PB82-254079
Modeling the Fate of Toxic Organic
Materials in Aquatic Environments
Rensselaer Polytechnic Inst,
Troy, NY
Prepared for
Environmental Research Lab.
Athens, GA
Apr 82
U.S. DEPARTMENT OF COMMERCE
National Technical Information Service
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NOTICE
Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
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ABSTRACT
Documentation is given for PEST, a dynamic simulation model
for evaluating the fate of toxic organic materials (TOM) in
freshwater aquatic environments. PEST represents the time-vary-
ing concentration (in ppm) of a given TOM in each of as many as
sixteen carrier compartments; it also computes the percent
distribution and half life of the TOM in each of the carriers.
Possible carriers include phytoplankton, macrophytes, zooplank-
ton, waterbugs, zoobenthos, fish, particulate organic matter,
floating organic matter, clay, and water (with TOM in the
dissolved phase).
PEST simulates TOM degradation by hydrolysis, oxidation,
photolysis, microbial metabolism, and biotransformation by high-
er organisms; it} simulates TOM transfer by solution, volatiliza-
tion, sorption, absorption onto gills, consumption, excretion,
defecation, biodeposition, mortality, and throughflow. These
are subject to time-varying environmental factors such as pH,
temperature, dissolved oxygen, wind, solar radiation, and bio-
mass and condition of organisms. . ,; .
i . _ i- ..
The model has been verified, with process-level laboratory
data and with ecosystem-level site data. The site data for
.fish ponds in Missouri and Israel and a reservoir in Iowa con-
stitute prototype data sets that can'be used to evaluate other
compounds. ... ...
PEST is an interactive, user-oriented model with twelve
commands; The user can edit parameters and driving variables,
display process-response curves for' all combinations of pro-
cesses and driving variables, run a simulation for any length
of time, print any or all -state-variable results,:debug load-
ings and rates during the simulation, tabulate the results,
obtain line-printer and graphics-device plots, dump COMMON
block contents, and access an extensive HELP file.
The model is written in standard FORTRAN IV and will run in
22k on a PDP11 with overlaying. It has also been tested on an
IBM3033. The program is well structured and highly modular and
is easy to understand. ' System-dependent features are restricted
to two optional subroutines: one which handles operations such
as file numbering and time calls and one which provides an
interface to graphics terminals and plotters. Instructions are
1 iv..
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FOREWORD
Environmental protection efforts are increasingly directed
toward preventing adverse health and ecological effects associa-
ted with specific compounds of natural or human origin. As part
of this Laboratory's research on the occurrence, movement, trans-
formation, impact and control of environmental contaminants, the
Environmental Systems Branch studies complexes of environmental
processes that control the transport, transformation, .degrada-r
tion, fate, and impact of pollutants or other materials in soil
and water and develops models for assessing exposure to chemical
contaminants.
Concern about environmental exposure to synthetic organic.
'compounds, has increased'-the heed for techniques to. predict the
behavior of chemicals entering the environment as a result of
the manufacture, use, and disposal of .commercial products. In ...
response to this need,. :a number of mathematical models have been
developed to provide information about the fate of these materi-
als as an aid to environmental researchers, planners, and mana-
gers. This report describes PEST, a dynamic simulation model
for evaluating the fate of toxic organic materials in freshwater....
environments that provides a particularly detailed analysis of
bioacc.umulation. - ,-.;;: .,. . . ...
David W. Duttweiler
:-.';' ' :';-;.Director _'' '
'v':;._ Environmental Research Laboratory
'-' ,.''. ..''-.Athens, Georgia , ,
111
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CONTENTS
FOREWORD. -.......' ill
ABSTRACT iv
FIGURES ix
TABLES xiv
ACKNOWLEDGEMENTS xv
1. INTRODUCTION 1
RELATIONSHIP TO OTHER MODELS 1
CHARACTERISTICS OF MODEL. . -. . . . 2
I .
2. PROCESS EQUATIONS AND PARAMETERIZATION. ..... 7
HYDROLYSIS 7
. Verification. . . . . 13
OXIDATION -. 15
PHOTOLYSIS. 16
Verification '.'."'. 21
.. VOLATILIZATION..... ... .....'._. .......... .,... , . . 24
Gas-phase Mass Transfer'Coefficient 26
Liquid-phase Mass Transfer Coefficient. ... .28
Verification 30
SOLUTION 31
MICROBIAL METABOLISM. . 32
,. SORPTION. . ...... .;, ..... . . .'.- . 40
GILL SORPTION 41
....CONSUMPTION . . . .-. . . . . . . . . .'.-.-,. .-.... 44
Ingestion 45
/. BIOTRANSFORMATION..- . . ....... ........;. . -.,..... . ... . 48
3. DATA REQUIREMENTS . ; ":'. .-,.. -.. . . . ... 51
COMPOUND-SPECIFIC PARAMETERS. ......... 51
1. Hydrolysis 51
2. pxidation 52
3. Photolysis 52
4. Volatilization 52
5. Solution 52
6. Microbial Degradation .......... 52
7. Sorption 53
8. Bioaccumulation 53
i
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given for converting the program .and data files from the dis-
tribution tape to the user's computer-installation.
This report was submitted in fulfillment of Grant No.
R804820-03-4 by Rensselaer Polytechnic Institute under the
sponsorship of the U.S. Environmental Protection Agency. This
report covers the period August 1976 to January 1981, and work
was completed as of January 1981.
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SITE-SPECIFIC CONSTANTS AND. DRIVING VARIABLES . . 53
ORGANISM-SPECIFIC PARAMETERS 54
4-. VERIFICATION 57
PARATHION IN ISRAELI FISHPONDS 58
PENTACHLOROPHENOL IN MISSOURI FISHPONDS 61
DIELDRIN IN AN IOWA RESERVOIR 63
5. USER'S MANUAL 68
LOGON 68
Example 68
EDIT '68
. Syntax. 68
Examples. . ........... 69
START ........... ........ . 69
Syntax. . . . . . . ..'.'...: . ,:..'. . . . .. 69
... Example . . .... .... . . . ... . ..., . . ...-........ . . 70
: -PRINT .;. . '.' .".-.'. . .". V ."'."".'. V.............. . 70
Syntax ...... . . . ... ..... . .. .'..'71
' Example ..'..' 71
DEBUG 71
- Syntax. . ...... . . 72
Example 72
TABULATE ' . 73
Syntax. ...... ...... 73
Example 73
PLOT. .'..'.'. 73
Syntax. . . . 74
Example ............... 74
DISPLAY 75
Syntax ." 75
Example . . . "".' ",""';-. ...... 75
DUMP. ....... 76
. Syntax. ....;' : 76
Example i, . 76
HELP. 77
-.-.' ... Syntax. 77
. . . Example . .- . .... ;'. ... . 77
QUIT 78
Syntax 78
Example 78
6'.' PROGRAMMER'S GUIDE. .. 79
' INTRODUCTION. . . . ... . . . . : . . ..... 79
FILE UNITS. 79
COMMON BLOCKS ........ 81
BUILDING A MODEL. . . . ... .. . .... . . . . 82
7. REFERENCES 84
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APPENDICES S2
Parameters and Loadings used in Simulation
of Pond A-7, Dor, Israel . 92
Parameters and Loadings used in Simulation
of Treatment-3 Ponds, Columbia, Missouri 112
Loadings used in Simulation of Treatment-2
Ponds, Columbia, Missouri 131
Parameters and Loadings used in.Simulation
of Coralville Reservoir, Iowa . . . 135
GLOSSARY OF PARAMETERS AND VARIABLES 156
INDEX . 162
Vlll
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FIGURES
Number Page
1 Compartments in the PEST model 3
2 PEST process flow chart . . 5
3a Hydrolysis of parathion 9
3b Hydrolysis of peritachlorophenol . 10
3c Hydrolysis of methoxychlor. 10
3d Hydrolysis of 2,4-D 11
.4a Sensitized photolysis of methoxychlor ........ 23
4b Unsensitized photolysis of. methoxychlor ...... 23
.1 ...
4c Photolysis of pentachlorophenol .......... 24
5- Two-film model of volatilization from the
surface of water. . . * . .... ............ 25
6 Volatilization -of 'a hypothetical li'quid-phas'e
: -''. .. control compound* ". .*". . . ';-/"!;.,..*- .' ....... 30
7 Malathioh remaining vs. incubation, time ...... 34
8 Effect of dissolved oxygen on microbial
.... . degradation of pentachlorophenol.. .. . . ... . . . . . 35
9 . Effect of pH on microbial degradation of
pentachlorophenol .36
10. . Effect of temperature on microbial degradation
.--..]'' of pentachlorophenol.' 37
.. 11 ._ "Relation .between the concentration of
methoxychlor in a fish and time .......... 45...
12.""' 'Relationship between -BTRANS (g transformed/g :,..
. ' organism/day) and TOM concentration (g TOM/g) . . .. .-50
ix
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13a Comparison of predicted and observed concentra-
tions of Parathion in dissolved phase in Pond
A-7, Dor, Israel 58
13b Comparison of predicted and observed concentra-
tions of Parathion in zooplankton.in Pond .A-7,
Dor, Israel . . 59
13c Comparison of predicted and observed concen-
trations of Parathion in carp in Pond A-7,
Dor, Israel. 59
13d Comparison of predicted and observed concen-
trations of Parathion in Tilapia in Pond A-7,
Dor, Israel 60
13e Comparison of predicted and observed concen-
trations of Parathion in silver carp in Pond
A-7, Dor, Israel . . 60
14a Comparison of predicted and observed concen-
trations of Pentachlorophenol in dissolved
phase in Treatment-3 ponds, Columbia, Missouri ... 61
14b Predicted concentrations of Pentachlorophenol
in clay and particulate organic material in
Treatment-3 ponds, Columbia, Missouri 62
14c Predicted concentrations of Pentachlorophenol
in phytoplankton and zooplankton in Treatment-
3 ponds, Columbia, Missouri 62
14d Predicted and observed concentrations of
Pentachlorophenol in large-mouth bass in
Treatment-3 ponds, Columbia, Missouri 63
15a Comparison of predicted and observed concen-
trations of Pentachlorophenol in dissolved
. phase.in Treatment-2 ponds, Columbia,
. . Missouri . 64
15b Comparison of predicted and observed concen-
trations of Pentachlorophenol in bluegills
in Treatment-2 ponds, Columbia, Missouri 64
15c Comparison of predicted and observed concen-
trations of Pentachlorophenol in large-mouth
bass in Treatment-2 ponds, Columbia, Missouri. ... 65
15d Comparison of predicted and observed concen-
trations of Pentachlorophenol in channel
catfish in Treatment-2 ponds, Columbia, Missouri . . 65
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16a Comparison of predicted and observed concen-
trations of Dieldrin in dissolved phase in
Coralville Reservoir, Iowa from 1968 to
1977. 66 '
16b Comparison of predicted and observed concen-
trations of Dieldrin in carp in Coralville
Reservoir, Iowa 67
16c Comparison of predicted and observed concen-
trations of Dieldrin in the dissolved phase
in Coralville Reservoir, Iowa 67
Al Temperature loading used in simulation of
Pond A-7, Dor, Israel 107
A2 pH loading used in simulation of Pond A-7,'
Dor, Israel . . . 107
'A3. Dissolved oxygen loading used.in simulation.of
' . Pond-A-7, Dor, Israel ........ 108
A4 Wind velocity loading used in simulation of
Pond A-7, Dor, Israel . 108
A5 Solar radiation loading used in simulation of
Pond A-7,1 Dor, Israel 109
A6 Phytoplankton biomass used in simulation of
Pond A-7, Dor, Israel ........... .'"'.' . . 109
A7 Zooplankton biomass used in simulation of
Pond A-7, Dor, Israel . 110
A8 : Water bug biomass used in simulation of
Pond A-7, Dor, Israel .; ..... 110
A9 .Carp biomass used in simulation,of Pond A-7,
Dor, Israel . . Ill
Bl Temperature loading used in simulation of
Treatment-3 ponds, Columbia, Missouri . . . . ... . 127
B2 pH loading used in simulation of Treatment-
3 ponds,iColumbia, Missouri .' 127
B3 Dissolved oxygen loading used in simulation
of Treatment-3 ponds, Columbia, Missouri. ...... . 128
B4 Wind velocity loading used in simulation of
Treatment^3 ponds, Columbia, Missouri . 128
XI
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B5 Solar radiation loading used in simulation
of Treatment-3 ponds, Columbia, Missouri 129
B6 Phytoplankton biomass used in simulation
of Treatment-3 ponds, Columbia, Missouri 129
B7 Zooplankton biomass used in simulation of
Treatment-3 ponds, Columbia, Missouri ....... 130
B8 Bluegill biomass used in simulation of
Treatment-3 ponds, Columbia, Missouri ....... 130
Cl Temperature loadings used in simulation of
Treatment-2 ponds, Columbia, Missouri 131
C2 pK loadings used in simulation of
Treatment-2 ponds, Columbia, Missouri 131
C3 Dissolved oxygen loadings used in simulation
of Treatment-2 ponds, Columbia, Missouri 132
C4 Phytoplankton biomass used in simulation of
Treatment-2 ponds, Columbia, Missouri 132
C5 Zooplankton biomass used in simulation of
Treatment-2 ponds, Columbia, Missouri 133
C6 Bluegill biomass. used .in simulation, of
Treatment-2 ponds, Columbia, Missouri . . .... . . 133
C7 Pentachlorophenol loadings used in simulation
of Treatment-2 ponds, Columbia, Missouri. ..... 134
Dl. Temperature loadings used in .simulation of
Coralville Reservoir, Iowa. ... 150
D2 pH loadings used in simulation of
Coralville Reservoir^ Iowa.-. . . . . ... . . . ..150
D3 Dissolved oxygen loadings used in simulation
of Coralville Reservoir, Iowa 151
D4 Wind loadings used in simulation of
Coralville Reservoir, Iowa 151
D5 Particulate organic matter loadings used in
simulation of Coralville Reservoir, Iowa. 152
D6 Clay loadings used in simulation of
Coralville Reservoir, Iowa 152
xri
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D7 ' Phytoplankton biomass used in simulation
of .Coralville Reservoir, Iowa 153
D8 Zooplankton biomass used in simulation
of C6ralville Reservoir, Iowa . 153
D9 Carp biomass used in simulation of
Coralville Reservoir, Iowa 154
DID Water flow loadings used in simulation of
Coralville Reservoir, Iowa 154
Dll Dieldrin loadings used in simulation of
Coralville Reservoir, Iowa 155
XXll
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TABLES
Nuinber Page
1 STATE VARIABLE EQUATIONS 3
2 .. PARAMETERS (1/M day) FOR SELECTED COMPOUNDS ... 9
3 COMPARISON OF PEST HYDROLYSIS HALF-LIVES WITH
LITERATURE VALUES FOR VARIATIONS IN pH AT
CONSTANT TEMPERATURE 14
4 COMPARISON OF THE SOLAR INTENSITY BREAKDOWN
IN PEST WITH THE RESULTS OBTAINED BY ZEPP AND
CLINE .(1977). 18
5 ATTENUATION COEFFICIENTS (AS PRESENTED BY
HAUTALA, 1978) 19
6 ATTENUATION SENSITIVITY ANALYSIS OF PENTA-
CHLOROPHENOL UNDER MIDSUMMER SUN 20
7 COMPARISON OF PEST PHOTOLYSIS HALF-LIVES WITH
LITERATURE VALUES 22
8 CALCULATED HENRY'S LAW CONSTANTS. ........ 27
9 LE BAS ADDITIVE VOLUMES.'TO-CALCULATE LIQUID
.'.MOLAL'.VOLUME (cc/g mole) ;-..... ............ 27
10 'CALCULATED OR OBSERVED MOLAL VOLUMES FOR
SELECTED-COMPOUNDS. ...'. 28
11 VERIFICATION OF TEMPERATURE CORRECTION. ..... 29
12 LIQUID-PHASE MASS TRANSFER COEFFICIENTS AT
25°C 29
13 COMPARISON OF PEST VOLATILIZATION RATES WITH
LITERATURE VALUES 31
xiv
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ACKNOWLEDGEMENTS
This project has been financed with federal funds from the
Environmental Protection Agency under Grant No. R804820-03-4.
We wish to thank all the other people who contributed to this
project, including: Ronald Avery, Sipra Choudhury, Steve Cohen,
Pascal deCaprariis, Audrey Depelteau, George Estel, Shirley
Gully, Robert Haimes, Alan House, Tammy Kimmel, Donna Leung,
Diana Merchant, Phil Perry, George Pierce, Steve Plust, William
Reeves, Eric Ruff, and Corey Trench. Also, we appreciate the
guidance provided by Ray R. Lassiter, who was the project
officer during the initial stages of the research. .
The pentachlorophenol data were provided by Terence P.
Boyle, Everett F. Robinson-Wilson, and Foster L. Mayer of the
Columbia (MO) National Fisheries Research Laboratory, Fish and
Wildlife Service, The parathion data were provided by Avital
Gasith and A. S. 'Perry of Tel Aviv University, Israel. The
dieldrin data were provided by Jerald L. Schnoor and Donald B.
MacDonald of the 'university of Iowa. The generosity of these
individuals in freely offering advice and published and un-
published data is gratefully acknowledged.
xv
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SECTION 1
INTRODUCTION
RELATIONSHIP TO OTHER MODELS
The PEST model has been under development for the past four
years in .response to the need for a detailed, chemically- and
biologically-realistic model to predict the fate of toxic
organic' materials in .natural 'aquatic-.environments. ' As such,
its development .has paralleled that of several other fate,.. "
models; however, each model has its particular emphasis, and
.PEST fulfills a need for detail and biologic realism that is
no.t addressed ,by other -models-. (Park et al. ,. 1980; ,A-lbane.se . -
'et-al. 1981) . ' . ' . '
PEST:'can "be considered an evaluative -model in the sense of
Lassiter (1975). As such, it is intended to be used primarily
to indicate the relative importance of the various processes
under well defined environmental conditions and to determine
.the environmental.compatibility of .particular organic materials..
Many"--of'the demands placed on the -.'EPA'" relative -to -evaluating
..new materials can be :,ans-wered .through the- expediency of such a.
process-or'ient'ed evaluative model. -The model can-'also'as-sist'
in the extrapolation of data from laboratoryexperiments and
microcosms to natural, eh:vironments...,.-.(Park,'Tndyke and Heitzman,
in press) . , .. "_, '-'--' '"
'PEST is often compared with the EXAMS model;, both are
components of the fate modeling program of the Environmental
Systems Branch of the Athens- Environmental Research Laboratory.
EXAMS was developed as an in-house: effort (Lassiter, Baughman,
and Burns, 1978; Baughman and Burns, 1979; Burns, Cline and
Lassiter, in press). EXAMS differs from PEST in that: 1) it
partitions the chemical into ionic species; 2) it represents
bioaccumulation as a bioconcentration factor for the ecosystem;
and 3) it is a steady-state model. EXAMS is designed for use
As'-a- quick screening tool, while PEST provides a more detailed
'analysis, ..especially with respect to bioaccumulation.
'""-... A similar .-model,. based--in part on the SRI model. (Smith et
al., 1.977), was programmed by Schnoor et ,al (1979). It re-
presents' non-steady state -. and- considers-..bioaccumulation in
.several" fish types; therefore it comes somewhat closer to 'the
conce'ptv.of PEST. .A model developed at DOW Chemical Company
"'" """ "" ' ''' ' 1 ' ' ''"- - " '
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(Neely and Blau, 1977; Branson, 1978) is simpler in concept
but does distinguish between uptake and depuration in fish.
The SERATRA model (Onishi, and Wise/ 1979) is characterized
by good hydrodynamic resolution. Its .-chemical and .biological
realism is less than PEST, but it; is-.probably .better, than PEST
in representing physical., transport of ...toxic" organic, materials
in riverine and estuarine environments.
Another group of fate models- emphasize bioaccumulation, but
ignore chemical processes. Thomanri '(1978) models bioaccumula-
tion in relation to size'of organism. Weini'nger '(1978) consi-
ders detailed bioenergetics in .simulating the uptake of poly-
chlorinated biphenyls (PCBs) by lake trout;- 'his approach is
similar to that used in modeling bioaccumulation in PEST,
although the extreme lipophilic nature of PCBs. permits some
simplifications that are not taken in PEST.
Each of these models serves a specific purpose. However,
only PEST combines detailed chemical kinetics and bioenergetics
to permit examination and.evaluation of the behavior of toxic
organic materials in the context of the entire aquatic eco-
system. Of course, use of such a..complex model requires an
understanding of the many assumptions and parameters; as well
as a knowledge of the mechanics of 'the program. The purpose of
this report is to acquaint the potential user with the details
of PEST so that the model can be used- both easily and wisely.
CHARACTERISTICS OF MODEL . - '"
PEST is capable of]simulating.the time-varying concentra-
tion of a ;toxic organic material (TOM)-.in each-of as many as
sixteen carrier compartments. The sixteen.state variables can
be parameterized to represent a variety of. TOM-carrier associa-
tions typical of aquatic ecosystems (for 'example Figure 1).
;'...! The s»tate-variable~ equations are .-ordinary differential
equations with source and sink terms for the various processes
that result, in additions to,., and losses-from, the-carriers
(Table 1). Broad categories include TOM in: plant's, such as
phytoplankton and macrophytes; animals, such as zooplankton,
waterbugs, zoobenthos, and fish (different species and/or age
classes); dissolved phase, either in the water column or in
interstitial water; particulate organic matter, either suspended
or as bottom sediment; floating organic matter, usually as a
surface film; and clay, either suspended or as bottom sediment.
The source and sink terms for the state variables are
represented by process equations. Most of the process equations
are non-linear, and many involve several different environmental
factors .(Figure 2) .
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\ \ ~~ ci
Figure 1. Compartments in the PEST model"." FMACRO = "floating
macrophyte, MACRO = macrophyte, FOM = floating .or-
ganic matter, POM = particulate organic matter,
WBUG = water bug, Z003 = zoobenthos,' ZOOP = zooplank-
ton, PHYTO = phytoplankton.
TABLE 1. STATE VARIABLE EQUATIONS
ANIMALS(zooplankton,zoobenthos and/or fish)
State Variables 1-10
dC/dt = CONS-EX-DEF-MORT+BSORP+GILSRP-BTRANS+
LOADS
(Eq. 1)
where
C = concentration of toxic organic material (TOM)
CONS = intake of TOM through consumption
EX = loss through excretion
DBF = loss through defecation
MORT = loss through mortality of carrier organisms
BSORP = intake through passive sorption onto body
GILSRP = intake through sorption onto gill as a consequence
of respiratory activity
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BTRANS = biological transformation of TOM
LOADS = input of TOM into ecosystem segment as a result of
movement of carrier organisms
PLANTS (phytoplankton and/or macrophytes)
State Variables 11 and 12 . '
dC/dt = BSORP-BTRANS-MORT-CONS+LOADS-MMET (Eq. 2)
WATER (DISSOLVED PHASE)
State Variable 13
dC/dt = -HYDR-OXID-PHOT-VOLAT+SOLU-BSORP-GILSRP-
SORP+LOADS-MMET (Eq. 3)
where .
HYDR = loss through hydrolysis
OXID = loss through oxidation
PHOT = loss through photolysis
MMET = loss due to microbial metabolism
VOLAT = loss through volatilization
SOLU = addition through solution
SORP = jLoss through sorption (or gain through desorption)
PARTICIPATE'ORGANIC MATTER '(POM) . --^---..
State Variable 14 ' ': . ' ' .
dC/dt = -HYDR-OXID+MSUM+DEF-MMET-CONS-PHOT+
SORP+LOADS (Eq. 4)
where
MSUM = addition of TOM due to mortality of carrier organisms
FLOATING ORGANIC MATTER (FOM)
State Variable 15
dC/dt = -VOLAT-MMET-CONS+DEF-HYDR-OXID-PHOT+
SORP-SOLU+LOADS (Eg. 5)
CLAY
State Variable 16
dC/dt = -MMET-CONS+DEF-HYDR-OXID-PHOT+SORP-
SOLU+LOADS (Eq. 6)
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DEFECATION
'Figure 2. PEST process flow chart.
Output from the model includes: (1) the time-varying con-
centration of the toxic material in each carrier (in ppm) , (2) '
the percent distribution of the toxic material among the
carriers, and (3) the halflives of the toxic material in:each "
carrier.. One can also obtain plots of the degradation rates,
both as they vary through time and as a function of. env.iron-
..inental factors.. . . ' "
The model has been verified with process-level laboratory
data for several compounds and with ecosystem data from fish .
ponds in Missouri and Israel and from a reservoir in Iowa. The
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site constants and environmental driving variables for these
ecosystems constitute useful "prototype" data sets that enhance
the value of the model for evaluative purposes. The process-
level results from our studies and from our summarization of
the literature are used as examples in the following section.
