentrations tended to be higher in the hypolimnion and increasing concentrations in the hypolimnion over time (e.g. at 30 m: 28 May ~ 18 July~ 18 Sept~ 28 Jan 91) were also clearly visible. Plunging inflows (which would require a high spatial resolution) such as that occurring on 28 May were not of primary interest. Thus, the most simple model which allows the description of the increasing concentrations in the hypolimnion was taken: the two-box model (Fig. 28). For the initial values, volume-weighted averages of the profiles were used. Fig. 29 illustrates the iterative model development. It shows the simulation results calculated with a preliminary model.

The next stage is the selection of the processes to be included. EDTA load, either as a constant surface load or split into a variable epilimnion and hypolimnion load, was included (Fig. 30). A comparatively good estimate for the total load was available from the field study (Tab. 9). Photolysis was neglected because no clear indication of photolytic degradation could be seen in the field data (except perhaps at 0 and 2 m on 25 June and above 7.5 m on 15 Oct). Further, the expected fraction of degradable Fe(III)-EDTA was negligible and no information about the kinetics of the processes was available. Proton 75 transfer reactions are not relevant for the behavior of EDTA, and can therefore be neglected.

The hypolimnetic c.oncentration which increased over time against the vertical concentration gradient can be explained in terms of adsorption and sedimentation only. Therefore, adsorption to particulate iron was included. Field data for particulate iron were available (Fig. 33) and were used as input parameter in the model. A linear adsorption isotherm and a fast sorption equilibrium was assumed (Eqs. 8 and 9). This type of adsorption process is implemented in a MASAS submodel and includes adsorption on particles, settling of the particulate species, input into the sediment and burial into deep permanent sediment layers. Further, particle resuspension from the sediment was included in the model.

The particle submodel requires two constants for which no data were available: the partition coefficient between particulate iron and water for EDTA,

Kp, and the sedimentation velocity of particulate iron, v8• These were estimated with simple calculations using a mass balance model for the epilimnion, applied to EDTA and iron field data:

EDTAsedi = EDTA;n - EDTAout - ~ EDTAepi [kg/d] with

EDTA;n EDTA input into epilimnion (constant, 5 kg/d}

EDTAout EDTA output from lake, depending on concentration in epilimnion (average: 1.1 kg/d; range: 0.9 ... 1.4 kg/d)

~ EDTAepi change in total EDTA in epilimnion from one sampling date to the next (average: 0.1 kg/d; range: -2.7 ... 3.2 kg/d)

EDTAsedi unknown quantity of EDTA eliminated by settling (average: 3.8 kg/d; range: 0.6 ... 6.6 kg/d)

The fraction f1 present as dissolved EDTA is given by Eq. 8, using CFe instead of cpoc. It varies for a Kp of 40 m3/g between 0.6 and 0.9 (Fig. 34). The following formula describes the relationship between Kp and v8 :

Kp= r (Eq. 1) CFe (Vg·Ctot - r) 76 with r specific EDTA sedimentation rate obtained from field data [g·m·2.d·l]. Mean value: 0.61 mg·m·2.d·l. CFe concentration of particulate iron (volume-weighted average 0 ... 10 m of field data) Ctot total concentration of EDTA in water column (volume- weighted average 0 ... 7.5 m of field data).

The resulting hyperbolic function v5 = f(Kp) is shown in Fig. 35. The differential equations of the model are identical for different combinations of (vs, Kp) as long as they follow the hyperbolic curve. The model calculations were therefore performed with one combination only.

Both EDTA models are summarized in Tab. 10. They include the same description of adsorption on particulate iron and transport to the sediment. They differ, however, in their description of EDTA loading and particle resuspension.

Tub. 10: Structure and process parameters of different EDTA models.

MODEL PROCESSES EDTA load Settling Resuspension from sediment A Constant load Load (epi.): 5 kg/d Vs 0.75 mid Constant: 0.003 d-1 Constant resuspension Kp 40 m3/d B Variable load (fitted) Constant load (epi.): same as A day 78: 0.003 d· 1 Variable resuspension 3kg/d day 288: 0.003 d·1 (fitted) Variable load (epi.): day 394: 0.03 d., 0·5kg/d (linear interpolation be· Variable load (hypo.): tween these dates) 0·13kg/d Range of total load: 3-20 kg/d Average load: 6.0 kg/d 77

Figs. 25-32 (following pages) constitute an illustrative example for MASAS:

~ Illustrative example for MASAS: Load lake data from system library. The user selects the command Open ... in the menu File to load the system library file Greifensee 1990 (EDTA) (file selection entry form not shown). All system parameters (Tab. 30) for the model setup are now available and can be modified interactively. General system parameters are used for all model types; Flow parameters define the inflow/outflow pattern of the lake on up to three levels (current example: inflow and outflow at surface); Model dependent System Parameters are available in our example for all models except the combi-box model. In the two-box column, ";" separates epi- and hypolimnion values. Some of the parameters are not included in the system library file, but instead are calculated from basic data (e. g. Hydraulic Rate, or Total Volume of n-box model, see. Eq. 1 & 15 in Tab. 31). Grey scroll- bars to the right and at the bottom of the window indicate that not all information is displayed within the window frame.

~ Illustrative example for MASAS: Load compound data from compound library. The user again selects File I Open and chooses this time a compound library file, EDTA.MCD, from the file selection entry form. MASAS reads the data and subsequently shows the compound window with all data now available: compound Identification, Physico-Chemical Parameters, and Reactivities (not shown) (Tab. 32). Undefined values are marked with * * *.

~ Illustrative example for MASAS: Get information about compound parameter (PER). The user chooses a parameter value (Molecular Diffusion Coefficient in Air) with a mouse click, selects the menu Edit I Get Info, and the parameter information is displayed: the identifier (ident, Tab. 30 and 32), used for unequivocal identification of each parameter, the status (read/write or read only), and, if available, the parameter comment. Typically, the parameter comment contains a reference to the parameter value and additional reported values. White scroll-bars indicate that all information is displayed. 78

~ Illustrative example for MASAS: Define new model with initial values. System and compound data are now defined, and the user starts by setting up the EDTA model. First, a new model is activated with the command File I New, picking the option model in the entry form Select type of new file. In the entry form Select model type, the user chooses Two-box model. Clicking OK activates the model window, displaying the model variables, the undefined initial values and the predefined standard processes. The initial values are then defined. The field of the corresponding initial value is selected, and the user selects Time Var., Epi/ Hypo to define two time series for the epi- and hypolimnion. In our case, the data will be Read from file. OK is clicked, the file selected in the entry form (not shown) and the values are defined. (The background tasks performed by the program to carry out the action described are shown in Fig. 62). These time series will be used both as initial values (the program calculates the value for the start time using linear interpolation) and for the comparison of simulated with experimental data.

~ Illustrative example for MASAS: Start simulation run. A minimal EDTA model with the predefined standard processes diffusion, advection, lake outflow and sedimentation (inactive because particle submodel has not been activated) is defined and a simulation run can now be started. First, three actions are performed (not shown): output intervals (menu command Output Setup) and simulation time (menu command Simulation Setup) are set and a graph window is created (menu command Windows I Show Graph Set). Simulation I Run starts the simulation, and the window Simulation displays the information of the ongoing run (simulation time, next output, etc.). In the graph window, results are plotted immediately during the performance of the calculations. The results of this first model are clearly not satisfactory: the simulated concentrations both for the epilimnion and for the hypolimnion (EI H) are too low, and the peak values occurring on days 148 (28 May 90) and 232 (20 Aug) are not at all reflected in the modeled values. The model thus requires iterative improvement. Explanation of graph legend: Time: Independent variable with unit and scaling Total compound in water: Simulated concentration, scaling 0 ... 6 µg/m3. 79 c12: Field data (EDTA), averaged for two-box model. Same scaling; value on start day used as initial value. On the y-axis, a relative scaling of 0 ... 1 is used.

~ Illustrative example for MASAS: Add new process to model and next simulation run. The previous model did not even employ an EDTA input into the lake; we therefore define such a process now with the command New Process. In the entry form, Define Process, all available process types are presented (Tab. 24). The process Loading from infiow (mass/ day) is chosen, entitled EDTA load (into epilimnion), and after clicking OK, the process appears in the model window. The daily input is set to 5 kg/d, which is within the range of values measured (Tab. 9) (not shown), and a simulation run with the improved model is started. In the graph, the previous run is re- tained for comparison purposes. Simulated concentrations are now significantly higher than experimental values, and the variations are still not reflected by the model; further improvement is required.

~ Illustrative example for MASAS: Model with particles, settling and sediment; next simulation run. The hypothesis that EDTA adsorbs on particulate iron (Section 7.3) will now be tested. The particle submode} is activated with the command Particles. In the entry form Define particle bound species of compound, we choose the option Include. The particle type is chosen to be POC, as particulate iron is not available in MASAS. We will put the data of particulate iron into the variable of POC (not shown). Because the mathematical descriptions of adsorption to POC and adsorption to particulate iron are identical, the results will be correct. Data are available from field campaigns (Sigg et al., 1991), and will not be modeled: a case for the option Predefined input parameter. After clicking OK, the processes for the new model variable Compound in sediment are added in the model window. The definition of the process Sediment resuspension is not shown. Sedimentation for Total compound in water is automatically activated, echoed on the screen in bold-faced type. An additional graph window for total EDTA in the lake and the sediment is defined by the user (menu command File/New ... Graph), and a new simulation run is started. Now, results are encouraging: The overall, slightly increasing trend of the measured lake content(Total mass for c12, - - , scaling 0 ... 1Q9mg ~ 1000 kg) is reflected in the results of the simulation (Total mass for cl, -). In the left 80 window EDTA in Greifensee, the simulated and experimental curves for the epilimnion and hypolimnion are shown. The increasing trend in the hypolimnion (field data, --o--) is quite well reflected in the model calculations (-+-). However, short-term dynamics are still not reflected in the model.

&..a2;. Illustrative example for MASAS: Final EDTA model. The results are presented with a screen copy of the model window and three user-configured graphs showing all relevant input parameters and results of the model. Two processes, a variable loading into the epilimnion and hypolimnion, respectively, were added (window Complete model). The loading time series was fitted manually with iterative simulations (window Epi I Hypo- load, resuspension rate; epilimnion: -, hypolimnion:--, scaling 0 ... 13 kg/d). The resuspension rate ( ...... ) is time dependent, reflecting the increased sediment/water exchange during overturn. The model performs very well, both in the simulation of the total lake content (window Total EDTA (lake I sediment) and of epi- and hypolimnion concentrations (window EDTA in Greifensee). However, as experimental evidence for adsorption of EDTA on iron is scarce, this model gives just one possible explanation for the behavior of EDTA in a lake. These calculations will hope- fully stimulate chemists to delve more deeply into the details ofEDTA behavior in lakes. 1. Load lake data from system library

; lll1ill Edit Simulation Window New ••. :ICN -11• ·II Greifensee 1990 (EDTR) ------Jt---- General System Parameters Unit Values Close... 8CW Tota 1 Volume m3 1.508E+08 Saue 8CS Surface Area of Lake m2 8.492E+06 3.200E+01 Saue Rs ••• Maxima 1 Depth m Flow m3/s 4.280E+OO Hydraulic Rate 1 /d 2.451 E-03 Page Set Up ••• Moan Residence Time of Water y 1.1 f SE+OO Print... 8CP Catchment Area (Exe 1. Lake) km2 1.634E+02 Inhabitants 8.500E+04 Debug • Flow: Quit 8CQ Inflow Flow [m3/s] From/to [ml 0 4.280E+OO O.OOOE+OO; O.OOOE+OO

2 *** *** Outflow 0 4.280E+OO O.OOOE+OO; O.OOOE+OO *** *** 2 *** *** Model D•P.•ndent System Parameters: Ono-box Two-box n-box Total Volume m3 1.508E+08 5.895E+07; 9 .1 90E +07 Box Constant Surface area/Lake cross section mt2 8.492E+06 8.492E+06; 7 245E+06 Depth Variable Surface Area of Hypolimnion m2 7.245E+06 Epilimnion Thickness m 7.SOOE+OO Time Variable Sox Thickness m 1.776E+01 7.SOOE+OO; t .268E+Ot Box Constant Depth of Box Borders m Eddy Diffusion Coefficient cm2/s Time/Depth Var !l!i!i §.~-;::·•·-•~~ :I• •~ "'~" .~T.:M n-"••'" '~I pH (Acidity) Time Variable Time Var; E/H Time/Depth VarfO ci 1 1~rn1rn1mmmm11mrniim:mmmiiiii~mmmmmmmmmimmmmmmmmmmm:mmmmm:mm1mmmm:mrn:m::::mrnmmmmmmmmm1mmmmI:mmmmmmi:1 o 121 I 2. Load compound data from compound library I

leii Compound Library I Cl Rtrozine.MCD =UP-Pilatus I I Cl NTR.MCD Cl PER.MCD Cl Test.CMP ['J Testcompound Hydrolysis Cl llorio ~ EDTR.MCD Identification - Compound Name: Ethylenedfaminetetraacetio acid Synon\jms: EDT A;N,N'-1 ,2-Ethane-di\jlbis[N-(oarboxymethyl)glycine l;Havidote ;Versene CAS-Nr.: 60-00-4 Sum-Formula: CIOH16N208 ~sioo-Chemioal Parameters Unit Value L!~l A factor Molar Mass 2.922E+o2 Melting Temperature oc 2.200E+02 Boiling Temperature oc *** Boiling Temperature Pressure atm *** Molar Volume- cm3/mo1 *** *** Mo le-ou Jar Diffusion Coefficie-nt in W' ate-r cm2/s *** *** *** Mole-cular Diffusion Coe-fficie-nt in Air cm2/s *** *** *** Vapor Pre-ssure- atm *** *** *** /\qu'l'Otls Solubility mol/1 1.710E-03 25.0 *** Henry Coefficient atm*llmol *" * "* * "* + Octanol W'ater Partition Coefficient log(Kow) *** *** Partition Coefficient Organic Carbon/Wate-r m3/goc I 3. Get information about compound parameter (PER) I

PEH.MCD !dtnt1f!catim c...... ,....i-: 1,1,2,2·Tttt'achlor.. tt.,i- S\,lnonvms: PER, po!"d!lonlO, Noma CM-fir.: 127-18-4 3eD 5....-fcnroi.: CC12-CC12 .,.ttonstant l!!!Ysico·Cbemicfl PMametru :t.& L~l Hoi.rMan 1.65BE+02 Boa Conslan1 l'kiltlng Tomper•tur.. •c ·1.!IOOE+Ol lime Unrin!>le 9ol1!ng T..,_-•ture "C 1.211£+02 Ut.>p th tlnrial>!e Bofllng T.....,,...•tur• PrHsurt •tm 1.000E+oo Hoi.r Voll.mo cm3/mol lime uar.. EpV!lypo Molocuw Diffusion Coofficitnt in Vator cm2/s * * .. • * • Iimt> Ii Ot>!>lh Uarinblt> Molocuw Diffusion Co•fficiont in Afr cm2/s ..... 9.9 *. * ••• YliP'Of" Pr•ssur• 1tm 1.316£-()2 13.9 5,032£+00 I ,990£+03 To Output List ;.qu...,. So1'.j)1lity molll 2.420E-W 25,0 *** *** Sholl' Bou n,11 a How\! Coofficient •tm•llmol 1.2231:+01 25.0 2,136i+o3 8,407E+OO Uolume Wei!Jhted Ruern Octanol 'lr'..t.r P.n:ttion c.. ffloieftt log(Kov) 2.880E+OO *** Portttion Cotffloilnt Or90'ri<> c...t>on/Vattr m3/9oc *** *** *** *** Acidity Constant 0 .... *** Enter Comment ••• Rm!jytti!s (l?jsso!Y!d O!!!!ul) !!Ii!. L~l ~ Set Undefined .\cid Hl,jdro)Jsis m31molld *** *** _,.•! H\jdro)Jsis 1/d *** *** *** lllkam. Hvdro)Jsis m3/molld *** *** *** Hydrolysis Raw Obsorvod o lid * •• *** *** Gtntral First Ordor Rat.. Constant 0 l /d * *** *"* L91t .v...rption (bri>do) m3/mollm ****. o.-tum ...-Id O'!IO RoactionwtthSingltttll"'Jgen m3/molld * • * * Information about "Molecular Diffusion Coefficient in Rir lcm2/s)': Roaction will> 011-Rodical m3/mol/d * * • * Short identifier: da r,Bio&:;'::z.::·~1~11e;;.:.::;ac~t=1on~~~~~~~~~1~/d~~~~-*.::..:•~•:.._~_::..*~I re11d/111rlte

Comment: Ref: Part, T. et al. (1987): Mater them Phys 15, 397-410. Other data: Harry Watts (1971 ). Can J Chemistry 49, 1965-67: 0.0711 cm/sec at 25°t (corrected for nonlde1111ty). t OK J with initial values 4. Define new model Select model type: 0 One-boH model

~~' !r1l Combi-boH model: not auailable O n-bon model with bones Select type or new file: ID!JI O system ®model OK O compound O groph

Cancel

Variables !!dl!! One-box Total compound in .... ater Off*** Compoulld in s;;diment Off*** Particulat. organic carbon (POC) Off O.OOOE+OO

~din water: Stat Obs Calculation Mod;;IParamtt;;r Diffilsien On Off On Off MYeotioa Ualues for "Static ualue (Two-boH) of cl (mass/m3)": lak• outflew @Read from file O Saue to file O User defined Number of ualues for input parameters: Time (di IPMI Cancel ) I OK )J I 5. Start simulation run

• I Output Setup ... Simulation Setup ... Greifensee 1990 (EOTH) Untitled model mtot One-box P

mm.Meo ldent!f19ation Compound Name: Ethyl1mediaminetetraacetic acid a.a Synofll,lms: EDTA;N,N'-1,2-Ethanediylbis(N-(car CAS-Nr.: 60-00-4 0.5 Sum-Formula: CIOH16N208 ellYsico-Chtmipal Parameters Molar Mass 0.2 Melting Temperature °C Temperature oc 0.0 'ling Temperature Pressure atm 100.0 150.0 200.0 250.0 300.0 350.0 Molar Volume om3/mol Time [day! cm2/s !l!::..GP.h v~iables Minimum Maximum Unit cm2/s W Time 7.SE+OI 3.9E+02 day Total compound in water 0.0E+OO 6.0E+OO mass/m3 c12 O.OE+OO 6.0E+OO mass/m3 ~ I 6. Add new process to model and next simulation run I Define process: Name: IEDTR load (into epilimnion) I dent: Select Model •.. Type: Particles ... O Zeroth order reaction O Loading from inflow (concentration) Proton Transfer Reaction .•. O First order reaction ~Loading from Inflow (mass/day) O Sediment fluH, zeroth order O Input in boHes (man/day) O Sediment fluH, first order 0 Hydrolysis Delete Process ••• O Surface fluH, zeroth order O Direct photolysis Delete Rll Processes .•. O Surface fluH, first order 0 Biological degradation 0 Rir/water eHchange O Pseudo first order reaction EDTR in Greifensee lmg/m3l = Cancel

Untitled model Stat Obs rameter On Off Adv•otion On Off Lak• outflow On Off 100.0 150.0 200.0 250.0 300.0 350.0 Tin>e [da\i l Sedimentation Off Off LineQa~ ~ ~ ~ Y!li1 Simulation oo EDT A lead (into epilimnion) On Off Tim@ 7.8E+01 3.9£+02 d"ll - Total compound in watw Epi O.OE+OO 6.0E+OO rnus/m3 a ... mod•l !+;po Epi Hypo Epi O.OE+OO 6.0E+OO mass/m3 Bas•modt'l Hf,jpo i;ii I1. Model with particles, settling and sediment. Next simulation run Denne particle bound species of compound: O EHclude (dissoJued species only) ® Include (together with sediment and compound In sediment) Select Model ••• Particle type: ® Particulate organic carbon (POC)

Proton Transfer Reaction ••• O Total particles Modelling of particles: O Dynamic model uariable Reset Model. •• ~Predefined input parameter New Process ... Reactiuitles for particle bound species: ® Same as dissolued neutral sp Delete Process .•• Delete All Processes •••

Cancel

:o~ EDTH in 6reifensee [m /m3) ~~ Total EOTll (lake/sediment) (mgl Variables Relative sealing C0. .. 1 l R"lati"" scol ing CO. •• I J Total compound in water 1.0 1.0 Compound in sediment Particulate organic carbon (POC) Total comP.oo.md in watE

0.5 0.5 _J] ... LJ ...... ;.//// 0.0 100.0 200.0 300.0 0.0 :-:·:·::··::·:t::·::ci:········+:··::··::::-.:·~·i~.. --- c-=-. Time Cdayl 100.0 200.0 300.0 Line Gr;!!!h v¥iab'k!s !2!P.th Minimum Maximum Uni Time [dayl Time 7.8E+01 3.9E+02 d ~ Grarih variable-s !L4!1!th Minimum Maximum Uni - Total compound in water Epi O.OE+OO 6.0E+OO ma Time 7.8E+01 3.9E+02 da HiJpo -pcbl Epi O.OE+OO 1.3E+07 ma -- c12 Epi O.OE+OO 6.0E+OO ma -- pcbl Epi O.OE+OO 1.3E+07 ma -<>- Hypo -······· Resusp•msion rate Epi 0.0E+OO 3.0E-02 Q] 23April (113} Depth lml Depth [ml 0.0 I·1 • '·, r /' . • . l : ? \ • : ~ 1. • '· "\ /11 July (192} • • ":\ !. ~ ~ •• . . • .; . • .• l. . . . ,,...... · ...... -5.0 Oli • • • I . \'II . t . I : . ! .... . ·, ·1 . . -10.0 >~'" !· :;...... : . . . .. :...... \t . . ..-;·---1- . . ', . ; ,. .... _ . 8Au (220) :: ; I: ~· -

\. {165) '·:-... ,f,-'l( . -25.0 . . ''( ... ~. :· . "a~ ~ '...... ·: ...... :· ...... ~···... .. ) "'"]. : -30.0 ...... '. /~l ·.· ..... :...... ' .. . Q····· /~, • o· :

0.0 5.0 10.0 15.0 20. 0 [mg/rii3] 0. 0 5.0 10.0 15.0 20.0 25.0

~Particulate iron in Greifensee, 1990/91 (Sigg et al. 1991). The profiles show the typical elevated values in the hypolimnion during summer (11 July to 24 Oct). 90

1.0

0.8

Kp=40 m3/g • . 0.6 .. . ·: ...... ·:·;-;~· ... ·:·. r~ .. . : ·tt -~ . ti •·•. . . Epilimnion 0.4 ... -· ~ / .. ... ::~!~~~~.~···· :... \.. ·,,/ ..... -·~·--···-·~~...... ~ ..: ... :..-: .. ~·-···-· ...: ...: ...: ... ~.~ ...: ...: Kp =200 m3/g: • .•.. ~...... : ...... ··:·······" Hypolin:nion 0.2 • • • , • • • • • • • •* • • • • • • • .. • • • • • • • .. • • • • • • • .. • • • • • • • •• • • • ~ • • •

0.0 100.0 150.0 200.0 250.0 300.0 350.0 Time ldl 10Apr90 30May 19 July 7Sept 270ct 16000 ~ Fraction of total EDTA present as dissolved species, calculated for epilimnion and hypolimnion assuming different values for the particulate iron/water adsorption coefficient, Kp. Legend: • Epilimnion; no symbol: Hy- polimnion.

