PB83-170563
Verification of a Toxic Organic Substance Transport and Bioaccumulation
Model
Iowa Univ., Iowa City
Prepared for
Environmental Research Lab.
Athens, GA
Feb 83
U.S. DEPARTMENT OF COMMERCE
National Technical Information Service
NTIS
-------
EPA-600/3-83-007
February 1983
VERIFICATION OF A TOXIC ORGANIC SUBSTANCE
TRANSPORT AND BIOACCUMULATION MODEL
by
Jerald L. Schnoor
Narasinga Rao
Kathryn J. Cartwright
Richard M. Noll
Carlos E. Ruiz-Calzada
The University of Iowa
Iowa City, Iowa 52242
Grant No- R-806059-02
Project Officer
Thomas 0. Barnwell
Technology Development and Applications Branch
Environmental Research Laboratory
Athens, Georgia 30613
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30613
-------
TECHNICAL REPORT DATA
(Pleatr read laaructtrns rn ih( revinr before frmrlitingj
EPA-600/3-83-007
*EC>riENT s
3 170563
TITLE AMP Sf »TITLE
Verification of a Toxic Organic Substance Transport
and Bioaccumulation Model
E'OKT PATE
February 1983
RGANIZATION COPE
Jerald L- Schnoor, Narasinga Rao, Kathryn J, Cartwright
Richard M. Noll, and Carlos E Ruiz-Calzada
8 ^E W OWMING ORGANIZATION «£rO"T NO
f rEHf?HMI~G OflG»NIZATI»M
The University fff Iowa
Iowa City, Iowa 52242
CCULIA
1' CONTR»CT'O«»'>'T MO
R-806059-02
17
Envi r»nmental Research Laboratory-Athens GA
Office of Research and Development
US. Environmental Protection Agency
, Georgia 30613
Final, 10/79-12/81
EPA/600/01
A field verification of the Toxic Organic Substance Transport and Bioaccumula-
tion Model (TOXIC) was conducted using the insecticide dieldrin and the herbicides
rlachlor and atrazine as the test compounds. The test sites were two Iowa reservoirs
The verification procedure included both steady-state analyses and quasi-dynamic sim-
ulations using time-variable flows and pollutant loadings along with model coeffic-
ients derived from laboratory and literature data- Laboratory measurements were used
in simulations of alachlor, atrazine and dieldrin, and model predictions were well
within an order of magnitude of field observations. For the herbicide alachlor, for
example, laboratory protocol measurements were used directly in model simulations
with excellent agreement between model predictions and measured concentrations
The TOXIC model, therefore, was considered to be field verified. Moreover, the
successful field verification supports the validity of EPA's Exposure Analysis
Modeling System (EXAMS), which handles pollutant transport and transformation kinetic
in an almost identical manner.
KEY WO"»S
I F>rld/G)»up
P'ST «I»UTIPN STATEMENT
RELEASE TO PUBLIC
SECU«ITY CLASS (Tha
UNCLASSIFIED
J» SECURITY CLASSIThupogf)
UNCLASSIFIED
71 M» 9f PAGES
_ 177
11 '"ICE
2730-* l»-7»>
-------
DISCLAIMER
Although the research described in this report has been funded wholly
or in part by the United States Environmental Protection Agency through
Grant Number R-806059-02 to the University of Iowa, it has not been subjected
to the Agency's required peer and policy review and therefore does not
necessarily reflect the views of the Agency and no official endorsement
should be inferred.
11
-------
NOTICE
«
.THIS DOCUMENT HAS BEEN REPRODUCED
FROM THE BEST COPY FURNISHED US BY
THE SPONSORING AGENCY, ALTHOUGH IT
IS RECOGNIZED THAT CERTAIN PORTIONS
ARE ILLEGIBLE, IT IS BEING RELEASED
IN THE INTEREST OF MAKING AVAILABLE
AS MUCH INFORMATION AS POSSIBLE
-------
FOREWORD
As environmental controls become more costly to implement and the
penalties of judgment errors become more severe, environmental quality
management requires more efficient analytical tools based on greater
knowledge of the environmental phenomena to be managed. As part of this
Laboratory's research on the occurrence, movement, transformation, impact,
and control of environmental contaminants, the Technology Development and
Applications Branch develops and tests management and -engineering tools to
help pollution control officials achieve water quality goals.