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SECTION 2
PROCESS EQUATIONS AND PARAMETERIZATION
HYDROLYSIS HYDR
PEST represents the degradation of the TOM through hydro-
lysis as: .... ... ......
HYDR = (TCORR*KO+TCORR*KH*10~PH+TCORR*KOH*10PH~14
.- +KA*HA+KB*HB+TCORR*KCAL) *CONCEN (Eq. 7)
This includes the temperature-correction factor for the Xth rate
constant based on the standard Arrhenius energy equation:
EN EN
TCORR(X) = *. - 1.987(TCOPT)
where
e = natural exponent
EN = activation energy for effect of temperature on particu-
lar . reaction (cal/mole)
TEMP = ambient, temperature (°C)
TCOPT = temperature at which rate constant was obtained (°K)
with 1.0R7 being the universal 'gas constant and 273 being the
conversion to °K.
The other terms in Eq. 7 are:
KO = uncatalyzed rate constant (I/days)
KH = acid-catalyzed rate constant (1/M days, where M is
molality)
KOH = base-catalyzed rate'constant (1/M days)
pH = ambient pH . . .
. . KCAL = rate constant to account for colloidal, metal-ion,
; and phase-transfer catalysis adjusted for site
conditions (I/days) ..
" KA. = rate constant for Bronsted acid catalysis (I/days)
HA = concentration of Bronsted acid'(g/m3).
KB = rate constant .for Bronsted base catalysis (I/days)
HB = concentration .of Bronsted base (g/m-*)
CONCEN = concentration of TOM (g/m^)
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Eg. 7 assumes that the activity of the hydrogen ion in
natural waters is identical to the concentration. That is, the
activity coefficient of the hydrogen ion is taken'as 1. This
assumption is good for natural waters containing few dissolved
electrolytes (0 to 220 ppm), but will not apply in brackish or
salt waters, where the total electrolyte concentration can ex-
ceed 35,000 ppm. The effect of electrolyte concentration, may
be important (Walker 1976, .1978), although studies conducted in
support of this project show that it has little effect on the
hydrolysis .of some compounds such as atrazine (Herbrandson,
et al., 1977).
The uncatalyzed, acid-catalyzed, and base-catalyzed rate
constants chosen for these equations are the specific rate con-
stants that measure the contribution to the disappearance rate
due to specific types of catalysis. These are easy to measure
in the laboratory; determination of their values does not re-
quire detailed knowledge of the reaction mechanism. The kine-
tic data, however, may not fit for some compounds, and general
rate constants and another rate expression may be required.
The definition of hydrolysis as the disappearance of the
TOM through reaction with water does not imply a specific
mechanism, and generally includes several chemical processes.
Eq. 7 does assume that -the rates can ,be expressed in terms of
pseudo first-order constants, meaning that the second-order
rate is made pH-dependent. By combining these constants, as
in Eq. 7, the overall rate expression can-model several differ-
ent types of hydrogen-ion and Bronsted acid-base dependences.
For example, hydrolysis rates for atrazine, malathion, carbaryl
and methoxychlor can all be expressed in terms of these con-
stants, even though atrazine reacts due to nucleophilic sub-
stitution of the hydroxide ion for the chloride ion, malathion
decomposes in water due to elimination arid carboxylate ester
hydrolysis, carbaryl hydrolyzes by an elimination reaction,
methoxychlor undergoes base-catalyzed elimination of HCL.
The pH-dependence and importance of he hydrolysis reaction
vary greatly for different compounds. These variations are
expressed by the relative magnitudes of the KH, KO, and KOH
parameters (Table 2). Parathion is base-catalyzed (Figure 3a),
pentachlorophenol is slightly acid-catalyzed (Figure 3b),
methoxychlor is primarily base-catalyzed (Figure 3c), and
2,4-D is both acid- and base-catalyzed with the minimum at a
pH of about 4.7 (Figure 3d).
The value for KO serves as the minimum rate of hydrolysis
over the entire pH range. This is the rate constant at neutral-
ity, where the acid and base concentrations are equal. Because
the overall hydrolysis rate is the sum of the neutral, basic,
acidic and miscellaneous terms, it is important when choosing
hydrolysis parameters to note the overlap of KO into both acidic
8
-------
TABLE 2. PARAMETERS (1/M day) FOR
SELECTED COMPOUNDS
KH
KO
KOH
References
Methoxychlor
Parathion
1.9E-3 2.57E-3 31.10
Wolfe et al., 1977
Mabey & Mill, 1978
1.28E2 3.64E-3 2.46E3 Ketelaar & Gersmann,
1958
Pentachlorophenol 1.13E4 5.83E-3 3.34
2,'4-D ' 4.9E6 0
Akisada, 1964
2.61E6 Wolfe et al., 1977a
Zepp et al., 1975
fi!.
v
aco aoo
PH
tun
Figure 3'a. Hydrolysis of parathion.
.9
-------
s
o
X
a
a
2*
u
SJKI
PH
1UKI
Figure 3b. Hydrolysis of pentachlorophenol,
to
C3 -
$
'»
OJ
I
Figure 3c. Hydrolysis of methoxychlor
10
-------
£3
X
»s
&
8
to
PH
Figure 3d. Hydrolysis of 2,4-D.
and basic waters.1 If the overall rate constant (second order)
is .001 at pH=8, then a value of KO greater than this will over-
shadow any KOH calculation at that pH.
For example: if a-study shows that.the overall hydrolysis
rate of a compound is O.lrat pH=8 and ..001 at pH=7, then K0=
.001 and KOH=0.l/10~b=10~J. However, if the rate at pH=8 is
.0001, then KO cannot be .001. In this case .the.user has the
choice of selecting a negative KOH and keeping K0=.001, or mak-
ing up for the rate at pH=7 by choosing a large value for KH
(this would be the case if there were a high degree of acid
catalysis). '
In this way the three rate constants are interdependent,
especially around the transitions between acid and base cataly-
sis. Because most natural waters are between pH=6 and pH=9,
this range should be considered when choosing parameters. It
may be necessary to sacrifice accuracy at extreme pH's in order
to gain a more accurate model over this middle range.
Each of the'pseudo'first-order rate constants is easily
.obtained by dividing the rate at a given pH by the ion concen-
tration, giving units of g/m3 day M. In general there are two
forms in which hydrolysis parameters are available. The first
-------
is in the form of either first- or second-order rate constants.
These can be used (after conversion to pseudo first-order con-
stants) without considering the effects at transition regions of
pH. Secondly, the parameters can be obtained from half-life
values; however, care must be taken when calculating rate con-
stants not to allow an overlap of rates which would give in-
accurate results. The best way to do this is to find KOH. and
KH at pH's far enough away 'from neutrality that there is no
interference. Also, if there is a high degree of catalysis,
either acidic or basic, then there will be little effect of the
KO value at a more extreme pH.
KA. and KB add the effects of Bronsted type acids and bases
to those of the hydronium and hydroxide ions. Again, the
magnitude of these terms will vary with the TOM and the acids
or bases responsible for the catalysis. For example, Wolfe et.
al. (1976) demonstrated that the degradation of malathion is
not subject to general base catalysis. If the presence of acids
and bases other than hydronium and hydroxide ions is suspected
in natural waters, then experimentally determined rate constants
must be included as parameters in the model. Exclusion of such
data may eliminate a significant portion of the total rate for
hydrolysis (and provide a "worst case" simulation).
The empirical term KCAL in the rate expression accounts for
catalysis by suspended colloidal .materials, sediment organic
matter, metal ions, phase transfer catalysis, and any other
factors which increase or decrease .the rate of TOM hydrolysis.
Such effects may make a significant contribution to the total
hydrolysis rate. For example, Li and Felback (1972) demonstra-
ted that the presence of humic acids increases the rate of
atrazine hydrolysis 50-fold. Khan (1978) observed increased
hydrolysis rates for atrazine in water containing fulvic acids.
Herbrandson et al. (1977) as a part of this project have shown
that colloidal catalysis is highly dependent on the chemical
nature of the colloids, and'can vary, ..in the case of atrazine,
from significant acceleration to no effect at all. Armstrong
et al. (1967, .1968) concluded that sterile soil particles in-
crease the rate of atrazine hydrolysis almost 10-fold, while
White (1976) found evidence of the hydrolysis of many S-tri-
azines on the acidic surface of montmorillonite. On the other
hand Skipper (1978) found no S-triazine hydrolysis in allophanic
clay colloids. Harris (1967) correlated the rate of hydrolysis
of simazine VIII (a chloro-S-triazine) with the percent oxidiza-
ble carbon present in the soil.
Metal ions can accelerate, decelerate or have no effect on
pesticide hydrolysis rates in both aqueous solution and on the
surface of colloidal clays. Copper (II) ions accelerate hydro-
lysis of Dursban, Diazinon, Ronnel, and Zytron (organic phos-
phorus pesticides structurally similar to malathion) at 20°C.
Acceleration is proportional to the ratio of copper ion . .
12
-------
concentration to pesticide concentration. The rate increases
as this ratio increases until equal concentrations are present,
after which the rate stays constant. Co^Caq), Zn+"f(aq), Ni++
(aq) , and Ca++(ag) do not catalyze the hydrolysis of these com-
pounds, however. First-order kinetics are observed for both the
accelerated and uncatalyzed reactions, except in the case of
Dursban, which reacts at a rate proportional to the-square of
the Dursban concentration (Mortland and Raman, 1967). Similar-
ly, Ketelaar et al. (1956) observed a 20-fold rate increase in
parathion hydrolysis when both metal ion and pesticide are pre-
sent in milli-molar quantities. He found only doubling of the
rate for paraoxon present. First-order kinetic behavior is
found in both of these cases. Copper ions bound to soil organic
material do not catalyze the hydrolysis of organic phosphorus
pesticides (Mortland and Raman, 1967). These observations, and
the low, naturally occurring concentrations of Cu++(ag) (1.2 to
53 ppb)(Wetzel, 1975)have led at least one group to discount
metal-ion catalysis as being important in environmental systems
(Mabey and Mill, 1,978). More research in this area is needed,
however, before this effect can be ignored or included in fate
modeling.
KCAL has been included in Eq. 7 to adjust the overall rate
for these effects 'because no comprehensive theoretical express-
ion for the rate constant due to these effects has been pro-
posed, and because the magnitude of these effects vary with
site composition and TOM concentration. This term may be
determined empirically by measuring the rate of hydrolysis in
natural waters, then subtracting the known rate constants from
the overall rate'constant. Alternatively, it can be estimated
from data in the literature for the types of phenomena just
discussed. Eventually, however, catalysis from these other
sources will have to be demonstrated and quantified if the model
is to yield accurate predictions.
Because the pH-dependence. of hydrolysis can cause orders-
of-magnitude differences in degradation rates within the range
of pH found in natural aquatic environments, it is necessary to
pay close attention' to the pH loadings used in a particular
'simulation. The pH in. .an unbuffered, highly-productive fish .
pond can vary by three units in the course of a day; this should.
be' represented by 'a carefully weighted average because the pre-
sent version of PEST does not simulate diurnal variations.
Verification
The,results of the hydrolysis calculation for several
compounds are presented in Table 3. The results are expressed
in terms of half-lives and are compared with literature values
under similar conditions. Without exception, the comparisons
of PEST half-lives with literature values show that the hydro-
lysis submodel is accurate- All the values are of the same or-
der of magnitude, and usually within 30% of the quoted literature
13
-------
TABLE 3. COMPARISON OF PEST HYDROLYSIS HALF-LIVES WITH
LITERATURE VALUES FOR VARIATIONS IN pH AT CONSTANT TEMPERATURE
Pesticide
Conditions
PEST
Literature
1) Carbaryl
2) 2,4-D
3) Methoxychlor
4) Malathion
6) PCP
pH=5
pH=6
pH=7
pH=8
pH=9
pH=6
pH=9
pH=9
pH=6
pH=7
. -pH=8
pH=9
pH=6
pH=7
pH=7 . 5
5
6
19
1
4
39
267
228
23
. . 2
69
173
210
.2
.3
.9
.5
.4
.96
.4
.29
.234
years
months
days
days
hours
days
hours
days
days
days
days
days
days*
days
days
3
4
13
1
3
44
270
150
15
1
0
n
.6
'.4
.3
.2
.96.
.0
.5
.15
.a.
years
months
days
days
hours
days
hours
days
days
days
days
days
The slight acid catalysis shown here is a result of the photo-
lysis observations, of Akisada (1964),-.in which photolysis was
3 times faster under acidic conditions. Because there is no
method for modeling ionic influence in the photolysis program,
any catalysis will have to be accounted for in the hydrolysis
calculation... . . ..,
values. A brief analysis of these results follows:
Carbaryl PEST half-life values are consistently 31%
'higher than the literature numbers. The pH sensitivity ir very
good, and the-fact that the difference in value is 31% over the
whole range shows that the acid-base relationships are reason-
able. The discrepancy is due to the choice of parameters. The
value chosen by Wolfe et al. (1976) for the pseudo first-order,
base-catalyzed constant is 2.94 E+5, while that chosen for PEST
is 4.34 E+5, based on Wolfe 'et al. (1978).
2,4-D (methyl ester) Because the parameter values for
2,4-D were calculated directly from the half-life values in the
literature, the results were fitted to the 'correct values. It
is revealing to note that a perfect fit was not obtained at a
pH of 6, the difference in values being about 10%. The accuracy
under acidic conditions was sacrificed for accuracy in the basic
range because: 1) the majority of the pH loadings for the
14
-------
validation site at Columbia, Missouri, are above 7.0, and 2)
from the half-life values it appears that 2,4-D is base-cata-
lyzed, and it was judged more important to have greater accuracy
where .half-lives are short.
Parathion Because of the number of studies done to
determine the rates of hydrolysis of parathion under various-
conditions, it is not surprising that the half-life values do
not correlate as well as the others. The literature values
quoted here were calculated from the average second-order rates
compiled by Wolfe et al. (1976) while the parameters were taken
from work done by Ketelaar (1950), which is included in the
Wolfe compilation. The Ketelaar values were chosen because of
the availability of other data in that report, including the.
activation energies for catalysis. Also, these half-lives were
.shorter and varied less with pH. The predominant form of
catalysis is in the basic range, and these half lives are.15%
.different from-the quoted literature values...
Malathion As is the case with carbaryl, the results for
malathion ;show the same pH relationship as their literature
counterparts, but with different base values. This is again due
.to the parameterization.
OXIDATION ' . OXID
Autooxidatioh reactions may be initiated by free radicals
present at low concentrations in naturally occurring substances
such as humic acids (Steellink, 1977); Schnitzer and Khan, 1972)
by free radicals formed thermally, or by metal ion catalysis
from peroxides. Those formed from photochemical processes are
considered as a part of photolysis'in .PEST. .The rate expression
used is .-based on first principles:- .,
OXID = KP/KT°-5*KEFF*RAD°'5*CONCEN (Eq. 9)
where
KP = the rate of the reaction between the TOM and alkoxy and
peroxy radicals (I/day)
KT = the rate of the competing reaction between two radicals
resulting in non-radical products (I/day)
. RAD = the concentration of radical initiator present in the
environment
. KEFF = the rate of the radical initiation reaction
CONCEN = the concentration of TOM (g/m^)
In the case of carbaryl, 2,4-D, malathion, and atrazine,
chemical' oxidation contributes little or nothing to the dis-
appearance rate (Wolfe et al., 1976). This may be due to the
15 . '"-..
-------
presence of naturally occurring, chain-terminating compounds
such as amines and phenols, very low rates of initiator genera-
tion, or unreactivity of the TOM towards singlet oxygen.
Methoxychlor, on the other hand, reacts with oxygen if
hydrogen peroxide is. present' in catalytic quantities (Wolfe, et.
al., 1976). Mabey and Mill'(1978), however, have estimated that
the propagation step in a peroxy-radical reaction scheme would
be so slow at concentrations expected in the environment that
oxidation of most organics would not occur by this process.
. . Chemical oxidation was not modeled as a part of the verifi-
cation of PEST. Oxidation of a given TOM will have to be
demonstrated for the site waters being modeled if this term is
to be included. The rate constants KT, KP, and KEFF will, in
any case, be empirically determined.
PHOTOLYSIS . PHOT
The degradation of the TOM due to interaction with .light
includes both direct and sensitized photolytic reactions. Dir-
ect photolysis is treated mechanistically by PEST whereas sensi-
tized photolysis is treated empirically. The formulation is:
PHOT = (PSIA*KA-fPSIB*KSEN)*CONCEN(I) (Eq. 10)
where . .
KA = the sum of the wavelength-specific, direct photolysis
rate constants (I/day)
PSIA = the direct photolysis quantum yield for the TOM
(unitless)
PSIB = the sensitized photolysis quantum yield for the TOM
(unitless) . :. .
KSEN = the rate constant for sensitized photolysis, deter-
mined empirically (I/day) ,
CONCEN(I) = the concentration of the TOM (g/rrr)
The first term in Eq. 10 accounts for direct' photolysis and
is based on the work of Zepp and Cline (1976). Rates are com-
puted for each of twelve ultraviolet wavelengths (297.5, 300.0,
302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 323.1, and
330.0 nanometers):
KA = KLAM(297.5)+KLAM(300.0)+...KLAM(330.0) (Eq. lOa)
KLAM(I) = IILAM(I)*ELAM(I)*1NT/(6.02E20*
ALPHA(I)) (Eq. lOb)
IILAM(I) = (IDLAM(I)*(1-10**(-ALPHA(I)*LD))+
ISLAM(I)*(1-10**(-ALPHA(I)*LS)))/D (Eq. lOc)
16
-------
LD- = D*1.14
LS = -D*1.2
IDLAM(I) = INTENS(I)*FRACD(I) (Eq. lOd)
ISLAM(I) = INTENS(I)*FRACS(I) (Eq. lOe)
INTENS(I) = INTEN*LAM(I)
where
ELAM(I) = molar extinction coefficient for the compound at
the Ith wavelength (I/mole cm)
.INT = the width of the wavelength interval on which the
' . designated wavelengths, are centered (nm)
....ALPHA(I) = extinction coefficient for site waters at Ith
wavelength. (I/cm) - . .-.--.
" ..b'= median depth of water (cm)'
FRACD(I) = fraction of irradiance that is direct at Ith
wavelength (supplied in program) (unitless)
FRACS(I) = fraction of irradiance that is indirect (sky)
(supplied in program) (unitless)
LD and LS = effective direct and diffuse underwater path
lengths for irradiance assuming a refractive in-
dex of 1.34 and solar inclination of 40° (cf.
Zepp and Cline, 1977)
LAM = wavelength (nm)
There are several constants in. the formulation.' 6.02E20
is a factor to convert from- -photons. to mole of photons (photons
I/Einstein cm^). 1.14 is the secant of the refracted angle of
light and 1.2 is 'the correction ..for the refraction of diffuse
light. , . ; '
ELAM and PSIA represent the molar extinction coefficients
at the twelve wavelengths, and the direct' photolysis quantum
yield,. There have been many studies to determine quantum
yields, and most extinction coefficients are available from
spectroscopy references (such as the Sadtler series). It is
important to note the solvent used for the extinction measure-
.mentj as a non-organic solvent is preferred.
The difference between the approach used in PEST and that
of"'Zepp.and' Cline -(19.77) is that PEST uses loadings rather than
calculating solar intensity as a function of latitude and
.season. The values for "solar intensity are entered as weekly
values of Langleys/day, the most common unit of solar irradiance;
these data'-are available for all. U.S.-Weather Bureau stations.
17
-------
The intensity at each wavelength is determined by multiplica-
tion of the intensity loading by the arrays FRACS and FRACD.
These two twelve-unit arrays represent the fraction of direct
and sky (diffuse) radiation at the .wavelengths considered.
These have been calculated from data.published by Green (1976),
and Bener (1972), and.compare reasonably well with the results
of .the Zepp and Ciine (1977) model (Table 4)'.
TABLE 4. COMPARISON OF THE SOLAR INTENSITY BREAKDOWN IN PEST
WITH THE RESULTS OBTAINED BY ZEPP AND CLINE (1977)
Wavelength
Direct
Diffuse
Total
W
(Zepp &
Cline)
297.
300
302.
305
307.
310
312.
315
317.
320
323.
330
5
5
5
5
5
1
.3033
.9590
.3291
.1188
.1243
.1707
.2019
.2412
.2814
.6881
.1467
.4038
E
E
E
E
E
E
E
E
E
E
E
E
12
12
13
14
14
14
14
14
14
14
15
15
.5035
.2309
.4997
.1544
.2556
.3706
.5414
.7488
.1027
.1122
.1571
.4521
'E
E
E
-E-
E
E
E
E
E
E
E
E
12
13
13
14
14
14
14
14
15
15
15
15
.8067
.3268
.8288
.2732
.3800
.5413
.7433
.9900
.1309
.1810
;3038
.8559
E
E
E.
E
E
E
E
E
E
E
E
E
12
13
13
14
14
14
14
14
15
15
15
15
.648
.219
.657
.163
.274
.444
.643
.836
.103
.121
.226
.762
E 12
E 13
E 13
E 14
E 14
E 14
E 14
E 14
E 15
E 15
E 15
E 15
Assumptions: '
1) Midsummer sun (approximately 550 ly/day) at 40° north
latitude
2) Atmospheric Ozone content equal to .320 cm STP
3) Index of refraction of-water equal .to "1.34
The advantage of the loading approach used in PEST over the
computation approach is that variations in total solar intensity
due to cloud cover and elevation are accounted for. However the
distribution over the ultraviolet range is affected by both
cloud cover and ozone variations, and this is not accounted for
in PEST.
The site constants for direct photolysis are the depth of
the section being studied and the attenuation of the site water.
The depth (D) is the median depth of the water and has units of
centimeters. The twelve-member array ALPHA represents the
attenuation coefficients of the site water, and has units of in-
verse centimeters. These coefficients are an important set of
parameters for the photolysis calculation, and the overall rate
of photolysis can vary 50-fold over the reported range of values.
18
-------
If no precise values for attenuation are available for the
site studied, it is helpful to have at least a qualitative
description of the clarity of the water, so that an educated
guess can be made as to which set of coefficients will be used.
Two sets of coefficients are listed in Table 5, the first for
pure water (Hautala, 1978) and the second for a sampling of
southern river waters (Zepp aidCline, 1977). For most applica-
tions a scaled-up set of coefficients could be derived. The
values for river water should serve only as an upper limit, as
these are taken from waters that are highly colored, and may be
as much as 95%different (by admission of the authors). If a
set of coefficients is estimated based on qualitative observa-
tions (or Secchi disk measurements) it is important that the
relative values at the twelve wavelengths be similar to those
listed in Table 5. Thus the attenuation in the UV region will
decrease with increasing wavelength, and will approximately
double from 297.5 to 330 nanometers. Table 6 presents the re-
sults of a sensitivity analysis which is helpful in judging the
effect of increased attenuation on the photolysis half-life.
TABLE 5. ATTENUATION COEFFICIENTS (AS PRESENTED BY
HAUTALA, 1978, AND ZSPP AND CLINE, 1977).
Wavelength Pure .Water River Water
297.5
300.0
302.5
305.0
307.5
- 310.0
312.5
315.0
317.5
320.0
323.1
330.0
.0028
.0028
.0026
.0025
.0024
.0023
.0022
.0021
.0020
.0019
.0018
.0015
.123
.121
.119
.117
.116
".114
.112
..111
.109
.107
.105
..100
The .direct photolysis rate expression, as written, does not
include the effect of hydrogen ion concentration. If this .
effect has been shown to be important, as it has for 2,4-D (Aly
and Faust, 1964), and 2,4,5-T(I)' (Crosby and Wong, 1973), then
allowance must be1 made by changing the form of the equation
appropriately. For example, if it can be shown that the re-
actionate for 2,4-D is proportional to the equilibrium concen- "
tration of the 2,4-D-Chloropheroxyacelate anion (2,4-DOO-) (II),
19
-------
TABLE 6. ATTENUATION SENSITIVITY ANALYSIS OF PENTACHLOROPHENOL
UNDER MIDSUMMER SUN
ALPHA
.0025
.0050
.0100
.0250
.0500
.1000
Half-life Percent reduction
for Photolysis from ALPHA=.0025
00.539 days
00.746
01.25
02.99
05.98
11.96
«
38%
132%
455%
1010%
2119%
the concentration of which is itself proportional to the hydro-
gen ion concentration, then KA might be rewritten as
KA=
*KEQ/10"PH (Eq. 10b)
where KEQ is the equilibrium dissociation constant at the site
temperature . .