Kp [m3/g] 120 .-.---,--_,..i--..., ------.----. 1 ! i i i 1: :=]:~j~]~]~J.~::-~ I i ! · 1 : i E ! i i : i 60 ...... ,...... f'''""''""""'1"""'""""'"'1"'"'""'"'""'f'''"""'""'"' ! . : : i 40 ...... !...... ! 20 ...... ! ...... + ...... 0 ...._ _ __.i __ _._ ! __ _._ __..._ __ ...._ _ _. 0 0.5 1.5 2 2.5 3 vs [mid} ~ EDTA in Greifensee 1990/91. Adsorption on particulate iron. Rela- tionship between sedimentation velocity v8 and particulate iron/water partition coefficient for EDTA, Kp. calculated from field data applied to a one-box model for the epilimnion, assuming a linear adsorption isotherm and fast sorption equilibrium. Data from days 232 and 394 have been omitted because ofhypolimnetic EDTA input and overturn, respectively. Value used in model A and B indicated by• (v8 =0.75, Kp =40). · 91

RESULTS OF MODEL A

Model A includes the following processes: constant EDTA load into epilimnion; adsorption on particulate iron, with subsequent settling of the particulate species to the sediment; and resuspension of particles from the sediment at a constant rate. This model can reproduce the increasing EDTA content of the lake observed in the field (Fig. 31, window Total EDTA) and the EDTA accumulation in the hypolimnion water towards the end of the season (day 345 and 394; Fig. 31, window EDTA in Greifensee). The model failed to reproduce the EDTA peak of day 232 (20 Aug), which is not surprising when using a model with constant parameters. The calculated total accumulation of EDTA in the sediment was 700 kg, or 83 mg/m2 sediment (Fig. 31, window Total EDTA).

RESULTS OF MODEL B

Model B includes the same processes. However, the EDTA load is temporally variable and split into epilimnion and hypolimnion loads; the resuspension rate is also temporally variable. This model reproduces the field data both qualitatively and quantitatively very well. The simulated total EDTA content of the lake and the epilimnion and hypolimnion concentrations are compared with field data in Fig. 32 (windows Total EDTA and EDTA in Greifensee).

Input of EDTA into epilimnion and hypolimnion was fitted independently, yielding a mean load of 6.0 kg/d (Fig. 32, window Epi I Hypo-load, - epilimnion, - - hypolimnion). This value is in agreement with the exper- imentally determined input of 4.3 kg/d (Fig. 24), which does not include high- water loading. The variations were within a plausible range except for the period between days 199 and 232, where an unrealistic total load of 20 kg/d had to be assumed to simulate the strange profile of day 232.

The increase in the resuspension rate (Fig. 32, window Epi/Hypo-load, resuspension rate, ...... ), justified by stronger sediment/water interaction during lake circulation (M. Sturm, pers. comm.), caused a significant remobilization of EDTA from the sediments and a decrease in the sediment concentration. SUMMARY

Two simple models were developed which describe the observed EDTA concentrations in Greifensee. The following processes are proposed to be relevant: loading, adsorption on particulate iron, settling of the particle-bound EDTA-fraction, input into and remobilization from the sediment. No evidence for a significant photolytical or biological degradation could be identi- fied, neither in the field data nor by the model calculations.

Based on field data, unknown parameters for the proposed adsorption on particulate iron could be estimated and validated with the model calculations. Using a settling velocity of 0. 75 mid, a particulate-iron/water partition coefficient for EDTA of 40 m3/g seems reasonable. However, Kp·values derived from adsorption studies are much lower, in the range of l0-5 - 10-3 m3/g (Rueda et al., 1985; Chang et al., 1983; Buffie, 1988, p. 332). The discrepancy could be due to non-equilibrium conditions, to differences between the type of particulate iron used in laboratory experiments and that occurring in the lake, or to other, as yet unknown processes involved in EDTA-settling. The accumulation in the sediment calculated with the model was 2.45 kg/d or 0.3 mg ·m·2 ·d·l.

The model study revealed several open questions: - Is EDTA adsorbed on particulate iron or on other particles? What are the values of the partition coefficient and kinetic constants involved? - Is adsorption on particulate iron relevant under the conditions prevailing in Greifensee? Can EDTA be detected in sediment traps? - Does EDTA occur in lake sediments? What are the EDTA concentrations, accumulation rates, and exchange rates with overlying water? - Under which conditions is photolytic degradation relevant in the lake environment? Which is the fraction of EDTA occurring as Fe(III)-EDTA- complex in lake waters, and which are the kinetics of its formation? 93

4.4. NTA MODELS

SETUP OF THE MODELS

As in the case of EDTA, the NTA models were set up in three stages. First, lake and compound data were loaded into the program. For the lake description, the same data file as for EDTA was used. NTA data were compiled and stored on a library file.

Next, the model type was selected. Then-box model was used because NTA profiles showed a marked vertical structure. Above 15 m, NTA profiles were very variable, both with respect to time and space; below 15 m they tended to be rather constant. Therefore, 18 boxes with different thickness were considered to be adequate for the model (0-3 m; 3-15 m: 1meach;15-21 m: 2 m each; 21-25 m; 25-32 m).

Tab. 11: Structure and process parameters of different NTA models. MODEL PROCESSES NTA load NT A degradation A Constant load 4.5 kg/d 0.035 d-1 Constant degradation B Constant load 4.5 kg/d Values used in Eq. 51: 1 Temperature-dependent r0 = 0.03 d' degradation rate calculated To = 10"C using the Arrhenius equa- Ea = 46 kJ/mol tion (Eq. 51 in Tab. 32) Examples for typical temps.: 15°C: 0.042 d-1 20°C: 0.059 d·1 C Variable load (fitted) Base load (into epi.): 1kgtd 0.035 ct1 Constant degradation Surface high-water load: 0 6.5 kgid Subsurface high-water load (5-1 Om/4-8m): 0 4.5kgld Range of total load: 1 -11.3 kg/d Average load: 5.7 kgtd D Variable load (fitted) Base load (into epi.): 1kg/d asmodelB Temperature-dependent Surface high-water load: degradation rate 0-10kgld (Arrhenius relation) Subsurface high-water load (5-10rnt3-8m): 0 ·4.7kg/d Range of total load: 1 -15.7 kg/d Average load: 6.5 kgtd In the third stage, the processes for the model were determined. NTA loading was split. A constant base load into the epilimnion and a constant or temporally variable high-water load into different water layers was assumed. Further, biological degradation, defined as a first order reaction, was included in the model. Other degradation processes were found to be of minor or no relevance and were thus ignored. Four different model types were set up which differed in the complexity of their process descriptions (Tab. 11).

By means of simple mass-balance calculations, a first estimate of important process parameters was obtained. The range of the first-order rate constant for biological degradation, kb, was estimated to be 0.02 - 0.04 d-1 using a simple one-box model for the epilimnion (details given in Tab. 12). Maximal plausible NTA load was estimated to be 5.7 kg/d with the same one-box model approach (details in Tab. 13) for periods with a marked increase in NTA content. NTA input below the thermocline is not considered in these estimations.

RESULTS OF MODEL A

This model uses constant process parameters in the range obtained using the simple calculations described above. Fig. 36 shows the simulated (-) and measured (--)total NTA content of the lake. The large temporal variations in the NTA content of the lake cannot be described with this simple model.

RESULTS OF MODEL B

In this model, the NTA degradation rate was calculated for the actual lake temperatures using the Arrhenius equation (Eq. 51 in Tab. 32) and an activation energy, Ea, of 46 kJ/mol (Larson et al., 1981). Unfortunately, this model performs slightly worse than model A, because the temperature-corrected degradation rate is higher in summer, yielding an even lower NTA content. It seems that the assumption of a constant load is an oversimplification. 95

Tab. 12: Estimates of first order rate constant kb [d-1] for NTA degradation in Greifensee in 1990/91. The estimates were obtained using measured NTA lake concentrations and a steady-state one-box model applied to the epilimnion. Exchange with the hypolimnion was neglected and a probable value for the NTA load was assumed. Periods with a marked decrease of NTA content and little or no high-water NTA input were selected. An assumed input of 0 kg NTA/day yielded the absolute lower limit for the degradation rate kb, a load of 1 kg/day gave a probable estimate. (Due to high-water overflows containing higher NTA concentrations in the period of 15 Oct-12 Nov, the NTA load may be > 1 kg/day; thus kb may be underestimated.)

Period NTA degradation rate kb [d-1] Mean eplllmnion load: Okg/d load: 1kg/d teml!erature [°CJ 19 Mar-30 Apr 0.02 0.037 10 18 Sep - 15 Oct 0.023 0.033 17 15 Oct- 12 Nov 0.004 0.011 20

Tab. 13: NTA load estimates for Greifensee in 1990/91, obtained using measured NTA lake concentrations and a steady-state one-box model applied to the epilimnion. Three different values for the rate constant of biological degradation, kb, were used:

Period Estimated NTA load [kg·d·1] Conditions for@ using different scenarios:

Tab. 14: NTA load for Greifensee 1990/91, estimated using field measurements, empirical values and data from the catchment area. a: Fraction of load discharged as overflow within WWTPs during high-water events (field data: measured NTA concentrations (spot samples) in overflow; flow recordings of WWTPs). WWTP Maur: few rain catchment basins and rain outlets in the drainage system; sampling period January - September 1990. WWTP Monchaltorf: several rain catchment basins and rain outlets; January - October 1990. The values represent a lower limit because discharge above WWTPs is not included. b: Fraction of load discharged as overflow within entire sewage system (estimated with empirical values). c: Estimation of NTA load, for three probable values for the load fraction discharged as overflow. Legend: Inhab. =Inhabitants; Pop.Eq. =population equivalent; Degr. = degradation rate in WWTP (Koch, 1990). The rows Sum of 4 and 5 correspond to River Aa and River Aabach, respectively (Tab. 9). The Pftl.ffiker- see outflow does not contribute significantly to the NTA load and was neglected. Ii) Estimation of overflow within WWTP lfleld data\ WWTP Maur WWTP M6. Fraction of time with highwater 4.1 % 0.7 % Water discharged at surplusing works during hlghwater 28 % 20 % Fraction of load discharoed as overflow 1.1 % 0.2 % Estimation of overflow within sewaae svstem Cemolrlcal values) Fraction of lime with highwater discharae Aatal 1,100 450 98% 12 0 12 6 12 31 Sumof4 26,398 15,950 1819 0 1801 158 1728 780 5 Egg-Oetwil 10,000 3,000 97% 164 0 162 55 155 273 5 Gossau 9,500 1,000 85% 735 0 728 49 698 245 Sumof5 19 500 4 000 899 0 890 104 854 518 Total 76,433 40,400 3419 0 3385 443 3248 2214 Sum WWTP and direct 3419 3827 54&2 RESULTS OF MODEL C

In this model a constant NTA degradation rate and a variable load was used. The load was split into surface and subsurface loads. The surface load was composed of 1 kg/d dry weather load and a variable high-water load. The subsurface load is variable and depends on inflows plunging below the thermocline. Precise field data were unavailable, and therefore both load functions were fitted by repeated simulation runs. Conformity with the profiles above 10 m and the total lake content were used as a fit criterion. Below 10 m, the concentrations were near the lower limit of detection and not used for fitting. Individual peaks, e. g. on 18 September/day 261 at 15 m depth, were not fitted. No attempt has been made to simulate fluctuations in the surface concentration at 0 and 1 m, as these seem to reflect short-time variations in NTA input.

In general, a good agreement with field data was achieved with this relatively simple model. The simulated total lake content is shown in Fig. 36 (.-·-). Selected profiles calculated with model Care shown in Fig. 38. It was necessary to vary both surface and subsurface load within a large range to obtain satisfactory results. The epilimnion high-water load, which varied between 0 and 6.5 kg/d, is shown in Fig. 37 (-). Load approximations calculated using data on the catchment area and on high-water events yielded an average load of 5.5 kg/d (Tab. 14, using a fraction of 5 % for the water discharged as overflow, V. Krejci, pers. comm). This value corresponds very well with the mean value of the fitted load of 5. 7 kg/d. The measured average load into Greifensee (3.0 kg/d, Fig. 24) corresponds to the load discharged by WWTPs (3.2 kg/d, Tab. 14), and may underestimate the real load, because high-water events were obviously not representatively covered by the sampling program.

The existence of an NTA input below 10 m was obvious in the profiles taken on 18 July, 20 August, and 18 September. To avoid unjustified model complexity, however, the model was not extended to reproduce the observed profiles. NTA concentrations in the hypolimnion are underestimated on several dates (e.g. 25 June, 18 Sept). Most probably, degradation is not constant over the entire water column due to varying environmental conditions (temperature, oxygen; Larson et al.,1981). Since NTA in the hypolimnion is close to or below the lower limit of detection and since it does not significantly contribute to the total NTA Total NTA in lake [kg] 300

240

180

120

100.0 150.0 200.0 250.0 300.0 350.0 Time 10 Apr90 30May 19 July 7Sepl 270ct 16 Dec

~ Comparison of the total NTA content in Greifensee simulated with four different models (all with 18 boxes).

Highwater load into epilimnion [kg/dj 10 r----.. I \ I I I \ __ :..._ t · I · . 8 ····>······-:·······t·······:·l··············-······ t • I t · I . . ~ . I . 6 ...... • • • • • • • . . .I .••...• .-. I ... ·r=:=i·..... , ...... I · I --- . :Modelo: I ·I .. : - -....,., ~---- I · I I .· ! . 4 . . ., ...... {· .. ·\· ...... <· .. · l ·:- ·1· .... ._..,...,..,..,..,..,..., . ,---~· I : 7 I . ! . 2 · · ·: · · ·l·Mode1c · · · · ·, · · · · · · ·.· · · · · · · ·.· · j . . . ·u·· · · ·.·. · · · · · · I . . . . t ' . 0 ----'--•I ----I · 100.0 130.0 200.0 330.0 250.0 300.0 Time 10 Apr 90 30May 19 July 7 Sept 27 Ocl 16Dec

~ Fitted high-water NT A load into epilimnion for Greifensee, 1990/91. 99

Depth lml o.o

-5.0

-10.0

-15.0

-20.0

-25.0

-30.0

0.0 SF .F S •. : I. : : 12 Nov (316):1 { : ·-·{ 16 'sept s I:F '\ 28 Jan 91 (394) . -5.0 ·...... ·'if" .. ·>- pe1 l ' ' . ' " ' ' ' J':' ' ' . '•.. ' ' ' ' ' . A . • • -10.0 ..: .. ·1(... -: ..... : ..... ·,{tY':I'.'.'''' : FIS : . -15.0 .. . ·:···h:' '···:·····:· ····· , I '. . : -20.0 t! ! ...... ' ...... {, ". ' ,,', " ' ...... · II . ' • I ! ; : I . : . -25.0 .1. ! ..·.' ... : ..... i j • '.• .... : ..... l j ! ' •••• ' •••• ' > •••••••••. ' ••• -30.0 ' ••• .-.·.-.~ ·;·······.·.·.-.·r.·... ' . f· 11 Dec (345) ·

0 0.7 1.4 2.1 2.8 3.5 0.7 1.4 2.1 2.8 3.5 3 [mg/m ] [mgtm3 )

~ Selected NTA profiles calculated with model C for Greifensee 1990/91. S Simulated data; F: Field data. 100 content of the lake, fitting of the degradation rate constant for the hypolimnion was not attempted.

The value used for the rate constant of biological degradation was 0.035 d-1, which is low compared to data in the literature (Tab. 7). Higher rates would have required NTA loads exceeding those permissible, whereas with a lower rate, the decrease in the NTA content could not have been modeled (Tab. 12).

RFSUL'IS OF MODEL D

Model D used a degradation rate depending on the actual lake temperature and yielded slightly better results. Due to higher degradation rates during the warm period, the average fitted load was slightly higher (6.5 kg/d). Peak values of the total load (15.7 kg/d) were clearly above the plausible range. In the hypolimnion, lower degradation (temperature: 3.7 - 6.7 °C at 20 m) yielded results which were satisfactocy (25 June/day 176) or at least better than those obtained using a constant degradation rate (18 September/day 261) results.

SUMMARY

The behavior of NTA in Greifensee can be described in terms of two major processes, loading and biological degradation. The high variability of NTA concentrations in the lake can be explained only with an NTA load varying by a factor of 10. Model D, which uses temperature-dependent biological degradation, yields only slightly better results than model C. Therefore, model C with a constant degradation rate of 0.035 d-1, corresponding to a half-life of t112 = 20 d, can be considered as the most simple model which satisfactorily describes the behavior ofNTA in Greifensee.

The following further conclusions can be drawn: A sampling rate of at least once a month is necessary because NTA profiles reflect the histocy of only one to two months at most. High-water events with high loads are essential for the understanding of NTA in a lake, and should therefore be carefully monitored. Microbial degradation is the major elimination process, but the factors influencing it are not fully understood. Further insight could be obtained from field or laboratory studies investigating the degradation process under various conditions, i.e. various temperatures and oxygen and NTA concentration. 101

4.5. PER (TETRACIIl..OROETHYLENE) MODELS

SETUP OF THE MODELS

The PER models were developed based on data from two field studies. 18 profiles, including a series with very high PER concentrations caused by an unknown contamination incident, were available for the period of 10 October 1982 (=day 277; day 0 =1January1982) to 18 November 1985 (=day 1417). Another series was available for 19 March to 15 October 1990 (days 2999 to 3208). The input of PER into Greifensee was estimated twice at the major inflows (Fig. 24). In 1990, PER concentrations were significantly lower than in the period of 1982-85 and therefore only a qualitative interpretation of the 1990 data is possible.

Lake parameters were available on the system library file for Greifensee which provided the necessary average data to model the long term behavior. PER data were available on the corresponding compound library file, which was loaded into MASAS (Fig. 27).

As a next step, the model was set up with two user-defined processes: air/water-exchange and loading. The mass transfer coefficient for air/water- exchange was not available. Therefore, the two-film model was activated (see Fig. 39, window Air/water-exchange, first line in table Summary Parameters). The Henry coefficient is a required input parameter for this model. On the PER library file, a value of 12.23 atm·l·Mol-1 is given for the Henry coefficient (25°C). The parameters aH and bff, used for the calculation of the Henry coefficient for other temperatures, are also on file. The Henry coefficient could thus be calculated for the mean temperature of the lake surface (12°C), yielding 8.24 atm·l·Mol·l (Eq. 50 in Tab. 32). The transfer velocity through the gas film (Gas Film*H IR/T) was much higher than for the liquid film (51 mid compared to 0.15 mid), and therefore, the gas film layer was switched off for the calculation of the overall mass transfer velocity (Eq. 49).

The next question was whether seasonal variations in wind speed, influencing vf (Transfer Coefficient Oxygen), and surface temperature, influencing r1 (Ratio DwOx/ DwCmp) have to be considered. Surface temperatures, available from the system library file, were plotted in the window Surface Temperature; for the wind speed u10, typical values for Swiss lakes were taken: 1.6 mis in summer, 2.0 mis in winter (David Livingstone, pers. 100 comm.). v1 was calculated for different wind speeds and different values of r1 (Tab. 15).

The ratio r1 of the diffusion coefficients could not be calculated directly, as the molecular diffusion coefficient of PER in water was not available. Therefore, three different procedures (Tab. 26) were applied and compared:

- Use of a value typical for many organic compounds: 0.35 Approximation with the Hayduk-Laudie formula for constant temperature - Hayduk-Laudie formula, with influence of temperature.

It was found that the influences of temperature and wind speed roughly cancel each other out in summer and winter. A constant value of 0.15 mid was therefore used for the model calculations.

The rates of both elimination processes, air/water-exchange and lake outflow, were compared. The elimination rate due to air/water-exchange is given by:

Vtx>t; - 0.15 d-1- d-1 mean lake depth 17.6 -0.0085

Fili· 39 Cfollowinii pa~): Modeling air/water-exchange with MASAS. (a) Screen copy of three windows. The window PER.MCD shows physico- chemical parameters of PER. In the window Air I water-exchange, the air/water mass transfer coefficient is estimated with the two-film model and auxiliary calculations, activated by the user depending on available data. Examples: Henry Coefficient (Library Constant f(TJJ: calculated for the selected surface temperature with the A- and B-factors given in the compound library file. Surface temperature: A value of 12°C is entered by the user, using the information window Surface Temperature, which displays the values in the system library file. (b) Entry form to select the Henry coefficient calculation. The Info-buttons can be clicked to obtain further information (c) on the calculation and the input parameters required. 103 Fig, 39 a "C ...."C omtS/m()l cmt2/sto ... cmt2/1to 6!!50E-02 9.9 aim t.316E-02 13.B 5.032E+OO 1.990£+03 rnolll 2.420£-03 211.0 *** .... atm•l/mol ll!l!llB211.o 2. 136E+Oi 9.407E+OO 2.eeoE+oo * •• ••• ... • •• *** Rlr-w11ter euch11n e Surface tempen1tu1 C,J.lm.ihijgo t12dt !lila Tve Fflm H•d•1 1.519E-OI Oa 1.519E-01 Off 5.097£+01 1.446E+o2 L•rorv f(T) 9.2.5E+OO lofo Gt- i~; 1.600£.+00 O.OOOE.+00 J· Solnr¥H•bacll & ill. f(•IO) 4.341E-01 ''.~ Dir•ot £ntrv 3.500E-01 0.0 200.0 1.000E+OO Tl•e ldoyl '·uit!ll:J~ l.l!!llll Sohv.-z•altaoh & •1. f(t1IO) 5.337E+o2 Tlme . : ~ . !. ;j •I~. Tt atur+ 0.

Calculation of Henry Coefficient H: b O Direct Entry O Llllr•ry Constant ®Library 1m 0 Llbnlry pO/ cw O llbnlry pO(T)/cw(ll l Info/Missing Poremeters

~ I OK I

Information about celcu•etlon: C Type: Library constant with temperoture dependency Formula: H • 10'(8H-AH/HgeH•IO)j, 10•273.15

Parameter(s) used for c:alculetion: Home: I dent: Sletus: R Coefficient for ApproHlmatlon AH defined 8 Coefficient for Appro11lmntlon BH defined Surface Temperotun T tgu defined lOi

The hydraulic rate (displayed in system window) is 0.0025 d-1. The values have the same order of magnitude, and therefore both processes have to be included.