Concern about environmental exposure to synthetic organic compounds
has increased the need for reliable techniques to predict the behavior of
chemicals entering natural waters as a result of the manufacture, use, and
disposal of commercial products. In response, mathematical models have
been developed to aid in evaluating the environmental consequences of
pollutant exposure. An essential step in the development and use of these
models is field verification. This report describes a recent study in
which the Toxic and Organic Substance Transport and Bioaccumulation Model
(TOXIC) predicted pesticide concentrations that were well within an order of
magnitude of levels actually measured in two Iowa reservoirs This success-
ful field verification of TOXIC also supports the use of EPA's Exposure
Analysis Modeling System, which incorporates similar modeling procedures
David W Duttweiler
Director
Environmental Research Laboratory
Athens, Georgia
m
-------
ABSTRACT
The model TOXIC (and versions thereof) was calibrated for the
insecticide dieldrin and the herbicides alachlor and atrazine using data
from Iowa reservoirs. Steady state analyses and quasi-dynamic simulations
with time-variable flows and loadings were accomplished Model coefficients
were derived from laboratory data and in some cases literature data.
For the herbicide alachlor, laboratory protocol measurements were used
directly in model simulations with excellent agreement between model pre-
dictions and measured concentrations. Thus the model may be considered
field-validated. Laboratory measurements were used in simulations of
alachlor, atrazine and dieldrin, and results were well within an order-of-
magnitude of field data.
One of the most important aspects of the dieldrin simulations was the
choice of time and space scales. Especially in quasi-dynamic applications,
where one variable varies in time and space while another is held constant,
averaging problems arise. To simulate both the exposure concentration and
mass fluxes accurately, one must use a fully dynamic, spatially variable
model in which flow, suspended solids, toxicant concentration, pH, and
other state variables are functions of time and space. Fortunately most
applications do not require such accuracy, and we may settle for steady
state or quasi-dynamic models such as TOXIC. TOXIC was utilized in a
management decision by the Iowa Conservation Commission to lift the ban on
commercial fishing in Coralville Reservoir in 1979.
Sediment has been a small net source of dieldrin to the water column
of Coralville Reservoir, especially important under low flow conditions
Coralville Reservoir contains approximately 50 kg of dieldrin in the sed-
iment. It will take 6-10 more years to achieve less than detectable
(<0.002 ug/n) dieldrin in the water column, primarily due to continued in-
flow as well as sediment desorption.
Lastly, field bioconcentration factors when normalized on a lipid
basis were approximately equal to laboratory-derived bioconcentration
factors similarly weighted The bioconcentration factors were also pro-
portional to the octanol water partition coefficient. Bioconcentration is
the primary mode of pesticide uptake in Coralville Reservoir and food items
are generally of less importance.
This report was submitted in fulfillment of Grant No. R-806059-02 by
the University of Iowa under the sponsorship of the U.S. Environmental
Protection Agency. This report covers the period October 1, 1979, to
December 31, 1981, and work was completed as of December, 1982.
IV
-------
TABLE OF CONTENTS
Foreword ill
Abstract i v
List of Tables x - xi
Acknowledgments xi 1
Chapter I. Dieldrin in Coarlville Reservoir:
Model Development and Calibration 1
Introduction 1
Model Development 2
Mass Balance for Pesticides in Reservoirs 2
Bioaccumulation Model 6
Multicompartment Solutions 8
Results and Discussion 10
Fate and Transport 10
Two Compartment Model 10
Eight Compartment Model 13
Unsteady Flow Simulations 14
Summary 14
Chapter II Sediment, Water and Biota Interactions
of the Pesticide Dieldrin in Coralville Reservoir ... 37
Introduction 37
Field Observations 37
Modeling Sediment - Water Interactions 39
Compartmentalization 39
Solids Balance 40
Pesticide Balance 41
Biotic Uptake 42
Bioconcentration 42
Bioaccumulation 43
Summary 44
Chapter III. A Dynamic Simulation Model for Alachlor and Atrazine:
Field Validation Using Microcosm and Laboratory Studies
Introduction 67
Model Devel opment 67
GASP-IV Simulation Language 69
-------
Materials and Methods
Bitransformation Tests
Microcosm Test
Field Tests
Results and Discussion
Biotransformation Test
Microcosm Test
Field Tests
Summary
Chapter IV, Conclusions and Recommendations ,
Conclusions
Recommendations
Appendix TOXIC Documentation
Program Structure
Common Blocks
Running TOXIC
Program Codes
TOXIC
TOXIC - 30 Compartment version
TWOCOMP
TWOCOMP1
Sample Input
DATA File for TOXIC
Sample Output
TWOCOMP
TWOCOMP1
PESTY2
TOXIC - 9 Compartment
TOXIC - 30 Compartment
70
70
70
71
71
71
72
72
73
87
87
88
89
90
91
99
101
102
113
126
128
131
132
134
134
136
137
140
150
-------
LIST OF FIGURES
Chapter I.