KEQ = KA = (H+(aq)) (2 , 4-DOO) /^/ (2 , 4-D) (Eq. lOc)
The molar extinction coefficients used for each wavelength
would then be those associated with the anion. The rest of
Eq. 10 remains the same in this case.
Sensitized photolysis is calculated using an empirically
determined, site-specific rate constant; this should be modified
at the user's discretion to reflect the observed kinetic be-
havior of the target compound i
The relationship between KSENS and the incident direct and
r-sky light' intensity also must be empirically determined. The
simplest model assumes a linear relationship where
KSEN = KMEAS*IITOT/IIMEAS (Eq. lOd)
where
IITOT =
IIMEAS is the light intensity integrated over the same wave-
lengths and time period as IILAM.
LSM, LDM are experimentally measured effective path lengths
for light in the reactor, IDMLAM, ISMLAM are the measured direct
20
-------
and diffuse light intensities at a particular wavelength in
the reactor, and ALPHA is the absorption coefficient of the
water used in the reactor at a particular wavelength. Most
literature gives the total incident power and some impression
of the wavelength distribution in the reactor. To a rough
approximation, then, if the reaction vessel is not too large, if
distilled water is used in the medium, and if the wavelength
interval of the irradiating list is about 295nm to 330nm, the
reported incident power IIMEAS can be used after it is converted
to approximate units (photons/cm^). Better equations exist in
the literature; see, for example, Boval and Smith (1973).
Very few quantitative data are available in the literature
on the kinetics of sensitized photolysis in natural waters. The
parameters are usually determined by back-calculation from an.
observed half-life value. There are few studies that provide
the. experimental data necessary to fit the required parameters.
Most studies provide only a half-life observation under certain
solar conditions (e.g., midsummer). The routine sums up the
'energy absorbed at each wavelength, and uses this sum as the . ..
driving force for .the sensitized mechanism.. The total energy
is multiplied by the sensitized.'rate constant (KMEAS), and.is,
divided by the energy for which the rate was determined. In
this- way the rate at a given energy is corrected for the energy
of 'the site.
In determining values for KMEAS for half-life measurements,
a trial and_ error method is used. It is helpful..to. h4ave_..a .self-
,c^ntained version of PHOT .. so that values for KSEMS* (the pve.rai-i'-'
se'hs'i'tized" rate) 'ciari be "read directly and compared "with 'the .' :
reference half-life. The sensitized quantum yield (PSIB) may
be assumed equal to the direct quantum yield if no other in-
formation is available.. .IIMEAS, the experimental energy value,
may be set to a standard 3 E13- (photon/cm^s). At this point
KMEAS can be varied until the half-lives obtained compare
favorably with the reference 'values;
Verification . ..........
'The results of the photolysis calculations fd'r several
compounds are compared with the literature values in Table 7.
It should be noted that the sensitized half-lives for both
Methoxychlor and Malathion are much longer than the literature
values quoted. This is because the literature values are taken
froitKexperiments done in river water, which contains higher
amounts of colored and other material which may serve as sensi-
tizing agents. The desired rates in lake water were judged to
be- lower, and so smaller values for KMEAS were chosen.
'.-.-. The relationship of photolysis to solar intensity and" "the
effect of sensitized photolysis are illustrated by Figures 4a,
4b, and 4c.
-------
TABLE 7. COMPARISON OF PEST PHOTOLYSIS HALF-LIVES WITH
LITERATURE VALUES
The range of PEST half-lives corresponds to a range of in-
tensity from 400 to 600 ly/day. This corresponds to the
season from inidspring to midsummer. The conditions column
refers to the season in which the literature value was taken.
Pesticide
Condition
Literature
PEST
Carbaryl
Atrazine
Methoxychlor
(sensitized)
(unsensitized)
Parathion
Malathion
(sensitized)
(unsensitized)
Pentachlorophenol
2,4-D
midsummer
artificial
midsummer
midsummer
midsummer
September
September
2.
1.
2-
4.
6.
14
04
5
5
81
15
*
t
2.9
days
days
hours
months
days
hours
days
1
8
2
9
11
5
0
28
.66
.723
.4
.13
.93
.2
.5
.86
.9
- 2
- 1
- 12
- 3
- 14
.-16
- 8
- 1
- 43
.48
.08
.5
.18
.8
.8 '
.2
.28
.3
days
days
hours
years
days
days
months
days
days
Malathion photolysis is described as the slowest-of any pesti-
cide studied under non-sensitized conditions by Wolfe et al.
(1.976). However, unlike methoxychlor sensitization, malathion
sensitization has only been observed in one water sample.
.Therefore it-is recommended that malathion photolysis be con-
sidered only as a direct mechanism.
Photolysis of pentachlorophenol was"described by Crosby (1972)
as being complete in 5 to 7 days in natural waters. Taking
five half-lives as the length of time for 95 percent degrada-
tion, ..total degradation would take. .-4.3 to 9.4 days as calcu-
lated by PEST'.
As with hydrolysis, photolysis proceeds at different rates
for different compounds and in different chemical environments.
Direct photolysis is probably not as important as hydrolysis for
atrazine, methoxychlor, malathion, or 2,4-D, but may be the rate-
controlling process for carbaryl degradation. Sensitized photo-
lysis is very rapid, however, for methoxychlor and malathion
in natural river waters (Wolfe et al.f 1976).
22
-------
»
X
ro
o
\
CO
(0
"
i-
59&BO 78QJJQ SStUffl
Solar Radiation (ly/day)
Figure 4a. Sensitized photolysis of methoxychlor,
CO
at
GH^
aj
to
5
23QJO S^LCO 78JUW
Solar Radiation Uy/day)
Figure 4b. Unsensitized photolysis of methoxychlor.
23
-------
X
>»
(0
o
OJ
4-1
CO
TSQJU
Solar Radiation Cly/day)
Figure 4C. Photolysis of Pentachlorophenol.
VOLATILIZATION . - . '-/ ' , - VOLAT
The rate at which a toxic organic material (TOM) will
volatize can be expressed as:
VOLAT = CONCEN*KLEXPT/(1/KLIQ + 1/KGAS) (Eq. 11)
where . ',
VOLAT = mass transfer rate (moles/cm /hr)
\ COHCEN = concentration (moles/cm )
: KLEXPT = correction factor, where experimental data are
available, otherwise = 1 (unitless)
KLIQ = liquid-phase mass transfer coefficient (cm/hr)
KGAS = gas-phase mass transfer coefficient (cm/hr)
.The denominator is the sum of the liquid- and gas-phase mass
transfer resistances.
24
-------
Because of the difficulty in measuring interfacial condi-
tions, it is convenient to express the transfer rate in terms
of an overall driving force and the total resistance, made up
of the individual resistances in the gas and liquid films. This
treatment is directly analogous to that used in treating con-
vective heat transfer using the Whitman two-film theory (Whitman,
1923). '
This approach' considers the existence of a gas film and a
liquid film forming an equilibrium interface. Within the films
transfer is by diffusion, providing a resistance to flow. The
thickness of the films, and consequently the resistance, is
considered to be a function of the nature of the fluid and the
turbulence within the fluid. This concept is illustrated by
Figure 5. ,
Figure 5.- Two-film model of'volatilization from the surface of
water (from Sharma, 1979) .
The concept of such discontinuities is perhaps physically
unrealistic. However, the approach has proven useful as an aid
.for visualizing processes at the interface and for simplifying
theoretical calculations of exchange rates. In this appl'ica- '
tion, we are concerned with a TOM dissolved in water and diffus-
ing into the air. The TOM will encounter resistance to transfer"
through the water immediately adjacent to the interface between
the water and the air. This is the liquid film. The TOM must
then diffuse through a gaseous film where it again encounters
resistance to flow. .
25
-------
Gas-phase Mass Transfer Coefficient KGAS
Not mar.:/ values are available for gas-phase mass transfer
coefficients. An empirical relationship as a function of wind
is based on Liss (1973) :
KGAS (water) = (0.1857+11 -36*WINDV*100)/100 (Eg. 13)
where
WINDV.= wind velocity (m/sec)
This coefficient can then be used to obtain the gas-phase
coefficient for the TOM, corrected for the diffusion coefficient
ratio (Othmer and Thakar, 1953; cf. p. 29 this report):
KGAS = KGAS(water)*HENRY*(VH20/VTOM)**0.6/ (Eq. 14)
((TEMP+273.15)*8.206*10**-?)
where ..... ... .....' '"
HENRY = Henry's Law Constant (atm-cm /mol)
VH20 = molal volume of water (cm3/mol)
VTOM = molal volume of TOM (cm3/mol)
TEMP = ambient temperature (°C)
The 8.206*10**-? factor is the gas constant (cm .atm/mol K) and
273i15 is used, to convert °Q- to °K. . Henry's Law constant may
be calculated as: . . ' - ' . . '
HENRY = VPRESS*MOLCWT/(760*SOLUB) (Eq. 15)
where:
MOLCWT = molecular weight (unitless)
SOLUB'= solubility (mol/cm3) . . :. . . .
VPRESS = vapor pressure (atm)
Table 8 lists- values for several compounds calculated using
this, procedure. The molecular volume VTOM can be calculated
as the sum of the contributions of each element in the compound.
Table 9 lists the contributions of common elements and struc-
tural configurations (Perry, 1963). Calculated values are given
for water, benzene, and selected pesticides in Table 10.
It has been shown that environmental values of KGAS and
KLIQ are such that KGAS/KLIQ normally lies in the range of 50
to 250 (Sutherland, 197"8) . In addition, it may be noted that
for values of HENRY below 5 E-6 (corresponding to relatively high
26
-------
TABLE 8. CALCULATED HENRY'S LAV7 CONSTANTS
Compound
HENRY
Atrazine
Carbaryl
Dieldrin
Malthion
Methoxychlor
Parathion
Pentachlorophenol
2,4-D
2.58 E-9
1.32 E-6
5.40 E-5
3.74 E-7
1.00 E-5
6.06 E-7
3.10 E-6
3.15 E-8
TABLE 9. LE BAS ADDITIVE VOLUMES TO CALCULATE LIQUID MOLAL
' ' ' VOLUME (cc/g mole)
Atomic Volumes: ' ""'
5
As
Bi
Br
C
Cr
30.
48.0
27.0
14.8
27.4
F 8.7
Ge 34.5
H 3.7
Hg 19.0
I 37.0
P 27.0
Pb 48.3
S 25.6
Sb 34.2
Si 32.0
Chlorine -
Terminal (as in R-C1)
Medial (as in R-CHCL-R)
Nitrogen -
Double bonded / . ;.''
Triple bonded
In primary amines (R-NH2)
Secondary 'amines (R-NH-R)
Tertiary amines (R3-N)
Oxygen - 7.4 (except as noted below)
In methyl ester
In methyl ethers
In higher esters, ethers
In acids
In union with S,P.N
Deductions -
Three member ring
Four member ring
Five member ring
Six member ring (benzene)
Naphthalene ring
Anthracene ring
Sn 42.3
Ti 35.7
V 32.0
Zn 20.4
21.6
24.6
15.6
16.2
10.5
12.0
10.8
9.1
9.9
11.0
12.0
8.3
- 6.0
- 8.5
-11.5
-15.0
-30.0
-47.5
27
-------
TABLE 10. CALCULATED OR OBSERVED MOLAL VOLUMES FOR SELECTED
_, COMPOUNDS
Compound , -- Molal Volume
Dissolved Oxygen ' 14.8
Water . 18.7
Benzene 96.0
Atrazine . : 229.3
Carbaryl . 218.7
2,4-D 239.6
Dieldrin . 314.2
Malathion 345.7
Methoxychlor 345.3
Parathion 302.6
Pentachlorophenol . 192.9
Simazine ' 207.1
solubility or low vapor pressure) the transfer is gas-phase con-
trolled. On the other hand, for values above 5 E-3 the liquid
phase controls. ' " . . ' " '..''
Liquid-phase Mass Transfer Coefficient
The computation of the liquid-phase mass transfer co-
efficient (KLIQ) involves two equations according to wind
velocity. For velocities below 3 m/sec, where calm water pre-
vails, ah empirical, linear equation is used:
KLIQ = ((WINDV*100*1.287)/300+2.5)*1.016**
(TEMP-20)*(VBEN/VTOM)**0.6 (Eq. 16)
where ' .
VBEN = molal volume of benzene -. (cm /mpl)
The temperature correction factor (second line of the equation)
is based on the reaeration studies of Streeter et al. (1936) and
was verified as a part of this study (Sharma, 1979). The fit
is quite good for benzene, but only the trend is shown for
toluene (Table 11).
The third factor corrects for the relative rates of the TOM
and benzene. Benzene is usejjd as a standard in PEST because 1)
experimental data areravail4ble for various wind velocities, 2)
it is a much larger;.; molecule than oxygen (the other standard
used in volatilization"'" studies), and 3) it has a ring structure
similar to that oWinany TOMs.
28
-------
'TABLE 11. VERIFICATION OF TEMPERATURE CORRECTION
Compound Temperature (°C) KLIQ Observed KLIQ Predicted
Benzene
Benzene
Benzene
Toluene
Toluene
Toluene
20
25
30
20
25
30
2.9
3.14
3.4
2.91
2.92
3.18
2.9
3.13
3.39
2.91
3.15
3.41
(20°is reference temperature)
In a liquid with a dilute concentration of pollutant KLIQ
is. proportional to the diffusivity of the pollutant. Othmer
and Thakar (1953) found that the diffusion coefficient in dilute
aequeous solutions is inversely proportionate to the 0.6 power
of the molal volume. Therefore, the transformation factor to
permit the benzene data to be used is:
(VBEN/VTOM)
i .
Table 12 gives experimental results obtained in this study
and used to calibrate the model (Sharma, 1979).
TABLE 12. LIQUID-PHASE MASS TRANSFER COEFFICIENTS AT 25°C
Compound Temperature KLIQ
..-. Benzene
Toluene
Atrazine
Methoxychlor
,.,..- Carbaryl
'.25
25
. 25
25
. 25
2.
2.
7.
2.
2.
90
92
09
29
01
X
X
X
10-2
10-2
10~2
.- .
At wind velocities above 3 m/sec there is turbulent flow
With waves; under these conditions Eq. 16 becomes:
.(VBEN/VTOM)0'6 (Eq. 17)
The first factor 'is based on experiments by Cohen, Cocchio and
MacKay (1978) using benzene that showed that: -
.KLIQ = 11.4*RE°°195-4.1 (Eq. 17a)
29
-------
with turbulence or "roughness" represented by the dimensionless
Reynolds Number (RE):
RE = (WINDV*100)0.17
(Eq. 17b)
where 0.17 is the kinematic viscosity of air (Sabersky et al.,
and Acosta (1964) .
The other two factors correct for temperature and the re-
lative diffusiyities.of Benzene and the TOM as in Eq. 16. The
effect of wind'is shown in Figure 6.
OJ
SfcJSO S4K) HUB 17c8S
Wind .Velocity (i/s)
Figure 6. Volatilization of a hypothetical liquid-phase con-
trolled compound. Note the discontinuity at 3 m/s,
Verification
The results of volatilization calculations are compared
with reference values in Table 13. Although no precise values
for volatilization could be found in the literature, the
vaporization indices published by Haque and Freed (1975) serve
as an indication of the accuracy of the submodel. For all the
compounds except 2,4-D the PEST and literature ranges of values
roughly coincide. For 2,4-D the only literature value available
was for the acid, whereas PEST was parameterized for the methyl
ester.
30
-------
TABLE 13. COMPARISON OF PEST VOLATILIZATION RATES WITH
LITERATURE VALUES
The PEST values represent the range of rates corresponding
to wind velocities from 7 to 15 m/s.
Vaporization Approximate
Pesticide
Carbaryl
Malathion
Parathion
Atrazine
2,4-D*
Pentachlorophenol
Dieldrin
Index
3-4
2
3
na
1
na
T
Conversion
1.37 - 2.74E-3
.548 - 8.2E-4
.959 - 1.78E-3
<2.74E-5
.. 1.33E-3
PEST
Values
.611
.106
.118
.203
.257
.118
2.01E-4
- 8.53E-4
- 1.47E-4
- 1.58E-3
- 2.83E-5
- 3.59E-3
- 1.65E-3
- 2.79E-3
* .
The literature value for 2,4-D is for the acid, while the PEST
calculation is for the methyl ester.
j. .
'This value was calculated from a half-life value published by
.Mackay and Leinonen (t 1/2=1.44 years).
SOLUTION . '' SOLU'
Solution is treated in a very straightforward mannerr.,.in:.-;/ "
PEST; the formulation is intended only to keep the TOM in the .
dissolved phase from exceeding the solubility:
SOL = CONCEN(15)-EXTRA ' ' ' ' (Eq. 18)
if CONCEN(13)+CONCEN(15)-SOLUB*TCORR<0: (Eq. 18a)
EXTRA =0 ' ' - ' .
I' " ' .
otherwise:
EXTRA = CONCEN(13)+CONCEN(15)-SOLUB*TCORR (Eq. 18b)
where
CONCEN(13) = concentration, of TOM in water
CONCEN(15) = concentration of TOM in particulate form
TCORR = temperature correction (Eq. 8)
-------
MICROBIAL METABOLISM MMET
The rate of raicrobial metabolism resulting in the degrada-
tion of TOM is computed in the subprocess routine MMET. Micro-
bial metabolism is defined as any biochemical conversion of the
parent compound by a microbial assemblage. PEST models micro-
bial metabolism -as:
A*TADPT*METMAX*CONCSN
KS+CONCEN
where
MSTMAX = chemically and photochemically corrected TOM trans-
formation rate by an "adapted" mixed assemblage
under non-limiting conditions of H+, dissolved
oxygen, temperature, nutrients and mixing (I/day)
CONCEN = concentration of TOM (g/m3)
KS = constant equal to TOM concentration of 1/2 METMAX
(g/m3)-- - .- - - - .
A = activity coefficient which reduces METMAX due to site
conditions (unitless)
TADPT - effective TOM-degrading microbial biomass (g
organism/m3)
In order to model microbial metabolism of TOM, a .maximum
value (METMAX) must be determined which can be reduced by site
correction.factors (A and.TADPT) as specified in the above
equation. V
This maximum value ideally is the rate of degradation of
the chemical by ,a microbial assemblage with a high degree of
species diversity. This assemblage should have been exposed
to the compound for at least several generation times, and be .
'grow.ing under optimal conditions of DO, temperature, pH, nutri-
ents' and mixing. During this time some enrichment may also
occur.
In order to provide for a wide range of biochemical activi-
ties , inocula from sources that are undergoing complex organic
decomposition should be combined and used for the determination
of METMAX (e.g. soil, marsh water, sediment). In practice,
the flocculant layer of a lake sediment is well suited for
these determinations by virtue of its interfacial position.
Although it has a rather constant, or gradually changing, ther-
mal environment, it is subject to random fluctuations in nu-
trients and dissolved oxygen as a function of mixing. As such,
it is adapted to a variety of intermittent environmental condi-
tions. This system, amended with soil and marsh inocula, can
be used for TOM degradation studies. Samples obtained at peak
seasonal temperatures for the .system should be used because
this condition would produce the most metabolically active
assemblage. ._ .
32
-------
Values for METMAX have been derived for malathion, atra-
zine, and 2,4-D. The rates of degradation of these compounds
were measured under anaerobic and oxygen-saturated conditions
in shake flasks at 23-30°C. Sediment was taken from the
littoral area of a lake '(Imdepth in Lake George, New York)
during the growing season (June and July). This site has a
significant macrophyte cover which contributes organic matter
to the underlying sediment through sloughing and death and pro-
vides for a great deal of microbial species diversity. Samples
were taken of the metabolically-active surface sediments. These
sediments were diluted to approximately 1. g (dry weight) per
liter with lake water from the same site. The sediments were
incubated with pesticide at concentrations of 50 and 100 mg/1.
The time course of TOM disappearance over a period of up
to 2 weeks was followed by solvent extraction and gas chroma-
tography using established methods. These rates were corrected
for photochemical and chemical degradation with dark and in-
activated systems (Kg, antibiotics, boiled). Figure 7 is an
example of the efficiency of microbial metabolism under ideal.
conditions. In the absence of these detailed measurements of
TOM disappearance, the METMAX can also be estimated from the
biochemical oxygen demand (BOD) for the compound.
The need to relate biodegradation capability to the site
conditions is met through the A factor. This factor modifies
the maximum metabolic rate.of the effective biomass (IADPT) to"
the rate possible1under site conditions. Thus it is a reduc-
tion factor determined by the site conditions and particular
TOM. The environmental factor that is most restrictive to
microbial growth and decomposition of the particular TOM deter-
mines A. For example, if the TOM is aromatic and the parti-
cular compartment being modeled is the sediment during summer
conditions, the limiting parameter might be DO and a correction
factor based on the influence of dissolved oxygen on the meta-
bolic rate of aromatic ring, degrading organisms would be used -
(DOCOR) to limit METMAX. " " ' : ''
""' A = .-min (DOCOR,PHGOR,TPCOR,TMIX). ,....' -.-- -(Eg. ISa)'"
where each term is a unitless' reduction factor for "suboptimal
conditions.
..The dependence of the microbial. assemblage on oxygen
(DOCOR). can be expressed as: . ... .-.-
if DOCOR < DOMIN then DOCOR = DOMI-N
33
-------
ng cal/1
Incubation (hours)
Figure 7. Malathion remaining vs. incubation time.
34
-------
where
D02 = dissolved oxygen concentration (g/m J
DOMIN = minimum effect under anaerobic conditions (unitless)
MK02 = half-saturation constant for oxygen (g/m^)
For aromatic compounds, no degradation of the aromatic
ring occurs under , anaerobic conditions. Side chains can, how-
ever, be degraded, e.g. the acetic acid moiety of 2,4-D; there-
fore, a minimum limit (DOMIN) is imposed on the reduction
factor. Aerobic metabolism is practically independent of oxygen
tension above a critical value (about 0.01 atm for pure cul-
tures) (Figure 8) .
Because of the environmental conditions and the array of
metabolic types in a natural assemblage, the rate of non-aroma-
tic degradation is probably independent of oxygen, with anaero-
bic utilization of TOM occurring when oxygen becomes limiting
for aerobic metabolism. Therefore, DOMIN = 1.
Figure 8.
SJD ROD
Dissolved Oxyoen (ppo)
Effect of dissolved oxygen on microbial degradation
of pentachlorophenol.
Most natural environments have pH values between 5 and 9
(.Brock, 1970). Most bacteria grow best under, neutral or slight-
ly alkaline (pH 6.8-8) conditions,, whereas most yeasts and..fungi
.prefer slightly acidic environs (pH 5-6). Under otherwise
optimal conditions, the pH response curve of a natural assem-
blage (of environmental pH 5-9) exposed to an instantaneous pH
-------
shift can be represented by:
"KpH*e(pH-PHMIN)
?HCOR=JKPH*e(PHMAX-PH)
if pH <_ PHMIN
if pH >_ PHMAX
otherwise
(Eq. 19c)
where
KPH = adaptive constant for pH (uni'tless)
pH = pH
PHMIN = critically low pH . .
PHMAX = critically high pH
This can be parameterized to yield a broad peak reflecting
the composite effect of many different organisms with differing
pH optima within the 5-9 range (Figure 9).
nja
pH
Figure 9. Effect of pH on microbial degradation of
pentachlorophenol.
Given sustained conditions of pH values outside of the 5-9
range, restricted populations will develop that are tolerant of
those conditions. Acidified lakes (pH 3.5) and mine drainage
(pH 4.2), and alkaline lakes (pH 9.5) develop specialized
microflora. Some of these organisms exhibit pH optima close to
the pH of the environs and others are simply tolerant of those
conditions. The population composition will be a reflection of
the competition between the acidophilic or alkalophilic .:"
36
-------
organisms and the tolerant organisms. The population density and
growth rate however will not be as high as it would be if that
same system were neutralized (e.g., dystrophic lake) since it
is believed that microorganisms must expend energy in order to
maintain their internal neutral conditions in an acid or basic
environment. The overall effect is a broadening of the curve,
but a lowering of the activity and density of the population.
The KPH, PHMIN, and PKMAX parameters can be adjusted for these
unusual conditions.
The temperature reduction factor is formulated similarly
to that for pH to provide a plateau of adaptation (Figure 10):
5?
K o
Figure 10.
44JO
Tespersture CO
Effect of temperature on microbial degradation of
pentachlorophenol.