Next, the spatial model resolution required in the PER model was determined. All processes occur in the surface layer; in the hypolimnion, PER can assumed to be conservative. Thus, two average PER-concentrations for epilimnion and hypolimnion respectively, describe the profiles, with the exception of the thermocline, sufficiently well. During winter, one mean value is enough to describe the PER concentration throughout the mixed water column. The combi-box model is therefore a suitable choice in this case. This conclusion is supported by profiles representing typical summer and winter situations (e.g. 4 October 1982 and 17 January 1983 in Fig. 22). A summary of the five PER models is given in Tab. 16.

Tah...lli Air/water mass transfer coefficients Vtot [mid] for PER estimated with MASAS. a: Use of a typical value of 0.35 for the ratio r1 of the diffusion coefficients (compound/oxygen) in water. b: Approximation of r1 with the molar volume of the compound (Hayduk- Laudie). c: Approximation of temperature-dependent diffusion coefficient D1 with revised Othmar-Thakar equations (Hayduk-Laudie, f(T)). Formulas and symbols of the approximation algorithms are given in Tab. 26.

WINDSPEED MASS TRANSFER COEFFICIENT [m/d] [m/s] a b c 4 °C 12 °C 22 °C 0 0.12 0.16 0.13 0.14 0.15 1.0 0.13 0.18 0.14 0.15 0.16 1.6 0.15 0.20 0.16 0.17 0.18 2.0 0.17 0.22 0.18 0.19 0.21 3.0 0.23 0.30 0.24 0.26 0.28 100

RF.SUIJIS OF MODEL A

The simplest model included a constant PER load and a description of the air/water PER exchange using the previously determined air/water-mass transfer coefficient. It was applied over the period 4 October 1982 (day 277) to 6 May 1985 (day 1221}. PER load, for which very little or no data were available, was fitted using repeated simulation runs. A value of 130 g/d was obtained.

The results of this model are satisfactory. It explains the long-term development of the total amount of PER in Greifensee (Fig. 40}. The temporal development of the PER concentrations in the epilimnion and hypolimnion (Fig. 41, e.g. day 445-711}, however, are not covered by this model.

The contribution of loading, air/water-exchange and lake outflow to the differential ~,uation describing the PER concentration in the epilimnion is displayed in Fig. 42, confirming the previous estimates of process relevance.

RESUL'IS OF MODEL B

This model demonstrates how a simple but reasonable model can explain the field data (Fig. 43). All settings remained unchanged, except load, which was fitted by repeated simulation runs and varied within the plausible range of 15 300 g PER/day (Fig. 43, Window PER load).

Tab.16: Structure and process parameters of different PER models. MODEL PROCESSES SIMULATION TIME PER load Alr/w.ex. A Combl-box Load: 130 gld 0.15 mid 10 Oct82 - 6 May85 Constant load day 277 • 1221 B Combl-box Sulface load: 15 · 350 gld 0.15 mid same as A Variable load (fitted) Average: 126 gtd C Combl-box day 277: 200 gld 0.15 mid 10 Oct 82 -15 Oct 90 Linearly decreasing load day 1417: 80 9'd day 277 • 3208 day 3208: 20 gtd (linear interpolation day 1417: 18 Nov 85 between these dates) D One-box SameasC 0.15 mid Sarneasc Linearly decreasing load E Contamination incident Peak load: 1.4 kg/d 0.15 mid 6 May 85 -18 Nov85 n-box model (17 boxes) (day 1238 -1262) day 1221 - 1417 Variable epilimnion depth Total PER discharged during incident (temporally during incident: decfining from 4 to 8 m. 48 kg 100

Total PER in lake [kg] 50

40 \ • • • 30 . . ~--~<~iel~ ~~ta· ..... ;· ...... 20 ·····~····-..;··"··············\. ' ...... ' . Model A · "'.'. : .. ~-.:.:: 10 ...... , ...... ,~;.-"'····

0 i...... ~~~~• .-.--.-~~-.-...-.-.--.--.-~~ 500.0 750.0 1000.0 15 May 83 20 Jan 84 27 Sept 84

~ PER content in Greifensee calculated with the combi-box model (model A). simulated values; -- field data (volume-weighted average).

PER concentration [mgtm3] 0.40

400.0 600.0 900.0 1000.0 1200.0 4 Feb83 23 Aug 83 11 Mar 84 27 Sept 84 15 Apr 85

~ PER concentrations in the epilimnion and hypolimuion of Greifen- see calculated with the combi-box model (model A). - simulated values; -- field data (volume-weighted average calculated from profiles using an epilimnion depth of 5 m). 107

~ Comparison of the three processes included in PER model A using MASAS. The figure shows the relevance of the processes in terms of their contribution to the differential equation describing the epilimnetic PER concentration (Eq. 27, see also Fig. 58):

dCepil Unit: [mg·m-3·d·l] ~ ProcessJ. Values of curves below zero represent elimination processes (air/water exchange, lake outflow), whilst those above zero represent production/input processes (loading). The effect of air/water-exchange is about three times larger than that of lake outflow. The figure shows when a steady state has been reached: day"' 400: no steady state (sum of elimination processes >> input process). day"' 1000: steady state (sum of elimination processes "' input process).

1 [mg ·m •3 · d · 1 0.01 .- _-~f'_E_R_l~~~i~~C!_e!~L- __ --:- ______._ o:~ v~~:;:::;:::~~-'~''.-

400.0 600.0 800.0 1000.0 1200.0 4 Feb 83 23 Aug 83 11 Mar 84 27 Sept 84 15 Apr 85

RESUL'IS OF MODEL C

With this model, the simulation was extrapolated to 15 October 1990 (day 3208). Concentrations in the lake were significantly lower in 1990, therefore a linearly decreasing load was assumed (-...... in Fig. 44). In 1990, the load reached 20 g/d, which is in good agreement with the few available field data (Fig. 24). The simulated total PER content is shown in Fig. 44 (-+- field data, -- model C).

The profiles taken after the incident of 3 June 1985 (day 1249) showed a marked vertical structure (Fig. 22) for which the combi-box model was not suited. This is a typical case where a more complex model must be employed (model E). l~

RESUL'IS OF MODEL D

Model Dis a one-box version of model C with exactly the same process parameters. The simulated PER content of the lake (Fig. 44, -) was slightly lower than in the case of model C. In the combi-box model, the compound is not removed from the hypolimnion during summer. In the one-box model, PER is removed more efficiently during summer, reducing the time needed to reach steady state. For a rough estimation based on the available field data, model D would however still be sufficient.

RESUL'IS OF MODEL E

Models A-D describe long term trends of the PER concentration. However, an incident with a marked vertical structure, such as that which occurred in May/June 1985, cannot be described adequately with these simple models. Model E was therefore developed. It is a 17-box model, with sizes adapted to the vertical structure of the PER profiles (0-10 m: 1 m each; 10-20 m: 2 m each; 20-25 m; 25-32 m). Two additional parameters, epilimnion depth and vertical eddy diffusion coefficient, had to be provided for this model. The change in epilimnion depth with time was determined based on temperature data. For critical periods during the incident, no temperature data were available and reasonable values were assumed. For the vertical eddy diffusion coefficient, typical values for Greifensee were employed.

A possible scenario for the incident, which is represented in the model, is the following: Between 6 May (1221) and 1 July 1985 (1277), an unknown amount of PER was discharged into the lake. High epilimnetic concentrations re- sulted (3 June, day 1249). The data measured after this input event showed decreasing concentrations in the epilimnion, and a concentration peak in the layer 6 m below the water surface, which can be explained in the following way: due to a storm or a temperature change, the epilimnion depth increased after 3 June for several days from 4 m down to 8 m (assumption). During this time, PER was mixed into deeper water layers. After the subsequent change of the epilimnion depth back to 4 m, PER was buried in the layer below 4 m, where no elimination processes occurred, thus delaying its removal from the lake. PER, combi-bOH Total PER in lake PER concentration [mg/m3] 0.40

'I 0.32 \·:······:······:······'.······:·: l~~,-,,,,,, · ·· .. ··•·I : ~--....,----..... -----~---·--:· \ . . . . 0 I l ; I I I ; t I I j i t I : l i I i 0.24 .. ' .· ...... " ...... ·...... \ : : Hypolimnion : · 400.0 600.0 800.0 1000.0 1200.0 4 Feb 83 23 Aug 83 11 Mar 84 27 Sept 84 15 Apr 85 Lint> ~P.h v M"iab les: 0.16 Simulated data (model B) Field data 0.08 PER load 1./~x~.·····----~E·1· p11mrnon·.~v : : ·,~-...... : : Mass in 0 400.0 600.0 800.0 1000.0 1200.0 4Feb83 23Aug83 11 Mar84 27Sept84 15Apr85 Time !day! 1 t ' !.iD! Gr <111h varia!)les 500.0 750.0 1000.0 - Simulated data (model B) 15 May 83 20 Jan 84 27 Sept 84 - - Field data Lint> Gr!f!h variables - Mass in inflow 0 400 l2J

~Simulation of the PER concentrations in Greifensee with the combi-box model (model B). 110 Total PER in lake (kg] PER load 50 . . 200

\ . \ .. ,. . ... Combi-box mOdel (C) ... :- ...... 160 40 l I : . . /Field data · 30 /·:········,'•·······:······· 120

PER load 20 .. .. 80 . . "<:.... ~: .. ~... :.:... :.. :... :.?' .. ~ ...... ,, 10 ...... )·.. , ...... :..·.'' 40 ''"•·-·:... -...... One-box model (DJ

500.0 1000.0 1500.0 2000.0 2500.0 3000.0 15 May 83 27 Sept 84 9 Feb 86 24 June 87 6 Nov 88 21 Mar90

~ Screen copy of total PER content and PER load in Greifensee, 1982- 1990 as simulated using models C (combi-box) and D (one-box). The PER peak during days 1221...1417 is not simulated because of insufficient spatial resolution of the models. PER load decreased significantly during the period of simulation.

0.0

-5.0

-10.0

-15.0

-20.0

-25.0 ...;: . il' ~· -30.0 ...... · ...... -: ..... ' ..... ,• .... ·:· .... .

0 0.12 0.24 0.36 0.48 0.6 0 0.12 0.24 0.36 0.48 0.6 PER concentration [mgtm3]

~ Screen copy of PER profiles calculated using model E (17 boxes) for Greifensee. At some time previous to 3 June 1985, a contamination incident led to a significant increase in the epilimnetic PER concentrations. Parts of the PER were buried below 4 m due to a decrease in epilimnion depth with time. Graph legend: - simulation results, model E, ...... field data. Total PER in lake [kg] PER in inflow [kg/d] 50 2

/lr.;::~~ ~m (model E) .. 25 1 ...... , ...... , ...... ' ...... -~ Field data · ---- 0 1221.0 1271.0 1321.0 1371.0 0 1.,_,.....,...... ,_,..=:;:::;:::;::::;:::;:::;:::;:::;:::;:::;;::;:::;:::;:::;:::::;::J Time 6May85 25June 14Aug 30ct 1221.0 1271.0 1321.0 1371.0 Time 6May85 25June 14Aug 30ct

m Eddy diffusion coefficient [cm 2/s] 10.0 0.1 ......

below 17 m . . / . : i : ...... ,, ...... :...... ' . . . . . ~ . . . ~ 0.05 5.0 . . , ...... · . . . . . above 15 m / ,,.--~~~- 0 '-r--~~-...~~~~~~~~...-.-...~~...-J 0.0 1221.0 1271.0 1321. 0 1371.0 Time 1221.0 1271.0 1321.0 1371.0 6May85 25June 14Aug 3 Oct Time 6May85 25 June 14Aug 30ct

&.J&_ Simulated total lake content and input parameters (load, epilimnion depth, vertical eddy diffusion coefficient) of model E (17 boxes). 112

The calculated profiles, which are in very good agreement with measured data, are shown in Fig. 45. The time-dependent parameters used in the model (epilimnion depth,,PER load, vertical eddy diffusion coefficient Kz) and the total PER content of the lake are shown in a screen copy {Fig. 46). The total load released during the incident is approximately 50 kg. This value has been determined by means of model calculations, assuming a temporally variable epilimnion depth.

SUMMARY

The PER model study illustrates how the complexity of the model employed must be chosen to suit the problem to be solved . The one-box model (D) is sufficient for a rough estimation of long-term trends. The combi-box model (A, B, C) is required to describe adequately the spatial distribution of PER under normal conditions. The most complex model available in MASAS (n-box, E) is required for a correct description of the dynamic behavior, including the removal time, of particular incidents with marked vertical structures.

The MASAS implementation of air/water-exchange with the two-film model was used to estimate a mass transfer coefficient for PER. Using different process approximations, which included the estimation of unknown compound parameters (diffusion coefficient of PER in water, D1) and the calculation of dependence on wind speed and lake temperature, a constant value of 0.15 mid could be established.

Using models C and D in combination with the field data (lake), it was possible to estimate the unknown PER input. A clear decrease was found: 1982 · 1985: 100 - 250 g/d; 1990: 20 g/d. The total PER load released during the incident in May/June 1985 was found to be 50 kg. 113

4.6. ATRAZINE MODELS

SETUP OF THE MODELS

Vertical variability within the measured atrazine profiles was small and lay within the analytical error bounds (Fig. 23). For the given data quality, the one-box model was appropriate and was selected for all model calculations; speaking metaphorically, we tried to avoid using a sledgehammer to crack nuts, or, in German "wir wollten nicht mit Modellkanonen auf Datenspat- zen schiessen".

The same system data file as for the NTA model was used. Compound data were available in the atrazine library file. First-order removal rates were calculated for all possible degradation processes (Tab. 17). Processes with rates significantly smaller than the hydraulic rate of the lake were neglected.

Tab.17: Evaluation of relevant degradation processes for atrazine in Greifensee based on apparent first order rate constants, k. The values were either directly available or calculated for a one-box model. All data from Grover (1989), except: * Ambiihl (1990); t Sigg et al. (1991). Process Estimation of apparent first order rate constant Lake outflow k = 2.45· 10-3 d-1 Hydrolysis observed k = 9.10-4 - 9.9·10-3 d-1 (25°C)

acid 3.37·1 o-3 m3·mo1·1.d-1 (25°C) pHmin = 7.5 -+ k"' 1·10-7 d-1 (Tab. 28) * pHmax = 8.2 -+ k""' 2·10-8 d-1 (Tab. 28) * alkaline 6.57·1 o-3 m3·mol·1.d·1 (25°C) pKw (12°C) = 14.5 pOHmin =6.3 -+ k "' 2· 10-6 d·1 (Tab. 28) * pOHmax = 7.0 -+ k""' 3·10·7 d·1 (Tab. 28) * Biological degradation k =7.7·10·3 d-1 (24.5°C) Sorption to particles koc = 2.16· 10-4 m3/gpoc [POC] =0.34 - 1.24 g·m-3 -+ 1 g·m-3 t f1 = 0.9998 (Eq. 8) k = 3.1 o-5 d-1 (Eqs. 44 and 45) Volatilization H = 2.85· 10-6 atm·l·mo1-1 (Henry coefficient) Vtot ""' 2.10-5 m·d·1 (Gas film controlled) k "' Ho-a d-1 (Eq. 48 and Tab. 26) 114

Only two processes were found to be potentially relevant: hydrolysis and biological degradation. The latter was omitted since these two processes are indistinguishable from the point of view of the model calculations.

Various reported hydrolysis rates were tested using MASAS (Fig. 47). These rates included acid, alkaline and observed overall hydrolysis rates (Tab. 28). The relative importance of acid and alkaline hydrolysis was evaluated for different pH-values, using the MASAS option to show the contribution of each process (Fig. 48). Both acid and alkaline hydrolysis were found to be irrele- vant at the pH-values encountered in the lake.

In the next step, different models were set up with no, small and large observed hydrolysis rate constants (Tab. 17). For each model, the atrazine load was fitted and compared to the measured load; as usual, the coincidence of simulation results and field data was used as the fit criterion.

Tab. 18 lists the models with the parameters; simulated concentrations and atrazine load are shown in Figs. 49 and 50.

Fig. 47 (following paire): Definition of a hydrolysis process in MASAS. a: Entry form shown when a hydrolysis process is defined. Various options are presented. On the left, the type is selected from among all options for which the required input parameters (system and compound) are available; when data for the calculation of temperature dependence are available, a second button is shown which allows the process rate to be made dependent on lake temperature (see Libr., Observed 0 I 1). b: Information window for the option Library data; alkaline hydrolysis I pKw = [(temperature). The description and the formula for the option are displayed, as are the parameters required along with their values or ranges. Missing parameters are indicated by* * *. c: Screen copy of the model window after all processes have been defined. It includes the atrazine models A-D and the evaluation of the hydrolysis reactions. Different models and processes can be loaded simultaneously. They are switched on using the click fields in the column Stat(us). Active processes are shown in bold type. The settings illustrated here are for the evaluation of acid and alkaline hydrolysis. 115

8 Definition of proteH: Hgdrolysls (Alkaline Hydrnlysls (II, pKw • !(temp) )): for species~ Total concenlrallon of compound With reactiuttles for. Dissolved neutra•

ca1culotlon of process rote: constlr(ll 0 Dlteel entry 0 O llbr., Obserued o 0 O libr., Obserued 1 0 Ubr., Obserued 2 I lnro/Missing Porometers J 0 llbr., Obserued 3 I lnro/Mlsslng Parameters l 0 llbr., Reid I lnro/Mlsslng Parameters I libr., Neulrel I Info/Missing Parameters J 0 llbr., ftllllne I Into/Missing Parameters I ® Libr., Rik., pKw(T) lftfo1M1ssmg 'orametets I OK I b t11formetion about cclculotion of Hydrolysis rate consltud Type: library Constant or flO lempernture dependent pKe for calculation of [OH) from pH Formula: kh • constant or temperature dep~mdent [OH)• IO'(pH-pKw), pKw • 14.92 - 0.0368•temp

Pan11meter(s) tor caltuletton: Delue(s): Unit: !dent: Source: fflkellne Hydrolysis 6.57£-03 m3/mol/d kbD compound Temperature for kb 2.SOC+OI °C TtbD compound R

c MS Rtnazin Modell Total 9omt1oynd in water: Stat~ Calculation Mode/Parameter Diffusion Off Off Advection Off Off Lake outflov On On Sedimentation Off Off A ; Constant load Off Off Mass in inflow (mass Id) Inflow level (0, 1 ,2) B : Variable load Off Off Mass in inflow (mass/d) Inflow level (0, 1 ,2) C : Variable load Off Off Mass in inflow (mass/d) Inflow level (0, I ,2) C: Hydrolysis observed/large Off Off Libr _,Observed 0, Const. D: Variable load Off Off Mass in inflow (mass Id) Inflow level (0,1,2) D: Hydrolysis observed/small Off Off Libr., Observed 3, Const. Acid Hydrolysis On On Libr., Acid, Const. Alkaline Hydrolysis (IV, const pKw) Off Off Libr_, Alkaline, Const_ Alkaline sis Libr ., Al y T Const_ 116

Tab. 18: Structure and process parameters of different atrazine models. MODEL PROCESSES Atrazlne load Degradation (Hydrolysis) A Constant load Load: 94 g/d none No degradation processes • B Variable load (fitted) Range of load: none No degradation processes 0-490g/d Average load: 75 gld C Variable load (fitted) Range of load: Highest reported hydrolysis rate: High hydrolytic degradation 0-930 g/d 9.9·10-3 d"1 Average load: 437 gld D variable load (fitted) Range of load: Lowest reported hydrolysis rate: Low hydrolytic degradation 0-560g/d 9.1 o-4 d-1 Average load: 103gld

Fig. 48 (following page): Evaluation of the relative importance of degradation processes for different environmental concentrations. The figure shows the contribution of the processes to the differential equation of the one-box model (Eq. 27): dCI Unit: [mol·m·3.d-1] dtprocess J. Lake outflow, acid and alkaline hydrolysis are shown (left-hand y-axis) together with the pH-value (right-hand y-axis). The [OH]-concentration for the alkaline hydrolysis is calculated with a temperature-dependent pKw. Neither acid nor alkaline hydrolysis are significant at the pH-values found in Greifensee (a, different scaling!). Acid and alkaline hydrolysis would be significant at pH-values of3 and 11, respectively (b, c). 117 a [mg·m-3·d"1] pH 0 --,--- .... ------:------.. ------:--~--~-..-- -- 12 Acid 'hydrolysis •1000 : ;...... : ..... _. __ -0.2 ...... '., , ..... :...... '.. ·.. .:..·"'~ .. ·:"·:·:...... '. ... 10 : ...... ;...... :.. ,....., Alkaline hydrolysis ,...... •1000 -0.4 )'~":.~.:::.~!.··:~:<~-. :_: _: ~: _:_ :_ :.. :. :. : .. :. :. :.:. :_. -·- -~· -·-. -...... -.- ..... -,. . 8 : pH (field data) : : '. -0.6 6

""'-~~-. . -0.8 · · · · · · · · · · · · · '. · · · Lake outflow · · '. · · · · · · · :. · · 4

-1.0 124.0 174.0 224.0 274.0 324.0 374.0 4 May 90 23 June 12 Aug 1 Oct 20 Nov 9 Jan 91 1 [mg ·m·3 · d" J pH b 0 ...... ~.·~~~~;~~~ ..~·~~~:·;~:;:··:··~·~~~·:- ...... ~ ...... ~...... 12

·0.2 10

-0.4 ...... '. .. L:a.k~.o~~flo~."":--,.,..: ..... <· . .:.,.,,.'":'".~~~~ 8 • • ---- ¥"'~ • -0.6 ...... : ... ·/·<·".~.. ,, .: ... ,, .. :. .. 6 '._.. ,,r, : Acid hydrolysis : ·0.8 ../.""" ..,...... ,,, .:. · · · .,.,.,,/."""; · : H · · · · ·. · · 4 :.. .-·-, -.... -~-~::_~ :.~-, -,-·~· -.~.~ .~. ~, ~.~:~. ~z. ~- .. ,~ ...... -·-·-.-·., -.-. -. -1.0 ..,_~,....,.....,....,...... ,...... ,.....,...... -.-~...... -.-...... , ...... -.-,...... ,-.-~,...... ,..-.--'2 124.0 174.0 224.0 274.0 324.0 374.0 4 May 90 23 June 12 Aug 1 Oc1 20 Nov 9 Jan 91

[mg·m·3 ·d"1] pH c 0 --~Ac~-hycirolysi5~1ooo------~------:- 12

-0.2 ·:·:·:·:- :·: ·:-~-~-7-: ·:·:~:-··: ·:·: ·:· :-~-:-:·:·:-:-. .. ~ .. r.:~"~:.~:~~~.;:.F:·=: 1o

-0.4 ...... :... :.. : .. A~kaline hydr~lysls~ .. ;...... :<": .. :...... ;.. '. . _,...... · '-. Lak~ outflow -0.6 .. :... :"·:··...... : ... •:"'.. 6

. , -0.8 • •' '•' ·.•••••••I*'••''',' ..... , .. 4

-1 .0 ..,._,_,.....,....,.....,.....,-,...... "T""ir-.-...,.....,...... ,--,.....,.....,-,.._;...-,.-,....,.....,...,,...... _, 2 124.0 174 .0 224.0 274.0 324.0 374.0 4 May 90 23 June 12Aug 1 Oct 20 Nov 9 Jan 91 118

RE&JL'IS OF MODEL A

The results of this model clearly show that the simplified assumption of constant loading employed here is unable to account for the variations in mean atrazine concentration observed in the lake.