Figure 1-1 Pesticide Fate and Transport 22
Figure 1-2 Physical Configurations of the Reservoir Model 23
Figure 1-3 Bioaccumulation Kinetics of the Pesticide Transport
Model 24
Figure 1-4 Model Results for Dieldrin Concentration in the
Coralville Reservoir Outflow 25
Figure 1-5 Input Loading Function to Coralville Reservoir 26
Figure 1-6 Calculated Mass Flux of Dieldrin to the Sediment 27
'Figure 1-7 Sensitivity Analysis of the Effects of the
Partition Coefficient, K 28
Figure 1-8 Sensitivity Analysis of the Effect of the
Sedimentation Coefficient, K 29
Figure 1-9 Field Data and Model Results for Dieldrin in
Coralville Reservoir, Numbers represent the
number of data points and percentages are the
percent oil or lipid content of the catch 30
Figure 1-10 Field Data and Model Results for Dieldrin in
Coralville Reservoir Fish, Normalized on an Oil
Basi s 31
Figure 1-11 Eight Compartment Model Results for Dieldrin in
the Water Col umn 32
Figure 1-12 Eight Compartment Model Results for Adsorbed
Dieldrin on the Sediment 33
Figure 1-13 Unsteady Flow Simulation 34
Figure 1-14 Unsteady Flow Results 35
Figure 1-15 Unsteady Mass Flux to the Sediment 36
VI1
-------
Chapter II,
Figure II-l Estimated Aldrin Usage (In million pounds) In
Iowa and Total Dleldrln Concentration In the Iowa
River at Iowa City, 1968-1978 51
Figure II-2 Hypothetical Scenario for the Self-Purification of an
Impoundment After the Introduction Introduction of
a Hydrophobic Toxic Organic 52
Figure II-3 Solids Loading and Flow Below Coralville Reservoir
at Iowa City 53
Figure I1-4 Rate of Erosion versus Bottom Shear Stresses in
dynes/cm' (calculated from data of Partheniades,
1965) 54
Figure II-5 Solids and Dieldrin Loading Below Coralville
Reservoir at Iowa City (note: no dieldrin record
exists for 1978) 55
Figure II-6 Solids Loading and Total Dieldrin Concentration
Below Coralville Reservoir at Iowa City (note: no
dieldrin record exists for 1978) 56
Figure II-7 Sediment Core Analyses for Percent Volatile Solids
and Dieldrin Concentration Near the Inflow to
Coralv1l1e Reservoi r 57
Figure II-8 Sediment Core Analyses for Dieldrin Concentration on
a Dry Weight Basis and on a Volatile Solids (VS)
Basis 58
Figure II-9 Compartmentalized Approach to Sediment - Water
Interactions in Coralville Reservoir 59
Figure 11-10 Estimated Dieldrin Sediment Concentrations and
Loading Scenario in Highly Depositional Zones of
Coralville Reservoir 60
Figure 11-11 Dieldrin Residues in Fish vs. Percent Oil Content,
Coralville Reservoir, 1979 Field Data. Equation
for Lease squares regression: Y = 2640 x -10,8,
r = 0.77 61
Figure 11-12 Iowa Field Bioconcentration Factors in Fish Oil
vs. Octonol/Water Partition Coefficients 62
Figure 11-13 Ecosystem Contamination Levels Based on Field
Observations and Laboratory Microcosm Experiments .... 63
vm
-------
Figure 11-14
Figure 11-15
Figure 11-16
Chapter III.
Figure III-l
Figure III-2
Figure III-3
Figure III-4
Figure III-5
Figure III-6
Figure III-7
'Figure III-8
Figure III-9
Figure 111-10
Figure III-ll
Bioaccumulation Model Schematic , ,
Dieldrin Residues in Game Fish Simulation for
Coral vi lie Reservoir . - . , . . . , ,
Dieldrin Residues in Bottom - Feeding Fish for
Coral vi lie Reservoir ,,.,.,, ,.,,,,..,,,.
Alachlor Degradation in Iowa River Water with an
Acti vated SI udge Irvnocul um ,.,,,,
Alachlor Microcosm Concentrations 1n Filtered Water
Sdmpl es . . , , , , , ,,.,,,,, ,,,.,,
Atrazine Microcosm Concentrations in Filtered Water
Sampl es , , , , , . , , ,.,,.,.
Model Inflow to Lake Rathbun
Model Inflow to Lake Rathbun
Measured Alachlor Inflow Concentrations and
Model Input . , . , , , , , . , . . , , , ,
Model Results and Measured Alachlor Concentrations
in Lake Rathbun, 1978 . . ...
Measured Atrazone Concentrations and Model Input
Model Results and Measured Atrazine Concentrations ...
Measured Dieldrin Concnetrations and Model Input
Model Results and Measured Atrazine Concentrations ...
64
65
66
. -76
77
78
'79
80
81
82
83
84
85
86
IX
-------
LIST OF TABLES
Chapter I,
Table 1-1
Table 1-2
Chapter II,
Table II-l
Table II-2
Table II-3
Chapter III.
Table III-l
Appendix
Table 1.
Table 2.
Table 3.
Table 4.
Table 5.