TPCOR =
KTP*e
KTP*e
(TEMP-TPMIN)
(TPMAX-TEMP)
where
if -TEMP <_ TPMIN
if TEMP :> TPMAX
otherwise
(Eq. 19d)
KTP = adaptive constant for temperature (unitless)
TEMP = ambient temperature (°C)
TPMIN = critically low temperature (°C)
TPMAX = critically high temperature (°C)
The usual range of temperature required for growth.of a
37 .
-------
given organism is 30-40 degrees. It is very rare that tempera-
tures in nature exceed 50°C, especially in water.(with the
exception of hot springs). During seasonal variations in temp-
erature, microbial populations in lake sediments maintain a
temperature optimum -corresponding to'the maximum temperature
attained by the lake or higher, (Boylen-and Brock,. 1973) . This
may result in reduced decomposition activity during'the periods
when temperature is less than' 25°C. The- 'actual' effect, how-
ever, will depend' upon the controlling limiting variable for
growth, i.e. growth may be greater .at 10°C than 15°C if there is
limiting energy subtrate. at- 15°C. Psychrophilic---organisms that
may develop are not abundant enough.to affect the .overall rate.
In contrast, systems that are permanently cold or hot develop
predominantely psychrophilic -or thermophilic populations and
TPMIN and TPMAX can be changed to-reflect these unusual condi-.
tions. " - -
Evidence from studies at Lake George, New York (Clesceri,
Boylen and Park, 1977) has shown the direct effect of mixing
on cellulose degradation. These studies were done in filled,
closed flasks to separate the effect of aeration from agitation.
At the present time mixing is. represented simplistically
as a function of wind speed in PEST:
TMIX = <
WINDV
WMIX+WINDV
0
if DPHLIM < DEPTH
if. DPHLIM < DEPTH
DPHLIM = .KDEPTH*WINDV
(Eq. 19e)
(Eq. 19f)
.where .. . ";.. - ''-.-,.-
... WMI'X' = windspeed at' 1/2 maximum stirring, effect (m/sec)
WINDV = windspeed (m/sec)' "'
KDEPTH = constant relating wind energy to depth
. DPHLIM- ='p depth at which wind energy is- unimportant (m)
DEPTH = depth of water . (m)
The presence of other compounds has been shown to'facili-;
tate the.metabolism of recalcitrant molecules (Horvath, 1972;
Merkel and Perry, 1977). If natural inocula and associated
substrates are used in the METMAX determination, it is likely
that such cosubstrates will be present.
It is unlikely that an aquatic sediment will be deficient
in mineral nutrient (eg. N,P) for the metabolism of the avail-
able energy substrates. Minerals are recycled within the sedi-
ment system which becomes enriched in these nutrients (Clesceri,
Boylen, and Park, 1977). Our studies for the determination of
METMAX for 2,4-D, atrazine and malathion with lake sediments
38
-------
have revealed ho increase by the addition of inorganic nitrogen
(as NO," and NH,"1" or inorganic phosphorus (as H^PO. ) .
The TADPT factor allows for the development of raicrobial
biomass capable of TOM biodegradation. Adaptation may be gene-
tic and occur through mutation or it may be the acquisition of
new genetic elements (transmissible plasmids, Meynell, 1972).
It may also be simply the time required for induction of suita-
ble enzymes within the existing population. The factor is
derived through the use of an adaptation potential which ex-
presses whether or not the adaptation will occur during the time
of exposure to TOM. If the adaptation time exceeds the exposure
time, the adaptation potential has a fractional value, represen-
ting the fraction of potential TOM degraders present at that
.time.. If the adaptation time is less than the exposure time
the adaptation potential is 1. Thus the TADPT is determined by
the microbial biomass in the system and the adaptive^capability
of -the organisms: ' ... .. ' " : ... ,'..',... ...
TADPT = BACB*
0
if . [EXPT/ (MMGT*STRU*A] <;0 " ' "- -
1 if [EXPT/(MMGT*STRU*A]>1 (Eq. 19g)
EXPT/(MMGT*STRU*A otherwise :
where
BACB = microbial biomass (g organism/m )
MMGT = generation time under METMAX conditions (day)
STRU = structural activity factor (unitless)
EXPT = time of exposure to TOM (days)
EXPT = TIME-STTOM+1
(Eq. 19h)
where
TIME = Julian date in simulation
STTOM = Julian:date of'introduction of 'TOM
TADPT can be determined for a specific TOM along with the
.METMAX measurement. It can also be calculated for related com-
pounds using the A term, the generation1time of the assemblage
under METMAX conditions (MMGT) and the structural factor
(STRU). . . '
Mutation rate is dependent on generation rate under most
conditions, but observed-to be independent of generation rate
under amino acid- or nitrogen-limited -conditions in..continuous
culture (Kubitschek and Bendigketi, 1961). In addition to the
mutation rate, the'ability, to .develop .a biodegradation capa-
bility for a certainTOM depends on the structural factor
(STRU) which may be estimated by means_of..structural, activity..
39
-------
relationships (Jaffe, 1953; Kapoor et al., 1973). Structure-
activity studies developed for mammalian systems have a great
deal of applicability to microbiological transformations. The
concept that biological activity can be predicted from chemical
structure is very old (Crum-Brown and Fraser, 1869). Expanding
the distribution prediction based on partition coefficients
(Leo, Hansch and Elkins, 1971) to include transformation pre-
diction based upon substructural fragments seems feasible for
ecosystems as well as animal systems.
SORPTION . . SORP
The adsorption of TOM to the surfaces of the organic and
inorganic components is treated with a relatively simple algor-
ithm. However the approach is well grounded in physical chem-
istry. The initial calculation determines the amount of
TOM both dissolved in the water and on the surfaces of the
various components of the system:
TOTPST = £CONCEN(r)*SAREA(I) (Eq. 20)
, I
where
TOTPST = the total concentration of TOM in the environment
(grams TOM/m^)
CONCEN(I) = the concentration of TOM in the Ith compartment
(grams TOM/m^) . , .
SAREA(I) = the percentage of TOM in the Ith carrier at
the surface (unitless)
The parameter SAREA is used to indicate that of the total con-
centration of toxic material only a fraction is actually at the
surface of the organism or particle and thus available for the
physical process of adsorption. This allows the representation
of rapid initial adsorption to the surface to be separated from
the slower migration of the TOM into the carrier, as noted by
Kenaga and Goring (1980). .
The second part of the sorption routine uses the octanol-
water partition coefficient to indicate the equilibrium concen-
tration in each carrier, assuming no limitation on the quantity
of TOM available. This function:
NEWAMT(I) = KPART(I)*CONCEN(13)*LOAD(I)*
SAREA(I)*lE-6 (Eq. 21)
where
NEWAMT(I) = the equilibrium concentration for the Ith
carrier (ppm)
K?ART.(I) = the octanol-water partition coefficient for the .
40
-------
TOM being modeled and the Ith carrier
CONCEN(13) = the concentration of TOM in the water
LOAD(I) = the mass concentration of the Ith carrier
(g/m3!
(g/m3)
calculates the concentrations in units of parts per million and
again utilizes the parameter SAREA to indicate only the surface
of the carrier is involved in this process.
The final concentrations, after adsorption has occurred is
calculated:
CONCEN(I) = NEWAMT(I)* « +OLDAMT < I > {Ec2- 22>
where
CONCEN(I) = the new concentration of TOM in each Ith carrier
... . . (g TOM/m3) . '
OLDAMT(I) = the concentration of TOM within the carrier, not
affected by adsorption (g/m3)
The middle term in this function is used to normalize the amount
'of adsorption taking place so that mass balance is maintained.
This sorption algorithm differs from the others of the
PEST model in that1.it returns a concentration rather than a
rate. This is because the rate at which TOM adsorbs to a sur-
face is at a time scale much shorter than that at which the
model runs (Kenaga and Goring, 1980; Kenaga, 1975; Hague, 1974)
and can therefore be represented as occurring instantaneously.
GILL SORPTION " ... ' ' ' GILSRP
! ."""
The major route of uptake of TOM by fish has been consider-
ed, to be the result of active transport through the gills
(Macek, et al., 1977). As the organism respires, water is
passed over the outer surface-of the gill and blood is moved
through the inner surface. The exchange of TOM through the
gill membrane is assumed to be facilitated 'by the same general
mechanism as the uptake of oxygen and relea|e of carbon dioxide,
following the approaches of Fagerstrom and Asell (1973, 1975)
:and Weininger (1978).
... "" The formulation developed to calculate the uptake of TOM
by the gills was. designed to represent the actual pathways of
accumulation. Assuming a lipophilic material (although that
assumption'is not necessary, non-lipophilic materials can be
represented as well) we can conceptualize an organism as having-'
"three areas of import to this process: fat depots..where the
41
-------
material is stored, the gill membrane, and blood which provides
the link between the two. The flow of TOM can then be thought
of as first from the water through the gill membrane and into
the blood and then from the blood into the fat where it accumu-
lates.
To represent this process we must know the relative con-
centrations of toxic material in the water and in the blood and
their variance from the equilibrium concentrations. The amount
of TOM transferred would be a function of this partitioning and
the partitioning between the blood and the fat. The rate at
which the TOM is transferred would be a function of the effici-
ency with which the material could be passed through the gill
to the blood and the rate of blood circulation to move the
material to the fat. To calculate the gradient along which the
TOM will move, the concentrations on each side of the gill
membrane must be calculated. To find the concentration in the
blood the.respiration rate is calculated using a function
developed for the MS.CLEANER ecosystem model (Park et al.,
1980) : . .;.- ; -; - ' . .- - .-:.
CRS(J) = EXP(KTEMP(J)*(TEMP-TOPT(J))*RMAX(J)
. . *BIO(J)+(KRESP(J)*(ZCTWO(J)-ZDTWO(J))) (Eq. 23)
where . . ''..- '..."
CRS(J) = the rate of biomass loss due to respiration
(g/m3 day.) ......... . . . . . .
KTEMP(J) = a coefficient:describing .rate of increase of
respiration with temperature (1/°C)
TEMP = the water temperature (°CJ
TOPT(J) = the optimum temperature for respiration (°C)
RMAX(J) = the respiration rate at .starvation (g/g/day)
BIO(J) = the biomass of the Jth organism (g/m3)
KRESP(J) = a coefficient relating respiration to metabolism
.ICTWO(J) = the total rate of consumption by the Jth
organism (g/m3 day) . .
.-ZDTWO (J) = the total rate of defecation by the Jth organism
(g/m3 day)
The concentration that passes through the gills in the
blood, BCIRC (in g TOM/m3 day), is calculated by:
BCIRC(I) =
where
CRS(I)*02RESP(I)*CONCEN(I)
BLD02(I)*PCBLD(I)*PCFBL*BIO(J)*PCFAT(I)
(Eq. 24)
02RESP(I) = coefficient relating oxygen uptake to respiration
(g 02/g biomass) _ _.
42
-------
PCFBL = fat/blood partition coefficient (assumes the TOM is
concentrated in the fat, i.e., lipophilic
'CONCEN(I) = concentration of TOM in Ith organism, (g TOM/m3)
PCFAT(I) = percent fat in the Ith organism (g fat/g biomass)
3LD02(I) = coefficient for oxygen capacity of blood (g 02/
g blood)
PCBLD(I) = percent blood (g .blood/g biomass)
The volume of water processed is also calculated as a
function of the respiration rate, CRS, and used to determine the
amount of TOM in the water that passes through the gill. WCIRC
(g TOM/m^ water processed day) is calculated as:
WCIRC(I) - CRS(I)*02RESP(I)*CONCEN(13)
where
CONCEN(13) = concentration of TOM in the water (g TOM/m )
D0.2 = concentration of dissolved oxygen in the water -
(g 02/m3)
The total "exposure" of the blood of the organism to the TOM
must be checked to prevent the inclusion of more TOM than -is
present in the system. This check is necessitated because an
organism may circulate its entire blood supply through its gills
many times per day (Nicol, 1960) and because gill sorption is
assumed to occur almost instantaneously.
To calculate the exchange of TOM the gradient between the
concentrations in the blood and water must be established.
Since the toxic material will tend to adsorb onto organic
particles and this activity can be related to a partition co-
efficient (.Kenagal 1975;. Chiou et al., 1977), the. gradient.....
along which the material will move can be calculated using this'-
coefficient. The formulation is: .;- ..... ... .'. .
DIR(I) = WCIRC(I)*PCBLW-BCIRC(I,). .... ; . - -(Eg. 2.6).
where . ... ' ,..-......
DIR(I) = the difference in concentrations (g TOM/m^ day)
. PCBLW = the partition coefficient for blood to water for the
particular TOM
The blood-water partition coefficient, PCBLW, and the fat-
blood partition coefficient, PCFBL, are used to represent the.
series of partitionings from the water to the lipid (Kapoor et.
al., 1973) as noted in the description of the conceptual basis
of. the function. If the particular TOM being modeled is not
lipophilic the fat-blood partition coefficient is not
necessary and may be set egual to one. The values for these
.43
-------
coefficients may be determined experimentally or may be calcu-
lated (Leunq, 1978).
The total of the differences between equilibrium and pre-
sent levels in each organism is summed and used to normalize
the rate of gill sorption:
NORM =
ZBCIRC(I)+EWCIRC(I)
I I .
IDIR(J)
I
This normalization insures the maintenance of mass balance and
is necessary due to the instantaneous nature of the actual pro-
cess of sorption as opposed to the sequential calculations.
The actual rate of gill sorption, GILSRP (g TOM/m day),
is calculated by:
GILSRP(I) = EFFEN(I)*DIR(I)*NORM (Eq. 27)
wh'ere
EFFEN(I) = a coefficient describing the 'efficiency of the
gill membrane in the transport of the TOM through
it (unitless) . ...
The efficiency coefficient, EFFEN, is used to modify the rate to
comply with the assumption, basic tp'/the formulation: the trans-
port of TOM is facilitated by.the same general mechanism as
oxygen. This coefficient is similar to that of Weininger
(1978) in that it compares the diffusivity of the toxic material
being modeled with that of oxygen and modifies the predicted
rate of gill sorption to account for this difference.
The final calculation of.this function is the subtraction
of each individual rate from the.concentration in the water to
maintain mass balance. To illustrate the response of this pro-
cess with time of exposure, gill sorption was calculated assum-
ing a constant 3.0 ppm in the water, a basal respiration rate,
and constant biodegradation. The response is illustrated in
Figure 11 and is compared with the data of Lockhart et al.,
(1977).
CONSUMPTION CONS
This routine calculates the rate of change in the TOM con-
centration of an organism as the result of ingestion, defeca-
tion, and excretion. The effects of these individual processes
are added to obtain a single rate, CONS, in units of g TOM/m3
day.
44
-------
Figure 11.
8CUJB
TIME (HR).
Relation between the concentration of methoxychlor
in a fish and time. Data points are averages from
measurements of Lockhart et al., 1977.
.Ingestion .
The rate of ihgestion is calculated primarily as a func-
tion of the biomass concentrations of prey I and predator J,
which are supplied1 to the model as driving variables. The pre-
dators may follow one of three general strategies for ingestion
as indicated by the parameter"FEDSWT: FEDSWT=0 indicates
carnivores; FEDSWT=1 indicates filter-feeders; and FEDSWT=2
indicates filter-feeding, benthic organisms that produce pseu-..
dofeces. Carnivores and non-depositing filter-feeders both
utilize a saturation-kinetic equation, modified from CLEANER
(Scavia and Park, 1976) .
. For carnivores ingestion of biomass is calculated as:
CTWO =
CMAX(J)*W(I,J)*BIO(I)*BIO(J)*LIM*TRED*02COR
Q(J)+Z(W(I,J)*BIO(I))*LIM
. . I .......
(Eq. 28.)
where
CTWO = the ingestion of prey I by predator J (g/m3 day)
GMAX. .= the maximum -rate 'of ingestion (g/g day)
W(I,J) = the preference for prey I by predator J (unitless).,.
BIO(I) - the biomass concentration of the Ith prey (g/m3)
45
-------
310 (J) = the biomass concentration of the Jth predator (g/m3)
Q(J) = the half-saturation constant for feeding (g/m3)
The three terms LIM, 02GOR, and TRED are correction functions.
LIM (Leung et al. , 1978) is used to indicate reduction in
ingestion rate due to low prey concentrations:.
LIM = 1.0-(BMIN(J)/EBIO(I) ) " " ......... ..... : ....... ' (Eq. 29)
I
where
BMIN(J) = the. prey concentration at which the predator
begins feeding (g/m3)
The reduction of ingestion rate that results from low dissolved
oxygen concentrations is calculated by the function 02COR
(Park et al . , in prep.):
02COR = D02/(K02.(J.)+D02) --.;.-., ~. :- - -. (Eq. 30)
where
D02 = the dissolved oxygen concentration (g/m )
K02 ( J) = the saturation coefficient for oxygen limitation
(g/m3)
TRED. is the .reduction, factor for non-optimal temperature, ori-
ginally developed by. O'Neill et .al.' (1S>72) and modified by
Scavia and Park (1976') for application to aquatic ecosystems.
It is formulated to reflect the response of organisms to varia-
tions in temperature as:
TRED = VX*e(X*(1~V)) '. ." - '(Eg. 31a)
V = TMAX-T . "(Eq. 31b)
TMAX-TOPT .... ... v "
31C)
40CT -
w = In (Q10)* (TMAX-TOPT) . (Eq. 31d)
where
TMAX = the maximum temperature at which process will
occur (°C)
TOPT = the optimum temperature (°C)
Q10 = the rate of change per 10°C temperature rise (unitless)
T = the water temperature . (°C).,.._. .: ............... _ ....... ........... . .
46
-------
The calculation of ingestion by. saturation-type filter-
feeders is accomplished by replacing the maximum consumption
rate parameter, CMAX, with a maximum filtering rate, FMAX (g
filtered/g day).
The production of pseudofeces by filtering organisms is
calculated in three steps (Albanese, 1979). First the water
processing rate:
FIL = FMAX(J)*TRBRED*(£BIO(I)-BMIN(J))/
.1
(Q(J)+IBIO(I)-BMIN(J)) (Eq. 32)
I
where
FIL = the rate 'Of filtering (g/g day)
TRBRED = a reduction factor for filtering rate due to high
concentration of inorganic particles
and the other parameters as previously defined are calculated. ...
The second process, that of biodeposition, is calculated as:
. BIODEP = EXP(BDSLP(J)*BIO(I)-BDINT(J)) (Eq. 33)
where .
BIODEP = the rate of production of pseudofeces (g/g day)
BDSLP(J) and BDINT(J) = regression coefficients relating
biodeposition to food concentration
i
The rate of ingestion is then the difference between the filter-
ing rate and the biodeposition rate.
The ingestion of TOM'is calculated using the rate of in-
gestion of biomass and the concentration of TOM in each prey:
CPTWO = CTWO*CONCEN(I)/BIO(I) (Eq. 34)
'where . -- - .' ...... -., ... ... . . ..... ...= -
CPTWO = the rate of ingestion of TOM by each predator (g
. . . TOM ingested/m3 day)
'CbNCEN(I) = the concentration of TOM in prey I (g toxic/m3)
The defecation of'TOM is then calculated:
DEF1 = CPTWO*E(I,J) ' (Eq. 35)
where . . "'...
DEF1 = the rate of defecation of..TOM....(g/m_; day.)
47
-------
E(I,J) = the percentage of TOM in the Ith prey that is
egested by the Jth predator
The TOM that is biodeposited by filter-feeders is added to that
produced by defecation and both are transferred to the parti-
culate state-variable compartment. The rate, of ingestion of
prey I by predator J, CTWO, is also used to calculate the rate
of TOM lost by the prey as the result of grazing by summing
CPTWO by I, assuming that the total consumption must equal the
total grazing.
The loss of TOM through excretion is calculated as a
function of the respiration rate:
. EX('J) = CRS(J)*KEXCR(J) (Eq. 36)
where
EX(J) =_the rate of excretion of TOM by the Jth organism
(g/m3/day) ~- '
CRS(J) = the respiration rate (see GILSRP)
KEXCR(J) = a coefficient relating excretion to respiration
(unitless)
If the TOM being modeled is lipophilic, the rate of re-
lease by excretion must be corrected as the fat reserves, hav-
ing the highest TOM concentrations, would.be utilized first.
The correction factor . for .this, is based, on the ratio of the .
predicted ingestion rate to the maximum ingestion rate, which
is supplied as either CMAX for FMAX depending on the feeding
strategy of organism. As this ratio increases the organism
is assumed to increase its fat content from the basal level as
given by PFTBOD at the rate, FATRAT* In this way the condition
of the organism is accounted for ,in the calculation of .TOM re-
leased. ' .
The calculated rates ,of ingestion, defecation, arid excre-
tion are then combined and passed to obtain the ..value of CONS.
BIOTRANSFORMATION BTRANS
This routine predicts the rate of biotransformation of the
TOM to its daughter products; for some compounds it is a signi-
ficant process of degradation (Khan et al., 1972). The rate is
a function of the length of exposure to the TOM, the ambient
temperature, and the concentration of the TOM in the organism.
- MAX*CONCEN(I)
"
KBTRAN(I)+CONCEN(I)
48
-------
where
MAX. = the maximum - rate of biotransformation under the present
'environmental conditions (g transformed/g biomass day)
CONCEN(I) = the concentration of TOM in the Ith organism
(g TOM/m3)
KBTRAN(I) = the half-saturation coefficient for biotrans-
formation (g TOM/m3) --
The rate of biotransformation is reduced for low metabolic
capacity and for suboptimal temperature:
MAX = BTMAX*METCAP(I)*TRED (Eq. 38)
where
BTMAX = maximum rate of biotransformation (g transformed/ ..
' g biomass day), . ' ..-,.''
:;!.-.TRED =. reduction factor .for; temperature (.see Eq.. <°3-la), . (unit-
' .less) . .-.. - - -.- -->- "'-' --- - -
..__ ^ METCAP (!,),.=the percent metabolic capacity for degradation
:''- ' ' ' of the TOM (unitless) .'-. "*
METCAP(I) = (TIME-STTOM+1)/BTTIM(I) ''(Eq. 39)
where- -....-,'' '..... .
.TIME = the day of the- year ('Julian date)
STTOM = the Julian date --of Introduction of the TOM to the
environment ...,.-.
. BTTIM(I) = the number..of'days required to reach full meta-
bolic capacity . -;
, 'The, relationship; of ..jthis; ,f-tincti.6ri--.to-.concentration is .,..
illustrated,-in.-Figure 12.'.'.'...' ;....''"/ .--:
49
-------
020 0.40
CONCENTRATION (G/G)
(L60
Figure 12.
Relationship between BTRANS :-(-g transformed/g
organism/day)' and TbM;'concentration (g TOM/g) . Data
are for aldrin epoxidation. :in"bluegill fry (+) and
in adult bluegills (X) (Stanton and Kahn,-a973).
50
-------
SECTION 3
DATA REQUIREMENTS
Data requirements depend on the intended use of the model.
If PEST is to be used as an evaluative model, as originally
intended, then default data on prototype sites (such as the
verification sites) may be sufficient to characterize the be-
havior and fate of a toxic organic material; therefore, site
data would be unnecessary. If the model is to be applied as a
diagnostic tool in order to better understand the fate of a
compound at a particular site, then an accurate characterization
of the site is required. If the problem involves bioconcentra-
tion in a particular group of organisms, then it will be
necessary to accurately characterize the metabolic requirements
and feeding preference of the organism.
COMPOUND-SPECIFIC PARAMETERS
To characterize all types of compounds for simulation by
PEST, the following parameters are of interest. However, for a
given compound some parameters., such as quantum yield for
sensitized photolysis, may not be applicable or may be unavail-
able. The user of the model should exercise discretion in
determining what parameters are necessary for a particular
compound. Some parameters, such as the Henry's Law constant,
can be derived from first-principle relationships and are
calculated in PEST. For many parameters, use of a zero value
will have the effect of cancelling a term in a process equation.
The parameters for characterization of a compound include: .-
1. Hydrolysis ....... ... . .
EN activation energy for effect of temperature (cal/mole)
p. 7
KA ... ' rate constant for Bronsted acid catalysis (I/days) pp..
' - " 7, 11 ; .' . ' ' ,
KB rate constant for Bronsted base catalysis (I/days)
. PP- 7, 11 ' . ......
KCAL rate constant to account for colloidal, metal-ion, and
phase-transfer catalysis (I/days) pp. 7, 12
51
-------
KH acid-catalyzed rate constant (1/M days) pp. 7, 9
KO uncatalyzed rate constant (I/days) pp. 7, 9, 11
KOH base-catalyzed rate constant (1/M days) pp. 7, 9, 11
TCOPT temperature at which rate constant was obtained (°K)
p. 7 .....'