RE&JL'IS OF MODEL B

In this model, the loading was allowed to vary over time and degradation was neglected. The mean load of 75 g/d, obtained by fitting, is in the same range as the observed load of 84 g/d (Fig. 24 and Tab. 9). Temporal variations of the load seem plausible: the high load from 10 May till 14 July (days 130- 200) reflects the use of atrazine in corn cultures. Higher values in October coincide with several rainfall events. Simulated concentrations are in good agreement with field data, except during two periods, where the simulated decrease was too small, although the input was set to zero (20 Aug - 18 Sept, days 232-261, and 12 Nov - 28 Jan, days 316-394).

RESUL'IS OF MODEL C

In this model, the highest observed hydrolysis rate was used. Simulated and observed atrazine concentrations corresponded perfectly when the load was fitted using the ordinary procedure. However, the average load of 437 g/d obtained is far above the plausible range.

RE&JL'IS OF MODEL D

This model, with a small hydrolysis rate, yields a load similar to model B (average: 103 g/d). Simulated concentrations correspond perfectly to observed values; the decrease during the periods from day 232 to day 261 and from day 316 to day 394 is simulated better with model D than with model B. However, on the basis of the available field data, it is difficult to say whether model D is definitely preferable to model B. 119

Atrazine concentration (µg/m 3] 310

288

266

244

222 ...... -:· ...... ·> ...... ·: .... .

200 '---~~~~~~~~~~~~~~~~~~~~~--' 120.0 170.0 220.0 270.0 320.0 370.0 30 Apr 90 19 June 8 Aug 27 Sept 16Nov 5 Jan 91 ~ Atrazine concentrations in Greifensee calculated with different one-box models (models A-D). Simulation period: 30 Apr 90 (day 120) - 28 Jan 91 (day 394).

Atrazine load (kg/day) 1.0

i"'··. ·,. . 0.8 . . . . . ·~· ~ ...... ' .· ...... " ...... · . . . ' . . . ~ .. . . . / :\ M~del C . 0.5 .. . / t~\ \_/~· ..... }-·----- ...... 0.4 j /1X' ..;.;d~i.; ' :: / ~., .J<: :: :~:- -_: 0.2 ... ·}M d I a~.;·· · · ·.· · · · · · · · '· · · · · ·: .,. · · · · · · · · · ·

0.0 .... .,,,/' oe: ·~";,; "'·..,·...... L-- .. ····"·.;;...:::·: .. j· _: .. :··:· .. ·>~ ·;:::.., ...... ___ ,...... ~ ...... , ...... 120.0 170.0 220.0 270.0 320.0 370.0 30 Apr 90 19 June 8 Aug 27 Sept 16 Nov 5 Jan 91 ~ Temporal development of atrazine load (Greifensee 1990/91). The values were obtained by fitting, using models A-D. The peak between days 130 and 200 (10 May - 14 July) reflects atrazine application in corn cultures, the smaller peak around day 300 (27 Oct) is due to high-water events. SUMMARY

The relevant processes were found to be loading, lake outflow and, possibly, hydrolysis or biological degradation, using MASAS and simple calculations, in combination with fielq studies. Simulations performed with a simple one- box model provided satisfactory results.

Loading is clearly time dependent, and reflects the time pattern of atrazine use in corn cultures. High-water events later in the season can bring more atrazine into the lake.

The in situ degradation rate obtained with the model calculations corresponds to the smallest values cited in the literature and lies in the range of 0 - lQ-3 d-1 (t112 ~ 700 d). Larger rates (model C) required a load which is significantly above the observed atrazine input. 121

5. MATHEMATICAL MODELS

In this chapter, the mathematical models of MASAS and CHEMSEE are presented. The general formulation of the continuous model equations is given in Section 5.1. The equations used for the chemical species are described in Section 5.2. Subsequently, in Section 5.3, it is explained how the specific MASAS and CHEMSEE equations are obtained from the general equations. Sections 5.4 and 5.5 describe the discretisation of the continuous model equations for the n-box model and the one- and two-box models, respectively. Sections 5.6-5.8 refer to the MASAS system and describe the processes, and the data sets used for the characterization of lakes and compounds.

5.1. CONTINUOUS 1-DIMENSIONAL VERTICAL LAKE MODEL

This model describes the lake in terms of one spatial dimension, lake depth. It calculates vertical gradients of model variables and ignores horizontal variations. The model equations are given in Tab. 19; the transport and transformation processes are described in detail below. Lake topography is described by the function A(z), i.e. isobath area as a function of depth. The relative importance of the sediment, expressed by the local ratio of sediment dA area to water volume - -d- , is important for all exchange processes A· z between water and sediment.

In the following, we explain the terms and symbols of the model equations (numbers refer to Tab. 19 and Fig. 1). Sign conventions are given in Fig. 51.

(1) Lake inflow

This process does not explicitly appear in the model equations. Water renewal is described by lake outflow, and loading processes are described as net fluxes into the water column (see below). 122

Surface fluxes: >0 <0 Indices: Depth definitions: 0 0 01 >0 <0 1 Vertical fluxes: 2 f ' z-axis, Indices: box i z boundary j+1 positive downwards

~ Index and sign conventions used in the one-dimensional vertical lake model. Symbols are explained in the text.

(2) LaJu:! outflow r(z,t): Hydraulic rate [d-1] z: Depth (vertical coordinate, zero at lake surface, positive downwards) [m] t: Time [d]

(4) Vertical advective flow due to undenDater inflow or outflow

Qvert(z,t) = u(z,t).A(z), with Qverl(z,t): Vertical flow [m3·d·l] u(z,t): Vertical advection velocity [m·d-1]

(5) Verli.cal mixing by eddy diffusion

Kz(Z, t): Vertical eddy diffusion coefficient [m2·d-l] A(z): Isobath areat [m2]

t In MASAS and CHEMSEE, the expression lake cross section is used instead of isobath area. 123

Tab. 19: Differential equations of the one-dimensional vertical lake model for reactive substances in deep lakes as used in MASAS and CHEMSEE. Symbols and terms of the processes are explained in the text.

Ci= Ci (z, t): total concentration ofi-th variable (compound, suspended solid, or any biological or physical property) in water column [moJ.m-3] *) Number of variables in water column: i =1...nc Units of equation: [mol·m·3.d-l] or [g·m·3.d-1J

_Al. (Qvert.C.·) + 1 d (K A dCi) f dCi a:z A' dz z· dz - i,p·Vi· dz

Lake outflow (2) Vertical advection (4) Diffusion (5) Serumentution (6)

(7) First order reactions (8) Second order reactions (9)

s dA c I dA (2) vi . A·dz. i F l.. Ad . z

First order fluxes into sediment (10) Sediment/water exchange (11) c; =c; (z, t): total concentration of i-th variable adsorbed to POC in mixed layer of the sediment as a function of lake depth z [mol·gp~cl dC~ l v.· f C F s. - A Vj C cs y= M · i,2· 1 + 1 p·M· Poe· i +

Sedimentation (6) (11) To permanent layer (12)

First order reactions (13) Second order reactions (14)

*) Concentration units used: compound: mol·m·3; POC, particles, etc: g·m·3

In MASAS, the compound concentration unit is mass·m·3 (mass = either mo!, µmol, etc. or g, µg, etc. may be used as long as the reaction rate units are adapted accordingly). In CHEMSEE, the user must define the unit of the variable as g·m·3, mol·m·3, °C, etc. In both programs, m3 must be used as the volume unit. (6) Settling ofparticks and particulnte species

This process describes the settling of particles (6a) and particle-bound species of a compound (6b) into deeper water layers and down to the sediment. Depending on whether l?articles or particulate species should be modelled, one of the following descriptions is used in the model equations:

(6a) Particles: v;(z, t): Settling velocity of particle i [m·d-1]. f\,2(z,t) = 1 (all particles are subjected to settling)

(6b) Particulate species:

This description refers to dissolved compounds which have a particle-bound species, either described as the fraction adsorbed on POC or on total particles. In the following, only POC will be mentioned: the particle option is implicitly included.

Vj(Z, t): Settling velocity of the particles on which compound i is adsorbed [m·d-1]. f;,2(z,t): Fraction of particle bound species of compound represented by variable i (see Section 5.2).

The compound is transported to the sediment with the settling particles. Settling is therefore an input process for the compound in the sediment described by sediment variables (Eq. 3). The following additional parameter is required in this case:

M: Mass of POC in mixed sediment layer [g·m-2]

(7) Net flux into water column

Sum of zero order reactions and boundary fluxes, including loading, zero order mass exchange at water surface, and zero order sediment flux (i. e. mass exchange across sediment-water boundary) [mol·m-3·d·l]

Fluxes through the sediment-water intei:face cannot be related to sediment variables. They are pure boundary fluxes for variables in the water column. 125

The exchange between water and sediment for compounds which occur in both compartments is described by process (11). Lake topography is included in the following way:

-F?· _M_ 'th (4) 1 A·dz'Wl

Ji* (z, t): Sediment input/output, corrected for the local ratio of sediment area to water volume (mol·m·3.d-l] Mass flux of compound i through sediment/water-boundary (mol-m·2.d-l]

(8) (PseucJo..) First order reacllons

The rates of all first and pseudo-first order reactions (e. g. hydrolysis, photolysis, first order surface fluxes, etc.) are sununed to yield one overall reaction rate Rk,i· The equation used when species are involved is given in section 5.2.

Rk,i(Z, t): Total reaction rate (d-1]

(9) Second order reacllons

The total reaction rate is defined analogously to first order reactions:

(10) First order sediment flux

The flux of a compound (or a solid variable) into or out of the sediment is proportional to its concentration in the water column. The relative importance of the sediment is calculated in the same way as for the zero order sediment flux. Again, this process is a pure boundary flux for variables in the water column. v ~(z, t): mass transfer velocity of compound i through sediment-water l interface (m·d-1] 126

(11) a: Particle resuspension, b: Pore water diffusion

Boundary flux between sediment surface and overlying water occurs by means of particle resuspension or diffusive exchange of pore water. This process always includes two model variables; viz. the concentration of a compound in the water column and in the sediment, respectively.

(5)

(6) Partition coefficient of variable i between particulate organic carbon and water [m3·gpoc·1] fi,1(z, t): Dissolved neutral fraction of the compound described by variable i in water column Rex(z, t): Resuspension rate [d-1].

For pore water diffusion, Rex can be interpreted as: Rex= vex , with (7) Kp,i·M Vex(z, t): Mass transfer velocity [m·d-1] due to diffusive exchange of sediment pore water with overlying water

(12) Transport ofparticles from mixed sediment layer into permanent sediment f3(z, t): Preservation factor of POC (fraction of POC reaching permanent sediment layer). Cpoc(z, t): Concentration of POC in water column

(18) First order reactions in the sediment

Rth, t): Total reaction rate [d-1]

(14) Second order reactions in the sediment 127

5.2. CHEMICAL SPECIATION

Chemical compounds which are represented in the model by a dissolved variable may occur in different forms (species): 1: dissolved neutral species 2: particulate, neutral species (adsorbed on POC or particles) 3: dissolved single charged anion (deprotonated acid) 4: dissolved single charged cation (protonated base)

We assume that all these species are linked by fast reactions. In this case, only the total concentration of the compound has to be calculated as a dynamic model variable; and the concentrations of the species are given by the equilibrium partitioning equations (Tab. 20). The following definitions are required:

Ci: Total concentration of compound i [molm-3]

n 8 : Number of species f1, 5: Fraction of total concentration of compound i which occurs as species s. fi,s = 0: species s not considered for variable i

Ci,s: Concentration of species s of compound i [mol·m-3], with

(22)

The total concentration of the compound is given by ns L f1,s·Ci = Ci (23) S=l

When only certain species are involved in a given process, the total concentration is replaced by the concentration of the species involved for the calculation of the process rate. For example, for first order processes:

ne I: Rk,i.ck , with (24) k=l Tab. 20: Equilibrium partitioning equations used for species in MASAS and CHEMSEE. Sorption to POC/particles is assumed to be fast and reversible, with linear sorption isotherm. Symbols used ;:c ~ !. in equations (further symbols explained in the text): ~- Koc,i partition coefficient [g·gpoc-1] cpoc concentration of POC/particles [g·m-3] ~ u 1:11 Species of dissolved compound, represented by variable i (") i::: Speclatlon 1: dlssolved neutral 2: particle 3: dissolved an- 4: dissolved cat· 0 bound, neutral Ion (-1, deproto· Ion (+1, proto· ::s [M.t nated acid) nated base) ii1 f11 = f1 2 = f13 = f1 4 = S' .;;-- 1 No species q, 1 0 0 0 2, (dissolved) ;:c '1' "'(1) 1 ~ 2 SOrption to POC (8) 1 - f ~ 1 (9) 0 0 a. (1) "' 1 + Koc,i · CPOC "' 1 "'0 3 Acid (10) 0 1 0 ...... 1+10PH-pKa 1 f·1 "' (") - •1 1+1QPKa·PH (11) ~ 1 4 Base 1 - fi,4 (12) 0 0 (13) § 1+1oPH·pKa p. 5 Acid with 1 Pi' ,with a= (14) Koc,i·CPQC 1opH-pKa sorptlon to POC a (16) (17) 0 a a ~ 1 +Koc,i·CPQC + 1 OPH-pKa (15) ...::.:

Koc,i·CfQ!;! 10PKa·PH 6 Base with l {20) 0 (21) sorptlon to POC p ,withP= (18) p p 1 + Koc j·CPQC + 1 OPKa-PH (19) 129

5.3. EQUATIONS FOR MASAS AND CHEMSEE

The model equations used in MASAS and CHEMSEE are obtained from the general equations (Tab. 19) with the following restrictions:

5.3.1. MASAS MODEL WITIIOUT ADSORPTION TO POC

This model describes a dissolved compound in the water column, ignoring the effect of adsorption to POC and the sediment. In this case one single model variable is sufficient. It describes the total concentration C1 of the compound and is calculated by Eq. 2.

A proton-transfer reaction can optionally be included in the model. In this case, the concentrations of the following species are calculated: • C1,1 concentration of dissolved neutral compound (Eq. 10 or 12 in Tab. 20) • Ct,3 or C1,4 concentration of dissolved anion or cation (Eq. 11 or 13 in Tab. 20)

5.3.2. MASAS MODEL Wim ADSORPTION TO POC

This model includes adsorption on POC and the sediment. Therefore, two additional variables are required for the compound in the sediment and for POO in the water column (nc =2, one sediment variable).

The total concentration C1 of the compound is described by Eq. 2. The concentrations of the following species are calculated: • C1,1 concentration of dissolved neutral compound (Eq. 8, 14, or 18 in Tab. 20) • 01,2 concentration of neutral compound adsorbed on POC (Eq. 9, 16, or 20 in Tab. 20)

A proton-transfer reaction can optionally be included: • C1,3 or 01,4 concentration of dissolved anion or cation (Eq. 17 or 21 in Tab. 20)

The concentration of POC, 02, in the water column is described by Eq. 2, and the concentration C~ of compound in the sediment by Eq. 3. In all MASAS models, the terms for first order reactions (8,13) can be simplified in the following way because only one compound is modelled: 130

nc=l= (26)

Second order reactions (9,14) are not included in MASAS.

5.3.3. CHEMSEE MODEL

CHEMSEE offers an arbitrary number of variables of any type and the model equations hold without restrictions. The concentrations Ci of compounds, particles or other physical or biological properties in the water column are described by Eq. 2. The concentration c: of compounds in the sediment are described by Eq. 3.

Adsorption on POC can be optionally included for compounds in the water column. In this case, the dissolved neutral and the particulate species are calculated by Eq. 8 and 9, respectively.

5.4. DISCRETE ONE-DIMENSIONAL VERTICAL LAKE MODEL (N·BOX MODEL)

To solve the differential equations numerically by computer, the continuous equations had to be transformed into their discrete equivalents. Various methods for doing this exist (Noye, 1984; Roache 1972; Chapra and Reckhow, 1983; Peaceman, 1977). The control volume approach, which leads to box models, was employed. The continuous depth dimension was replaced by a number of adjacent boxes. Continuous model variables Ci(Z) were replaced by discrete variables Cij for variable i at the center of box j.

In all terms without spatial derivatives Ci(Z) is directly replaced by C;j.

Second order spatial derivatives ;:2 which appear in the eddy diffusion term were replaced by centered differences (PEACEMAN, 1977, Section 2). Advective flow and settling fluxes were described by upstream differencing, which is numerically very stable (NOYE, 1984, p. 190). The general equations are given in Tab. 21; index and sign conventions are depicted in Fig. 51. Boxes are numbered from the lake surface down to the bottom, starting with 0. Positive boundary fluxes signify transport into the system and vice versa. 131

Tab. 21: Equations for then-box model in the general formulation of the MASAS and CHEMSEE models. The numbers below the terms refer to the text and to Fig. 1. Symbols and terms are explained in the text.

CiJ (t): Total concentration of i-th variable in box j in water column [mol/m3] nc: Number of variables in water column i: Variable index L.nc j: Box index O... n 00x-1

dC·· __1_,J + ~- --dCi,jl +--dCi,jl +--dCi,jl + dC··1 dt - dt out dt ad dt diff dt sedi

(2) (4) (5) (6)

nc nc nc

J;j + LRk. ··Ck. + Rk k 1· .. ck .. ck · ,IJ ,J r l> 2· ·l 1J 2.J k=l k1=lk2=lr

(7) (8) (9)

I v~ .·'Yj·C [mol·m-3·d·l] (27) - l ,J i , j - F.l,J ·"Yi

(10) (11)

s CiJ(t): Total concentration of i-th variable in box j adsorbed to POC in mixed layer of the sediment [moJ.gp()1c1

(28)

First order reactions (13) Second order reactions (14) The equations for MASAS and CHEMSEE are obtained with the same restrictions as described in Section 5.3.

General definiti.ons:

Vr Volume ofboxj [m3] Aj: Surface area of top ofboxj [m2] zr Depth of top of box j, [ml below water surface hr Thickness of box j [m]: hj = Zj+l • Zj Distance between centers of box j and box j+1 [m]: dj = 0.5-Chj+l + hj) Ratio of sediment area to box volume for box j [m-1]: Aj+l - Aj Aj+l - Aj (29) Yj A··h· = VJ· = J J

(1) Inflow ofwater

External water inflow into box j [m3·d·l]

This process does not explicitly appear in the model equations. It is used for the calculation of the vertical advective flow.

(.2) Outflow ofwater

dCi · 1 Qoutj . =- V· ·Cj = - rfCj, with (30) T out J rj(t): Hydraulic rate of box j [d·l] Qoutj{t): Water outflow from box j [m3·d·l]

The total flow with the assumption of constant lake volume is given by

llb-1 llb-1 Qin = Qout = L Qin.j = L Qout.j (31) j=O j=O 133

(3) Epilimnion mixing

The mixing of the .epilimnion is calculated using a simple algorithm which does not appear in the differential equations. The epilimnion is defined in the model as a completely mixed water layer reaching from the surface down to the depth of the epilimnion de(t) [m]. The number of epilimnion boxes ne is given by:

1, for de= 0 ne(t) = Satisfies the condition: Zne-1 Znb

The mean concentration is calculated as the volume-weighted average of the concentrations in all epilimnion boxes:

l ne-1 * Ci,j = V · t: Cis·Vr with (33) e j =0 Concentration of variable i in epilimnion after mixing De-1 Volume of epilimnion: Ve= L, Vj (34) j:O

(4) Vertical advective flow

The total mass flux [mol·d-1] from box j-1 into box j due to vertical advective flow is given by

a _ Qvert.c- .. J j - j lJ (35)

To obtain the contribution to the differential equation, the difference between inflow and outflow is calculated and divided by the box volume:

(36)

The following definitions are used:

Vertical flow from box j-1 to box j [m3·d-l] 134

Concentration used for the calculation:

(37)

Vertical flow is calculated by means of the following formula:

j Q:'ert = AQr ' with (38) J . l 1 J= Net external water inflow into box j [m3·d-1]: AQj =Q;nj • Qoutj (39)

Boundary condition:

(40)

(5) Vertical eddy di/fusion

This is calculated from the taylor series of the continuous equation using centered differences (Peacemen, 1977, p.35). 'I'he mass flux [mol/d] from box j-1 to box j is given by:

(41)

Contribution to the differential equation:

dC; 1 d d ~dt·1 =v:

Kj( t): Vertical eddy diffusion coefficient at top of box j [m2/d]

Boundary condition:

d d Kz o = K,. n = 0, or J J = 0 (43) ' 'b 0 = nb 135

(6) Sedimentanon

The mass flux per area from box j-1 to box j due to sedimentation [mol·m·2.d-1] is given by

(44)

For the differential equation, the flux difference must be divided by the box thickness hj:

dCiJl 1 s s dt =h:(F. -F·+1) (45) sedi JlJ

Boundary condition: F~ =0 f;J,p(t): Particulate fraction of variable i in box j Vij(t): Settling velocity for variable i in box j

(7,8,9,13.,14) Net fluxes, first aml secoml order recwtions

The transformation from the continuous equation is straightforward.

Jij(t): Sum of inputs and outputs of variable i for boxj [mol·m·3.d-1] Rk,iJ(t): Overall first order reaction rates in box j [d-1]

Rk 1,k 2,iJ(t): Overall second order reaction rates in box j [moI-l.m3·d-1]

(10) First order flux through sediment-water interfcwe viJs (t): Mass transfer velocity through sediment-water interface for variable i in box j [mid]

(11, 12)

Transformation is straightforward. 5.5. ONE-, TWO- AND COMBI-BOX MODEL

The equations of the one-box and two-box models are a simplification of those employed in the n-box model. The corresponding adaptations are shown in Tabs. 22 and 23, respectively.

The combi-box model uses the two-box model during summer stratification, and the one-box model during winter overturn. Sediment variables are always described with the two-box model, as they are not affected by mixing.

Tab. 22: Equations for the one-box MASAS model. The table lists modifi- cations of then-box equations (Tab. 21). Number of boxes: Lake geometry: Mean depth: ho= Vtot/Ao Surface area of lake: Ao= A(z=O) l.-1 Ratio of sediment area to water volume: 'Y=uo Advection (4) and diffusion (5) terms disappear

Tab. 23: Equations of the two-box model of MASAS. The table lists modifi- cations of the n-box equations (Tab. 21). Number of boxes: Ilb =2 Lake geometry: Eqs. (8t)-(llf) in Tab. 3.11, Ch. 3.8

Eddy dilfu~o~i:J l,:P:•:~:·: .::,:llowing way.