Table 6,
Table 7,
Chemical Structures of Selected Iowa Pesticides 19
Photolysis, Hydrolysis, Biolysis, and Sorption
Coefficients for Selected Iowa Pesticides 21
Annual Total Dieldrin Concentration Statistics from
Below Coralville Reservoir at Iowa City, 1969-1979 46
Two-Compartment Model Results with Aggrading Sediment,
Coralville Reservoir 47
Results of 25 Compartments Simulation for Dieldrin
in Coralville Reservoir 48
Kinetic Biodegradation Rate Constants 75
TOXIC code in Extended Fortran for PRIME 750 computer 101
30 - compartment TOXIC code in Extended Fortran for
PRIME 750 computer 113
TWOCOMP code for Solids balance in a 2 - compartment
reservoir with volume 126
TWOCOMP1 code for soldis balance in a 2 - compartment
reservoir with variable volume in the sediment compart-
ment 128
DATA file for input to TOXIC 132
TWOCOMP output for hypothetical solids balance in
Coralville Reservoir 135
TWOCOMP1 output for hypothetical solids balance in
Coralville Reservoir (variable volume sediment
compartment) 136
-------
Table 9. Sample Output for the 9 - compartment TOXIC model 140
Table 10. Sample output for the 25 - compartment TOXIC model 150
-------
ACKNOWLEDGMENTS
We gratefully acknowledge the interesting discussions and help of EPA
personnel at the Environmental Research Laboratory, Athens, Georgia,
Including Mr. Thomas Barnwell, Dr. James Falco, Dr. Lawrence Burns, and
Dr. Robert Swank. Also the entire research team of Mr. George Baughman,
Dr. Lee Wolfe, Dr. Richard Zepp, Dr. Samuel Karickhoff, Dr. William Steen,
and Mrs. Doris Paris have been most helpful in their explanation of toxic
organics reaction rates and enthusiasm for this project. Special thanks are
due to Mr. Lauren Johnson of The University of Iowa Hygienic Laboratory for
transmitting unpublished data and Ms. Tina Swartzendruber and Ms. Kay
Chambers for preparation of this manuscript.
-------
CHAPTER I
DIELDRIN IN CORALVILLE RESERVOIR:
MODEL DEVELOPMENT AND CALIBRATION
INTRODUCTION
Agricultural usage of pesticides 1n Iowa is widespread, particularly
grass and broadleaf herbicides and row crop soil Insecticides One of the
insecticides widely used for control of the corn rootworm and cutworm from
1960 to 1975 was the chlorinated hydrocarbon, aldrln. Aldrin is microblally
metabolized to its very persistent epoxide, dieldrin. Dieldrin is itself an
insecticide of certain toxicity and is also a very hydrophobic substance of
limited solubility 1n water (0.25 ppm) and low vapor pressure (2.7 x 10'° mm
Hg (? 25*C). It is known to bioaccumulate to levels as high as 1.6 mg/kg wet
weight in edible tissue of Iowa catfish [1], Table 1-1,
Aldrin application in Iowa during the mid-1960's amounted to some 2.94
x Ifl6 kg/yr on 5.0 million acres (2 x 10'" irr). However, the corn rootworm
grew increasingly resistant to aldrin and, after 1967, usage decreased across
the state by approximately one-half. Finally, the pesticide was banned in
1975 and very little was applied after 1976. Although aldrin was no longer
labeled, dieldrin residues in excess of the Food and Drug Administration
"action" level (0.3 mg/kg wet edible tissue) were recorded for Coralville
Reservoir fishes, and commercial fishing was banned in 1975. The problemwa-.
to determine the fate and transport of the pesticide dieldrin and to assess
when the rc-idual concentrations would be acceptable for commerical fishing.
Coralville Reservoir is a mainstream impoundment of the Iowa River in
Eastern Iowa. It drains approximately 7978 km^ of prime Iowa farmland and
receives extensive agricultural runoff with 90% of its drainage basin in
intensive agriculture. It 1s a variable-level, flood control and recrea-
tional reservoir which has undergone considerable sedimentation since it was
created in 1958. At conservation pool (680 ft. above msl), the Reservoir has
a capacity of 4.69 x 10? nr, a surface area of 1.98 x 10^ m?, a mean depth
of approximately 2,44 m, and a mean detention time of 14 days. In 1958, the
capacity at conservation pool was 6.63 x 10' m^.
Several models have been developed to assess the fate and transport of
agricultural chemicals including the Agricultural Runoff Model (ARM) [2],
the Nonpoint Source Pollution Model (NPS) [3], the Stanford Research Insti-
tude Kinetic Model (SRI) [4], and the Exposure Analysis Modeling System
(EXAMS) [5]. The first two models are primarily designed to simulate the
delivery of soil particles and agricultural chemicals to the edge of the
stream bank. They make extensive use of the modified Universal Soil Loss
Equation and knowledge of the chemical partitioning between soil and water,
together with a given flood hydrograph. The SRI and EXAMS models ar?