2. Oxidation
KEFF rate of radical initiation reaction, p. 15
KP rate of reaction between TOM and alkoxy and peroxy
radicals (I/day) p. 15
KT rate of competing reaction between two radicals result-
ing in non-radical products (I/day) p. 15
3. Photolysis
ELAM molar extinction coefficient for TOM at each wave-
length: (I/mole em)- p. 17
FRACD fraction of irradiance that is direct at each wave-
length (unitless) p.. 17
FRACS fraction of irradiance that is indirect at each wave-
length (unitless) p. 17
KMEAS rate constant for sensitized photolysis (I/day)
pp. 20, 21.
KSEN empirical' rate constant for sensitized photolysis
(I/day) pp. 16, 20
PSIA direct photolysis quantum yield for the TOM (unitless)
pp. 16, 17
PSI3 L 'sensitized photolysis quantum yield for the TOM
(unitless) p. 16
4. Volatilization ' - . .
HENRY Henry's Law constant(atm.cm /mol) pp. 26, 27
KLEXPT correction factor for volatilization (unitless) p. 24
(an empiricism to provide greater precision where
necessary)
VPRESS vapor pressure (atm) p. 26
VTOM molal volume of TOM (cm /mol) pp. 26-28
5. Solution
SOLUB solubility (mol/cm3) pp. 26, 31
6. Microbial Degradation
52
-------
KS half -saturation constant for microbial metabolism
(g/m3) p. 32
METMAX - maximum rate of microbial metabolism (I/day) pp. 32,
33
STRU structural activity factor for microbial degradation
(unitless) pp. 39, 40
7. Sorption
KPART octanol-water partition coefficient (unitless) p. 40
8 . Bioaccumulation
BTMAX maximum rate of bio trans formation (g/g biomass day)
p. 49
.BTTIM number of days required to reach full metabolic
'"""'' ': :. capacity (day) p. 4'9
''£'"- .. .pe±centage of"TOM""in orey that is egested (uni'tless)
pp. 47, 48 "''" " ..-..
''EFFEN ''coefficient -for dif fusiv.ity >:6f" TOM through gill
membrane (unitless) p. 44
EX ' rate of excretion 'of TOM by each organism .(g/m day)
P. 48 .. ... ; . . .
KBTRA-N half-saturation coefficient for bio trans format ion (g
: .-.;;.-; TOM/m3) p. 49" ......;.,. . ':: ', . -..- "
'PCBLW ' bloodrwater partition coefficient, 'p. 43 '"'"
"-'. . j . ,, . ' -. .. -
PCFBL . £at:blood partition "coefficient , p. 43
SITE-SPECIFIC ' CONSTANTS:,.- AN.D..-DRlVlNi3'.: VARIABLES ' .'' '..'
'..'... In. order- to. run 'the ...model- 'f or" a -particular site, .other
than the representative. .prototype 'sit.e.s,.. it 'is necessary to
have values-, fo-r th'e following-.site' characteristics:
'ALPHA. -extinction -coefficient for . site, water at each wave-
! length (I/cm) pp. . 17-20 "
CONCEN . 'initial concentration of toxic organic material
"..'..'."'' /(g/m;3) p-. 7..;..'; ;.-,;''" .- : .
D:.,:,.;. . .median, depth of water'7 (cm) .-pp... -.1.7, 18
DEPTH- .: depth of '.water (m) p. .'"38".
HA .. ..concentr'ation.-.of. .Bron'sted acid .(g/m3) p. 7
HB :,:....,. ;;;,.conc!entratibn. of- Bronsted. base-.t-;(g/m?) .p.- 7 - .-
RAD c'qncentration 'of radical .initiator- present , p. 15
. 5-3
-------
STTOM
Julian date of introduction of TOM, pp. 39, 49
Because PEST is a dynamic, time-varying model, it is also
necessary to have time series of data for driving variables for
the time period being simulated. The driving variables are:
BACB . microbial biomass (g/m3) p. 39
BIO biomass, of each organism (g/m3) pp. 42, 45-
D02 dissolved oxygen concentration (g/m3) pp. 35, 43, 46
IIMEAS observed light intensity (photons/cm) pp. 20, 21
LOAD concentration of each carrier (g/m3) pp. 40, 41 (=
BIO)
pH ambient pH, pp. 7, 13, 36
.T water temperature (°C) (= TEMP) p. 46
TEMP ambient temperature (°C) pp. 7, 26, 37, 42
,WINDV ... wind velocity (m/sec) pp....26, 38
.ORGANISM-SPECIFIC PARAMETERS .... ,. ,
The following information is desirable for adaptation of
the bioaccumulation submodel for particular species:
BDINT coefficient for biodeposition (unitless) p. 47
BDSLP coefficient for biodeposition (unitless) p. 47
BIODEP rate' of production of pseudofeces (g/g day) p. 47
BLD02 coefficient for oxygen capacity of blood' (g 02/g
' . .blood;)' p. 43 , . ..'... ....... -.-' .. -:..-
BMIN the prey concentration at-which 'predator,, begins f eed-
ing (g/m3) pp. 46, 47 ':-- ' '
CMAX maximum rate of ingestion (g/g day) p. 45
FIL ; rate'of filtering (g/g day) p:' '47 '"
KEXCR proportionality coefficient for excretion as a func-
tion of respiration (unitless) p. 48
K02 saturation coefficient for oxygen limitation of
ingestion (g/m3) p. 46-.. ;
KRESP proportionality constant for respiration as a function
of metabolism (unitless) p. 42
KTEMP coefficient relating respiration rate to temperature
(1/°C) p. 42
LIM reduction in ingestion rate due to low prey concen-
54
-------
MAX
02RZSP
PCBLD
Q
Q10
RMAX
SARSA
TMAX.
TOPT '
, TRB.RED
TRED
W
tration (unitless) pp. 45,. 46
maximum rate of biotransformation under ambient en-
vironmental conditions (g/g biomass day) p. 49
coefficient relating oxygen uptake to respiration
(g 02/g biomass) pp. 42, 43
porcent blood (g blood/g biomass) p. 43
half-saturation constant for feeding (g/m3) pp. 45-47
rate of change per 10°C temperature change (unitless)
p. 46
respiration rate at starvation (g/g day) p. 42
percentage of TOM at surface of carrier (unitless)
p. 40
maximum temperature at which process will occur (°C)
p. 46 ...,....- '
optimum temperature (°C) ppy'42,' 4-6
reduction factor in ^filtering..rate, due to-.high
turbidity (unitless) p. 47
reduction factor, for non-optimal temperature (unit- .
less) pp. 46, 49
preference of predator for prey (unitless) p. 45
The following is needed to adapt the submodel for parti-
cular microbial assemblages:, ',' .._;,. ''.'.''. "".'' ' V .
DOCOR.- reduction of microbial degradation due -to;suboptimal
-'"'..'. . .. oxygen levels .(unitless) p. 33 .
"DOM-IN' '..minimum value 'of "oxygen reduction under anaerobic
conditions (unitiess)' :.pp.. 33, 35
KPH adaptive constant- for pH effect on..microbial degrada-
tion (unitless) pp. 36, 37'-
KTP adaptive constant for-.effect of temperature on
^microbial degradation (unitless) .pp. 37, 38
MK02 half-saturation constant for effect of oxygen on
microbial degradation (g/m3) pp. 33, 35
MMGT microbial generation time under optimal conditions
. (days) p.- 3'9 ' .. '. - .- . ... '!,
PHMAX critically high pH for microbial degradation, pp. 36,
37 . . ;. ..:-.' ; .-.
PHMIN criti'cally lowpH for> microBial degradation, pp. 36,
"37 ' ..; . ;- .;_ ..._ ., .-. '_,. ; .;.;.'
TMIX" '.-.- ;.;; reductibn factor, -for 'ef fee t.-'of .sub'bptimal mixing'on..
.. - ..-; microbial. degradation .(unitless) _ pp. .33 ,: 38
'55
-------
TPCOR reduction factor for effect of nonoptimal temperature
on microbial degradation (unitless) pp. 37, 38
TPMAX critically high temperature for microbial degradation
(°C) p. 37
56
-------
SECTION 4
VERIFICATION
Our philosophy of verification has been to use available
parameter values, confirm the yalidity of the process equations
by inspecting the process-response curves (such as are pre-
.sented in the previous section), and then apply the model to
the particular site without calibration. If the fit to the
observed data was 'not acceptable the formulations were re-
examined and improved, but the parameter values were not
changed. This approach was taken because it was felt that
there would not 'be opportunity or rationale for "fine-tuning"
the parameter values in PEST using observed data when it was
used as an evaluative model for new compounds.
However, in developing the model, this constraint proved
.to be frustrating. Correction'of obvious deficiencies in the
formulations sometimes led to worse fits to observed data. In
fact, the simulations presented here represent just such a
case; we discovered during the final stages of documentation
that mass balance ;was not being maintained and -that the TOM
was being "lost" due to a programming error; we corrected the
error and now the *TOM does not disappear fast enough! This
discrepancy, in most of the..,.simulations may., indicate, another
problem in the formulations; or":it" may. be a'reflection of the
uncertainty involved in the. parameters for processes such as ....
uptake by organisms, biotransformation, sensitized photolysis,
and colloidal-catalyzed hydrolysis :r these all. tend to be
conservative and lead to "worst case" simulations in the sense
that the TOM is-more persistent. There are"-also -uncertainties
involved in the driving-variable data and residue data for the.
sites. Therefore, the simulations can only.be used to suggest
that the model is behaving reasonably well. Unfortunately, the
data are not sufficient for either validation or invalidation
of PEST.
Based on our modeling experience, we recommend that future
...field verification studies include the following considerations:
the mass (or biomass) of each carrier group,
time series of TOM concentrations in the carriers,
some idea of the bioenergetics of the biotic carriers
57
-------
(such as consumption rates and prey preference), and
enough information so that degradation pathways can be
pinpointed (this could involve short-term use of
radioactive-labelled material coupled.with standard
analytical determinations of TOM concentrations).
PARATHION IN ISRAELI FISHPONDS- . .
As described by Perry and Gasith (1978), Parathion was
introduced as a single 0.05 ppm application to each of two
eutrophic fish ponds at Dor, Israel/on March 8, 1978; the
experiment was concluded 67 days later. The ponds were 400 m^
in area and averaged 1 m in depth. Data were given for concen-
trations in phytoplankton/ zooplankton, waterbugs, benthic
invertebrates (not modeled)/ carp, grass carp, silver carp,
Tilapia, and water.
The driving variables and parameters used in the simulation
are given in Appendix A. The principal source of uncertainty
was in the diurnal variation of pH, which is very important in
base-catalyzed hydrolysis of paratnion (see Fig. 3a, p. 9).
The .simulation results "are^ given in 'Figures 13a-e.
dtnil or I no
77JD 87JDO . 87JD
Julian Date .
VJJD
Figure 13a.
Comparison of predicted and observed concentrations
of Parathion in dissolved phase in Pond A-7, Dor,
Israel. Data from Perry and Gasith, 1978.
58
-------
experimental
B7J3
T7JJD 87JB 87JB
Julian Date
V7JDO
Figure 13b.
Comparison of predicted and observed concentrations
of Parathion-in"zooplahkton in Pond A-7, Dor,
Israel. Data from Perry and Gasith, 1978.
doulstlon
7 JO
77JO 87JOO 37JO
Julian Date
vnjio
Figure 13c.,
Comparison of predicted and observed concentrations'
of Parathion in carp in Pond;A-7,:Dor, Israel.
Data from Perry and Gasith, 1978
59
-------
Figure 13d.
Figure 13e.
3
t-i
fel
.etuulstlan
,expBrijefrtsl
TUB 87A)
Julian Date
87JDB
HlflO
Comparison of predicted and observed concentrations
of Parathion in Tilapia in Pond A-7, Dor, Israel.
Data from Perry and Gasith, 1978.
.emulation
* ttXptS "lAo ilkl
IfO
TltB B7JD S7JQO
Julian Date
VTJD
Comparison of predicted and observed concentrations
of Parathion in silver carp in Pond A-7, Dor,
Israel. Data from Perry and Gasith, 1978.
60
-------
PENTACHLOROPHENOL TN MISSOURI FISHPONDS
Pentachlorophenol was introduced as. a single application
of 1.0 ppm in each.of 3 low-nutrient ponds without macrophytes
at the Columbia, Missouri, National Fisheries Research Labora-
tory on,,May 22, 1978 r the experiment was concluded 142 days
later '(T. P. Boyle and E.. F. Robinson-Wilson, personal communi-
cation). The ponds were 297.28 m2 in area and had an average
depth of 1.20 m. Data were given for concentrations in large
mouth bass and water.
The driving variables and parameters used in the simula-
tion are given in Appendix B. The principal source of uncer-
tainty was in the role of pH in the sediments as opposed to
the water column; water-column values were used in the simula-
tion, but the more acidic sediments may have been quite
important due to the acid-catalyzed hydrolysis of Pentachloro-
phenol (see Fig. 3b, p. 10). Also, Pentachlorophenol degrada-
tion is sensitive to the effects of light attenuation on photo-
lysis (see" Table-6,, p....2"0') . The simulation, was further '
.hampered, .by._the availability-of. biomass data; the .zooplankton
v'ai-u'es are':ojtly",approx-±mate---conversio'ns' ..'an'd': the fish .values
were given only for the beginning and end of the experiment.
The simulations.are shown, in Figures.. 14a-d... ..
slnuistton
22BJDO
Julian Date
'Figure 14a'/
Comparison "of:." p'r'edicted "and' observed concentrations
of Pentachlorophenpl .in dissolved.phase in Tr.eat-
:ment~i-21 ponds,"'Cbrumbia,;iMi'ssb'uri.. ..Data from Boyle
and '. Robinson-Wil son (iiripub. j .
61
-------
.Part. OM
.Clay
Julian Date
26CUM
3HJJO
Figure14b.
Predicted concentrations of Pentacn±oropnenol in
clay and particulate organic material^ in Treatment-
3 ponds, Columbia, .Missouri.
Figure 14c.
. Zooplenkton
a Phytnplanktun
22DJID
Julian Date
SffiXflO
Predicted concentrations of Pentachlorophenol in
phytoplankton and zooplankton in-Treatment-3
ponds, Columbia, Missouri.
62
-------
slnulstion.
«.experiaernsL
Julian Date
'Figure 14d.
Predicted and observed concentrations of Penta-
chlorophenol in large .mouth.bass in Treatment-3
ponds, Columbia, Missouri. Data/ front;. Boyle, and
Robirison-rWilson '(unpub.;)'."iV"r'.-'-_-V'' ''/.':"''v..'.';' '-.' "
In a concurrent treatment, Pentaehlorophenol was^introduced
in 4 applications of ; 0..2 , , 0. 2 ,. .0,. 4 and 0.4 ppm in each of .3,. ;
lowr-nutrient: ponds :without-;-macr.pphytes-. The ponds -were 297.28
m?;,,in .area, and had an aver age.,.depth; .of 1.4 8,:m. .. :Data were given
for concentrations in bluegi-lls., large mouth bass, channel
catfish, and..water. ': '..,.. : ''. ' '",. ':,.'"' :- ;'''. .
....... The driving variables .used -in. the simulation are given in . .
.Appendix C. ; .The. simulations;-are shown: in Figures 15a-d.
.blELDRIN'lN AN IOWA RESERVOIR" ''"'... . '- : '' ' ..... :
'.'.' Dieldrin has been monitored in the Iowa River since. 1968 . .
.and analyses for fish in Coralville Reservoir are available from
1970 to present; the compound has been quite persistent, al-
though aldrin, its precursor, has been banned since 1975.
-------
.gliulatlan
,. experlienul
aeioo
82SJOO
Figure' 15a.
24fijDO
..._., Julian Date
Comparison 6"f predicted and observed concentrations
of Pentachlorophenol in dissolved phase in Treat-
ment-2 ponds, Columbia.,; .Missouri. Data from
Boyle and Robinson-Wilson (unpub.).
« emulation
Figure 15b.
24SJD
Julian Date
Comparison of predicted and observed concentrations
of Pentachlorophenol in bluegills in Treatment-2
ponds, Columbia, Missouri. Data from Boyle and
Robinson-Wilson (unpub.).
64
-------
m fljlffiil srlon
Figure.15c,
. { . Julian Date
Comparison of predicted and observed concentrations
o'f Pentachlorophenol in large mouth bass, in Treat-
ment-2 ponds, Columbia, Missouri. Data from Boyle
. and: Robinson-Wilson, (unptib.,). r ......... .
Figure 15d.
Julln Drte;
Comparison of predicted and observed concentrations
.,of Pentachlorophenol. in channel catfish..in Treat-
ment-? ponds, Columbia, Missouri; Data from Boyle
and Robinson-Wilson (unpub.). ,
65-
-------
The driving variables and parameters used in the simulation
- are .given in Appendix D. Sources of uncertainty include the
dieldrin loadings to Coralville Reservoir (downstream
concentrations were.used) and.the biomass of organisms available:
to take up dieldrin. However, the simulations are clearly
incorrect because outflow and sedimentation were not taken into"/
consideration. We anticipate that later releases of the code
.will facilitate simulation of these important processes. The
simulations are shown in Figures 16a and b. ..
.eisulHtion
Tiae (days)
Figure 16a.
Comparison of predicted and observed concentra-
tions of Dieldrin in dissolved phase in Coral-
ville Reservoir, Iowa from 1968 to 1977. Data
from Schnoor et al. (1979).
However, when we used the sedimentation loss coefficient
used by Schnoor (1979) the pattern of response was improved,
but the results were still off by an order of magnitude
(Figure 16c) .
66
-------
CJ
,tn nation
Figure- 16b.
' ' 2DQQ<0 '' '-'SfldBir
Ti« (days)
Comparison- of > predicted ancF observed .concentrations
of Dieldr-in in carp..;in-...Goraiville Reservoir, Iowa.
Figure 16c. 'Comparison o'f" predicted and observed concentrations
>of^ieldfin;';;& -
cRes'er,yb'i-r''j'-->loWa>y-';i"s-edimieht;atlpn--l6Vs' coefficient was
:used.-.ih tl
.67
-------
.... SECTION .5
USER'S MANUAL
The PEST model is designed for interactive use, but can be
used in a batch environment. There are ten commands in the
model, including a HELP command that provides on-line assist-
ance. "Yes?" is the prompt for a command.
LOGON
Having logged on. to,-'.your" system" and begun' execution of
PEST, you will have "to supply the name of the data file and
indicate whether or not., you. are on a graphics device (DEC VT55
or similar). ' .... -' .' ; . ...
Example ' ' -'- "" - ; ''' '. .
Are you on a graphics device? .N .' ' .
Name of parameters-file: PSPOP3.DAT.' ,. . .
Yes?- '" . ' -'' '. ''"'
EDIT . - - - . ... .-,..- ..; . ' ''..,;, ,,,v-';. . ....:. .-.. .-
"''.; The EDIT command allows the user.-to edit any of the model's
parameters, i/e.,. the data for;.- the program. Because, these data
are in binary form, they cannot be altered using a. conventional
text editor, so this feature has been added to the model. The
edit mode gives a "->" prompt to the user, after which an edit
line may be entered. The EDIT mode is terminated by a "."..
Syntax '. : . .
. ..EDIT
= [,= ...]
where
is any variable listed in the parameter
index file (I/O unit 10)
is a list of one or more values to be
assigned, or is an integer followed by an asterisk
followed by a value to be assigned to the next n elements --
68
-------
of the parameter,, or is an integer followed by an
asterisk to display the next n values of the parameter
brackets indicate'optional' string^O'-f, input ' .' :. .'
Examoles . . . .' . . .... ... ': ...
Yes?' EDIT-'-..:: -MV:- --.-'-
-> TLAST-* " '
TLAST=
91
-> TFIRST=*
TFIRST= :
1 . .....' . ' -
-> DO2=4* . . , . ..- .
D02= ' ' ;..-. . . M ' .
-. ..11.50 '" "' -lo.fto,-.--' . .'s'.'Sob-- -'.-3--;.ooo~
.-> D02(2.) = 3*'. . . -....;, '.;.::-
D02-(2).= - :--. , V ''. . . ..'.-.
lO.iJO ..3-'900' ..^..000 :' '...
->D02:(3)=*J
D02(3.:) ='".".''
3-900'
D02t3)= .
4.00 '. .
Yes?' '
START ... .. ....'. ..
; .The" 'START . command; is, the. Very-;-heart of the'.model. .This
command causes, a simulation .to be performed.. At the. minimum, it
will, print a heading and, a table of pesticide amounts according
to day of simulation. Other information, may be printed out
depending oh. what previous.,.commands -the .-user may have .entered
'(i.e;'; DEBUG; and. PRINT; affect. START'S, output) . The. heading
contains the abbreviated state'yariable names (which may be
altered by 'the EDIT command)"." A; ^ , :-- ; , ..
Syntax ;.':''
START"-
69:
-------
Example
Yes? '-' I -iR.'l .' . .-.;..; V.M'.. .'.-".
..- Pe-sticide .* niE-LDRTN . 0CORALYIL'L'lt '''
TlME-'-'200:P:L''',
You did not stsrl
i: i . 0.450- - ;'
i: 31. 0.743
i: 64. 0.380
1.* 94, 0.221
WBUGS' "'""" CARP P;_"',. ''' PHYTO ' ''. UA'TEf<
', with e-ctiJi'.Ii.briuiii conditions. . .' . '
0-«.2'5p";'.';-:..';".--l:'."00 ' - ... .;:0^400 .. ' OUOOE-05. -
0.236 "';" ;l.01.'.'.. 0.740 -' 0. 422E-05.
0. 220 ;-- 0/9.92 ' 0.556 0 . 439:£-05 ' " '
0.2Q'i'" ... 0.936 - 0.376- ' - 0.451E-05
Would you like s summai-y of the results ? Y
End of simulstion.
Summsry of resultsJ
Compsrtment
ZOOPL
U BUGS-
CARP ,
BUFALQ
CHCATF' '::-..' .
CRAPIE. -" .
UA.LEYE "
'". ': S .-:' *' ''.":'.'"- "4
-:< 9 » ' '..'. '..''. .
«o»
PHYTO :'-: -:- ..-,.'.
MACRO "' ' "".
WATER.. . ...
:.,>-'--';'-,
POn
CLAY
Initisl -smourit
Final _smoijnt
Finsl
PF-ITl
22092
20128 .... -
93617
92592
97330 ".V.----.VV.
93695
99941- v-. :-'..,.,
o ' --
-o
0 ... "
3763S "''-'--'
30561
45103,E,-''05''---
o' . . " :"
45944E-01
43615E-01
of p-esticide
of pesticide
amounts ;
-' ^x'ni^*3 . .
ii24688E-08
,:-..40255E-09
.... .15S27E-05
.20687E-05
;;-v-yi,1.165E'-05
,6;8054'E.-06,.
:,...23922.£-rO.<5.'.
'':' i'Ci- "' -
.0 .
:. ..;0 ';:..
'-". 16149r06..-
.43240E-07
"'>4'5-10oE-0.5 ,.
""f.-o ....
.45?44E-^07
.21S07E-07
in ecosystem
in ecos«stetii
%' Distrib.
1 .23573E-01
... -...38436E-02- -
. 15.112 .
19.752
"Wl'0.660. /..;
' 6. 4973- : -
n '^O A -i .-..'.
. ;. *i'» fcWT A - . . .
.' .0
.0
-.*': ' ;.
.0
16.000-- -
16.000
£/cubic meter
si/cubic meter
PRINT
The user may wish to obtain output for state variables
other than those included in the default table. PRINT will
70
-------
include or exclude a state variable from the output; the command
acts as a toggle, reversing the print status of a state varia-
ble. Note that this does not exclude a state variable from the
simulation! It is useful in limiting the output for small
terminals. The command also controls the output units
Syntax
PRINT -
I
where
- is the name of a state variable or ALL
If "ALL", all state variables are printed; ordinarily state
variables 1,2,3,11,13, and 16 are printed.
Example
~"T'es?~ ~ST'ARi"r-~" """ «----"-- . - ..