(46) kex,j(t): Water exchange rate for boxes 0 and 1 (j = 0,1) [d-1], with kexj =Qex I Vj (47) Qex(t): Water exchanged between epilimnion and hypolimnion boxes [m3·d·l] Concentration difference: ACi,0 = Ci,l • Ci,O ACi,l =Ci,0 - Ci,l =- Aci,0 137

5.6. MODELING OF PROCESSES IN MASAS

An overview of all processes available in MASAS is given in Tab. 24. Processes for which approximation algorithms or various calculation options are provided are described in detail below. The relation between the calculation of the species in the model and the definition of the processes is explained in Tab. 25.

5.6.1. AIR-WATER EXCHANGE

Air-water exchange, which summarizes volatilization and absorption, is relevant for volatile compounds and is caused by molecular diffusion through the air/water interface. It is frequently described using the two-film model of Whitman (1923). Detailed presentations are given in Chapra and Reckhow, (1983, p. 359fl), Mackay (1985) and Schwarzenbach and Imboden (1984). It is assumed that the dominant resistance to transport is located in two laminar boundary layers, situated on either side of the air-water interface, through which transport occurs only by molecular diffusion.

Gas exchange is characterized by a mass transfer coefficient which deter- mines the rate at which a concentration gradient between water and air is reduced:

dCi,0 I Vi,tot ( Pi C· ) (48) dZ hmix . Hi - i,O gas exchange

Vi,tot: mass transfer coefficient for gas exchange [mid]

Pi: partial pressure of the compound in the atmosphere [atm]

Hi: Henry's law constant for the compound [atm·l·mol-1]

The formulas which have been included in MASAS to calculate Vi,tot are listed in Tab. 26. The options included allow the calculations to be adjusted to data available for the compound and the system, and to the model detail required, such as influences of temperature or wind speed.

Assuming typical values of 0.84 mid for VJ, 84 mid for vg, and 12°C for the lake surface temperature, the relative resistance r in the water film is given by 138

1 VJ r- --- (49) - _!__ - 0.234 atm·l Voot; 1 + H mol

Three ranges of r can be distinguished:

Liquid film controlled: r;e: 0.95 for H> 5 atm+mol·l

Gas film controlled: r ~0.05 for H < 0.01 atm·l·mol·l

Liquid and gas film controlled: 0.01 atm·l·mol·l

Tab. 24 (following page): Overview of all process types available in MASAS. Legend: No: Number of term in Fig. 1 and in differential equations (Tab. 19 and 21). Var type: Type of variable for which the process can be defined: D: Dissolved variable (compound in water); P: Particulate variable (POC); S: Sediment variable (compound in sediment). S: Predefined standard process;+: User defined process, multiple definitions possible; ·: Process not available. Species: Speciation for process of dissolved variable. ftot: total concentration of compound; 1: dissolved neutral species; 2: particle bound neutral species; 2: dissolved anion; 2: dissolved cation; >1: combination of more than one species possible. Approx: Approximation of process rate based on available system and compound data: I: Process rate directly calculated based on user settings from system and compound data during simulation. II: Approximation of process rate based on system and compound data in special window; manual copy of value to be used for simulation into model window. Var type Species (D) Approx Process type ti> Process parameters Unit DjP!S tot 314 >1 I II 1 Vertical eddy diffusion 5 Automatically coupled to system data - s s - x 2 Vertical advection 4 Automatically coupled to system data - s s - x 3 Sedimentation 6 Automatically coupled to system and compound data s s - x 4 Lake outflow 2 Automatically coupled to system data - s s - x 5 Particle input and burial 6,12 Automatically coupled to system and compound data - - s 6 Zeroth order reaction 7 Zero order constant mass·m·3.d·1 + s + x 7 First order reaction 8,13 First order rate constant d-1 + - + x x x x x x 8 Second order reaction 9,14 Second order rate constant m3.mass·1.d·1 - - - x x x x x x 9 Sediment flux, zeroth order 7 Sediment flux coefficient mass-m ·2.d· 1 + - - x 10 Sediment flux, first order 10 Mass transfer coefficient m-d-1 + - . x x x x x x 11 Surface flux, zeroth order 7 Surface flux coefficient mass·m ·2.d-1 + . - x 12 Surface flux, first order 8 Mass transfer coefficient m-d-1 + . . x x x x x x 13 Air/water exchange 8 (7) Mass transfer coefficient m·d·1 + - - x x x . 14 Loading from inflow 7 Concentration in inflow (mass/m3) mass-m·3 + s x (concentration) Inflow level (0 ... 2) - 15 Loading from inflow (mass/d) 7 Mass in inflow (massld) mass·d-1 + - - x Inflow level (0 ... 2) . 16 Input in boxes (mass/d) 7 Input in given depth (mass/d) mass·d-1 + - - x Level (minimax) m 17 Resuspension of adsorbed 11 Resuspension rate d·1 + - + x substance (') 18 Hydrolysis 8 Hydrolysis rate constant d·• + - x x x x x x 19 Direct photolysis 8 Direct photolysis rate constant d·1 + - - x x x x x x 2) Biological degradation 8 Biological degradation rate constant d-1 + . - x x x x x x 21 Pseudo first order reaction 8 Pseudo first order rate constant m3-mass·1.d·1 + . - x x x x x x Concentration of reaction partner mol·m·3 Tab. 26: Calculation of air/water-exchange in MASAS. Further explanations of the symbols are given in Tab. 32. a. Parameters at the highest hierarchical level Symbol, ldent [Unit] Ref. Overall mass transfer coefficient Vtot. mtc [mid] (1) Direct entry of a constant or a time series 1 1 1 (2) Calculation with two-film model -=-+-, [1,2,3] Vtot vr vg

Liquid-film mass transfer coefficient v1, vi [mid] ( 1 ) off: neglect liquid-film resistance v,=oo m o (2) on: include liquid-film resistance v1 = r1 • v1 [2] Typical value: 0.86 (true values usually within a factor of 5) [1]

Gas-film mass transfer coefficient (normalized) Vg', vgR [mid] (1) off: neglect gas-film resistance Vg' = oo

(2) on: include gas-film resistance Vg'= Vg. Ra-(T +Ko) [2] Gas-film mass transfer coefficient Vg, VQ (mid) n w Typical value: 86 (true values usually within a factor of 5) · (2, 1] v9 = r9 v9

Henry coefficient H', hgex [atm·l·Mo1-11 (1) (1) Direct entry of Henry coefficient (2) Compound library, constant value H' = H see remark (1) (3) Compound library, with temperature dependence (Eg. 50) H'= TF( aH. bH, T) [4] (4) Compound library, approximation with vapor pressure and aqueous solubility H'= p Is (4] (5) Same as (4), with temperature dependence (Eg. 50,Tab. 32) H'= TF( ap-as. bp-bs. T) Tab. 26 (continued) a. Parameters at the highest hierarchical level (continued) Symbol, ldent [Unit] Ref.

Surface temperature (2) T, tgex [°C] Typical value (Swiss lakes): 12°C Direct entry of a constant value

Wind speed at 10 m above water surface u10, u10 [m/s] Direct entry of a constant value b. Parameters for liquid-film layer Symbol, ldent [Unit] Ref. Mass transfer coefficient for oxygen in water v~, vlo [m/d) (1) Direct entry as constant or as a function of u10 (2) Empirical function of wind speed u10 v~ = 0.35 + 0.035·u102 [5]

, Ratio of molecular diffusion coefficients compound/oxygen r1 rl [·] (1) Direct entry as constant; typical value: 0.35 [6] (2) Calculation based on diffusion coefficients

(3) Same as (2), with temperature dependence (Eg. 50) r = TF( a - a bo - b T ) I 0 I 0°I' I 0°I' -(~)0.59 (4) Approximation based on molar volume r1- Vb" [7]

13.26·1 o-5 (5) Approximation of D using revised Othmar-Thakar D------[7] 1 1- µw(T)1.4. yb0.589

DI equation, with temperature dependence (Eg. 50) r1=TF( a0 f. bof, T)

Exponent m, el [·] Direct entry as a constant: two-film model: m=1, surface renewal model: m=0.5 [2] Tab. 26 (continued) c. Parameters for gas-film layer Symbol, ldent [Unit] Ref. Mass transfer coefficient for water in air v:, vgw [mid] (1) Direct entry as a constant or as a function of u1 o (2) Empirical function of wind speed u1 o V~ = 864·(0.3 + 0.2·U10 ) [5] Ratio of molecular diffusion coefficients compound/water rg, rg [·] (1) Direct entry as constant; typical value: 0.27 [6] (2) Calculation based on diffusion coefficients [2] r9:D9 /D: (3) Same as (2), with temperature dependence r = TF( ao - aow. bo - bow, T) g g g g g Exponent n, eg [-] Direct entry as a constant: two-film model: n=1, surface renewal model: n=0.5 [2] d. Parameters from compound library (see Tab. 3.12p): H, p, s, D , D , vb 1 9 e. Constants Symbol Unit Value Ref. Absolute temperature Ko [K] 273.15 Gas constant Ra [atm·l·mol·1.K-1] 0.08205 R J·mot·1.K-1] 8.31441 Do Molecular diffusion coefficient of oxygen in water at 12°C I [cm2/s] 1.6·10·5 [8] a: coefficient for calculation of D~(T) ao~ [K] 1066.1 [9) b: coefficient for calculation of D~(T) boO -1.052 [9] ! H Dw Molecular diffusion coefficient of water in air at 8°C g [cm2/s) 0.239 [10) a: coefficient for calculation of D:(T) aow [K] g Tab. 26 (continued) e. Constants (continued) Symbol Unit Value Ref. b: coefficient for calculation of D~(T) boW [-] g Molar volume of oxygen ~ [cm3/moij 27.9 [7] Dynamic viscosity of water (for 12°C) ~ [cps]=[1 03-kg-s-1 ·m-1] 1.235 [11] Calculation of µwas a function of temperature T, using the following function for ~(T) (validity range: 0-20°C): IOQ1o(µw) = _ + . _(;-~~; + . .(T- ) - 1.300233 998 333 8 1855 0 00585 20 2

(1 > H: Henry coefficient in compound library H': Henry coefficient used for calculation of air/water exchange (2) The heat capacity of water is much higherthan the heat capacity of air. It can therefore be assumed that the atmospheric boundary layer has the same temperature [12].

References: [1] Mackay (1985) [2] Schwarzenbach and Imboden ( 1984) [3] Chapra and Reckhow (1983), p.359. (4] Schwarzenbach (1990). [5] Schwarzenbach et al. (1991) [6] Fig. 2.3 in (2] [7] Hayduk and Laudie (1974) [8] CRC Handbook of Chemistry and Physics, 53rd Ed. [9] Himmelblau (1964) [10] In [8]. p. F-51. [11] In [8], p. F-40. [12] Wolff and van der Heidjde (1982) 144

5.6.2. DIRECT PHOTOLYSIS

Direct photolysis and the corresponding approximation procedures are not yet implemented in the first version of MASAS. When needed, direct photolysis can be modeled as a first order degradation process with a user-defined constant or time-dependent degradation rate. The implementation of photolysis, planned for the next program version, will be based on Leifer (1988).

5.6.3. FIRST ORDm REACTIONS

First order reactions, including sensitized photolysis, hydrolysis and biological degradation, contribute to term (8) of the model equations (Tab. 19 and 21). If species are involved, rates are transformed according to Eq. 25. The calculation options for first order reactions based on system and compound data are given in Tab. 27 (pseudo first order reactions), Tab. 28 (hydrolysis) and Tab. 29 (biological degradation).

(Tab. 26: pp.140-143)

Tab. 25: Relationship between species calculated for the compound (defined by the submodel type) and species required for a given process (defined within the process definition). The table lists how the processes are calculated for all combinations of speciations including cases where a species required is not calculated.

Definition of the Speciatlon of the compound (submode! type): process None (Ctot only) With species

Total concentration (c101) Use total concentra- Use total concentration to included in process tion to calculate calculate process process 1 ... many species Process excluded Include species which are both included in process from model included in speciation of com- (processes# 7, 10, 12) pound and in process definition. Other species excluded. One single species Process excluded The process is included in the included in process from model model only if the concentration (process # 18-21) of the species included in the process is calculated by the current submode! type. Tab. 27: Calculation of pseudo first order reactions in MASAS. Further explanations of the symbols are given in Tab. 32. Formula/Symbol Unit a. First order rate constant for differential equation Formula used: b. Calculation options for IC 1 (second order rate) (1) Direct entry of a constant (optionally time/depth dependent) k1 =direct entry

Sensitized photolysis: (2) Compound library, reaction with singlet oxygen, constant k1 = k102 (3) Compound library, reaction with OH-radical, constant k1 =kQH b. Calculation options for [X] (Reaction partner) [X] [mol·m-3] (1) Direct entry (constant, or time/depth dependent) [X] = direct entry Tab. 28: Calculation of hydrolysis reactions in MASAS. Further explications of the symbols are given in Tab. 32. Formula/Symbol Unit Ref. a. First order rate constant for differential equation Calculation options: (1) Direct entry (constant, or time/depth dependent) kh =direct entry (2) Compound library, observed first order rate constant kh = kobs (3) Acid, neutral and alkaline hydrolysis: kh = ka·[H+] + kn + kti·[OH·]

with: acidity (system library, current model) [H+] = 1o·PH [mol·m-3] b. Temperature dependence (options 2 & 3): Calculation options: (1) k (stands for kot,s, ka. kn. or kt,) constant k =constant (2) k calculated using F2 (Eq. 51 in Tab. 32) k = F2(r k Ea,k, Tk, T) with lake temperature from system library for current model type T [oC] c. Calculation of [OH·] for alkaline hydrolysis (option 3): [OH·]= 10(pH-pf

[1] Stumm and Sigg (1989), p. 45. Tab. 29: Calculation of biological degradation in MASAS. Further explications of the symbols are given in Tab. 32. Formula/Symbol Unit a. First order rate constant for differential equation Calculation options: (1) Direct entry (constant, or time/depth dependent) kb= direct entry (2) Compound library, constant value ~=~io (3) Compound library, with temperature dependence: calculation of the reaction rate for the lake temperature T (°C] given in the system library for the current model, using F2 (Eq. 51 in Tab. 32) 149

5.7. SYSTEM (LAKE) DATA OF MASAS

The system parameters describe the geometry, catchment area, hydraulics, sediment, particle balance, and chemical and physical characteristics of the lake (Tab. 30). The general system parameters are independent of the particular computer model, whereas the model- dependent parameters are compiled especially for a particular model. Certain parameters are calculated, either automatically or on request, from basic parameters (Tab. 31).

The complete dataset for a lake is stored on system library files, one file for each dataset. In the following, we will briefly explain how available lake data were processed for the system library files of the Swiss lakes.

Geometry, catchment area and flow parameters:

For most Swiss lakes, these data were directly available (Biihrer, 1988, Liechti, 1988). Flow parameters, which were not available, were determined based on data from water-gauges situated at the lake outflows. Detailed information about the flow regime (plunging inflows, subsurface sources or outlets) was not available, and therefore inflow and outflow were assumed to occur at the lake surface. Precipitation and evaporation are roughly equal under the climatic conditions prevailing in Switzerland, and were therefore neglected.

Chemical and physical parameters:

Temperature, acidity (pH), oxygen and conductivity profiles were available for the majority of the lakes; POC, particle and sediment data on the other hand were available only for a few lakes. These data were usually measured at regular (monthly) intervals at the same depths. For the system library, average profiles for each month of the year were calculated as the arithmetic mean of all available raw data profiles for a given month (example: mean April profile = mean of all profiles measured in April). Mean values for pH were calculated using H+-concentrations (lQ-PH).

When data were available over a sufficiently long period, maximum and minimum values were also extracted by taking the highest/lowest value which occurred for each month at each depth. 150

Depth of the epilimnion

Diffusive and advective water mixing processes in the surface layer create a well mixed zone of variable thickness, called the epilimnion. For a given sampling date, the depth of the epilimnion was determined in the following way:

The epilimnion depth was roughly fixed by eye or where the gra ·- ents exceeded a certain value (usually with graphical representa· tions). Gradients for typical parameters used were: • temperature: 0.5 °C·m·l • oxygen: 0.5 mg·l·l.m-3 • conductivity: 5 µS·cm-1.m-1 In general, the epilimnion depth was determined for several parameters measured on the same date

In the case of different epilimnion dept s for the various parameters, the smallest value was taken.

T e mean epi imnion ep y or by tak· ing the arithmetic mean over several ears. 151

Vertical eddy diffusion coefficient

Vertical eddy diffusion coefficients were calculated from temperature profiles by means of the flux gradient method with depth-dependent isobath areas (heat budget method, Imboden et aL, 1979). Mean diffusion coefficients were calculated using averaged temperature profiles . This method can be used under the assumption that all heat entering deeper layers of the lake is transported by vertical eddy diffusion (no advective heat transport, no significant geothermal heat flux). Based on the change of heat content occurring between time ti and 1'i+1 of the entire water body below a given depth, the vertical eddy diffusion coefficient necessary to transport this amount of heat can be calculated.

The procedure used is as follows:

Calculation of diffusion coefficients from temperature data (flux radient method im lemented in CHEMSEE)

Ne ative values dro ort

Values in the range of the molecular heat diffusion coefficient (approximately l.2· 10-3 cm2·s·l) are not reliable and were set to 10-2 cm2·s·l

Implausible values in surface layer (usually down to 5-6 m) dropped (not reliable due to solar radiation and subsurface inflows)

To most reliab e va ues extra

dropped (too small

If the majority of the values in a profile were eliminate during the previous procedure, the entire profile was dropped (non-diffusive heat trans ort, errors in calibration of tern erature data)

Missing values replaced by values interpo ate from neigh oring or t ical values

Unrealistic details in the Tab. 30: System (lake) parameters of MASAS. which are only calculated on user request are Full names, symbols used in formulas, identi- marked with (*). Lib: Parameter is included in fiers used in the MASAS program, and the unit library file. ()indicates optional inclusion. RO: are given for each parameter. Read-only parameter. Description: Topic characterized by the parame- Model type: Gen: general system parameter; 1, ter. Gen: general data; Ctch: catchment area; 2, C, N: model dependent system parameter Hydr: hydraulics; Part: particles; Sedi: sedi- (one../two-/combi-/n-box model). ment; PC: physical and chemical parameters. # Model type identifier. 1: one-box; 2: two- Type: Possible parameter definitions. C: con- box; 3: combi-box; N: n-&x. stant; CA: constant array (1 value given for $ Inflow level identifier. 0 ... 2: Inflow 0 ... 2; each box); TS: time series; PR: profile; TSEH: 3 ... 5: Outflow 0 ...2. two time series for epi/hypolimnion; TSPR: ** Parameter currently not used. Flow pa- series of profiles. n: n-box only; 2: two/combi-box rameters for two/combi-box model calculated only; 2n: two/combi- and n-box only. from general flow parameters. Options: Cale: Parameter calculated using a formula (number refers to Tab. 31). Parameters Description Type Options Model Type Name Symbol klent Unit GC Hp s p cc T p T TC L F ( 1 2 CN e t y a e c AS RS s a i Ce n c d r d E p I b n h r t i H R c

General system parameters Name of lake x x x Total Volume Vtot veil m3 x x x x Surface Area of Lake Aa sl m2 x x x x Maximal Defjh hmax hl m x x x x Fk:lw O=Oin=Oout q L m3/s x x x x x Hydraulic Rate r rL 1/d x x 1 x x x Mean Residence lime of Water 't taul y x x x 2 x x Catchment Area (Exel. Lake) - ctchl km2 x x x x Number of inhabitants - inhl x x x x Flow parameters Maximal Numoer 01 lntuumow Levels MFL - x constant= 3 x Number of Inflows (nlnflow) - nk1 - x integer number 3 x x Number of Outflows (nOutflow) - nOut - x integer number 3 x x ln-/Outflow on Level O q0/q3 m3/s x x x 4 x x Oin,O Oout,O ln-/Outflow on Level 1, 2 q1 ,2/4,5 m3/s x x x x x 0111,1,2 Oout,1,2 Level Borders (up/low) 0, 1, 2 uln$ m x x x x Below Epilimnion (not yet implemented) bEpi x boolean (true/false) x Tab. !3Q (i;:Qntinu~dl Description Type Options Model Type Name Symbol !dent Unit Ge Hp SP cc T p T TC L F <: 1 2 c N e t y a e c AS RS s a i Cle n c d r d E p I b n h r t i H R c

Sediment parameters nge e wit rlying x rex x xx xx xx (x x Mass of Mixed Sediment Layer M mMixL g/m2 x xx xx xx (x) x Preservation Factor Beta b betaL x xx xx xx (x) x Suspended Solids/Porosity porl g/m3 x xx xx xx (x) x Model dependent parameters: One-box Box Volume Vo VOil ms x x 5 x x Box Surface Area Ao sl m2 x x 6 x x Box TI!ickness {mean depth) ho h1 m x x 7 x x Model dependent parameters: Two- and combi-box Box Volume (epi/hypolimnion) Vo,1 vol# m" x x 10 x x x Box Surface Area (epi.'hypolimnion) Ao,1 s# m2 x x 8 x x Surface Area of Hypolimnion A1 shy# m2 x x 9' x x x Depth of EpiHmnion de ed# m x x x x x Box TI!ickness (epilhypolimnion) ~ h# m x x 11 x x x Inflow into Epilimnion (fraction)" . fin# x x x (x) x x Outflow from Epilimnion (fraction)" . tout# . x x x (x) x x Vertical Exchange Flow Between Boxes . qex# m3/s x x x x x x Switch Time to One-!Two-Box Model . sw1, sw2 d x x x Tab. 30 (~ntinllll1:U Description Type Options ModelType Name Symbol kfent Unit GC Hp SP cc T p T TC L Ii c 1 2 c N e t y a e c AS RS s a i c e n c d r d E p I b n h r t I H R c Model dependent parameters: N-box Box Volume Vj volN m:s x x 15 x x Lake Cross Section Aj sN m2 x x x x x x x Eddy Diffusion Coefficient Kz kexN cm2/s x x x x x x x x x Depth of Epilimnion de edN m x x x x x Box Thickness hj hN m x x x 13 x x Depth of Box Borders Zj dbN m x x x 12· (x) Depth of Box Centers ~ dcN m x x x 14 x Zj Default Number of Boxes - defltNbox - x integer number x x Eddy Diffusion Coefficient Kz kexN cm2/s x x x x x x x x x Model dependent parameters for all model types Temperature I temp# ¢(; x x n x n 2n n 15· x x x x x pH (acidity) pH pH# - x x n x n 2n n 17' x x x x x Oxygen Cl2 o2# g/m3 x x n x n 2n n 16* x x x x x Conductivity cond# µSiem x x n x n 2n n 16* x x x x x Sedimentation Veloclty v vs# mid x x n x n 2n n x x x x x Particulate Organic Carbon in Water cpoc poc# g/m3 x x n x n 2n n 16' x x x x x Fraction of Organic Carbon in Particles fpoc foe# x x n x n 2n n 16' x x x x x 156

Tab. 31: Formulas used for the calculation of MASAS system parameters. Numbers in the first column refer to Tab. 30. G enera syst em parame t ers Formu a c om men t 1 Hydraulic rate r = 0 I Vtot 2 Mean residence time of water 't= r Specific flow Darameters 3 Number of in-/outflows All ln-/outflows-:;:. Oare automatically counted. Maximum: 3 4 ln-/Outllow on level O Oin,o = Q. Oin,1• 0.,,2 Sum of partial flows equals to- ~ ~ ~ tal flow, automatically eva- Oout.O = 0 · Clout, 1• Oout,2 luated for time variable flow. One-box model 5 Box volume Vo= Vtot same program variable 6 Box surface Ao= Ao same program variable 7 Box thickness ho= Vo r Ao mean depth of lake Two-/combl-box model 8 Box surface of epi· Ao= Ao Value copied to another fhypollmnion A1 = A1 (shy) program variable 9 Surface area of A1 = A(de) Optionally calculated from lake hypolimnion cross section 10 Box volume of epi· Volume formula for frustrum of Vo= 3·(Ao+A1+ Ao·A1 ) /hypolimnion de "- cone V1 = V101 ·Vo 11 Box thickness of epi- ho=de /hypolimnion h1 =V1 I A1 N-box model 12 Depth of box borders . hirac Boxes of equal thickness; can ZJ=J· nbox be overwritten to obtain boxes of variable thickness. 13 Box thickness hi= z j-1 • z J 14 Depth of box centers Not shown in system window Zj=0.5· (Zj+Zj+1l 15 Box volume VJ = Volume formula for frustrum of cone h 1·k(Aj + Aj+1 +'1 Aj·A1+1)

Parameters for all model t es 16 Temperature, Oxygen, Optional calculation of volume-weighted average for one-/ two·/ Conductivity, POC, fpoc, combl·box model from n-box data (profiles for parameter and initial values lake cross section). 17 H acidi same as 16, exce t use of 10·PH for calculation of avera e. 157

5.8. MASAS COMPOUND DATA

Compound parameters include identification, physico-chemical information and reactivities (Tab. 32). Optional input of distinct reactivities for charged (cation +1, anion -1) and particulate bound species, and of additional information required for the calculation of the temperature dependence of coeffi· dents and reactivities is possible. A comment, typically including the reference and further details, can be stored for all parameters.