1
-------
kinetic formulations which describe the chemical, physical and biological
reactions which a pesticide can undergo once it reaches the water body.
Chemical hydrolysis, volatilization, photolysis, and biological degradation
comprise the reactions considered in these models. Instantaneous adsorption
and desorptlon equilibrium is also assumed. Relative importance of the
pathways of pesticide fate (hydrolysis, volatilization, photolysis, biolog-
ical degradation, oxidation, and sorption) may be assessed in the laboratory
[4].
Previous models have not been extensively verified with field data, and
they have not combined fate and transport modeling with the biological
effects (bioconcentration). In this research, a pesticide transport and bio-
concentration model is developed and applied to Coralvilie Reservoir to
assess the fate and effects of dleldrin 1n the ecosystem. Results have
aided the Iowa Conservation Commission in their decision to lift the
commercial fishing ban on November 7, 1979.
MODEL DEVELOPMENT
A schematic of pesticide fate and transport within a reservoir is
presented in Fig. 1-1. The solid lines are in accordance with the SRI model
formulation [4]. Modifications in both the kinetics and transport (which
might add increased realism to the model) are represented by dashed lines.
A two-compartment or "pond" representation of the reservoir was assumed since
there exist few in-reservoir data with which to calibrate a multi-
compartment model. Fig. 1-2 gives the physical configurations of the
completely mixed compartments utilized in the model- Coralville Reservoir
dimensions were used in the pond configuration for model calibration, and
simulations were later performed using the lake configuration as well.
Although the field data reflect individual storm events, the goal here
was to represent annual average concentrations and mass flows. Therefore,
constant annual average inflow and outflow rates were assumed, together
with an average annual volume for the reservoir. Coralville Reservoir does
not thermally stratify to any great extent, so the failure to include a
hypolimnion compartment in the pond configuration is not viewed as a serious
problem.
In addition to chemical reaction pathways, fish uptake and depuration
(excretion and metabolism) was included 1n the model. The bioconcentration
part of the model formulation is depicted separately in Fig. 1-3, Biouptake
is proportional to the product of the fish biomass and the dissolved pesti-
cide concentration. Pesticide is removed by the fish as water passes the
gill membrane. Biouptake from sediment and/or food (prey) items could also
be included in this portion of the model. Here it is assumed that the
pesticide residue is metabolized within the fish, but in some cases it may
be necessary to recycle the depurated pesticide as a dissolved input.
Mass Balance for Pesticides 1n Reservoirs
The distribution of pesticides 1n reservoirs is established by applica-
tion of the principle of continuity or mass balance. Each phase, the
-------
dissolved and particulate, is analyzed separately, taking into account the
interaction with the other. Thus for the dissolved component, the mass
balance includes various reaction pathways [4] in addition to the inflow and
outflow. The basic differential equation can be written to include the sum
of the first order or pseudo-first order reactions (hydrolysis, biological
degradation, biological uptake, photolysis, and volatilization) as well as
adsorption and desorption kinetics as a function of particle size distribu-
tion:
in which
V - reservoir volume, I3
W = rate of mass Input of the dissolved component, M/T
t = mean hydraulic detention time, T
t = dissolved chemical concentration, M/L3
4
.A k- = sum of the first order decay rate constants including the
1-1 T following:
k
k- =
;, = ki [Bacteria] = pseudo-first order biological degradation
rate constant, T-1
,- = kp [OH*] = base catalyzed, pseudo-first order hydrolysis
rate constant, T"1
k, = k; (quantum yield) = first order dirsct photolysis rate
constant, T"^
k. = first order volatilization rate constant, T"1
n
t^
k, - sum of the adsorption rate constants for the j size
j=l j fraction, n total fractions, L3/M-T
M. = suspended solids concentration in the j size fraction, M/L3
J
=l r . = sum of the desorption rate constants for the j size
J fraction, n total fraction, T'1
C = partlculate chemical concentration due to the j size
pj fraction, M/L3.