Pesticide: JDIELDRIN U'CGRrtLUilLUI
TIME ZQOPL
WBUGS
CARP
PHYTO
WA i ER
CLAY
iou aid not start with equilibrium conditions.
i: 1. 0.450 0.250 l.OC 0.400
31. 0,743
(44, 0.380
94. 0.221
0,236
0.220
0.201
1.01
0.9*32
0.740
0.556
0.376
0.400E-05 0.400E-0.1
Q.422E-05 0.410E-01
0.439E-05 0.427E-01
0.4S1E-05 0.436E-0:
Would you like s summsry of the results ? N
DEBUG
The DEBUG command causes values to be printed for the
indicated processes during the simulation, or it will cause
loadings, rates or time to be printed during a display. There
are eleven valid process names:
71
-------
Process Name
BTRA
CONS
GILS
HYDR
MMET
MORT
OXID
PHOT
SOLU
SORP
VOLA
Meaning
Biological transformation
Consumption
Gill Sorption
Hydrolysis
Microbial Metabolism
Mortality
Oxidation
Photolysis
Solution
Sorption
Volatilization
State Variables
Affected
1-12
1-12,14-16
1-13
13-16
' 14-16
1-12,15
13-16
13-16
13,15
1-16 .
13-14
This command is especially useful for diagnostic purposes; it
has the potential for generating large amounts of output!
Syntax
DEBUG [,...]
where
is the name .of a process
DEBUG [, ]
where
is L for loadings, R for rates, or T for time
Example
Yes? DEBUG GILS
Yes? START
Pesticide: DIELDRIN PCORALVILLE
TThE ZOOPL
WPUGS
CARP
PHYTO
WATER
CLAY
You did not start with et.-uilibriuni conditions.
i: l. 0.450 0.250 1.00 0.400
0.400E-05 0.400E-01
C-ILSRP:
0.1029253028E-14
-0.1156394316E-12
0.0
WCIRC :
0.3409434668E-13
0.3394711595E-11
0,0
0.0
-0.2176511123E-13
0.0
0.572811112SE-14
0.2115475330E-11
0.0
-0.3319572265Z--12
0.11S7643191E-13
0.1089410680E-11
0.5101936235E-11
0.7028356S46E-12
-0.632955ci5o2E-12
0.0
0.<3i45307568E-ll
0.0
72
-------
ECIRC :
0.-i>97072c080E-13 0.1172730880E-14 0.3433615614E-08 0.5393749483E-C8
0. l5«5iOS530£-OS 0, C.OS3629156E-09 0 a733071045c-10 0 . 0
0,0 0.0
JO IR :
0.726057668SE-11 0.1230369973E-11 -0.2341699501E-OS . -0.4465007919E.-OS
TABULATE
Assuming a simulation has already been performed, this
command will print out a table of results, with the header and
without any debugging and blank lines. This is especially use-
ful if you have debugged some processes, run a simulation, and
want uncluttered output.
Syntax
- :" TABULATE
Example
Yes? TABULATE
DIE1.DRIH eCORALVILLE
TIriE ZOO
i: i.
i : 31 .
i: 64.
i: 94.
..Yes? '
0.
0.
0.
0.
PL
450 .
743
380
221
WBUGS
0
0
0
o
.250
*236
; 220
,201.
.CARP
1
1
0.
'" 0.
.00
.01
.982
936
PHYTO :.
0
0
0
0
.400
.740
.556
.376
UATER . CLAY
0 .
0..
0.
0.
400E-05'
422£-05
439E-05
451E-05
0.400E-01
0.410E-01
0.427E-01
Q.436E-01
"PLOT
command assumes that a simulation has been done al-
ready, (there should be some data in files attached to I/O units
"2v and"' 3) and' plots the simulation results for the named state
variable .". XYPLOT .subroutine can be modified to suit the user;
it takes 3 -arguments: an X vector, a Y vector,, and the number
of points. The state-variable name entered must match one of
the heading ..titles for a simulation.
,. -. PLOT. -normally' provides printer plots; however, if at the
beginning of a run the question "Are you on a graphics device?"
73
-------
was answered "Yes", it assumes a configuration similar to that
in the Center for Ecological Modeling: a DEC VT55 and an HP722
plotter with proprietary software. The XYPLTA subroutine is
used for the HP plotter; it is included as an example of how
device-dependent calls can be used.
Syntax ' . ......'' " ' ;
PLOT . . . . . '
where "" "' . '-
is the name of a state variable; it must match
one of the heading titles for the simulation
Example
T'e<:
o
0.
0,
0.
0,
0.
0.
0.
0,
0.
0.
0.
0 <
0.
0.
0,
0 .
o;
0,
.0.
A +
0 .
0,
0.
ov
0.
0.
0.
0,
0.
0.
li
T °LOT PH'
147.00
97 ,00
44478
43217
41956
40695
39-<34
38173
36913
35652
34391
33.130
31869
30609
29348
23087
26826
25565-
24305
23044
21783 .....-
20522
19261 '
160-00
16740 .
15479
14218
12957
11696
10436
91747E-01
79139E-01
147,00
97,00
r'TO , . -
" . " '..;.-..- " ...... -
* "" .- ' -
*
'*.....
*
' * ' . -..
.*.,., . . .
* . ' '": --
.-.... . ' ' ..*.........;..; ''-..-.!.:; :;; ..-.-.-: ': " :..-..'. .-:.-'...
* .,.-.....- ' . . '
Jjt .. . '
.-* ", ' ..-.-:-
. . - * .- ... .. -
'*
X; x.
* *
* #
* *
* * *
$*#$###
74
-------
DISPLAY
This will cause a process (see DEBUG) to be plotted as a
function of an independent variable, one of the driving varia-
bles denoted by a number:
No.
1-16
17
18
19
20
21-36
37
Driving Variable
carrier compartment
temperature
windspeed
PH
dissolved oxygen
toxic organic matter in carrier 1-1C
solar radiation
The plotting is device-dependent (see PLOT). On a CRT,
such as the DEC VT55, it plots a well defined profile. The re-
sponse curves used in the process documentation (SECTION 2) were
obtained using this command with an HP plotter. DISPLAY uses
the LOOK subroutine and either the XYPLOT subroutine or the
XYPLTA subroutine;these.can..be modified for different devices.
Syntax
DISPLAY
where
is a process
Examole
YEi? DISPLAY VOLft
Uhi.ch state variable number- is to be uee3 ?13
WJ-.ich loadins is to be varied (loading nuniber)?18
Eriter the Tiintmc::'values for the losdina I 1»5
Do yo-j went a helf;-life Plot ? tl
1 ,0000
0.15349E-03
C-.14927E-C3
0.14504E-03
0.14081E-03
0.13659E-03
0.;;<2JiE-03
0.12814E-03
0.12391E-03
0.11969E-03
0.11546E-03
0.11124E-03
0..10701E-03
0.10278E-03
0.98S59E-04
0.94333E--04
0.90108E-04
0 . 85882E-04
0.81657E-04
0.77431E-04
0.7320SE-01
0.689aOE-04 '
0.64754E-04
.0..60529E-04
0.56303E-04
0.520'7E-04
0.47852E-04
6.43626E-04
0.39401E-04
0.35175E-04
0.30950E-04
4.9600
*
X*
**
. **
t*
. ***
t*
tit
..-.-... . / :- . . *'»*' ' ;"
-r»
'..'' *** '
. ' *** '" ...
. : ".." ' ** . ' "' '-- '
- «**
...- **'* . .
***
. . ***
*** ''
**
' ' **-*
*** ' ' .'
**
' *** ' .
x* ...
** '.'
*** .
.** '... .
»» '.....
**
** '. ' . .--'
1.0000
4.9600
75:
-------
Yes? SISF'LAV (J0',f>
Uh:crr stste variable nuniOer is to ee nnn»*B:: vsluee for the losoina : 1>S
Do sou usr,*, s nalf-lifg ?iot T Y.
1.0000 ' ... .
4.7600 '
DUMP
This
22391..
21775.
'21136.
20542.
19"2o.
19309. .
16693.
. 18076. :
1 7460-. '
16843;
16227...
15611.
14904. .
14378.
13751.
131-15.
12528.
11912.
11296.
10679.
10063. .
9446.3 '.
8829.9
8213.5
7597.0
o930.6
6364.2
5747. S
5131.3
4514.9
1 . 0000 .
command
* . ...
* .'''
*
X
*'
*
**"' : .''' . .. ..
* '
* ' '
x-
. *« . . ' .:.....-.-
* '
*
X*
**
**
a*
»*»
". . . « « . . .-
*«*
**x
***
-.-.; ' . . . .- .,.*** . . ...
' ' - ' ' '" ttft-' ''
' »x«*
»««*««
XtXXXXtl
. *»»**»*»*»
....'... ' .... 4.940C
is intended for use in model developme
mt
and
should not.be used by the casual User.
DUMP allows the -user to look at an entire common .block.
It accepts as an argument the number of. the common block to be
printed: "
Name---
MISC
RPARMS
DEBUG
'TIME .
DRIVER
Description
character parameters
.real parameters
logical parameters
integer parameters
loadings
Caution: ..blocks 1 and-4 "will-usually generate reams of output!
Syntax ' '
DUMP '[ n ...]..
where
is the number of the common block
Example
Yes? DUMP 3"
76
-------
HIJriLLi : 4 c 0 0 0 0
'*, 0 0 0 3 2 20 30 20 6
"( nOOOOOOu^
O 0 0 0 0 21 18772
S'-IIT'.'.HJ
;:i
'i FIRST:
3<>/
itflsr :
1 17
HELP j
This command provides limited on-line assistance in running
the model. It is totally query-oriented and is self-explana-
tory. However, .it is intended to be used as a refresher and
not as a substitute for this documentation.
Syntax ...
HELP
Example
' Yes? HELP
This ... is the. place to be...... - - -
Enter a .period to get back to command''"'mode. *''' ' '
: What topic do you wish 'information on.? HELP
"'3 lihe(s)"labelled'Topics follow. ."
'.Continue? (Yes or .No) YES
.Legal topics are: COMMANDS, DEBUG, DISPLAY, DUMP, EDIT,'
KELP INSTALL, LOADINGS, PLOT, PRINT, PROCESSES, QUIT,
.: REMOVE, START, TABULATE
'What topic do you wish information on? REMOVE .
3 line('s) . labelled Meaning follow. .
'Continue? (Yes or No) Y - -. . .....,'
This command allows--the user, to eliminate a process .from,
the simulation. It can be used to. .debug nast.y processes ,.-...-
.. or to .see..what the system does.if a process' is inhibited.
'"'3"lin-e(s) 'labelled Syntax follow.' _.-. - " '
77
-------
Continue? (Yes or No)Y .
The syntax of this command is:
REMOVE
where ' is a. process name.
1 line(s) labelled Example follow.
Continue? (Yes .or .No). Y . ... .......... . .
REMOVE CONSUM removes consumption from the model.
What topic do you -wish information -on.
QUIT
This command terminates the program and returns control to
the operating system.
Syntax
':.. QUIT
Example "- -. . ;
Yes? QUIT
STOP' '--'
78
-------
SECTION 6
PROGRAMMER'S GUIDE
INTRODUCTION
This manual is a guide in bringing up PEST at a particular
installation. It is intended to offer the programmer an under-
standing of the inner structure and rationale of PEST. PEST
was- written in what we believe to be a structured and modular
fashion. This design allows a considerable amount of tampering
without acomparable'number of nasty'side-effects. Thus, any
user .seeing fit to change a process may do so without much
grie.f.' If you find any inadequacies, such as poor commenting -in
a portion of code or spaghetti code, please use the Software
Report Form 'in the back of this report.
The pesticide model is written in FORTRAN IV, using reason-.
ably standard features. Any system dependencies, .are hopefully
confined to the 'SPOO'subroutine, thus making'it easy for the
programmer at. a ..particular site- to .add any system-dependent
features (e.g., file;openings, program profiling or timing, time
of day printout, etcl) according.to his sense of aesthetics.
i
The adage "Small is Beautiful" applies to many of the com-
ponents in PEST. We have tried to keep the amount of code per
subroutine down to- a-minimum. This hopefully insures easy read-
ing and understanding. .
FILE ..UNITS
.. .PEST uses the following units for I/O:
"' 1 Parameter index file
. .2 Rates of change of concentration
.3 - Concentrations. . . .
..... 4 ..-..Scratch file for conversions
5 User input (terminal)
6 User output (terminal)
8 Help file' " ' '
9 .. Parameter; file (data)
Files 1 and 8 are text files which are on your distribution
79 .
-------
tape. File 9 appears on the tape in alphanumeric form and must
be converted into an unformatted file before use. See the sec-
tion "Building a Model" for a description of how to get the
parameter file converted. ..
The parameter..index is used by the parameter editor, and
each line of this file is in (Al, 6A1, 416) format. The first-
column of this file is reserved for an end-of-file marker.The
next six characters are the names of editable parameters. The
four integer fields are the common block number to which the
parameter belongs, the row and"column sizes'of the variable (>1
means it is an array), and the location of the parameter in its
common block, respectively.
Files 2 and 3 record the results of a simulation in unfor- :
matted form for later use by the:TAB and PLOT commands, or any
external routines the user may have. The first record of these
files is the pesticide name, as entered into the parameter PEST;
the rest of the records represent values at a particular time
during the simulation.- Using-unformatted-FORTRAN-I/O-they con-
' tain, the .simulation'time as the first word, and the next 16
words are the values (rates or concentrations) for each of the
state variables. On some machines these values will require two
or more words, since the term "word" is somewhat arbitrary to '.*'
begin with (specifically, DEC computers will use two words for
these values, that being the size of their real number). Thus, ,
it should be easy to save simulations for later examination
merely by saving those files 'which were attached to FORTRAN . :
units, 2 and 3. You ^should have 'enough informationnow to be '
able to retrieve .the data in .these files for printing and plott-'
ing, if you so desire. Note that there are a varying number of
records .in each file, depending on the values of the STEP and
TLAST parameters, so that any external routines you write for
these data should.-use the "END-" -construct. ... . ..... .
File 4 is a scratch file used by the subroutine TRVAL to
perform translations from alphanumeric-to other modes .using
FORTRAN I/O. It is not an elegant way of doing things., and
will probably be replaced. in the future. '
Files 5 and 6 should be associated with the user's terminal.
File 8 is the help file. It is in the following format: ;
topic name '.
xl
:
xl lines of text
J__
.-80'
-------
x2
x2 lines of text
xn
followed by xn lines of text
topic name - - ;' -
In other words, the file starts with a topic name. The
topic is divided into subtopics by putting lines in which con-
tain a number-of-lines-to-follow (i2 format), and the subtopic
name. Then comes the specified number of lines, followed by
..another subtopic name, or a topic name, which indicates the end
.of the previous topic. " -. '.''. . . ;.-. "'' :"
.-',. --File 9 is..a. sequential, .access f i'le consisting'bf -5 'unfor-'''
matted records, one for each parameter common block, whose
descriptions appear in the next section. Note that if you wish
to change any of these unit numbers you should change the
appropriate initializations in the. block data^ which appears in
p..main.. .ftn.
COMMON SLOCKS
The following common blocks are all declared in .the main
program.. ' '' . '' ,'"'"' " . '
DEBUG - contains the LOGICAL parameters for the model, and
is' -read in by subroutine':PARMIO. These parameters control- ice
cover and debugging, of processes. . :' ' . .:
EATIT "- information from the CONSUM subroutine for use by
other processes:." Contains consumption rates', respiration rates,
..and,.grazing rates of various, .carriers.
...... DEVICE - contains one logical'variable which, if true, in-
dicates that 'the'user.wants"to produce"plots -on a graphics
device. ! . ' : .
', DRIVER-- is'a parameter "common..:b"Tock read in by subroutine
PARMIO. It'contains'the .values of the driving variables, along
with their times of: occurrence,., if., any:. . .
PFLAGS.- controls the printing of...compartment (carrier)
.pesticide, concentrations during:rsimulations.." Each, -element of
the array in..PFLAGS is a. logica'l variable corresponding to a
.particular i:state variable. __ If .its yalue_.is_ false., _ the
-------
corresponding state variable is not reported on during a simu-
lation. ' ... ,,. .
PLQADS - in this common, block, is .stored the. .array..of load-
ings for the previous, time.. stepV : '"..."".. .... .'.......
LODING - contains..the ..ioadirigs" for. the' current time step.-
. MISC - contains the alphanumeric parameters, specifically
the titles for each ;of the .state .yariable,s,'-and. the name-of the
pesticide. ' .''." '"'-''"'^""..-.'. "":':':'.;'''""
IOUNIT - contains integers which dictate the I/O units for
various purposes.. ' ":'
REMSWS - contains a set-of flags'which determine'if a pro-
cess has been removed or not.. ; ,.
RPARMS - contains the .real'parameters for the model, and is
read.in by PARMIO; these-are-mostly chemical-specific variables.
SEG - reserved for expansion into multiple segments.
SOLUTN - at any one,.time ' this common block contains the
concentrations of pesticide1,in. eachTstate:. variable.
TIME - the integer'parameter block,.vread in by PARMIO. Con--
tains STEP, TFIRST, ':TLAST, ..and: the number,s of each, of .the load-
ings (driving variables).'.''.".:. v ;,!..'': 1...^.: // . ' ' i.
BUILDING A MODEL . ' ' .} .,-.,..
Each ..parameter file actually .defines a... different model,
since it contains chemical, environmental, and biological data :
that change from system to system. Any of the sixteen compart-
ments in ..PEST can be redefined to represent another carrier by.
resetting the parameters for that compartment. This makes it
easier to model what you want to model, as"long as. it falls in-
to one of three categories: ; plant," --animal, or non-living. :
Your distribution tape should contain a sample parameter
file for you to play with, along with source code for PEST, the '
source code for the parameter conversion program, the"parameter
index file, the help text file, and the documentation. The tape
itself is a 1600 BPI, EBCDIC tape formatted into fixed blocks
of 1600 bytes, with 80-byte logical records (FB(1600,80) is
the MTS designation), unless otherwise requested. Therefore, if
you're not sure of the format, try the above.
The very first file on the!tape should contain an index .
of what is on the tape, and where it is__(in__the__form_of an_MTS .
'.;--.:% 2 ';.;
-------
command file, which should be documented well enough to tell you
what's going on). As a backup, in case somebody fouled up, you
will be sent a copy of that index file along with the tape.
The first thing you should do is get all the files off the
distribution tape. Then, obtain the parameter file and prepare
it for use by PEST. To do this, perform the following steps:
1) get the parameter file off the tape.
2) get the parameter conversion routine (CONVRT.FOR).
3) compile and link CONVRT.FOR
4) execute the resulting program with unit 2 assigned
to the formatted parameter file, and unit M assigned
to the new unformatted file.
5) when the program asks "To Tape?", hit the return key.
The information in the new file is now usable by PEST, and
this file should be assigned to unit 9.
'"':' At thi's point, you-can-compile -the-PEST program (all those
files beginning with "P" , and ending with ".FTN"). If you get
any compiler errors, call (.518.), 270-6494 or send us-the Software
Report Form in the back of this report. We are striving to make
this program as portable as possible.
You. have a choice at this point. The SPOO subroutine is a
program designed to make the. environment as comfortable as the
programmer wishes. ! We use it to convert all input to upper
case, and :to assign unit numbers to files, so .that the user .
does not have to. If you don11. want the subroutine around, 'you
can delete the call to .it which :occurs in-the main program.
83
-------
'SECTION ?
REFERENCE'S.
Akisada, T. Colorimetric.Determination of Tetrachlorophenol and
Pentachlorophenol in: Commercial PCP.. BUnseki Kagaku,
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Albanese-, J.R. A Simulation Model for Coastal Zoobenthic
Ecosystems.., Report-16 r-Center for Ecological-.Modeling,
Rerisselaer Polytechnic institute, Troy, New York, 45 pp,
1979v - '- ' ''._' ' _ ' "': : " '-'. - ':
Albanese, JiR., C.D. Collins, C. I. Connolly, B.B. MacLeod,
and R.A. Park. The Potential ,Role..,of Ecosystem Models. In
H.-Gi Stefeh '(ed.) Reseirvoir :Management 'Surf ace Water
Impoundments, 1:576-584,.. 1981V':-?-' ^;vv:.--
Aly, O.M., and S.D. /Faus.t. .'.'Studi'es'Ion the^Fate of 2,4-D and
. Ester Derivatives in Natural^Surface .Waters. .J. Agr.
. ./-Food Ghem.., -'12 '
Armstrong:, D-.E-.. . and G; Chesters. 'Adsorption Catalyzed .
Chemical Hydrolysis of Atrazine. iEnvir. Sci. Techn.,,
. ,. Current,.Research.,-s2: (-9) : 68 3r6 8.9.^.^1968. '.:,,;: v -'. ''.,. . .
. ' " , . ' ' . ' ' i '' '.,'.'" " "-''"
, .. > .and RVFy..-; Harris1*,; 'Atrazine;:Hydrolysis ^in Soil.
' ' In: Praceedings-.of Soil Science-..Society of America,
31:61-66, 1967... ;.'. ....,.._ ;..--. '"".._ . ''_; . -
Baughman, .:G^L. " and L.A. Burns. Transport and Trans format ion
. .. : .of :Chemica.ls .in the; Environment;... .A .Perspective, ;::'Int.
'';.'-".' Handbook'pf," Environmental ''Chemistry,-''Vol. 2-', 'Part A: '."',
Reactions, and Processes, pp, .1-17, 0. Hutzinger, ed.
Springer Verlag, 1980.
Bener, P. Approximate Values of ^Intensity of Natural
Ultra-violet Radiation, for Different Amounts of Atmospheric
Ozone. U.S. Army Repor-t-DAJA :37-68-C-1017, Davos Platz,
Switzerland, 1972. 4^11' >
Boval, B. and J.M. . Smith'£*' Photodecomposition of
2,4-dichlorophenoxyacetic Acid. Chemical Engineering
Science, ;28:1661-1675, 1973.
'V84:
-------
Boylen, C.W. and T.Do Brock,, Bacterial Decomposition
Processes in Lake Wingra Sediments during Winter.
Limnology and Oceanography, 18:628-634, 1973.
Branson, D.R. Predicting the Fate of Chemicals in the Aquatic
Environment from Laboratory Data. In: Estimating the
Hazard of Chemical Substances to Aquatic Life, J. Cairns,
Jr., KoL. Dickson, and A.W. Maki, eds. ASTM STP 657,
American Society'for Testing and Materials, 1978, pp.
55-70o
Brock, T.D. High Temperature Systems. Ann. Rev. Eco.
Systems, 1:191-220, 1970.
Burns, L.A., D.M. Cline and R.R. Lassiter. Exposure Analysis
Modeling System (EXAMS): User Manual and System
"'..' Documentation, in press, 443 pp.
Chiou et al. Partition Coefficient and Bioaccumulation of
Selected Organic Chemicals. Envir. Sci. Techn.,
11(5) -.475-478, May 1977.
.Clesceri, LoS., C«,W8 Boylen and R0A0 Park. Microdynamics of
Detritus Formation and Decomposition and Its Role as a
Stabilizing Influence in Freshwater Lakes. Rensselaer
Fresh Water .Institute, at Lake George, Rensselaer
Polytechnic Institute, Troy, New York, FWI Report #77-12,
1977, 102..pp. -, ; . : '
Cohen, Ye, W. Cocchio, and D. MacKay. Laboratory Study of
Liquid Phase Controlled Volatilization Rates in the
Presence of Wind Waves. .Envir. Sci. Techn.,
12(15):553-558, 1978. ...
"Crosby, D.G. The Photodecomposition of Pesticides in Water.
Advances in Chemistry Series Num. Ill, 1972. .
'". ., and .A. S. Wong. Phbtodecomposi-tion of
2,4,5-Trichlorophenoxyacetic Acid (2,4,5-T) in Water.
<; Agri. Food-Chem. , 21-: 1052-1054 ,rT973. '" .---
Crurn-Brown, A0, and'T. Fraser. Trans. Roy. Soc. Edinburgh,
. 225:151r-693, ,18690
I .*-.)'. . - '
Fagerstrom, T., and B. Asell; Methyl Mercury Accumulation in
an Aquatic Food Chain,.A Model and Some Implications for
. .Research Planning. AMBIO, 2(5), 1973.
Gibson, W.P. and R.G. Burns. The Breakdown of Malathion in
Soil and Soil Components. Microbial Ecology, 3:219-230,
1977. . . '
:85
-------
Green, A.E.S. Solar Spectral Irradiance Reaching the Ground.
In; Program and Abstracts on Nonbiological Transport and
Transformation of Pollutant's on Land and'Water: Processes
.and Critical Data Required for Predictive. Description.
Haque, R. Role\ of, Adsorption- 'in, Studying -the Dynamics of
Pesticides. Plenum Press, :New York, -New.Yprk>'':'19.74.- ".;..
Hautala, R.R. Surfactant Effects on Pesticide Photochemistry in
Water and Soil. Environmental Research Laboratory Office
of Research and Development, U.S.. Environmental Protection
.Agency, Athens, Georgia, EPA-600/3-78-060, 1978, 71 pp.