Information about each compound is stored on a library file. Its compilation is straightforward: information about the compound is collected and verified. When necessary, it is converted to the appropriate units, auxiliary coefficients for the calculations of temperature functions are evaluated from raw data, and all the information is entered interactively into MASAS and stored on the file.

In the analytical chemistry group at ETH Zurich, a project is in progress which deals with the compilation and build-up of compound data bases including property and variability estimation methods (M. Farkas and E. Pretsch, pers. comm.). Close contact exists and the outcome of the project will be available for the MASAS system. 158

Tab. 32: MASAS compound parameters. Full names, symbols used in formulas, identifiers used in the MASAS program, and the unit are given for each parameter. Type: C: constant; CO: coefficient; CX: coefficient, with information for calculation of temperature dependence; RR: reaction rate, with information for calculation of temperature dependence (£ and $ stand for symboVidentifier of corresponding parameter). Lib: Parameter is included in library file. To keep the identifiers (ident) of the reactivities unique, the following suffixes were added: D reactivity for dissolved neutral species P reactivity for particle bound, neutral species A reactivity for dissolved anion C reactivity for dissolved cation

Type

Name Symbol I dent Unit ng1i1=1~ Compound Identification Compound Name Text Synonyms Text CAS-Nr. (Chemical abstract number) Text x Sum-Formula Text Physico-Chemical Parameters Molar Mass mm MM x x Melting Point Im tm oc x Boiling Point lb tb oc x x Pressure for Boiling Point Ptb ptb atm x Molar Volume vb mv cm3·mo1-1 x x Molecular Diffusion Coefficient in Water D1 dw cm2·s x Molecular Diffusion Coefficient in Air Dg da cm2·s x Vapor Pressure p p atm x Aqueous Solubility s s mol·1·1 x x Henry Coefficient H H atm+mol·1 x Octanol Water Partition Coefficient Kow Kow log() x Partition Coefficient Organic Carbon/Water Koc Koc m3·9oc·1 Maximal Number of Acidity Constants NpKaMax constant= 3 Actual Number of Acidity Constants npKaTot - integer Acidity Constant 0... 2 PKa.0 .. 2 pKa0 ... 2 . x Highest Positive Charge nNeutral . integer 159 Tab. 32

Name Symbol ldent Unit Reactivities for species: Dissolved neutral/Dissolved anion, -1/Dissolved cation,+ 1/Particle bound, neutral Acid Hydrolysis ka ka m3·mo1·1.d-1 x x Neutral Hydrolysis kn kn d·1 x x Alkaline Hydrolysis kb kb m3. mol ·1.d-1 x x Maximal Number of Observed Hydrolysis Rates NkObsM constant= 5 ax Actual Number of Observed Hydrolysis Rates nkObs integer Hydrolysis Rate Observed o... 4 kobs,0 .. 4 kObs0 .. 4 d·1 x x Maximal Number of General First Order Rate t-1<1GenMax • constant= 5 Constants Actual Number of General First Order Rate nk1Gen integer Constants General First Order Rate Constants 0.. .4 kgen,0 .. 4 kGen0 .. 4 d·1 x x Light Absorption £}.. eps m3·mo1-1.m-1 x Quantum Yield (Phi) Phi x Reaction with Singlet Oxygen k102 k102 m3·mol·1 ·d·1 x Reaction with OH-Radical kQH· kOH m3·mol·1.d·1 x x Biological Reaction kbio kbio d·1 x x

Temperature dependent coefficients and reaction rates: Coefficient t e CO : Temperature T £ T$ °C x Coefficient (type CX}. Additional parameters allow the transformation of the coefficient to another tern erature: Temperature T£ A Coefficient a£ A$ K x B Coefficient b£ 8$ a Formula (Schwarzenbach, 1990): F1(a,b,l) =k(T) =10 (b· T+Ko) (Eq. 50) Ko= 273.15°C Reaction rate (type RR). Additional parameters allow the transformation of the reaction rate to another tern erature: Temperature T £ T x Activation Energy Ea E$ J·mo1·1 x

Formula(Arrhenius): F2(r, Ea, To, l) = r(T} = (Eq.51) 160

6. IMPLEMENTATION OF CHEMSEE

This chapter gives an overview of the implementation of CHEMSEE. More details are given in the user's manual (Ulrich, 1989).

6.1. USER INTERFACE

CHEMSEE has seven menus (Fig. 52, top) and various entry forms, and it can display an arbitrary number of graph windows. The program has three states (Fig. 52, bottom): Standby (normal state; program awaiting user action, all menu commands available), Simulating (all edit functions disabled, menu access restricted), and Pause (simulation interrupted, certain edit functions enabled·, menu access restricted). The menu commands are briefly explained in the following (Remark: "... " after a command text indicates that the command activates an entry form):

MENU FILE

Open ... (3&-0): Read a model file into the program (lake parameters, variables, processes, graph scaling, box number, simulation settings). Save ... (3C-S): Save the model information into a file. Quit (31:-Q): Terminate program.

MENU SYSTEM

Depth ... : Define maximum lake depth. In- and Outfiow ... : Define lake inflow and outflow at up to three depth levels. Sum ofinflow and outflow must be equal. Cross Section A(z) ... : Define isobath area of the lake (cross-section) as a function of depth. Epilimnion Depth ... : Define depth of the epilimnion.

Menu bar (top) and program states (bottom) of CHEMSEE. For each program state, the access status of the menu commands is shown in the reduced menu bar (- command available, command disabled). Transition commands are indicated as arrow labels. 161 Fi&:;. 52

Open... KO Depth ... New llnrlable ... S1111e... KS In- and Outflow ... Define Particulate Organic Carbon ... Cross Section ll(z) •.. Change uarlable ... Quit KQ Eplllmnlon Depth ... Chonge lnlth:ll/Statlc ualue ... llertlcal Dltruslon Coefficient ... Delete Uerleble ••• Sediment ... Delele AU llorinbles ••• Temperature ... llERDI

New Process... i!tP Define Output File ... Slmulnllon Set Up .•• Chnnge Process ... Edit Process Constant ... New Graph... OOG Start Run OOH Delete Process ••• Mollify Graph ... Pause/Resume OOE Lock/Unlock Graph KL Stop KT Delete Rll Processes ... Cle11r6r11ph

Show Graph Set Tile Windows

Clear All Graphs ~B

End of simulation (t =tend) Start Run or Stop 162

Vertical Diffusion Coefficient ... : Define coefficient of vertical eddy diffusion. Sediment ... : Enter sediment parameters. Temperature ... : Define temporally variable lake temperature for the estimation of the coefficient of vertical eddy diffusion. VERDI: Estimate vertical eddy diffusion coefficients by means of the flux gradient method (p. 151). The raw result is stored in the program variable used for the eddy diffusion coefficient.

MENU VARIABLES

New Variable ... (3£..N): Define a new model variable. Define Particulate Organic Carbon ... : Define POC as model variable. This variable is a prerequisite to define further model variables which describe either a dissolved compound with adsorption to POC or a compound in the sediment. Change Variable ... : Change settings of an existing variable. Chance Initial/Static Value ... : Change initial value/static value of a dynamic/static variable. Delete Variable ... : Delete model variable. Not possible as long as the variable is utilized in a process. Delete All Variables ... : Delete all model variables.

MENU PROCESSES

New Process ... (X..P): Define a new process. Change Process ... : Change settings of an existing process. Edit Process Constant .... : Change process constant. Delete Process ... : Delete process. Delete All Processes ... : Delete all processes. 163

MENU MODEL

Set Number of Boxes ... : Define vertical model resolution in terms of the number (between 1 and 1024) of boxes of equal thickness.

MENU OUTPUI'

Define Output File ... : Choose variables for which an output file should be written during a simulation. New Graph ... ('i)i..G): Create a graph window to display the simulation results for one variable (profiles). For each model variable, an arbitrary number of graphs can be defined. Modify Graph ... : Change graph scaling, title and grid of active graph window. Lock/Unlock Graph ('i)i..L): Conserve or reactivate active graph window. Clear Graph: Clear active graph window. Show Graph Set: Create a new graph window for each model variable. Tile Windows: Arrange all windows neatly on the screen Clear All Graphs ('i)i..B): Clear all unlocked graphs.

MENU SIMULATION

Simulation Set Up ... : Define start and stop time and further simulation parameters. Start Run (00-R): Start simulation run. Simulation time is displayed in top right corner of active window. Sets program state to Simulating. Pause I Resume (00-E): Interrupt/reactivate simulation run. Sets program state to Pause or Simulating. Terminate ('i)i..T): Abort simulation run. Sets program state to Standby. 164

6.2. MODULAR STRUCTURE

The program CHEMSEE is composed of five functional units, called modules in Modula-2 (Fig. 53). Four of these are library modules, providing specific data structures and procedures used in higher modules, and the fifth is the main program.

The program is based on the DialogMachine (Fischlin, 1986). This program library provided the procedures for the processing of user actions (event loop) and specific procedures to build the user interface, including menus, entry forms, windows and graphs, file input/output, etc.

CSB(Base)

This is the principal base module which provides the data structures for dynamic arrays, parameters, variables and processes, and the procedures for their management.

Dynamic arrays:

In contrast to usual array variables which have a fixed number of elements, the size of dynamic arrays can be changed while the program is running. Such arrays are used for all data with variable size (time series, data tables, variables for the n-box model). They are implemented with pointers and are therefore called RAP (R eal A rray P ointer). The RAP arrays used for the n- box model, e. g. for the variable's value in each box, are managed with special procedures. The procedure lnitBoxRAP (Initialize Box RAP) creates a new dynamic array of real numbers with nBox (current number of boxes) elements and keeps track of all box variables. When a user changes the number of boxes, the procedure NewBoxNumber is called. It changes the size of all dynamic box variables used anywhere in the program, using the list maintained by lnitBoxRAP.

Parameters:

Parameters are used for lake data (e.g. lake isobath area, epilimnion depth), for compound data (MASAS only), for initial and static values of variables, for process constants, etc. In the programmer's language, a parameter is a pointer (address) to a data record, and a record is a structured data type (Wirth, 1986), which groups variables of most different types. The computer 165

Entry forms with Definition and modification of associated functions graph windows File input'output Window/file output procedures for simulation results

Definition and modification of parameters, Installation Definition variables and of mouse and processes handlers modification of Procedures for per- parameters, forming actions on all variables parameters/variables and processes Change of box number Heat budget method (VERDI) -----i--+-1-...:::;D.::::at"""a.:::St:..::ru"'"'ct""'ur""es'""'

DMMaster (Program control) OM Files DMMenus DMEntryForms DMWindowlO DMWindows DM2DGraphs OM Files DMEntryForms

&.filt Modular structure of the lake simulation program CHEMSEE. Each box represents a Modula-2 program module. The arrows show the principal relations between the modules. The program is hierarchically organized with four base modules providing different program elements and the master module which combines these elements int.-0 one functional unit. The program is based on the DialogMachine (Fischlin, 1986), which provides basic elements for constructing the user interface. 166

memory to store this data record, Par Rec, is reserved (allocated) dynamically at the moment when the new parameter is defined, and deallo- cated when the parameter is no longer required.

For the non-programmer, it is sufficient to know that a parameter is an object which only exists in the program as long as it is actually being used and which holds all information pertaining to it: - Parameter identification, with string variables (name, unit, short identifier, comment) - Input data (real numbers), with the following options: - Constant - Box Constant (MASAS only) - Time Variable - Depth Variable - Time Variable, Epi-/Hypolimnion - Time & Depth Variable - Output data for boxes (dynamic arrays of real numbers, size determined by number of boxes). These data are calculated by means of linear interpolation from the input data for a given time and box in the model. In this way, input data are decoupled from the data used for the model. - Interpolation table and auxiliary information for higher efficiency during interpolation. - For MASAS parameters, some additional information is stored, including a flag for distinguishing between array (n-box) and scalar (one- and two- box models) parameters, the procedures for calculating the value of the parameter and of any dependent parameters, and display information.

Variables:

These objects are used for dynamic and static model variables. They are organized like parameters (pointers, records), each record holding the following information concerning one variable: - Variable identification (name, identifier, unit) - Variable type (dissolved, particulate, sediment I dynamic, static) - Value of the variable (s, dynamic array) - Derivative of the variable (ds, dynamic array). Used for numerical integration (Figs. 55 and 58). Speciation 167

- Boundary conditions - Processes in which the variable is involved (dynamic list, MASAS only) - Scaling for graphs

Processes:

Process is the data type used for processes. Each process object holds the following information: - Process identification (name, identifier) - Process type (for MASAS see Tab. 24, p. 139) - Process contribution to differential equation (dynamic array dSik• Figs. 55 and58) - Process constants (stored as parameters) - Variable(s) used in the process, and their speciation - Trigger variables - Stoichiometry - Procedure to calculate the process contribution dSik (MASAS only)

Management procedures

A set of management procedures is provided for each of the four data types, or objects, discussed. Let us illustrate this for the type variables: the procedure NewVar(VAR u: Var) defines a new variable, DelVar(VAR u: Var) and DelAllVar delete one or all variable(s). Further procedures allow actions to be performed on all variables or on a subset of them. DoForAllDynVar(p: VarProc), for instance, executes procedure p for each dynamic model variable. This is used together with CS.SetlnitVal(u: Var), which sets the initial value for one model variable. DoForAllDynVar(SetlnitVal) automatically sets the initial values of all currently defined model variables.

Global variables and procedure VERDI:

The system variables interactively defined by the user (lake depth, sediment parameters, flow parameters, etc.) and the derived auxiliary variables calculated by the program (box volume, relative sediment area, flow parameters for each box, etc.), simulation and output variables (time, start time, output interval, etc.) are provided as global variables by CSB. 168

The procedure VERDI for the calculation of vertical eddy diffusion coeffi· cients by the flux gradient method (p. 151) is provided to estimate values for diffusion coefficients from temperature profiles.

CSMousellandler This module, together with the DialogMachine, organizes the mouse handlers. These procedures are called automatically whenever the mouse is clicked in a window. The BringToFront-Handler, for instance, initiates the redrawing of a hidden window, which is reactivated with a mouse click.

CSIO (Data input/output)

This module contains all entry forms used in the program. Each entry form is programmed in one procedure, which typically has the following structure:

(I) Define elements to be displayed, including default values and options, using procedures of the DialogMachine (labels, data fields, check boxes, radio buttons, etc.). (II) Display the entry form (procedure ofDialogMachine) (III) Perform actions requested, e. g. definition of a new variable, when user clicks OK.

Further, the module provides the procedures for file input/output (pa· rameters, model files) with the exception of simulation results.

~ Simulation loop of CHEMSEE. This figure shows the main calculations performed during a simulation run. It is divided into three parts: I. Initialization, II. Simulation loop, III. Termination. All elements are located in the program module CS (Master), except: Ill CSO( Window output), Ill DMMaster (DialogMachine). *) for variables with both dissolved and particulate fractions. 169

Start

Initialize variables for simulation f. lnlllallzatlon

Initialize model: 1 Epilimnion 2 All parameters (lake, processes) 3 State variables (initial values) 4 Static model variables (predefined values) 5 Boundary condition for state variables 6 Distribution coefficients for state variables •)

Initialize and generate output

II. Simulation loop

Process user actions yes

Calculate derivatives: 1 Implicit processes 2 Explicit processes

Integrate models: 1 Euler integration algorithm with variable step size 2 Update time ( t:= t + di )

Calculate model parameters: 1 Calculate thickness and volume of epilimnion and number of boxes included in epilimnion 2 Calculate parameters depending on epilimnion (if epi. changed) 3 Calculate time dependent parameters 4 Update static model variables 5 Calculate epilimnion mixing 6 Calculate boundary conditions for state variables 7 Calculate distribution coefficients for state variables *)

--::::::::::::::::::~~~!::::::::::::::::::::::=:y:es~GenerateOutput ? output no...-~~~~~~~~~~~-- no End of simulation (t:tend) I simulation sto d (by user yes Ill. Termination 170

CSO (W'mdow output)

This module is responsible for the definition and modification of graph windows, and for the output of simulation results into windows and files. Two procedures are explained as an illustration:

The procedure NewGraph, used for the corresponding menu command, performs the following tasks: (I) It lets the user select a variable for the new graph (II) It creates in a dynamic list a new record, GraphRec, where all information about the window is stored (reference variable for DialogMachine, title, size, axis information, data used for redrawing the graph, etc.). (Ill) It opens a new window to draw profiles of the selected variable in further simulation runs.

The procedure GenerateOutput is called from the simulation loop to generate the output for the actual output time. It writes the variable's values into all active output files, draws the corresponding profiles in all open windows, and stores the data in GraphRec.

~ Setup of differential equations and calculation of derivatives in CHEMSEE. The data structure, which is shown for the example of the Mn- cycle, defines the differential equations of the computer model. It is interactively created by the user with the commands New Variable and New Process. During a simulation run, the differential equation is calculated using the data structure in the following way: all processes are calculated individually; the contribution to the differential equation, dsik, is stored. The sum of all processes for each variable yields the derivative dsi. used by the integration algorithm to calculate the new values of the variables, Bi. Trigger: Processes P21 and P22 are active only if Vl is within a certain user- defined range. 171

Fi~.55

t ...... - ...... ~ .... '""'"' ...... :...... ; Trigger

.~tpjq~ig[Tl(!t!Y •••••••••••••••

User-defined variable i, created with command 'New Variable ... • Implicit processes are defined automatically for the variable *) vertical advection: calculated only if inflow and outflow at different levels

User-defined process k for variable i, created with command 'New Process .. ."

Array of reals, dyna· Contribution of process k to differential equation of variable i. mically adjusted to Calculated by specific process procedure box-number used in Derivative of variable i. Calculated as model sum of all process contributions dsik (example: n=6) Actual value of variable i Dynamic variable: Calculation with integration algorithm Static variable: Predefined value 172

CS(Master)

The module CS is the master program and is responsible for program control and for the assembly of all user-interface elements provided by lower modules. It is responsible for the program states and provides all menus with their associated functions, including the simulation loop, which is called from menu command Start Run.

In the simulation loop, the differential equations of the model are solved numerically and output of the results is generated on file and in graph windows (Fig. 54).

Within the loop, the model equations are calculated for the numerical integration. First, all auxiliary parameters are evaluated for the actual simulation time (Initialize model and Calculate model parameters in Fig. 54). In the second step, the derivatives of the differential equations are calculated (Calculate derivatives in Fig. 54, and Fig. 55). Specific procedures for each process type, located in module CS, are called from the master procedure, CS.DoProcesses, with the specific process parameters (process constant, variables involved, etc.) . They calculate the contribution of process k to the differential equations of variable i, d;k (symbols explained in Fig. 55). The derivatives for each model variable i are finally obtained by summarizing all process contributions into ds;; and evaluating stoichiometry and triggering when necessary.

The integration algorithm used is a first order explicit Euler method with variable step size, which calculates the new state (variable i in box j) using the following equation:

Sij (t+dt) = Sij (t) + dSik(t) · dt (Eq. 52)

The integration step dt is adjusted within the user-defined range Smallest I Largest time step (Fig. 13, p. 52) in such a way that the maximum relative change of the model variables does not exceed the value defined by Relative change. This algorithm is quite stable and well suited for the type of models calculated using CHEMSEE.

The CHEMSEE main program initializes all program variables and calls the procedure RunDialogMachine, provided by the DialogMachine. This procedure delegates program control to the DialogMachine, which from now on 173 processes all events, such as mouse clicks, keyboard actions, disk insertion, window activation, etc. in the main event loop. Whenever necessary, procedures of the CHEMSEE program are called. E.g. when the user selects a menu command, the corresponding procedure is automatically called. 174

7. IMPLEMENTATION OF MASAS

This chapter illustrates the implementation of MASAS. It presents major user interface elements and describes in brief the architecture of the program with the modular structure and the organization of the main tasks. Experiences gathered during the development of this tool will be summarized in the last chapter.

7.1. USER INTERFACE

Four permanent and five window-dependent pull-down menus, five interactive data windows, an arbitrary number of graph windows, and various entry forms constitute the user interface of MASAS. The behaviour of the user interface can best be understood when focussing on the windows: the windows form independent program elements whose existence is directly related to the availability of the corresponding data. All windows allow standard operations, such as file input/output (New, Open, Save, etc.), printing, and interactive editing, and each has an individual menu. The following windows exist in MASAS (associated data files are given in brackets):

- System window, for system data (system library files). - Compound window, for compound data (compound library files) - Model window, for data of variables, processes and simulation (model file) - Windows for process approximation (air/water exchange, photolysis; no file input/output, only printing) - Graph windows for displaying time series and profiles (data files of simulation results; no input of data files)

MASAS knows five distinct program states which depend on available data (system and model) and on whether a simulation is in progress (Fig. 56).