-------
If we do not partition the suspended solids concentration into various
size fractions or if only one size fraction is active in sorbing the chemical
of interest, then Equation 1 reduces to:
f - T - ' EkC ' kf MC + kr CP (2)
in which
Ek = overall decay coefficient of the dissolved chemical, T'1
For the partlculate chemical concentration 1n the j size fraction:
in which
W - rate of mass input of the parti cul ate adsorbed chemical of
pj size fraction j, M/T
k = sedimentation coefficient of the j size fraction, T"1
J
Summing the total over j size fractions or if only one size fraction is
considered, equation (1) reduces to:
dC Wn C_
_ E. = _E__E_kr_|,r+kMr (&}
dt V t s Lp r Lp Kf ML (*>
o r r
in which
k = overall sedimentation coefficient, T"1
kr = overall desorptlon rate constant, T'1
k, = overall adsorption rate constant, L3/M-T
Adding Equations 2 and 4 cancels the adsorption and desorption terms
and yields the rate of change of the total concentration CT in terms of the
dissolved and particulate:
in which
-------
CT = total concentration = C + C
W = total mass input
The sorption coefficients kf and kr are usually orders of magnitude
greater than the decay and transfer coefficients of the dissolved and
particulate phases. Thus instantaneous local equilibrium 1s achieved
between the two phases - 1.e, the rates of transfer and decay are so low
that, comparatively, liquid-solid phase equilibrium is achieved very
rapidly. The concentrations C and Cp may be replaced by their equivalents
in terms of Cy providing that the adsorption Isotherm is known and that
equilibrium is achieved. Linear adsorption isotherms have been reported
elsewhere [6].
Kp C , (6)
in which
K * linear adsorption partition coefficient, (M/M)/(M/L3)
r = amount of pesticide adsorbed per unit mass of dry sediment,
M/M
It follows that C and C_ may be expressed in terms of Cj under conditions
of local equilibrium: p
C *
+ K M)
CT KM
C "
P d + Kp M)
Substituting for Cp and C from the above relationships Into Equation 5 the
mass balance differential equation is:
dCT ,.,.» CT r. k K M
_ I . U(t) _T £k r s p - rq,
dt " V " tQ " 1 + K M LT ' 1 + K M LT ^'
v/hich, under steady-state conditions may be expressed as;
c . - - (10)
t + ^9* CEk + KPMI
-------
extend the analysis to a number of compartments (such as the lake con-
figuration of Fig, 1-2) with interflow and bulk dispersive transport between
compartments. The equations are linear and may be solved analytically or
numerically.
Bloaccumulation Model
The bioconcentration model follows the simple kinetics of Fig. 1-3.
The total pesticide concentration is the sum of the particulate plus the
dissolved concentrations, with instantaneous sorptive equilibrium assumed.
The total pesticide mass balance equation is identical to Equation 9 except
it is written more concisely with fractions:
dCT u/j.\ CT
V 'To- <*k>fl CT - ksf2CT .
in which
f , - T&- = /, . jry = fraction of dissolved pesticide
i LJ \ i + K. n)
c =
KM
(1 + K FfT = 'frract''on of particulate pesticide
The mass balance for the concentration of pesticides tied up in fish
biomass per unit volume of water, Cp is:
inf =ki fi CT- kdcF
in which
k, biouptaKe rate constant,
k. = depuration rate constant, T"1
1 (dissolved fraction) (k^day)
k =
d (Biomass) (Fish Partition) (kg fish) (yg/kg)
If one divides Equation 12 by the fish biomass, a final bioaccumulation or
fish residue equation results:
defining: dF= (k^cye) - kdF (13)
-------
F = whole body fish residue level, M/M wet weight
B = fish biomass concentration, M/L3 wet weight
The bioconcentration factor (BCF) between pesticide residue in whole fish
and the dissolved concentration is the ratio of the biouptake rate constant
to the depuration rate constant divided by the fish biomass, ki/k-jB. If
pesticide is not metabolized in the fish, Fig. 1-3 and Equation 13 are
modified to reflect excretion of pesticide back into the dissolved phase.
Equations 11 and 13 may be solved analytically for constant coeffi-
cients and simple pesticide loading functions, W(t), or they may be
integrated numerically. In the case of a pesticide ban, the W(t) might
typically decline in an exponential manner due to degradation by soil
organisms. For an exponentially declining loading function at rate a>, the
analytical solutions to Equations 11 and 13 are:
. CT = CT e"5* +^ (ewt - e'6t) (14)
o c
ifi -k,t (CT_ CTin. CTii
-nre ° I-T + -*r--rrj (15^
in which
C- = initial total pesticide concentration in lake, ML"3
'o
''Tin = initial total pesticide inflow concentration, ML"3
o
a> - rate of exponentially declining inflow concentration, T"1
Y - kd - . - (1/t ), T-i
6 -- « + (i/tQ), r1
E * «t + 1 - u>t . dimensionless
9 = kd - 10, T"1
-------
The steady state solution to Equations 11 and 13 reduces to Equation 10
for the total pesticide concentration. For the fish residue level at steady
state, the solution to Equation 13 simply yields the fish partition coeffi-
cient times the equilibrium dissolved pesticide concentration.