Herbrandson, H.F., W. Reeves, E.M. Partain III, and F.H.
Feher. Catalysis and Inhibition of the Hydrolysis of the
Pesticides Atrazine and Carbaryl by Micelles. Final Report
on Grant No. 804820020, Environmental Systems Branch,
Environmental Research Laboratory.,. Athens, Georgia, 15pp.,
1977. '.,'.'' i ' - '
Horvath, R.S. Microbial Go-Metabolism and the Degradation of
Organic Compounds, Nature, 36(2):146-155, 1972.
Jaffe,; H.H. 'A Reexamination of Jthe Hammett Equation, Chem.
Rev..,. 53:.;191-261,:::1953'. ' ! .3 ' ,- - ' . ..- "
Kapo.or.,- I.P., R.L. 'Metcalf, A.S. "Hirwe> JiJ. -Coats, .and .M.S.
Khalsa.. Structure. Activity Correlations .of ... , ,
. .Biodegradability ;of DDT Analogs. J. Agr. .Food. Chem.,
,. . . .21(2)5310-315, ,1973.. .;;,,:,|V;. -..Vv... :..;..,':. ';
Kenaga, E.E., and C..A.I. Goring. Relationship .Between Water .
'"'"'','.' Solubility, 'Soil Sbrption,. :Octahol-Water Partitioning, and
"' Concentration of Chemicals ;in Biota. In; American Society
' for Testing'and Materials, ;JiF.- Eaton, P.R. Parrish, and-:
A.C. Hendricks, eds., .1980, pp... 78-115. . . . . ;
_^ , Partitioning and Uptake,of Pesticides in .Biological
Systems. In; Ehvironmental'Dynamics of Pesticides, R.
Hague and V.H. Freed, eds; Plenum Press, New York, New
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Ketelaar, J.A.A. The Hydrolysis of .
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(Parathion and Dimethylparathion (EG 05)). Lab .for General
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, and H.R. Gersmann. Chemical.Studies on Insecticides VI.
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f
86::
-------
V ,' and'M.M. Beck. ""Metal-Catalysed"Hydrolysis of
Thiophosphoric Esters, Nature, 122:392-393, 1956.
Khan, M.A.Q., A. Kamal, R.J. Wolin, and J. Runnels. In Vivo
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Khan, S.U. Kinetics of Hydrolysis of Atrazine in Aqueus Fulvic
Acid Solution. Pestic. Sci., 9:39-43, 1978.
Konrad, J.F., G. Chesters, and D.E. Armstrong. Soil
Degradation of Malathion: A Phosphorodithioate
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Kubitschek, S.I., and H.E. Bendigketi. Latent Mutants in
Chemostats. Genetics, 46:105-133, 1961.
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. . Aquatic Environments.--. Interim Report, AERL Task 302,
... Program Element IBA609, The Environmental Protection
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______ G.L. .Baughman, and .L;A. Burns. Fate of Toxic Organic
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:Lockhart, w;Li,""D.A. 'Metner, and J. Solomon. Methoxychlor
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87
-------
Mabey, W. and T. Mill. Critical Review of Hydrolysis of
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of Iowa, 1979.' ; "'"' - - --.. -:
Macek, K.J., 'M.E. . Barrows, R.F. Frasny, .and B.H. Sleight III.
Bioconcentration of 14C-Pesticides by Bluegill Sunfish
during Continuous Aqueous Exposure. .'In;
Structure-Activity Correlations in Studies of Toxicity and
Bioconcentration with Aquatic Organisms, G.D. Veith and D.
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Mackay, D. and .P.J. Leinonen. j Rate.of Evaporation of Low
Solubility Contaminants from Waterbodies to Atmosphere.
Envir. Sci. Techn., 9:1178-1180, 19750
Merkel, G.J. and J.J. Perry* .^Increase -Cooxidative
: Biodegradation of Malathion. .Soil via Cosubstrate
Enrichment, 25 (5) :1011-1012;,. 1977. .,/ .
Meynell, G.G. ' Bacterial Plasmide. The MacMillan Press, London,
1972. ; ' .-...,-. ;; ..'.. ; .;'.....,,. :..-.., ';
Mortland, M.M. . and K.Vi^Raman,.'-.'.'Catalytic ;Hydrolysis-"of Some ' :
Organic Phosphate Pesticides .by Cu(.II) . J.. . -Agr. Food :
Chem., 15
Neeley, W.B. : and G.E. Blau. .The Use of Laboratory Data to
Predict 'the bistributon of. Chlprpyritos in a .Fish Pond,
; ,:i977. ; . , '- :. .. ' -.;:.: -..-, ., -. .. :.- .--.',-..-.
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:: O'Neill, R.V., R.A. . Goldstein, H.H. Shugart and'J.B. Mankin.
Terrestrial Ecosystem Energy Model. Eastern Deciduous
Forest Biome Memo Report.#72-19, -1972.
. ' , " . ' - ' I '' "' " - '""' - ' '
:0nishi, Y. and S.E. Wise. Mathematical Model, SERATRA, for.
Sediment Contaminant Transport .in Rivers, and Its
Application to Pesticide Transport in Four Mile and Wolf
Creeks in Iowa. 1979. . .-| . '
Othmer, D.F., and M.S. Thakar. : Correlating Diffusion
Coefficients in Liquid. Ind. Eng. Chem., 45:589,. 1953.
Park, R.A., C.D. Collins, C.I.-Connolly, J.R. Albanese, .and-
-------
B.B. MacLeod.. Documentation of the Aquatic Ecosystem
Model MS.CLEANER. A Final Report on Grant No. R80504701,
U.,S. Environmental Protection Agency, Athens, Georgia,
"112 pp. 1980.
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_
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91:
-------
APPENDIX.A . ' .:/";
Parameters and Loadings used in Simulation of Pond A~l, Dor, Israel
HE»OIP:
TIDE rocpi «noas C»BP sicusp cue sup tmri Pen CLM
PEST : ,
J»7,13B»Il
tltLEl:
TOOt I
111112:
8BDGS
111111:
CHRP
TI1LE1:
SICABP
UitE5:
it TI1LE6:
.rum
Si TMlEl!
fi «7»
Si IMLIB:
tltttl:
tflLEB:
FHI1C
T11LIC:
nucnc
TlltEC:
BUI IB
IITLEE:
tllLEf:
FOR
-------
-'iiitioiv' ;" -. -; -'i ''-. '
ct»f ' vvT-: .-- ' '-
Ct»f
tint
pooiil : -, ' :'- >'
0.20'OE-flt.. '0.20QE-01
00002. ':' ;. '." :' ; :
i.oo' '-. ... ' !
0.151 . ; O..fl51 '. '. O.'.«5l 0.1(51 . O.»5l 0-151 O.«51 . 0.151 0.951 0.151
0.280E-02 0.280E-C2 C.260E-02 0.250E-02 0.2HOE-02 0.230R-02 0.220E-02 0.210E-02 0.200E-02 O.I^OE-02 0.1SOE-02 0.152K-02
B»CB :
0.200E-02 0.100R-C2 C.500E-02
8DIN1 :
0.0
BDSI.P :
0.0
0.0
0.0
0.0
o.'.o
0.0
0.0
. "o.o
o.o
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
BIDC2 1 , : .
.I; O.SROE'CH 0.588E«C
-------
vo
0.300
0.0
0.300
0.0
8.300
0.0
0.300
0.0
0.300
0.0
0.300
o.o
- .00
- .00
- .00
- .00
- -00
- .00
-1.00
-1.00
ElAH :
0.480E>04
EH :
OiU7E»05
ESfCF :
0. 1001-02
OilOOE-07
FEDSRIt
1,00
FB»I :
0-600E-01
n» :
0.0
HE :
0.0
nERPt c
0.607(-05
IlflEftS:
0.300EH4
Kit :'
0.0
KB :
0.0
KBTBIR:
6.00
1.00 KOO V-'- 1.00 ' 1.00
..** .
0.300 . C.300 - ".0.300 . 0. 30O
0.100 1.00 1.00 1.00
0.100 1.00 .. 1.00 ,1iOO
0.100 1.00 ''. 1.00 1.00
'.- _ .
0.100 1.00 .' - -1.00 . 1.00
. '- «'
-i.oo -i.oo i'-.-i.ob;. M.QO
;'-- '.'-'
-1.00 -1.00 ' "^I.OQ7 -1,00.
-i.oo -i.oo . . -i.oo -i.oo
-i.oo -i.oo ; .--LOO -KPO
. . - '
;.: '
0-»50B»01 0.125E»01 0.375I!>04 0.325E>
o.i67E«05 o. i67e»os .p. isojefos '.'.;'
- ' . -.'.- ; " .
_ - . t .
. ' '.'.'. ... .':
p".ibOE-07 0. JOOE703 . Oi300E^03 .'0. J~PBC->
' ' -. :' '.- " .: - ' ." ^
o.o o.o :: .0.0 ! OiO ;.'
1.00 1.00 :' 1.PP .' .|ii)0
: ' - V. "- '":"'
o.o o.o p.O ' : ';.'"
0.0 0.0 OiO .
:.-. ;- .
* t- . . ,'
' ;' .
o.o o.o o.o ' . 'i
0.0 0.0 0.0
6.00 6.00 6.00:. 6.00
i.oo -i.oo -i.oo -i.no .-i.oo o.fl i.oo
0.300 ,-1.00 -1.00 -1.00 -1.00 0.0 O.O
uoo -uno -i.oo -i.oo -i.on o.o o.o.
i.oo -LOO -i.oo -i.on ri.oo o^o o.o
1.00 -1.00 -1.00 v '-IwOO- :-1.00 0.0 0.0
1.00 .-1iOO -UOO 1.0Q -1.00 . 0.0 0.0
-i.oo -i.oo -.U'oo. '.-i.oo - -.-i.oo : -T.OO . Ti.no
-i.oo . -i.oo -liflo . . -i.oo -1,00 ' -i.oo -i.oo
-i.oo " -!l.oo- -l.bb ' ' -i.oo ' -i.oo -1:06 . -1.6.0
-1.00 : -1.00: . -1.0P :.;: -1.00 . -1.00 : -V.OO.' -1.00
: .-.' ' ' : -';- .'.-''.'' :'-''' :- '" '" ' .-' .
.- . . '" .' ' ' \:. :- ' -. ' ' ' v " ' : ' '' . '.'. .'
01 0.275B»0 '' " '' ' ' ; ' '';'*"'''
6.00 6. OP A. 00; 6.00 6.00 6.00 . 6.00
-------
ncn !'.
O.p. ' 0.0 / 0.732 . 0.201E-OI
o.joot-or "
0. 157E-05 . .:'. .
iilt'r ':'''.;'-.' ' ' ' '':.
0^0 ' '.' 0.0 :.:' .: 0.0 f; 0.0
' '. . ' ; '...'. : '
' ' - ' :
RIFO .:: .: .- : ; ' ' : .
6.00.' . 6.00. ': ; 6.00 .': 6.00, ; 6.00 0.100', . _. ._/' .
KEicn :
0.300,. 0.300 V 0.300 ;. ' 0.300 , 0.300 0.300 ! 1.00 1.00 1.00 1.00
MI .' :* '""; : ; . ':': .. '''' "'. - . . .
o.' '. o.- .; o.o ' '
;0.0
V0..; j'- p.500E-C ;. - - '' '='.. : f V '.':.-. '.--..
KO :
0.302E-02 p.36/IE-p2 C-361E-02 0.36«Erp2 '.''....
KC2 ' :: '
0.100- ;>,;
', :' .-(
FOH ' S: '
Kt . J
o. o ' -:
ieb.
i.oo '"
KRtSE I
0.250
Its :
20.0
KT :
1.00
o.p- ;-...' o.tooB-oV 6.«ooB-oi' O.HOOE-OI b.iboi
H3.2. ] . «3.2 :' .«3. 2
0.0 . ' : 0.0. : 0.0
70.0 70.0 6.51
1.00 1.00 1.00
0.100 0.350 0.350 0.350 0,350
20.0 20.0 . '
1.00 1.00 1.00
0.0 0.0 0.0 0. 200E-01 0.500B-00
o.o o.o o.o
1.00 1.00 t.OO 1.00
0.10UE-0) O.IOOE-C1 0.100E-01 0.100E-01 0.100E-01 0.100R-01 1.00 1.00 1.00 1.00
-------
vo
KTf :
1.00
KTMIIP :
6.00
I milt:
0.200E-01
(IEin*I:
10.0
HKC2 :
0.200E-01
nnot :
2.00
KGlCd:
291.
HOED :
0.0
CCCRD :
-1.00
-1.00
-1.00
-1.00
-UOO
-UOO
-1.00
-1.00
0. 302E-02
0*0
0.0
0.0
UOO
0-2BOS-02
0-150£»01
0.3001 Ml
0.0
2.00
10.0
uoo
0*100
-uoo
-uoo
0.0
0.0
-uoo
-uoo
-uoo
0.600
0.200
0.100
t.oo
0.0
1.00
6.00
10.0
O.ZOOE-01
2.00
-1.00
-1.00
-1.00
-uoo
0.600
0.100
0.100
o.ioo
0.3611-02
0.0
0.0
0.0
uoo
0.260C-02
0.125E>0«
100.
0.0
0.313Er01
20.0
UOO
0.100
-uoo
-uoo
0.250E-01
0.0
-t.oo
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0.100
0.200
0.100
uoo
0.0
uoo . ;"-
10.0
0.200B-01
2.00
C. 1 00
o.ioo
-uoo
-uoo
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-uoo
0.0
0. 1 30 ,
0.361E-02
0.732
0.0
0.0
1.00
. 0.250F-02
0.375B101
0. 1IOE-03
0.0
0.33IE-01
20.0
1.00
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0.0
C-0
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0. 361B-02--
0. 201E-.OK
o.o
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0. 230F>01
0.0
0.0
-uoo- .: -
-uoo ;": .
-uoo
0.100
0.200
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uoo
0. 100E-02
6.00
0.390
0*250
0.100
0.100
0.100
6.100
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6.167E»05
6.o:: .:
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6. 2308-02
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7.00
0.0
67.0
6'. 00 .
290.
rUOO-
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o.o
o.o'
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uoo
0.900E-02
6.00 .
-uoo'-
-uoo
0.600B-01
,1.00 ':
1.00
1.60
1.06 -
1.00
o.o '---'
0.1678*05
O.o
0.0 .
. 303. . :
0.2208-02
0.23r.B»0«
0.100
0.0
0-0
6.00
298.
-UOO
0.250E-01
0.0
0.0
-UOO
-1.00
-1.00
-1.00
o.ioo
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-uoo
0.0
0.900E-02
uoo
uoo
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uoo
uoo
0.0
0.0
0.0
;6.0 .- ' '
0.157E»05
0.0
0.0 " ;
1».8 '
0.210E-02
0.200E>04
0.0
0.910ErOl
0.0 .
6.00
29B.
0.0
0.0 ': .
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- .60
- .60 .
- .00
- .00
- .00
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- .00
0.600E-01
0.0
o.'04
0.0
0.0
0.200E-02
6.00.
29B.
0.250
0.0
0.0 .
- .00
- .00
- .00 ;
- .00
- .00
- .00
- .00
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0.0
0.900E-02
0.9008-02
0.900B-02
0.900E-02
0.900E-02
1.00
UOO
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1.00
; »3.2 . ;
o.o
0: 0 :.-
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0. 190B-02
0. 155E«0«
0.0 '
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0. 100E-02
9.00 '
0.100B-OI
0:200
0.0
-i.oo
-1.00
-1.00
-uoo
-1.00
-1.00
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0. 390
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0.0
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0*0 .
0.0 :
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9.00
0.150
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0* 250
uoo
0.0
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0.0
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0*100
'0.0 . .:
6.100E-01
o.nooE-Oi
0. 100E-01 -
O.nooE-or
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0.1528-02
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9. 00
o.ioo :
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uoo
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vo
0.0
0-400E-01
0.500
0.451
0.2001-01
0.58ni->04
35.0
0. 2508-04
8-00
0.313E-OI
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0.600
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0.600
0.600
0.600
0.600
0.600
0.600
0.0
0.1QOI-01
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0. 100E-07
0. 100E-03
0.100E-02
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40.0
25.0
0.0
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00(10000000
uonouououu
onoucootoo
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onoooouooo
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ouoooooboa
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o.-o :
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o.o
0.451
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31.5
0.110
fl.OO
0.313E-01
1.00
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0.600
o.r.oo
0.609
0.600
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0.600
0.600
0.600
0.600
0.600
0.600
0.0
1.00
1.00
1.00
0. 100E-07
0.100E-03
0. 100C-02
1.00
1.00
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40.0
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0.0
0.500E-03
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-
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0.0
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0.0
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0.0
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0.0
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0.0
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0.0
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0.0
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0.0
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0.750E-03
0.0
101.
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0.0
0.0
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0.0
(1.0
0.0
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0.0
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0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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0.0
0.0
iiliso :
2.32
0.0
0.0
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0.0
0.0
0.0
ZOCPl 8
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0.5631-01
0.0
zcctil:
61.0
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0. 0 ;
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76.0
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0.0
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0.0
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'0.0
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o.'o
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-------
TJJOD 87JO 87.00
Tiae (days)
torn
Figure A.L.
Temperature loading "used Tri 'simulation "of P.ohd A-7 ,
Dor, .Israel..::.'. . /. "-.'-'.'.
; ;-87JJO
Time (days)
107JO
Figure A. 21"."
"'
pH. loading used -in: simulation' ;of. .Pond-rA-7 ,' Dor,:
Israel. ' ' .--. .'. ' ' ' ;; ' "";'Vi" '
107
-------
1D7JJO
77M 87JO 87JJQ
__.:.____,. _;Tiae (days) __
Figure A.3v Dissoiyed 'Oxygen:;,Toading used "in "simulation of
Pond A-7, .Dor, 'Israel"..
TIM 87JD 97JO
Tiffle (days)
107JJD
Figure A.4.
Wind velocity loading used in simulation of Pond
A-7, Dor, Israel. :_
108
-------
'-/.Figure A. 5.
7,00 77J8 67,00 97JJO 187JOO '
Tine (days)
Soiar radiation idading us'ed\.i~ri"simulation"'of Pond
A-7, '';Dor./.' Israel. ; ;,..'''' ' -
'.Figure A'. 6...> ...Phytoplahktoii-^bibina^s u'se'd.;iri:''simulafionr of Pond
-; .. - "...-.- .v.A-7>;.Dor., -Israel;.-;.-.::'...;. ' - "'-v-- ;-
L0'9
-------
Figure A.7.
Tiae
107.00
,,, .. .. ____________
Zooplanktbh biomass .used.. .ijn/. simulation of Pond
A-7, Dor, ' ' '
S7JOB
Tiie (days)
Figure A.8.
Water bug biomass used in simulation of Pond A-7,
Dor, Israel._ - _._.._....; .
: . .110 ";
-------
97.00
107.00
Figure A.9.
87JO
Tiae (days)
Carp:biomass used in simulation of Pond A-7,
Israel. ....
Dor,
111!
-------
APPENDIX B
Parameters and Loadings Used in Simulation of Treatmentr-3 Ponds, Columbia, Missouri
REUDFF: . . '..-'
Tir.E rccpi unoos BIOCU incftss cncAir «e» «7» «8» «?»
«o» mite SHIER . -tan '' ci./ti .
PfST S : '' '-; ' '
ICC STBT-3,COLUnBI»
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PtOGlt
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inotss
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«9»
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TIltEE:
rune
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1I1LEI:
TI1LEF:
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0.200E-OI 0.200E-01
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1.00 " 1.00 1.00 " 1.00 : 1.00 i.OO 1.00 1.0O . 1.00 I.OO
: eiCO :'.'
0.200E-02 0.100E-02 C.500E-02
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BIBC2 : '. " .'':' -" : /. >- . ; '. '} -'. ' . ' '
0.150 ' 0.300E-02 0.300E-01 0.300E-01 0.300E-01 1.00. 1.00 1.00 1.00 1.00
0".3t3I-01 0.313E-01 0.313f-6l " 0.3138-01 -0.3138-01 0.313E-01 0.313E-01 0. 313E-01 '. 0.313R-01 0.313E-01
: ''-'-*..
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8.00 8.00 8.00 v 8.00 -4.00 H. 00 8.00 8.00 8.00 8.OO
.1.00 " O.ioOErOJ 6.900F-02 J.0.900E-02 0.900E-02 1.00 . 1.00 1.00 1.00 1.00
cdicim "''." ' . ." v' '
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run :
0.252 El 01
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0.3IOJ-06
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.0.600 0.600 O.AOO 0.600 . 0.600 . 0.600 0.600 O.fiOO
0.600 0.600 0.600 0.600 . 0.600 j. 0.600 0.600 0.600
0.600 . 0.600 0.600 0.600 . ..O.fiOO .-,' 0.600 0.600- 0..600
'. . '.- ' --' -: ' " :'-'.-'"' -: ';'" v i'; . '.'.'' .- '; >'
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i>i326B«0» 0.363E>01 0.i390E»0» 0."l33E».6il 0^«65E»0'I ;0.503EiO« 0.r»10B«01 O^iniR*
V : ' - ': ' .' '.- : ' ' .-. : .'' :'.' '" °'-:' \ '' . ' ":. '. ,
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^ ; -: :' ; "-.'. '' :' ':..'" i'i: -"'^ -'" :- -' '. " ; v ; :" '; ~K. :';'; -.'
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0.0 '0^0 0.0 n.O
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1.00 ''' 1.00 1.00 1.00 ;1.00 0.100
.
0.300 . 0.300. 0.300. : 0.300 - .'0.300 KOO \ 1.00 1.00 1.00 1.00
nil'. -?':'. ..'"' :" - :'.-- ' ' ;' ;-:; ':?. :. -. '-'" '.' ' . '.' '.'.
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0.583E-P2 0.583E-02 0. 583E-02 . O.'SBIE^OJ. . :: .'/ : ;
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0.0 : ! ' -0.0 . -"; -.- : . .- . .'.;' % . ':. .. .: ' '
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90.0 .; , 7.16 . - :;. :,:' - ' .
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1.00
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KS : '. ''... .. :.' -..'-....-'..
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0.100E-01 O.IOOE-OI C.100E-01 0.100E-01 0, 100E-01 1.00 . 1.00 1.00 1.00 1.00
-------
fit :
1.00
HtBRE :
1.00
limit:
0.200E-01
10.0
KII02 :
0.200E-01
BBOT :
2.00
HOlCSIs
266.
nune :
0.0
OCCIIB S
0.0
0.0
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0.0
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0.0
0.0
0.0
0.5B3E-02
0.0
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1.00
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0. 106E>20
0.0
2.00
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1.00
0.100
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.900
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30CLOO
Figure B.I'.
182,00 22JUJO
...- Julian Date
Temperature loading used"in 'simulation of Treat-
ment-3 pond's, Columbia, Missouri. ' ...
"MOJOr
2SLIH)
SfflLDO
Figure -B'; 2...'
''" 182JJO- '.' "' 22JUIO "
-.:;,. ^Julian. Dat?>^;;.;.;r...';. '''' ; _' v -,,>.;. ......
.pH. . -loading r,:used';: in -: simul;a'.tion--of :T'rea'tment- 3
.ponds; Columbia., -Missouri.-;:-;. .- :>
127
-------
-
x
O
u
o
to
4MHI
2BOJTO
301X03
Figure B.3.
18000 220JDO
Julian -Date : ;,,
bis^olv^d .pkyg'en' loading vised In simulation o£
Treatment-3 ponds, 'CQl"umb'ia,,.'.'Mlss'ourx.
Figure B.4.
Wind velocity;
Treatment-3
30GJW
g used^R^simulation of
fcolumbia^Missouri.
128
-------
Figure 3.5.
18&00 99tyfin ?60.flO
Julian Date
Solar radiation loading used in simulation of
Treatment-3 ponds, Columbia, Missouri.
1-
00
^v.
3
cS
QQ
CD
H
18000-. 22&OD
Julian Date
-f-
?8Q.no
Figure's.'6".'' Phytoplankton biomass used in simulation of Treat-
ment-3 ponds, Columbia, Missouri.
129
-------
Figure B.7.
18CJJO TftUTP 28£Uffl 300JO
:,. Julian Dste
Zooplankton biomass used--In simulation-of'Treat-
ment-3 ponds, Columbia')" Missouri.
3
to
8§J.
OcT1^
Figure B.8.
220.00
Julian Date
Bluegill biomass used in simulation of Treatment-3
ponds, Columbia, Missouri.