Program states of MASAS. Each state is represented by a box showing the associated windows. Optional windows are shown in grey. The compound window is always optional, because MASAS allows simple models to be run without specific compound data. For the state System OK, the minimal system dataset required to run a model is shown in the box. .LIU

Fill· 56 • NewModel •Open Model (system not available)

• NewSystem • Close • Open System System (model available)

Optional

• Run Simulation completed (!=lend) Simulation run suspended • Continue Terminate .. J. ~=ci•oo "'";'"' ::'

'1 1

All menu commands disabled except: Terminate, Pause, few others 176

The following short description of all menu commands introduces the features of MASAS:

•-MENU About MASAS: Display of program version and address of devel- oper.

MENUFILE New ... (X..N): Create a new file associated with a window. Open ... (X-0): Open existing library or data file. Close (X.. W): Close active window. Save (SE:-S): Save contents of active window to existing file. Save As... : Save contents of active window to new file. Page Setup ... : Define page setup for printing. Print ... (X-P): Print active window (as displayed on screen). Debug: Submenu with commands used for program debugging. Quit (X..Q): Terminate program.

MENU SYSTEM (system window active) Description ... : Set available system descriptions (one-, two-, combi-, and n-box model data). Options ... : Activation of formulas for the calculation of unknown parameters (# 9 in Tab. 31, p. 156). Reset ... : Reset all system parameters.

MENU COMPOUND (compound window active)

Description ... : Set options for compound (number of pK8 -values and highest positive charge, number of hydrolysis rate constants and general first order rate constants, and available reactivities for compound species). Reset ... : Reset all compound parameters.

MENU MODEL (model window active) This menu gives access to the following operations: - Selection of main model and submodels (particles, acid/base reaction) with the predefined standard processes. 177

- Definition and removal of additional processes by the user, either with or without the support ofMASAS (Tab. 24, p. 139).

Select Model...: Select from available main models and set number of boxes for n-box model. Particles ... : Define submode} for adsorption of compound to particles or POC, including sedimentation and sediment. Proton Transfer Reaction ... : Define submode} for acid-base reaction. Reset Model ... : Reset model parameters. New Process ... : Define a new process for the compound in water. Processes for the compound in the sediment can be defined if an element in the table for sediment processes is selected. Delete Process ... : Delete selected process. Delete All Processes ... : Delete all user-defined processes.

MENU SIMULATION This menu summarizes all commands related to output definition and simulation. Output Setup ... : Define output interval (constant or variable intervals), and choose boxes for which time series should be plotted. Simulation Setup ... : Define simulation time (start/stop), parameters for numerical integration, and integration method. Run (00-R): Start simulation run. Display simulation window. Sets program state to Simulating. Pause I Resume (3«-F): Interrupt/reactivate simulation run. Sets program state to Pause or Simulating, respectively. Terminate (00-T): Abort simulation run. Sets program state to Model OK. Continue ... : Continue last simulation run. Clear Last Run (00-J): Delete data of last simulation run, update graphs. Clear All Runs: Delete data of all simulation runs, update graphs. 178

MENU WINDOWS Tile: Arrange all windows neatly on the screen Tile Graphs: Arrange all graph windows neatly on the screen Hide: Hide active window System (sg..1): Show system window Compound (sg..2): Show compound window Model (sg..4): Show model window Air/Water exchange: Show air/water exchange window Photolysis: Show photolysis window Graph Windows (sg..5): Activate graph windows serially Show Graph Set: Show graph of compound in water and initial/experimental value. Time series are shown for one- and two-box models, profiles for n-box models. If Calculate total mass is switched on, an additional window for total mass is displayed (not for one-box). Clear All Graphs (sg..B): Clear all unlocked graphs

MENU GRAPH (graph window active) Modify ... (sg..M): Modify grid Configure ... : Modify curves (including color and line type) to be displayed in graph (only if ~12 curves in graph). Show All Data: Show all curves Lock/ Unlock (3C-L): Conserve or reactivate graph Clear: Clear graph

MENU EDIT This menu refers to the selection appearing in the active window. Several commands define input data for parameters (from file or keyboard). Formats for parameter text files and the use of one-year time series for repeated years are explained in Appendix 1.

Define Value ... (sg..D): Enter new value (string, number, time series, etc.) Constant ... : Enter constant value (file read/write not possible) Box Constant...: Enter constant value for each box. When the box number is changed, the data is converted into a depth profile. Time Variable: Enter time series Depth Variable: Enter profile 179

Time Var., Epi/Hypo: Enter two time series for epi- and hypolimnion Time & Depth Variable: Enter series of profiles

Submenu Options, with the following commands: ./To Output List: Record parameter in list of output variables, or remove it (toggle function). A check mark (./) indicates that parameter is in list. In this case, the parameter can be displayed graphically with the command New ... Graph. Show Box Data: Show parameter values used for the boxes in the model (interpolated from profiles). For array parameters with ::;20 elements. Volume Weighted Average: Calculate volume-weighted average from profile or profile series (#16, 17 in Tab. 31, p. 156). Get Info ... (3C-l): Show information and comments on parameter. Enter Comment ... : Enter or modify comments on parameter Set Undefined: Reset parameter value to "undefined".

Several windows have click fields, which work like menus:

MODEL WINDOW mtot: On/off field in table of variables for activating calculation of total mass within the lake. Stat (us): On/off field in process table for switching selected process on and off. Obs (erve): On/off field in process table for activating the observa- tion of the selected process. Spec (iation): Activates entry form for defining the speciation for the selected process.

AIR-WATER EXCHANGE WINDOW Various self-explanatory click fields for approximating the air/water mass transfer coefficient (corresponding to formulas given in Tab. 26, pp. 140-143). 180

7.2. MODULAR STRUCTURE

MASAS is composed of 21 units with a hierarchical structure, separated into a kernel and a user interface part (Fig. 57). The kernel modules include, on the lowest level, three modules with auxiliary procedures and data types (NMathLib, Interpolations, Aux). The next level consists of the principal base module, NBase, which provides the main data structures. Based on this are the modules for the system and compound data. On the next higher level are the three hierarchically ordered modules NProcesses, NModel and NSim.

The user-interface modules are organized on two levels: base modules for general purpose objects, and higher modules for MASAS-specific objects. Three modules exist on each level for window, file and entry-form handling. Two additional modules, MScanner and MParser, constitute the library system. The user-interface modules are based on the DialogMachine (Fischlin, 1986).

5.2.1. KERNEL MODULES

Aux

This module contains procedures for error and alert messages, data types and procedures for array variables with variable size, and various auxiliary procedures.

Interpolations

This module supplies the procedures for one- and two-dimensional linear interpolation, and the data types for interpolation tables of variable size. Interpolation is optimized by storing the gradient, intercept, and validity range for further interpolations in the same range.

NMathLib

This module provides unit strings ("m", "mid", etc.), conversion factors, physical and chemical constants and functions (e. g. gas constant, diffusion coefficient of oxygen, Arrhenius function), and functions for array calculations (overlap of given arrays, flow, volume and average calculations for boxes). 181

This module is the principal base module of MASAS and provides the major data types for parameters, model variables, processes, output variables, simulation runs, etc. used throughout the program. Objects of the above- mentioned type can be created and removed dynamically while a user is working with MASAS. For instance, when a user selects the menu command New Process, the program creates a new object of type process to hold all the data defining the new process. A set of management procedures are supplied to create, modify or delete these objects.

The list manager contains the procedures for handling the dynamic lists used in MASAS. The elements of a list are, depending on the list type, parameters, variables, processes, windows, etc. These lists are dynamically created and modified during program execution and can hold any number of elements. Some examples illustrate their application:

- Main Parameter List <.NBase.mpl ): This list includes all parameters and is used for actions on all parameters. For example, the update of time-dependent parameters during a simulation run is achieved with one single command which goes through mpl and updates each parameter. - Real Array Pointer List (NBase.rapList ): contains all array variables of the n-box model. To change the number of boxes, the list is processed and the array size of each variable modified. - Display Information on Parameter (parA.displnfo ): Each parameter (par) has a list which stores the information about its display. Whenever a new window containing the parameter is opened, an element is added to the list. In this way, the parameter can be displayed in different windows and still be updated correctly.

The objects dynamic arrays, parameters, model variables and processes are handled in a similar way as in CHEMSEE, and have been described in Section 6.2 (p. 164).

Fie-. 57 (followine- pae-es): Hierarchical structure of MASAS and principal relationships between program modules. Kernel modules are shown on the left side, user interface modules on the right side. Each box represents a Modula-2 program module. For the sake of clarity, globally used data structures, variables and procedures are not shown. 182 Fi~. 57: Simulation and simulation MASAS kernel modules setu procedures

Model setup procedures

Procedures for maintenance and approximation of processes Procedures to System set MASAS pro- data, cesses procedures for Procedures for calcula· system setup lion of and data geometry calculation

Management of simulation runs Management of output variables and data storage Reporter installation procedures (parameter and output variable update)

Parameter management procedures (SetPar) Mousehandler procedures

Data 183 Fi~. 57: MASAS user interface modules

MASAS·specilic entry forms, info- windows for process Handling approximation of already defined windows Read· & write procedures for MASAS-specific files (library, model)

Reporter installation Read- & write procedures procedures (selection, for system open/close window) and Field update and compound modification library files procedures Definition, maintenance of windows, graphs and tables Open, update, close defined windows Keywords and scan procedures for library files Basic file input/ Trace window output (debugging) Entry forms for base Parameter data types read/write (Parameter, integer, procedures query, etc.)

Window definition File input/output and input/output procedures procedures

Menu definition File input/output and event loop procedures procedures

Entry form definition procedures

Entry form definition procedures 184

Further elements provided by NBase are: - string management - data structures and managing procedures for output data and simulation runs - data structures for windows, tables and graphs - basic model variables (time, depth, etc.)

NSystem

This module contains all system data (Tab. 30, pp. 152-155) and satisfies the parameter requirements of the various MASAS model types. It provides data structures for the general system data, and for the system data of particular model types. Several procedures are included to calculate dependent parameters (Tab. 31, p. 156).

NCompound

This module is organized in the same way as NSystem and provides all compound data for the various model types (Tab. 32, pp. 158-159). Its data structures allow the storage of general compound data, physico-chemical parameters and reactivities for different species of the compound.

Data structure consisting of three model variables and the transport and transformation processes representing a compound model in MASAS. The figure shows the EDT A model as an example. The data structure is created partly by the program and partly by the user interactively, using the command New Process. It represents the differential equations and is used during a simulation run to calculate the derivatives of the model variables in the following way: all processes are calculated individually with the corresponding procedure and the respective contributions dSik to the differential equation are stored. The sum of all processes for each variable yields the derivative dsj, used by the integration algorithm to calculate the new values of the variables, Sj. ds ik Contribution of process k to differential equation of variable i. Calculated by specific process procedure ds i Derivative of variable i. Calculated as Standard variables included in MASAS ~ Procedure for process 11£1 type x, calculates: di P, sum of all process contributions dsik x s i Actual value of variable i Standard process, cannot be deleted Array of reals, dynamically Dynamic variable: Calculation with User defined process k for variable i; adjusted to number of boxes integration algorithm created with command New Process ... ~ of model (example: n=6) Static variable: Predefined value This module contains, for all available processes, the procedures for calculating the contribution of each process (Tab. 24, p. 139), one procedure for each fixed process (#1-12, 14-17), and several procedures for processes which can be approximated (#13, 18-21). Air/water exchange, for example, requires 11 procedures.

Management procedures for the process approximations and the basic model variables are also included in this module.

NModel

The main function of this module is to assemble the model equations (one·, two-, combi-, and n-box; compound species; sorptive processes; standard and user processes). This includes the calculation of the derivatives of the differential equations (Fig. 58), the procedures for initializing the model for a simulation run (Fig. 60) and for calculating time-dependent model parameters during a simulation run (Fig. 61). The setup of the differential equations within NModel is organized as in CHEMSEE (p. 172).

NModel retrieves all necessary input data from the modules NSystem, NCompound, and NProcesses. It provides the procedures used in NMaster for the interactive definition of the model.

NSim

This module contains the simulation loop, the procedures for the initialization of a simulation, and auxiliary procedures for the interactive control of a simulation run. For the numerical integration in the simulation loop, four different integration algorithms of different complexity are available:

- Euler, with constant step size: First order explicit Euler method with constant step size. Very simple method: may lead to errors, because integration error is not controlled. - Euler, with variable step size: First order explicit Euler method with variable step size (Eq. 52). • Runge-Kutta-Fehlberg (4,5): This method is suitable for solving non-stiff and mildly stiff equations, but should not be used when high accuracy is 187

demanded (Shampine et al., 1976). For the purposes of MASAS, however, its performance is still better than adequate. - ODE: Very efficient integration algorithm using the Adams Method; adjusts order and step size to control the local integration error per unit step (Shampine and Gordon, 1975).

The Euler methods are implemented in NSim, whereas Runge-Kutta- Fehlberg and ODE are located in two additional library modules, which were translated from original Fortran programs to Modula-2 (D. Steiner, ETHZ) and adapted by P. Reichert (pers. comm).

The algorithm selected is called repeatedly in the simulation loop in order to calculate step by step the numerical solution to the differential equations. Further tasks performed in the simulation loop are the activation of the output procedures and the processing of user events during a simulation (pause/terminate).

5.2.2. USER-INTERFACE MODULES

NBaseW and NBaseWAux

These modules constitute all general procedures for defining window lay- outs. They are used to assemble particular MASAS windows in higher modules and to handle all objects associated with windows.

Definition of window data structures (NBaseWAux)

A window data structure contains all the information about the program variables to be displayed in a window, regardless of whether the window is displayed or not. NBaseWAux provides the procedures to define windows, tables and fields, and graphs and curves (time series/profiles). An arbitrary number of windows can be defined.

Window display (NBase W)

Once a window data structure is defined, the window can be displayed on the screen with one single procedure call.

Modification of window data structures (NBaseWAux) Modification procedures allow adaptation of the window to suit particular needs. Columns or rows of a table can be hidden, fields in a table can be written in different styles and colors, click fields (like push buttons) can be installed, graphs can be rescaled, etc.

Removal of window data structures (NBaseW)

The window data structure can be removed step by step in the same way as it was created: curves can be removed from graphs, and graphs or tables from windows. Finally, the window itself can be removed.

Windows management (NBaseW)

Several tasks are autonomously managed by NBaseW: - Selecting and editing within windows. The main program is informed about the current selections by the selection reporter (#5 in Tab. 34). - Window Update; used whenever hidden parts of a window become visible. - Update of parameter values: When a parameter is changed, its value is updated in all windows involved (#2 in Tab. 34). - Window output during simulation runs (initialization, curve plotting, termination).

(vi) Auxiliary functions (NBase W)

- Trace window: Debugging during program development. - Simulation window: Time information during simulation.

Simulation loop of MASAS. The figure shows the sequence of calculations performed during a simulation run. It is divided into three parts: I. Initialization, II. Simulation loop, III. Termination. The calculations summarized in the boxes Initialize Model Variables and Calculate Model Parameters are given in detail in Figs. 60 and 61. The simulation loop is located in the module NMaster, and combines procedures provided by various modules: II] Dialog Machine I) NBase B NBa~W llJ NModel llJ NSim. 189 Fig. 59

I. Initialization 190

Simulation loop of MASAS. Initialization of model variables. The figure shows the calculations which are performed by NModel when the model is initialized for a new simulation run.

Calculate lake

Calculate epilimnion • Thickness, volume, number of boxes included in epilimnion (if not time-dependent) • De endent arameters

Calculate flow

Calculate rocesses

Simulation loop of MASAS. Calculation of model parameters. This figure illustrates the sequence of calculations performed by NModel to update all time-dependent parameters during a simulation run.

Calculate time-dependent parameters for • general system data • model-dependent system data rocesses and model

arameters

Calculate

Calculate 191

This is the low level module for file inpuUoutput. It provides procedures for file handling (open, close, ... ) and for reading/writing different data types (boolean, dynamic arrays, parameters, output data tables). File formats are given in Appendix 1.

NBaseIO

This module provides entry forms for parameters (constanUvariable), integer and real numbers, strings and auxiliary entry forms.

NWindows

This module defines the particular layout of the data and graph windows using the low level procedures of NBaseW and NBaseWAux, and provides the procedures for adapting the window data structure when the structure of the displayed data has been changed (e. g. add a new line for a newly defined process (model window) or hide compound reactivities which are not included (compound window)).

MScanner, MParser, NFile

These modules constitute the MASAB library manager, and allow the user to access and create system and compound library files.

NFile contains further procedures to save and read model files (with model and simulation settings and all model variables and processes) and the procedures for writing graph data on file. File formats are given in Appendix 1.

NIO and NIOAux

The module NIO contains the MASAS-specific entry forms, which deal with high level data (system, compound, model, program setup, etc.). These entry forms are mere "data acquisition machines" and perform no further actions (in contrast to CHEMSEE, (III), p. 168).

NIOAux supplies the information windows used for the process approximation (air/water exchange, hydrolysis, etc.). NMaster and NWMenu

NMaster is the main program which composes all the elements provided by the other modules to one functional unit and which defines the logical structure of the program. Auxiliary functions have been transferred to NWMenu. The following tasks are performed:

- Menu setup and maintenance: NMaster constitutes the menu bar and contains for every menu a procedure which is called when the menu is selected. Typically, these procedures contain various other procedures provided by subsidiary modules and thus define the behavior of the user interface. Menu maintenance includes the update of menus to correspond to the current program state and the current active window. - File access and window handling: NMaster coordinates all actions involved in accessing library and model files and displaying the data in windows (New, Open, Save, Hide). - Program state (Fig. 56): depends on available data and ongoing simulation runs. - Simulation procedure: The elements provided by various modules are assembled to yield the entire simulation procedure with initialize, simulation loop and terminate sections (Fig. 59). - Initialization: At startup, NMaster calls the initialize procedures provided by each module in the correct order. Thereafter, the event loop located in the DialogMachine is started to process all subsequent user actions.

7 .3. LIBRARY SYSTEM

The MASAS library system is located in the modules NFile, MScanner and MParser and allows the user to create and access system and compound library files. The library files are ASCII-files (textfiles); each file contains one system or one compound dataset. The data are stored in a format defined by a syntax, like a formal programming language (Wirth, 1986). However, instead of a program text, the library files contain system or compound data with the following features:

Structured data storage: - The information is hierarchically stored on library files analogously to the data structure of the variables in the MASAS-program. Lake data, for in- 193

stance, are grouped into one component consisting of general lake data and another component consisting of model-dependent data.

Expandability: - New parameters, components or formats can be added to the library files with minor changes or no changes at all in the program, without losing compatibility with older library file versions.

Flexibility: - Optional comments on parameters - Components can be omitted if the corresponding data are not available (e. g. n-box lake data or reactivities for compound species). Parameters can be saved in any order - Parameters can be left out if they are not available (e.g. reactivities) - Parameters can be stored as defined in the menu Edit (constant, time series, etc.).

High-level procedures which create the system and compound library files are located in the module NFile; the actual job is done by MParser.

The procedures for reading the library files are located in three program modules: NFile, MParser and MScanner. NFile contains the high-level procedures for accessing library files. MScanner scans the library file and rec- ognizes specific code words, such as MASASFILE or LAKE, the unequivocal identifiers (ident, Tab. 30, p. 152 and Tab. 32, p. 158), and numbers. MParser interprets the information stored on the library files according to the syntax definition, and detects syntax errors. For instance, the code word LAKE tells MParser that the subsequent information on the file characterizes a lake, or PAR initiates the input of a system or compound parameter (format is given in Appendix 1). 7.4. PRICE OF USER FRIENDLINESS

One of the aims of MASAS was the development of a user-friendly computer tool which assists the user in handling a complex subject.

Models of various complexity should be constructed on a variable dataset for both the system and the compound. Whenever data are missing, approximation routines acting on different hierarchical levels should help to realize the best possible model. In all steps to be performed, the user should be able to obtain at least minimal assistance from the program, and all actions should be echoed correctly on the screen.

Well, these problems could be handled relatively easily with a program design which leads the user strictly from point A to B to C. Any subsequent actions would be clearly based on previous decisions. But such restrictions are looked upon as most annoying by users. Therefore, the goal was to avoid such restrictions, except when the program logic would be violated. The consequence is that each action influences many other settings:

In the case of three elements, A, B and C, this would seem to be manageable. During the development of MASAS, some very tough problems arose in con- nection with the almost infinite number of combinations of program states involved. The battle to obtain a functional program under these conditions was fought at two fronts. The internal state of the program should always be consistent and all the information presented on the screen should always be correct. Often, the timing of different actions executed by the program is critical (e. g. window update H delete data structures). To give an idea of the background operations required to offer an easy life to the user, we carefully listed, as an example, all steps, either performed by the user or the program, which are involved in the selection and alteration of a parameter value with a mouse click (Fig. 62). This action is described from the user's point of view in Fig. 28.

User friendliness is reflected in the size of the program parts (Tab. 33). The modules dealing with the model proper and with the simulation account for only 20% of the total size, less than the proportion devoted to window handling (22 %).

Commwiication within the program becomes important because every program part involved has to be informed about user or program actions. Tab. 34 lists the major communication pathways within MASAS. For instance, a parameter value is changed by a low level procedure (NBase); the module responsible for updating the value displayed is NBaseW, and needs to know about the event (#2 in Tab. 34).

For the future development of such tools, the most costly features and some alternatives which promise a significant reduction of programming effort with minimal loss in comfort are summarized in Tab. 35.

Tab. 33: Size of different program parts of MASAS. 1 page contains 88 lines. The table illustrates a typical situation found in user-friendly programs, where the core functions form a minor part of the program (here 16%). In comparison, window handling alone accowits for 22%.

Task Modules Pages "lo Of total Basic functions 90% NBase, NMathlib, Aux, 52 18 Interpolations User dialog with entry forms NBaselO, NIO, NIOAux 43 15 Windows and window 10% NBase, NBaseW, 65 22 input/output NBaseWAux, NWindows File input/output and library NBaseFile, MScanner, MParser, 37 13 system NFile Kernel of MASAS model NSystem, NCompound, 47 16 (system, compound, NProcesses, NModel processes, model) Simulation (excl. integration NSim 13 4 algorithms) Program control NMaster, NWMenu 34 12 196

~ Sequence of actions performed by MASAS when a parameter, displayed in a window, is interactively selected (I) and changed (II). The example shown here illustrates the numerous background operations which are necessary to provide a user-friendly interface.

1. Selection of a parameter with a mouse Ciiek:

User clcks with mouse on a rameter field

Dia Machine calls NBaseW.MasterConte

MasterConUmtHandler evaluates field and table which have been clicked

If another field was selected previously: Old selection is erased in window Old selection is cancelled in window data structure

NBaseW calls Se/ectlonReporterto communicate to NMasterthat the selection in the window was chan ed

NMaster adapts parameter menu to the newly selected parameter: available calculation options (constant, time-dependent, etc.) and auxiliary options (Show Box Data, To Output List. Volume Wei hted Ave e etc.