Multicompartment Solutions
Multicompartment solutions of Equations 11 and 13 must include inter-
flows and bulk dispersion as well aran assumption regarding suspended
solids and fish biomass distribution. For each constant volume compartment:
v^L
-lv +k \ f r \i + K f r v + Fa fr
\Ke * Kna ' T9"T* ^ca'^^a'a tft \^a
s pa c i sa & a a r a
where
V e compartment volume (m )
Cj = total pesticide concentration of the compartment (vg/fc)
t = time (d)
Q, - inflow of water from adjacent compartment (m /d)
d
0.^ = outflow of water to adjacent compartments (m /d)
C = total pesticide concentration in the adjacent compartment
a (ug/i)
f^ = fraction of the total pesticide in the dissolved phase
fg = fraction of the total pesticide in the particulate phase
K, = reaction rate constant for the dissolved phase (d" )
K = reaction rate constant for the particulate phase (d" )
d-1
k = settling rate constant from the above compartment (d" )
2
E = bulk dispersion coefficient for adjacent compartmants (m /d)
o
A * surface area between two adjacent compartments (m )
k = settling rate constant of the compartment (d" )
"
-------
j, = mixing length between midpoints of adjacent compartments (m)
V. = volume of above compartment (m )
a
The general mass balance equation for the compartments can be reduced
to a general matrix equation, Equation 17.
V
I u _J
v
where i = subscript denoting adjacent compartments
j * subscript denoting the j compartment
C; = total pesticide concentration in a compartment
J
C4 » total pesticide concentration 1n an adjacent compartment
I- / j. \
Q. - - flow into compartment j (m /d)
' r J
Q. .: = flow out of compartment j (m /d)
J»'
f, - dissolved fraction of a pesticide in compartment j
f, . = particulate fraction of a pesticide in compartment j
^ 'J
K. = sum of dissolved reaction rate constant (d" )
K = sura of particulate reaction rate constant (d" )
pa
k . = settling rate constant for compartment j (d' )
k . = settling rate constant from an above compartment (d' )
s fj
E. . * bulk dispersion coefficient between adjacent compartments
1>J (m2/d)
2
A. = surface area of compartment j (m )
J
i- = length between the midpoints of adjacent compartments (m)
i 'J
9
-------
V- = compartment volume (m^)
J
V.j = volume of adjacent compartment (m )
The equations comprise a set of linear, ordinary differential equations
which were numerically Integrated via a fourth order Runge-Kutta approxi-
mation technique.
RESULTS AND DISCUSSION
Fate and Transport
The first step 1n fate and transport modeling 1s to determine the
predominant reaction and transport pathways. Coralville is a short
detention time, flood control reservoir with a mean annual hydraulic
detention time of only 14 days. This corresponds to a washout rate of 0.0714
day'' or approximately 7% of the dissolved material is exported through the
outflow on an average day. Washout is expected to be a major transport
mechanism in Coralville Reservoir. Other reaction rates and partition
coefficients have been measured in laboratory studies and are summarized by
pollutant in Tab. 1-2 Dieldrin should strongly adsorb to sediments and
bioconcentrate, but degradation reactions are very slow. Furadan^, a
carbamate Insecticide, is quite reactive, but biological degradation should
predominate (Tab. 1-2), Selected herbicides and insecticides of usage in
Iowa are listed in Tab. 1-2with their laboratory protocol rate constant,
half-lives, and partition coefficients.
The dieldrin time series of Fig, 1-4 (dashed line) is from monthly
grab sample data collected by The University of Iowa Hygienic Laboratory
and indicates, a steady decline in the envelope of peak concentrations
during agricultural runoff events, as well as a decline in average annual
concentrations. It is believed that the decline in dieldrin from the
Reservoir outlet is due to the decreased aldrin application rates since 1967
as well as the microbial degradation of dieldrin by soil organisms on the
land, Dieldrin loading rates in a small watershed runoff study from 1974
have been computed and range from 1,0 x 10-11 to 1.0 x 10-9 kg/m2 .
-------
A sedimentation coeff cient (k?) of 0,18 per day was calculated from
suspended solids removal rates in the Reservoir while a partition coeffi-
cientof 6250 ug/kg per ug/1 (Kp) was estimated from field data [6]. The
average suspended solids (M) in the reservior from 1968-78 was 80 mg/1, so
KpM was 0.50, indicating the ratio between the partlculate and dissolved
pesticide. Initially the total pesticide inflow concentration was 0.05
ijg/1, but it was assumed to decline exponentially thereafter. The sum of
the first order and pseudo-first order rate constants for dieldrin are
believed to be quite small. The sum of the volatilization, biolysis,
photolysis, and hydrolysis rate constants was assumed to be 1.7 x 10"4 per
day or a half life of 11 years. At this rate, the decay reactions were
insignificant compared to the transport and sedimentation of dieldrin The
pesticide 1s acting as an adsorbing, conservative substance and is tracking
the input loading function of Fig. 1-5.