130
-------
APPENDIX C
Loadings used in Simulation of Treatment-2 Ponds,
Columbia, Missouri
With the exception of pentachlorophenol-induced mortality,
all the parameters are the same as given in Appendix B, as are
some of the loadings. Only the loadings that differ from those
of .Appendix B are illustrated here.
I
WCQ
1KLOO 22tWW ,2StUJO
Julian DateV.
*- '
30CJK)
Figure C. 1....'. Temperatufe.'.T6adings ..used--lii -simulation of-.
.... Treatment-2'":ponds,.,Columbia/ Missouri. ;
-------
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Figure C.2.
18QJK) 221CO
Julian Date
pH loadings used in .simulation of Treatment-2
ponds, Columbia, Missouri''.'
§T
n.-
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CD
IS
T3 .
CJ
o
tn
ens
142^33 1&QD 22!UIO 280JJO
Julian Date
Figure C.3.
Dissolved oxygen loadings used in simulation of
Treatment-2 ponds,.Columbia, Missouri.
132
-------
Figure C.4.
WCJB '
Julian Date
Phytoplankton biomass used in'simulation'of
Treatment-2 ponds, Columbia, Missouri.
28CUJO
333.03
Figure C.5.
mOO 22UJO
. Julian Date.
Zooplankton biomass-.used in simulation of
Treatment-2 ponds, Columbia, Missouri.
133
-------
tf)
18
.(O
28000
30000
Figure C. 6.
' 18000 22n.flD
,. ''.--'. -Julian Date... >
Eluegill-vbiomass used- -in simulation of Treatment- 2
ponds, Columbia, Miss.purl.'1" ' "'" '''
m.
tn*
u.
n.
o.
05
1SOJK)
Julian Date
270.00
stooo
Figure C.7.
Pentachlorophenol loadings used in simulation of
Treatment-2 ponds, Columbia,. Missouri.
134
-------
APPENDIX D
Parameters and Loadings used in Simulation of Coralville Reservoir, Iowa
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'0.0
0.0
o^ o;
3. mi
2.6(3
0.0
o a :'.
o-. o
o: o. ;
O.JI50E-01
O.'O
p;o
o.o
o.a
o.o
3.B1
3. no
0.0
0.0
0-0
0.0.
0.1J20E.-02
0.0 .
0.0 _ . :
0.6 :'
0.0 .
ff. 0 .'
3.11
3.10 ,
0.0
0.0
O.BHOE-02
0.0
-
0.0
0.0
3.1'i
3.26
0.0
o.n
6.f,noE-02
0.0
-
0.0
0.0
2."2
3.
-------
O
«J
o.
OJ
Figure-'D. 1,
0.00
30OO
40UK)
IQfUBJ .2HUB
; : '"' :Julian Date ; . , / '_
Temperkture:- loadings used'-;in' simulation of
Coralville Reservoir ,,Iowa-.
Figure D.2.
UHLDD 2MUB 30CJW
Julian Date
pH loadings used in .simulation of Coralville
Reservoir, Iowa.
150
-------
Figure D.3..
10CJ30 PfflTpflO ffif^itf) , 400.00
Julian Date .
Dissolved oxygen-, .loadings, used.'in simulation, of
Coralville .Reservoir, Iowa. '
10QJO"' '"''"' 2CJXDO'.'='
Julian ;Date
330JJO
.-Figure"D.4;:.
-Wihd loadings...usedjin simulation of-, Coralville
Reservoir, Iowa.
151-:
-------
Figure. D.5.
Particulate organic matter loading^ used 'in
simulation of Coralville Reservoir-, : Iowa.. .
1EUB 202JKJ
Julian Date
300JDQ
Figure D.6.'
Clay Ibadings used in simulation of Coralville
Reservoir, Iowa.
152
-------
tOUO
300,00
40000
Figure -D.7.
200JJO
.,... . (days) ...
Phytoplanktoh bipmass used in simulation of
Coralville Reservoir, Iowa.
Figure D.8.
Julian Date
Zooplankton biomass used in simulation,of
Coralville .Reservoir, Iowa.
153'-.
-------
6
cn
en
m
.to,
m
" ex
fe
O
3..
TLOQ
48040
Figure D. 9.:'
/. :' 2SKLOO'- » 3COJX)
Julian Date ;,-:
;Garp. biomass /us.ed. in sirfiulatipn of Coralville
Reservbir, "Iowa. " "^'""'V.'-''"""''""'' ' : ' '"
(days)
Figure D.10. Water flow loadings used in-.simulation of
Coralville Reservoir, Iowa.
154
-------
Figure D.'ll.
(days)
Dieldrin loadings used in simulation.of
,Coralville: Reservoir, Iowa;.
155
-------
A:
ALPHA:
BACB :
BCTRC :'
BD.INT :
BDSLP :
BIO:
BIODEP:
BLD02:'
BTMAX:
STRAINS:
BTTIM-:;.
..CMAX:
CQNCEN :
CONS: ..
CPTWO :
CRS:
CTWO :
D:-
.GLOSSARY OF' PARAMETERS- AND 'VARIABLES
ctivity coefficient for''microbial. degradation (unit-
extinction coeffiC'ient for. site water .at-each wave-
.len.gth ' ''
fnicrobial biomass (g/rii3j (p.;: 39) :.
cohcen-tratidn' o'f : TOM that passes through the gills in
the blood (g/m3 day): .(p. 42) . '
coefficient for biodeposition (unitless) (p. 47)
coefficient for: biodeposition (unitless) (p. 47)
: biomass. of "each brgariism' (g/m3) "(.ppv..-42V '45')
rate, of production of pseudofeces'; (g/g day) . :(p. 47)
coefficient for oxygen! capacity of Mood (g 02/g
blood) . ip." :~ :' ^-: "' -* ''"' '"'' '' ''
BMIN-: . . .the. .prey concenjtration' a£ which.i..predator begins feed-
maximum rate of bio trans formation (g/g : biomass day)
.(p. ..49). -.. ,v;:^ ,;-,- v " 2;;;"'.;. _' -..,.;.--
rate of : biotransf6rmation!,o,f TOM by higher organisms
'' ''
'.'number .pf days required^ fe6'-'r^ch;..f.u:Ll.:inetabo'lic
!'cap'ac'i'ty' ':'-:":''''- v''"''': "''"'" '" "
ma'xinruih rate of ingestibn. (.g/g . day) :(p^ 45) '. : :
concentration of toxic iprganic. material (g/m3) (p.. 7)
rate of change . of TOM in organism as a result of
ingestion, defecation, and excretion (g TOM/m3 day)
(p. 44)
rate of ingestion of TOM by predator (g TOM ingested/
m3 day) (p. 47) . . ;
rate of biomass loss due to respiration (g/m3 day)
(pp. 42, 43, 48)
total rate of consumption by each organism (g/m3 day)
(p. 42, 45)
median depth of water (cm) (pp. 17, 18)
156
-------
DEF1: t
DEPTH:'
DIR:
DOCOR:
DOMIN:
D02:
DPHLIM:
DTWO:
E:
;'£^;-: -
.E5FSN :
'.y '". "
.EL'AM:
°v.'!-' '
EN: ..
.
EX:
.'";:;.'
EXP.T.:,
EXTRA:
FIL:
FRACD:
" '"-.:
FRACS":
i'GILSRP:
.HA:
HB: .
H-ENRY-:.
rate of -defecation of .;TOM (g/m3 day) . (p. 47)
depth of water (m) tp. "
IIME'AS:
I-I.TOT:
; INT:-' :
difference in TOM concentrations in water and blood
(g TOM/m3 day) (pp. 43, 44) .,. '
reduction of microbial degradation due to suboptimal
oxygen levels (unitless) .(p. 33)
minimum value of oxygen reduction under anaerobic
conditions (unitless) (pp. 33, 35)
dissolved oxygen concentration (g/m3) (pp. 35, 43, 46)
depth at which wind energy is unimportant for mixing
effect on microbial degradation (m) (p. 38)
total rate of defecation by each organism (g/m3 day)
(p- 42)
percentage of TOM in prey- that is egested (unitless)
-1---"' '
,-cpef f ic.ient for -di.f;f usivityv.of .. .T.QM' through:.. gill-;-
1 (uniifeless'j''"-(p. 44-) ;*. ;--,'.,,' './:-.', . . ' ";',.' ' .
molar extinction coefficient for TOM at : each wave-
length (I/mole cm) ; -(p. ' 17);;';>;';:;,:. ;.:;: .. ...-.' . . . . . '.
activation .energy "for-:, effect of temperature (cal/mole)
:-(p.>-7) .,., .:;:,-::r;, :,:,.;'y ''^^^'^^^ -...v; ,...--
fate of 'excr'et-i:6n"';ro£ 'TOM .by":;each '"organism (g/m3 day)
'(p. "48) ; ' '.;,- '""-; ' . ;V:.MS:---:-^: .;;- :;V -;1 ' :.
time, of exposure to TOM '(days)/ (p.. \'39:) /.,-;.... ....
amount -of TOM not in .solution (g/m3) (p. 31) :
rate of filtering (g/g day) (p. 47)
fraction of irradiattce that is direct at each wave- '
length (unitless) (p. 17)
fraction of irradiance that is indirect at each wave-
length (unitless) (p. -17)
rate of sorption by gills (g/m3 day) {pp. 41, 44)
concentration of Bronsted acid (g/m3) (p. 7)
concentration of Bronsted base (g/m3) (p. 7)
Henry's Law " constant '(atm cm /mol) (pp. 2&,"27)
rate of -hydrolysis (g/m3 'day) (p. 7)
observed light intensity (photons/cm2) (pp. 20, 21)
total incident light (pho-tons/cm2) (p. 20)
width- of wavelength interval (jim) (p. 17)
rate constant for Bronsted acid Catalysis (I/days)
',15.7
-------
KB: .
KBTRAN:
,KCAL:
KDEPTH: ...
KEFF: .
KEQ:. .
'KE'XG'R:
KGAS: ..' '
.KH-:
KLEXPT:
KLIQ:.
KMEAS:
KO:
KOH:
K02: .;
"id? :.''' :,
-KPART: .
'KPH:
KRESP:
KS:
KSEN:
KT:
(pp. 1, 11; sum o-f Wave length-sped fie, direct ''photo-
lysis- rate constants..:;:(;l/day) (p. 16).
rate constant "for -:Bronsted' base:- catalysis (l-/-days) -
" '' ''
half-saturation- cbef fie ieht'.fQf:':biotrans formation (g
TOM/m3-) ...(p.-.':4-9');V--" V ^x--' ':'"'.. ''''''''"''
.rate.' constant' to; -account' -for "colloidal,'- metal-ion,
and phase-transfer catalysis' (I/days) (pp..- 7/ 12)
.constant relating .wind: .energy to depth. .:;....- ,.'. ."
rate of radical initiation reaction (I/day) (p. 15)
equilibrium .dissociation constant (not used) (p. 2'0)
.proportionality coefficient for excretion as a
function of respiration, (unitless) (p. 48)
...ga s^pha se ma'ss -.-.tria-ns f er .epef f icien t t&m/hr ) . ( pp . 24,- 26.)
acid-catalyzed "rate .constant" (1/M days) ..(pp. 7,. 9) '. .'"
correction factor ; for volatilization (unitless)
(P.: -;24.)-. .;,::,;;'.,.,,;::: .->:;" \;;-;-:it '.'/ .. . - ,.,. ,
liquid-phase mass transfer coefficient (cm/hf) (pp. 24',
28) - - -. v. , -,.,,.- '..
rate constant for sensitised' photolysis (I/day) (pp.
20, 21) . ..... -
uncatalyzed rate constant (I/days) (pp. 7, 9, 11)
base-catalyzed rate 'constant (1/M days) (,PP- 7, 9, 11)
-saturation -coefficient. fo,ic.- oxygen limitation -of ...
. ingestioh :'(g/m3) ;(p. 46) . : . ,- ..
.rate of reaction between TOM. ..and alkoxy and peroxy
radicals (I/day) (p. --15) '.'.' ' "-- - ......
octanol-water partition coefficient (unitless) (p. 40)'.'
adaptive constant for pH effect on microbial degrada-
tion (unitless) (p. 40) "''.. .... .
proportionality constant for respiration. as a function
of metabolism (unitless) (p. 42)
half-saturation constant for microbial metabolism
(g/m3) (p. 32)
empirical rate constant for sensitized photolysis
(I/day) (pp. 16, 20)
rate of competing reaction ^between two radicals re-
158
-------
KTEMP:
KTP: .
LAM:
LD:
LIM: :
LOAD:
L.S:
MAX: ::
METCAP: .
METMAX:
MK02:
MMGT :' .
MMET:
NEWAMT:
NORM:
'OLDAMT:
OXID:
02RESP:
' PCBLD;:
PCBLW-: ?'
PCFBL: :'
,PH:
-PHCOR: ,,;
.suiting in non-radical;-, products (I/day.) . (p.. 15)
coefficient relating respiratio.n rate to temperature
(i/°o' (p. .42) - ' ;..,:;;''\':: ' ,; ..,,:,,; ,... ...... ,......:;...
adaptive constant for effect of '.temperature, on-
microbial degradation (unitless) (pp . 3 7 , , 3<8.).
wavelength (nm) . (p. 17) ,. .'.; '. .-.;,': '-,...,., .-.,-...
effective direct underwater path length for irradiance
.(.cm)' (p.. i? )'.; ....-,:,: ;' ;:;. --.- , -.: :--':..:''-"-:. : ''"': - '
reduction in ingestion rate due to low prey concen-
tration (unitless) .; (pp. 45, 46)
concentration of each carrier (g/m^) (pp. 40, 41)
effective diffuse underwater path length for
irradiance (cm) (p. 17) . ....,.,;.:.
. maximum rate of -.biptr'ansformation- under ambient
environmental cond-itions....(g/g;biomass day) (p^ : 49) x-
.pe.rcent metaboli;c.rcapacity;. .for "degradation, .o,f-...TOM
.-'fuH-iti-ess) -I* '4 9)' '"'""^ ''"'";^'V-l>'.v::>: ' vv\-',''-'^^-;:;: .., '
.maximxim 'rate of microbial metabqlism (i/day:) (pp.
-32'/'33)' '-.. ...- -v-;: .;...; .:v,. :.,',.. "^^'^ :,-^'.. - . '' " -:.
half-saturation constant, for effect O'f oxygen on
:micrbbial degradation .. (g/m3) (pp. 33 ,; 35) '
micrbbial generation-.'. time- under ^botimai conditions''
(days) -^. ' ' " '
rate of degra'dation-idue to. micrpbiaT metabolism
(g/m3 day) (%. 32) '--. :/ ;;;;'" ; .:.' ;'..:;;;".;' -." '
equilibrium concentration for each .carrier (ppm) (p.
40)
rate of gill sorption" normalized for all organisms
(unitless) (pr. 44). .
concentration of TOM in carrier* not affected by
adsorption (g/m3) (p.' 41)
rate of oxidation (g/m3 day) (p. 15)
.coefficient relating, oxygen uptake to respiration (g
02/g biomass)-. (pp. 42, 43) .. . .
perc.e'nt ; blood (g .blopd/g- biomass); ,..(p. 43)
:blo6d : water partition" coefficient,, ';(.p>. 43) ...
f at: blood 'partition coefficient , (p. 43-) '..
;amh)ieht .pH,.; " " '
red.uc.tion,. f actor ror .:microbJlal.. degradation due to pH
; (unities?), (pp. : 33, .35,..;:;37); ' '" ,. ./. ' '.-.:,:.-.
159
-------
PHMAX :
PHMIN- :
PSIA:
.PSIBl"'
' -RAD.:
'.RE': '
.*/_
RMAX:
^SAREA:
SOLD:
SOLUB :
SORP:
STRU:
STTOM:
T: .
.,'TADPT !
-T&6PT:
TEMP: '
TIME:
TMAX:
TMIX:
TOPT :
TOTPST
critically high' pH. for micro.bial. degradation, (pp. 36,
"w .;:'' .;'>. - / ,-::;";. j; . " ; " '.
criticail'y. low. pH-. fofv-rnicrobiai 'degradation',, (pp.' '3'6,
37);;.,.. ;;;_ /; .,.,,.:;;V,-->C3'.':;::::;;..;:f-'Ay'.:'v';;:, .: ; .: "" V '"
...rate of,photolysl.s,;^g/m3..-day);, :(p.':-':16') ;..,;,... ..,,:
. direct pKotolys-issguarituin .yield' for the TOM (unitless)
(pp. 16, ''17).,-. ..-.-":; ' .;.:.: - .;;' ' '. '- . ; . . /
sensitized'photolysis qua'ntiim yield for/the .TOM (unit-
less) (p.. 16) '-°: ,'"';:'.,.. ;;"":': ' ''"...-.-. .,. , :.,/ = -.. '.' ...
half-saturation1 constairit for feeding' (g/in^) (pp. 45-47)
rate of change per 10°C temperature change (unitless)
/(P. 46)"; . ;...-. v '^ ;';:"" ' . . . = . "
concentration, of .radical, initiator present-, (p. 15)
Reynolds Number, (unitless) Cp.: ;30)
respiration rate at starvation (g/g day) (p. 42)
percentage of TOM at surface of carrier (unitless)
(p. 40)
amount of TOM in solution, (g/m^), (p. 31)
solubility (ppm) (pp.. 26,^-31)-
routine for calculating, TOM concentration due to
sorption, (pp. 40, 41)
structural activity factor for microbial degradation
(unitless) (pp." 39, 40)
Julian date of introduction of TOM, (pp. 39, 49)
water temperature (°C) (= TEMP) (p. 46)
effective biomass of TOM-.degrading microflora (g/m3)
. (pp; .32, 39) , :.;;.
temperature at which rate'constant was obtained (°K)
"Cpcvy ,. ,. ......
.Arrhehius temperature correction factor (unitless)
(p. .7) /' .
ambient temperature (°C) (pp. 7, 26, 37, 42)
Julian'datein simulation, (p. 39)
maximum temperature at which process will occur (°C)
(Pr 46> '= '. .. ' ,'. "' ' "'' .-:-
reduction factor for effect of suboptimal mixing on
microbial degradation (unitless) (pp. 33, 38)
optimum temperature (°G) (pp. 42, 46)
total concentration of TOM at /site (g/m3) (p. 40)
160
-------
TPCOR:
.TPMAX:
::,:.-'; ".-.
TR3RZD':
: .:/'
TREI)V ' -
;
VBEN:
VH20:
VOLAT:
VPRESS :
:-:VTOM.:.
W: ":
.WCIRC-: '
reduction' factor: ..for,. effect . of nonoptlmal temperature
on inicrobi;al degradatlpK. (unitiess) , .(pp. 37, 38)
critically high temperature , for microbial degradation
(;°c); -(p.. .37) ' ;,;-;;; ;,:,,, ^- ; _ .-, ;;;;>-;:.';_v:;:,,,.:; :,..,.,^.;-;;: -...- . : '
reduction factor in; 'filtering rate 'due to high.-,. .
' ;(unitless')';;ip'..;-47)- ; ^v/fv: '-..., ' .-.'/'.'..'.
' reductiott' 'factor for''';n6nfepptiinai te'mperature (unitr
. less) (pp. 46, 49) :":..'.''
mo.l a 1 volume of benzene (cm /rnol) (p. 28) : -
molal .volume of water , (cm /mol) ' (p. 26)
rate 'of volatilization (moles/m2' hr) (p. 24)
vapor pressure (Hg) (p. 26)
.molal,. volume of ; TOMr:(cm3/mol)';{pp. 26-28) .-.' ;'
...preference of predator,, f. or. prey (unltless) (p. 45)
amount ;.6f TOM ''.in. wa.t.er that ' passes, through gill (g
: : - TOM/m3 'iwater .processed. day;);:;':(^pv.-43 , '44)-':-'V:.!".":-. '
;.^ wind velocity Cm/sep) (pp'. .26, 3 8 )'....' : ;. : ; :
.- wiridspeed at bne-half-^.maximum stirring effect (m/sec)
''' " " ' ' '
161
-------
INDEX
'.Adaptation, 39
Aer6bic> 35
.'Anaerobic./ ' 35
Animals, 3
Aromatic . Ring-,i: 35
Ar.rheni'us Energy Equation, 7
Attenuation Coefficients, 18
Atrazine, 8, 12, 22, 27, 31
Benthic., 45-
Benzene,'28
Bioaccumulation, 53, 54-' '--
Biochemical Oxygen Demand, 33
Biodeposition,- 47
-Biotransformation, 48
Blood, 42
Bluegills, 64
Brpristed Acid, 7, 12'
Bronsted Base, 7, 12
BTRANS:,- 4, 48
Calibration,:57
;,Garbaryl, 8, 14, 22, 27, 31'
-Carnivores, 45
:Car-p>'-59
.Channel Catfish, 65
-Clayv ;47 62
'Colloidal Catalysis, 12
;'.C.O.;lu7tib!ia National Fisheries
/.Research Laboratory, 61
:Comrao;n'Block, 76, 81
Compound-Specific, 51
"CONS';" 3, 44
Consumption, 44 .'.''..
Goralville Reservoir, 63
Data Requirements, 51 : '
DEBUG, 71
.DEF> '3;> -44^ ' -'- '-:'
Defecation, 44 ,.: "
Depth of Water, 18 ., ; . ...
Diagnostic, 51 . ,
Dieldrin, 27, 31, 63
Diffusivity, 29, 44 ..-,-.
DISPLAY,"75
Dissolved Phase, 58, 61, 64, 66 .
,o Driving'Variables, 54, 75 .., '.
DUMP, 76
EDIT, 68
Electrolyte .Concentration, 8 ,.
Evaluative, 51
EX, 3
EXAMS, 1-
Excretion,* 44, 48«- -.
i -....-
Fat, 42, 48 : - :
Field .Verification, 57 . .
File ^nits,' 79 '
Filter-Feeders, 45
Fish Ponds, "58 " ' '., / ,
Floating Organic Matter (FOM), 4
Gas-Phase, 26
Gill Sorption, 41
GILSRP, 3, 41
Glossary, 156
HELP, 77
Henry's.Law Constant, 26
Hydrolysis, 7, 51
HYDR, 4, 51
Ingestion, 44
Iowa, 63
Israel,- 58
. ;. targe -Mouth Bas s > 6 3, 65
A; Lip6phyllic>. 41,, 48 . ,:
Liquid-Phase, 28
LOADS, 4 '
: LOGON:; ea :
Malathion,. 8, 12, .15, 22,. 27, 31
Metal-ion Catalysis, 13
Methoxychlor, 8,. 9, 10, 22, 27
162
-------
Microbial, 55
Microbial Degradation, 52
Microbial Metabolism, 32
Missouri, 61
Mixing, 38 '
MMET, 4 .,
Molal Volume,26
Molecular Volume, 26
MORT, 3 ... '" . .
MSUM,-.4 .... . .. ,.. -v':.;
.'-.>:';
Octanol-Water Partition .
Coefficient., 40 ' ;":
Organism-Specific, 54
Output, 5
OXID, 4, 15
Oxidation, 15, 52
Oxygen, 46
Parameter File, 83
Parameter Index, 80
Parameters, 51
Parathion, 8, 9, 15, '22, 27,
31, 58
Particulate Organic Matter
(POM), 4, 62.
Pentachlorophenol, 8,1 9, 10,
. 22, 27, 31, 61
pH, 8, 11, 13 '
PHOT, 4, 16
Photolysis, 16, 52
Phytoplankton, 62
Plants, 4 . '
PLOT, 73
Ponds, 61 . i .
PRINT, 70
Process Name, 72
Pseudofeces, 45, 47
i
QUIT, .78 ':' : . ";.
!
Respiration, 43
Sedimentation, 66
Sediments, 61
Sensitized Photolysis, 20
SERATRA, 2 > .
Silver Carp, 60
Simazine, 12 . ;
Site-Specific, 53
Solar Intensity, 18 '. < .
SOLU, 4, 31
Solubility, 31
Solution, 31, 52
SORP, 4, 40
Sorption,.40, 53
SPOO, 79, 83'
START, 69
State Variables, 2
S-triazine, 12
Structural Activity, 39 .
TABULATE,. 73 ''.. ''.. " '..,-
Tape, 82
Temperature, 37, 46 ;
Tilapia, 60
Uncertainties,/57
Universal Gas Constant, 7
Validity, 57
Verification, 57
,VOLAT, 4, 24
Volatilization, 24, 52
Water (Dissolved Phase), 4
Whitman Two-Film, 25
; Wind, 26, 28, 30
Zooplankton, 59, 62
2,4-D, 8, 9, 11, 14, 22, 27,
31
163
------- |