NMasterstores variable referenci 197

II. The parameter Is changed to Depth Variable: 1 User selects menu command Deoth Variable ...

DialoaMachine automaticallv calls the orocedure which has been assianed to this menu command J, Edit procedure of NMaster is called and displays entry form to ask whether the data should be Read from file or User defined and in the latter case the number of data ooints to be entered

User chooses User defined and 8 oairs of data ooints

2 x 8 storage places are dynamically allocated to take values from user (up to 2047 data pairs oossiblel

Entrv form to enter orofile ldepth and corresoondina data values\ displaved

User enters values and clicks OK

Check whether depth values are monotonically increasing. If It is not ok, the entry form is shown aaain

Data are assianed to oarameter J, Auxiliarv interoolation variables for oarameter inltialized

Calculation procedure assigned to parameter. The procedure will later be used to calculate parameter values for the boxes (n-box model) whenever number or dimension of boxes will be chanaed.

Value for all array elements (box centers) calculated with previously assigned procedure by linear interoolation from profile.

NBase calls NBaseW.UpdateMWParameter (Installed by NMasterto update display of the parameter in the windowlsll.

UpdateMWParameter updates display of the parameter in all windows where this is necessary. This is achieved by processing a dynamic list, which stores the windows, tables and graphs where the parameter is currenllv disolaved.

*All other parameters which are affected by alteration of the changed parameter are updated by means of a special procedure. The actions marked with -tr ... * are iteratively applied to all dependent parameters (assignment of new values, calculation of parameter value, update of display in windowlsll.

Set the flag which indicates that the window content was changed (used when the window is closed to ask user whether the chanaed content should be saved to file. J, End Tab. 34: Communication within the MASAS program. For major user actions, the table lists the main module involved and remote modules which must be informed about the event in order to execute secondary tasks. User action Processed by Repor- by calling to execute the following action ted to 1 Menu selection DialogMachine NMaster menu erocedure execute menu command 2 Change value of param- NBase.SetPar NBaseW ParameterUpdate- update parameter display in all windows eter ldirecil2'. or indirectlZ'.l Procedure 3 Delete output variable NBase.Remove NMaster OutVarUpdate- remove all windows displaying the ~directlZ'. or indirectli'.} OutVar Procedure variable 4 Mouse click within a DialogMachine NBaseW MasterContentHandler Evaluate which field was clicked and MASAS window erocess new selection 5 Selection of an edit field NBaseW NMaster Selection Reporter • update commands in edit menu in a window • record current selection 6 Change of window size Dialog Machine NBaseW MasterRedefHandler redraw window content, rescale graehs 7 Bring a background Dialog Machine NBaseW MasterBring ToFront- • set new active window window to foreground Handler • report new selection to NMaster (7a) .... with a mouse click • report new active window to NMaster {7bl 85 7a NBaseW NMaster SelectionReeorter same as5 7b NBaseW NMaster BringToFrontReporter change specific window menu for new active window B Close window by click in Dialog Machine NBaseW CloseWindowHandler • delete selections close box • report changed selection to NMaster (Ba) • inform all parameters which were displayed in window that window is closed • report to NMaster that active window has been removed from foreground (Bb) •graph windows: remove window data structure ~no longer usedi Ba NBaseW NMaster SelectionReeorter same as 5 Bb NBaseW NM aster RemoveFromFront- remove specific window menu from menu Re orter bar Tab. 35: Expensive features in MASAS. The table lists some of the most costly features, which can be assigned to four areas: I: interrelation of different data structures; 0: multiple options; E: free order of execution; W: display of data in windows. Type Example Expensive feature Possible simplification Consequences User control E Free order of Keep track of available Prescribe minimal order of + Number of combinations (user decides 0 loading data; reset meaningless actions, e. g. model reduced the order of system and user actions (e.g. definition strictly after - No switch from one lake or actions to be model selection of a model for definition of system and compound to another during performed by which no system data is compound data modeling program) available) - No provision of additional data during modeling (e. g. compound reactivities for s ecies. E Set com- All combinations of avail- • Compound species of + No processes have to be omitted 0 pound data, able compound data, the model cannot be from model (Tab. 25) species cal- compound speciation and switched as long as they - Testing of different models culated for species involved in a pro- are used in process complicated compound cess have to be consid- descriptions (model type) ered and processed cor- and species rectly (e. g. what should definition of the program do, if a a given pro- species which is used in a cess in free process is removed from order the model by the user?) • For one particular model + Number of combinations just one speciation reduced which cannot be - Testing of different models changed afterwards complicated '.[ab, afi; continued. T}'.E!e ExamE!le Ex£!ensive feature Possible sim£!11fication Conseguences Arbitrary size 0 Select box • Array variable which can Use of fixed arrays with + No need for management data of data series number for be dynamically changed upper limit for number of structure and box n-box model in size boxes Recompilation of program for number from • Data structure to record more boxes 2 ... 2047 every array variable . No possibility of reducing (theoretical (used to adapt array size memory requirements by maximum) whenever number of reduction of number of boxes boxes is changed} Display of w POCis Data structure which Strict concept: each + Simplification of data structure parameters I displayed in records for every parameter only displayed - Parameters cannot be displayed simultaneous- system parameter all windows in one window in remote window for, e.g., ly in different window and and tables where it is process approximation. windows in model displayed. window (initial value~ Display of I Hydraulic Data structure which Display raw data only; + No need for complex data read-only w rate in stores for a parameter all dependent parameters are structure to maintain display parameters system dependent parameters, displayed on request in information for parameter which are a window together with all windows, entry forms only. - Dependent parameters hidden, function of tables, etc., where these not readily accessible other parameters are displayed. rameters Display of pa- 0 Vtodmtc) in Same as above, plus: Same as above Same as above, plus: rameters I air/water information about currently - Impossible to create a user which are ei- w exchange selected calculation option interface for process ther a function window approximations of other parameters ordi- rectll'. entered Tab.35: continued. Type Example Expensive feature Possible simplification Consequences User- 0 Any • data structure for • Parameters cannot be + Data structure not required configured E parameter recording the parameter shown graphically in - No graphical information graphical W displayed in which should be saved windows output a window during a simulation run can be and displayed shown in a • data structure which graph records the windows where the parameter is displayed. Used when the parameter is deleted (e.g. delete process). • Parameter is shown + Data structure simplified once, not for each - Different parameter settings simulation run cannot be compared graphically Fi . 50 O Rescale and storage of numerical Graph windows with fixed + Numerical values not stored, no E resize values to recalculate size and scaling resize and rescale calculations w graphs graph - Unhandy I Display of •complex window data • Fixed set of graph + No need for complex data 0 one variable structure windows with structure w or parameter • independent data predefined variables - Comparison of different in different structure for data storage • Display of each variable variables and parameters in one graphs and for graph definition in one window only graph not possible (e.g. window (data storage once only, Epi!Hypo-load, resuspension multiple display) rate in Fig. 32) - Display of the same variable for different simulation runs in different windows not possible e .. Fi . 38 202 8. DISCUSSION

CHEMSEE

CHEMSEE has been applied in various research studies. In all cases, models were developed based on field data on the chemical or chemicals concerned. Concentration profiles, measured at regular time intervals in the lake, and data on boundary fluxes (loading, precipitation, flux across the sediment/water interface, etc.) were usually available.

The model of the Mn cycle contains reaction and sediment flux processes, all of which have already been investigated and described in the literature individually, but which have never been brought together into one dynamic model. The results are successful insofar as simulated Mn(II) concentration profiles were found to correspond very well to field data, and the parameter values employed were all within the range described in the literature. However, due to the complex nature of the processes of Mn oxide formation and settling involved, results were less satisfactory for the particulate Mn oxide. By means of the model calculations, it was possible to identify gaps in our knowledge which require further field and laboratory studies.

The model of the Cr cycle in Greifensee was employed to quantify the unknown Cr input in the inflows and the adsorption to particles with subsequent settling. This example illustrates how simulations using dynamic models can serve to give a more in-depth analysis of field data.

The same applies to the model developed for Lake Van. This model, which simulated the isotope ratios of 3He, 4He and 3H in the water column, was used to test hypotheses on the origin of the helium found in the lake. The model which gave the best description of the measured concentration profiles and isotopic ratios was one which included an input of helium from the earth manUe into the lake; thus, this model was employed to validate a hypothesis.

In a study on vertical mixing ratios in Lago di Cadagno, CHEMSEE was used to quantify unknown diffusive and advective mixing processes caused by eddy diffusion and the influence of underwater springs.

In all cases, computer simulation runs of the models developed were used to improve the analysis of field data, and to identify open questions for further field and laboratory investigations. 203

CHEMSEE has been used in courses on systems analysis for students of environmental natural sciences at ETH Zurich. During small case studies, groups of students developed simplified models for the behavior of HCB, CH4, 222Rn und CCl4 in lakes. The students were given a brief introduction to the problems to be solved, several publications, and the program with the user manual; a course room equipped with computers was put at their disposal. The students subsequently presented the results of their modeling activities to the others in short talks. On these occasions it was possible to obtain first- hand information on the reactions of users to CHEMSEE: it was obvious that working with CHEMSEE was stimulating and that the program was capable of being used as a teaching tool to promote understanding of the dynamic processes which determine the temporal and spatial behavior of chemicals in lakes.

In the case of CHEMSEE, the major limitation which became apparent is that the set of variable and process types provided by the program cannot be extended by the user, and neither can the vertical lake model be replaced by another model type. Thus, problems involving metal speciation, complex sediment reactions, active motion (organisms), uncommon processes, or lakes which are either very shallow or possess multiple basins cannot be solved. A future version might include an option to add user-specified variables, processes and models, either by interactive definition or by an option to define the user extensions by means of short programs which can be linked to CHEMSEE.

MASAS

The usefulness and applicability of MASAS in solving problems associated with the behavior of anthropogenic organic chemicals in lakes has been tested using four test compounds. Models were developed for each of these compounds and were used to answer specific questions. The required field data on the compounds were provided by a field study conducted in Greifensee and in its inflows. The results of the model calculations will be briefly discussed here.

For NTA, the in situ degradation rate was found to be approximately 0.035 d-1 (corresponding to a half-life t112 of approximately 20 d); the influence of lake temperature on the degradation rate was found to be negligible. Variable NTA loading seems to be the key factor in explaining NTA variations within the lake.

In the case of EDTA, the situation was more difficult: field data gave no indication of photolytic degradation; instead, it seemed that EDTA was transported down into the depths of the lake, which is contrary to common knowledge. A model which included adsorption on to particulate iron was set up and produced surprisingly good results. Elimination by adsorption and transport down to the sediment, however, is hypothetical, and requires further investigation in the field.

In the case of the PER models, the advantages of a hierarchical process description were able to be demonstrated. The air-water mass transfer coefficient was estimated using different levels of approximation, including the effect of dependence on wind speed and lake temperature. A constant value of 0.15 mid was found to be adequate. Simple models which were used to simu· late long term behavior revealed that PER input has decreased markedly from about 200 g/d in 1982 to about 20 g/d in 1991. A model with higher spatial resolution {17 boxes) was capable of simulating a particular contamination incident.

The atrazine models were based on a systematic evaluation of the relevant processes using simple calculations and the MASAS process description for hydrolysis. Field data were not too precise; thus, the use of a complex model was not justified, and a one-box model was used instead. Despite the simplicity of this model, it was still possible to establish a probable in situ degradation rate {s;lQ-3 d-1, t112 ~ 700 d) and to confirm the expected seasonal variation of the input.

User-friendliness and program development

Experience with CHEMSEE has confirmed, that modeling tools tend to be quickly adopted by a broader circle of users {researchers who are not model- ers themselves, students, etc.) as soon as they are made user-friendly (Ulrich, 1987, Fischlin et al. 1990, Ruchti et al. 1991).

The following is a brief summary of the criteria which must be satisfied in order for a program to be described as user-friendly: the user controls the program - the program always gives feedback about its current state - the user does not need to know the commands, they are shown by the program - analogous functions are executed identically by different programs - com- 205 mands can be undone, thus allowing the user to correct errors and to explore the program - information is displayed graphically instead of alphanumeri- cally - multiple windows are used for simultaneous display purposes.

These criteria apply equally to modeling and simulation software. At this point I should like to illustrate the importance of two of them based on my own experience. Students developed their models with CHEMSEE step by step. New processes were added one after the other, and the resulting system behavior was examined by simulation rwis, i.e. they explored the behavior of their models. Often, however, the appropriate range for the number of boxes, the suitable step size for the integration algorithm, and the process parameters were not known. Simulation runs calculated with unfavorable parameter combinations can, however, lead to program crashes, which cannot be described as user-friendly. Students experienced many such cases during their period of familiarization with CHEMSEE, and their frustration empha- sized the crucial importance of a user-friendly environment. Improvements are planned for future versions of CHEMSEE.

The advantage of multiple windows can be illustrated by means of the atrazine models developed with MASAS. The model window (Fig. 47c, p. 115) displays all currently defined processes; active processes are shown in bold- face type. Particular models (model A-D) are activated by clicking the corresponding on/off-buttons. Two additional windows display the results of simulation runs (simulated and measured atrazine concentrations, Fig. 49, p. 119) and the time-dependent loading function used for the model calculations (Fig. 50). In this way, users can see all relevant information at once.

Two further criteria for user-friendliness, concerning modeling and simulation software in particular, were found to be important. In MASAS, all defined processes are summarized in the model-window, as we have just seen. CHEMSEE, on the other hand, provides no such summary window, and users often feel uncertain about the current model setup. An option allowing the user to view the model setup, including the internal model structure (e.g. similar to Figs. 55 and 58) and the corresponding mathematical equations would be very helpful.

Secondly, both CHEMSEE and MASAS perform many background operations automatically in order to support users. However, such background operations tend to make users feel uncertain about the program's behavior if their operation is not clearly echoed on the screen. E.g. when the epilimnion depth 206 in the two-box model is changed in MASAS, the volumes and the surface areas of the epilimnion and hypolimnion box are automatically recalculated. Although the new values are indicated on the screen, users are not precisely informed about the actions of the program. Therefore the option to list any internal calculations which might be relevant to a user should exist.

The effort involved in developing and supporting a user-friendly program is very often underestimated. The fact that university projects, and Ph.D. projects in particular, tend to be limited, both with respect to time and man- power available, should be kept in mind. Scientists who develop software for use in their own field should always search for a clear compromise between user-friendliness and programming effort. Computer specialists should be consulted or brought in as partners.

The most promising solution to the problem of programming effort, apart from using adequate development systems, seems to lie in the iterative development of such tools. For instance, the development of CHEMSEE, achieved in a comparatively short time, was based on prior experience (Imboden and Schwarzenbach, 1985) and on precise development specifications. A consid- erable reduction in implementation effort was achieved by the use of entry forms instead ofinteractive windows for the user dialog. The development of MASAS, on the contrary, was itself a research issue. The subject was more complex and included a large number of different combinations (model types, hierarchical levels for processes, compound species, available/not available input parameters, etc.), and still, the program had to be user- friendly and able to deal with most of the possible combinations. Experience has shown, however, that dealing with too many combinations can slow down progress to an unacceptable degree.

User-friendliness can impair flexibility and vice-versa. For instance, the source code of a simple program, written in an interpreted program language (e.g. BASIC) can be interactively modified, whereas tools such as CHEMSEE and MASAS are applications programs closed to interactive mod- ifications. Adding new features is more costly, because secondary adaptations may be required. E. g., a new process type, introduced into MASAS or CHEMSEE, does not simply require an additional procedure for the process calculation, but may also require adaptations for file input/output and for display in windows and entry forms. '}[)7

Outlook

In the near future, MASAS will be applied to other lakes and compounds, and interested researchers will use and test the program. The extension of MASAS to other aquatic systems, such as shallow lakes, rivers, and aquifers is planned.

In order to make it available to a broader circle of users, CHEMSEE will shortly he converted to run under the MS-DOS operating system. Both CHEMSEE and MASAS are based on the program library DialogMachine, which is now available for MS-DOS computers (D. Keller, IDA-Center, ETH Zurich, pers. comm.). This will make the conversion of CHEMSEE to MS- DOS much easier. Depending on the success (or otherwise) of this conversion, MASAS may also be made available under MS-DOS. 9. REFERENCES

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1. FILE FORMAT FOR PARAMETERS Two different parameter formats are used (Tab. Al). #1 includes the table size and the data values, #2 includes further the code for the parameter type (Tab. A2)

File format Parameter Table Data Used for number tlpe code size table 0 x not used 1 x x data files for single parameters {MASAS and CHEMSEE~ 2 x x x within MASAS library and model files and CHEMSEE model files Tab, Al: File formats used in MASAS and CHEMSEE for parameters.

Parameter type: Parameter tlpe code 0 5 1 2 mnion 2 time series 3 4 Tab, A2: Parameter type codes used in MASAS and CHEMSEE .

2. REPEATED USE OF TIME SERIES (MASAS only) Time series (Parameter type 1,3,4), which start with day 0 and end with day 365 are reused automatically for subsequent years.

3. FORMATS OF DATA TABLES (for format #2) Tabulators(+) are used as separator between values. An optional text can be added as comment below the table. However, this comment will be ignored if the file is accessed and it will be lost when the file is overwritten by the program. 0 Constant, one or two values: One line containing the values: 16.74+ 7.51 A-2

5 Box Constant: First line contains number of values, next lines contain the values (example: depth ofbox borders of 18-bbx model for NTA [m]): 19 O.OOOOE+OO 3.0000E+OO 4.0000E+OO 5.0000E+OO 6.0000E+OO 7.0000E+OO 8.0000E+OO 9.0000E+OO 1.0000E+01 1.1000E+01 1.2000E+01 1.3000E+01 1.4000E+01 1.5000E+01 1.7000E+01 1.9000E+01 2.1000E+01 2.5000E+01 3.2000E+01

1 Time Variable: First line contains number of data pairs, next lines contain the values: time [d] I value (example: epilimnion depth [m] of Greifensee 1990/91): 14 6.4000E+01 + 2.2500E+01 9.2000E+01 + 6.0000E+OO 1.2000E+02 + 6.5000E+OO 1.3400E+02 + 3.0000E+OO 1.4800E+02+ 5.0000E+OO 1.7600E+02+ 3.5000E+OO 1.9900E+02 + 3.0000E+OO 2.32ooE+02 + 5.0000E+OO 2.6100E+02+ 7.5000E+OO 2.8800E+02 + 9.5000E+OO 3.1600E+02 + 1.1500E+01 3.4500E+02+ 2.2500E+01 3.7200E+02+ 3.2000E+01 3.9400E+02+ 1.7500E+01 This time series will not be used repeatedly for subsequent years (MASAS). A-3

2 Depth Variable: First line contains number of data pairs, next lines contain the values: depth [m] I value (example: lake isobath area [m2] of Greifensee): 14 o.oo+ 8492000.00 2.80+ 8082000.00 5.ao+ 7747000.00 7.8o+ 7177000.00 10.ao+ 6467000.00 12.80+ 5564000.00 15.30+ 4620000.00 17.80+ 3925000.00 20.30+ 3458000.00 22.80+ 2999000.00 25.30+ 2468000.00 27.BO+ 1918000.00 30.30+ 920000.00 32.60+ 100.00 3 Time Variable, Epi/Hypolimnion: First line contains number of data triples, next lines contain the values: time [d] I epilimnion value I hypolimnion value (example: mean temperatures [0 0] of Greifensee: 13 O.OOOOE+OO + 4.6287E+oo+ 4.6898E+OO 3.1 OOOE+01 + 3.5333E+OO+ 3.6390E+OO 6.0000E+01 + 3.8193E+OO+ 3.7995E+OO 9.1000E+01 + s.6714E+oo+ 4.7276E+OO 1.2100E+02 + 9.5998E+OO+ 5.9065E+OO 1.5200E+02 + 1.4609E+01 + 6.6492E+OO 1.8200E+02 + 1.7606E+01 + 7.1867E+OO 2.1300E+02+ 1.9227E+01 + 7.62B7E+oo 2.4300E+02 + 1.8736E+01 + 7.9417E+OO 2.7300E+02 + 1.5942E+01 + 8.0767E+OO 3.0400E+02 + 1.1604E+01 + 7.8897E+OO 3.3400E+02 + 7.2472E+OO+ 6.5666E+OO 3.6500E+02+ 4.6287E+oo+ 4.689BE+OO This time series shall be used repeatedly for subsequent years (MASAS). 4 Time & Depth Variable 1st line: number of depth and time values of the table, nd and nt 2nd line: nd depth levels [m] the following nt lines contain the time value [d] and the data values Example: 3 temperature profiles for day 0, 30, 60; for 5 depth levels 0, 1,2,5, 10 m: s+ 3 + o+ 1 + 2+ s+ 10 o+ 2.5+ 2.7+ 2.9+ 3.5+ 4.o 30+ 4.9+ 4.2+ 4.0+ 3.9+ 4.0 so+ 9.2+ s.9+ 6.7+ 5.2+ 4.1 After the data table, a comment text may follow ... A-4

4. PARAMETERS ON LIBRARY FILES Parameters are stored in library files using the format given in Tab. A3.

STRUCTURE EXAMPLE OF FILE CONTENT EXPLICATION (Acid hydrolysis rate constant for atrazine PAR (PAR) Code word for parameter (can be omitted for higher efficiency) id kaD Unique identifier. Used to find the program variable corresponding to the parameter on the file. COMMENT The COMMENT In aqueous solution, pH 3.1, Optional comment. staning with comment; cone. 40µg/ml. t 112 66 d. Product: the code word COMMENT, OH-atrazine. terminated with ; Ref 40 in Environmental Chemistry of Semicolons within the comment Herbicides, Vol.II. R. Grover & A. J. are automatically doubled: ;; Cessna. (eds.), CRC Press 1991.; n 0 Parameter type code (0 11 tant T 3.3700E-03 table format Ch. 1.3 Tab. A3: Parameter format of MASAS library files . CURRICULUM VITAE

Markus Maria illrich

8 November 1959 Born in Lucerne, Switzerland

1966-1971 Primary school in Lucerne

1971-1978 High school in Lucerne

1978 Leaving certificate, Matura C

1978-1984 Study at the Swiss Federal Institute of Technology (ETH), Zurich

1984 Diploma in Biology (Dipl. natw. ETH)

1985-1987 Member of project group CELTIA/IDA at the Institute of Automatic Control and Industrial Electronics, Swiss Federal Institute of Technology, Ziirich

1985-1987 Postgraduate study in Automatic Control Theory, Swiss Federal Institute of Technology, Zurich

1987-1991 Ph.D. thesis at Swiss Federal Institute for Water Resources and Water Pollution Control (EAWAG), Dubendorf I Swiss Federal Institute of Technology (ETH), Zurich