Fig. 1-6 presents the mass flux of particulate adsorbed dieldrin to the
bottom. Solids were assumed to be permanently lost from the water column,
so the model fails to include scour. Alternatively one could consider ks
to be an "apparent" or "effective" sedimentation coefficient, the net result
of sedimentation minus scour. The mass transport to the sediments involves
small quantities of pesticides, from 0.068 kg/day in 1968 to less than 0.014
kg/day in 1978, or about 46 percent of the total dleldrin Input. The rate
of decline in dieldrin transport to the sediment parallels, and is driven
by, the declining input function. Sixty-four percent of the total dieldrin
in the inflow to the reservoir model was in the particulate phase (KpM =
1.79). For a solids trap efficiency of 72% [20], a corresponding dieldrin
removal of 46% was indicated.
A sensitivity analysis was performed in order to assess the importance
?f the partition coefficient and is presented in Fig. 1-7. Fig. 1-7 indi-
cates that a several fold increase or decrease of KpM from 0.50 to 2.0 or
0.10, changes the totai concentration by-47 and+49 percent, respectively.
The participate fraction in Coralville Reservoir is only 33 percent of the
total dieldrin if KpM is 0.50 (0.33 = (KpM)/(l+KpM)). Kellogg and Bulkley
[1] indicated the same average in weekly samples from the Des Moines River,
Iowa, during 1973.
The sedimentation coefficient (ks) also affects the dieldrin removal by
sedimentation. Fig. 1-8 indicates that an increase of the coefficient from
0.18 to 0.28 per day results in a decrease of total dieldrin of 30 percent.
The mass flux of dieldrin to the sediment (ksCpV) does not fully double
when the sedimentation coefficient doubles due toa decreased particulate
concentration in the reservoir, Cp. This sedimentation coefficient
corresponds to a settling velocity of ks times the mean reservoir depth, or
0,44 m/day. The geometric mean diameter of a particle which settles at
0,44 m/day is about 3 jim, the fine silt/clay size range. Although size
distributions have not been determined for particles within Coralville
Reservoir, the mean particle size of the inflow is approximately 15 ym, a
silt size classification. It is expected that the mean particle size of
the inflow should be greater than the mean size within the Reservoir.
11
-------
Results presented in Figs- 1-4-8 did not include biological uptake by
fish. Fig. 1-9 1s identical to Fig. 1-4 except for including the effects
of biological uptake and metabolism. Fish biomass and productivity in
Coralvllle Reservoir is extremely large, estimated at 1,000 Ib/acre (0.11
kg/m') (46 mg wet weight per liter at conservation pool). Although the
fish biomass is large, the decrease in total dieldrin concentration due to
uptake by fish was less than 0-002 ug/1 after 10 years of simulation. This
fact is attributed to the rapid rates of pesticide washout and sedimentation
in Coralville Reservoir.
Fig. 1-9 presents the model results and field data for dieldrin
residues in sediment and the edible tissue of bottom feeding fish in
Coralville Reservoir. F1sh taxa Include blgmouth buffalo, carp, carpsucker,
catfish, and channel catfish. Fish were collected by shocking and were
subsequently filleted and analyzed [21, 22, 23]. Model coefficients
i ncl uded:
k, = 0.027/day uptake rate constant
k. = 0.0083/day depuration rate constant
B = 4.67 x 10' kg/1 biomass concentration
FQ = 1150 ug/kg initial whole body residue
kg = 0.005/day sediment biological degradation
A total of 1.4 percent per day of the dissolved pesticide is filtered
by bottom feeoing fish. The rate constants are in relative agreement with
those of Thomann [24]. The effective fish filtration rate was 600 liters
filtered per kg of wet fish per day and was utilized in the estimation of
k-j. The partition coefficient between fish and water was estimated from
field data to be 70,000 ug/kg per yg/1. Note that this is more than ten
times the equilibrium partition coefficient for dieldrin between suspended
solids and water. Dieldrin concentrates in bottom feeding fish due to
large uptake and relatively small depuration rates.
The sediment compartment receives the mass flux of dieldrin depicted
in Fig. 1-6 due to sedimentation. The mass of dieldrin in the sediment is
strongly partitioned into the particulate phase by adsorption. The ratio
of particulate dieldrin to dissolved dieldrin is equal to KpM or approxi-
mately 2,000, assuming a solids concentration in the sediment of 0.32 kg/a.
The decline in sediment concentration follows the declining mass input rate
to the sediment (Fig, 1-6), but it also biodegrades at a rather slow rate
of ^0.005 per day. Biodegr*dation in the sediment compartment 1s assumed to
occur for both dissolved and particulate dieldrin.
Bioconcentration of hydrophobic pesticides 1n Coralville Reservoir
fish is directly proportional to the oil or lipid cpntent of the catch.
By normalizing all of the fish residue data on an oil basis, it is possible
12
-------
to use Equations 12 and 13 to simulate all taxa of fish simultaneously.
The oil content is the fraction of the total wet weight which is extract-
able with petroleum ether Fig. 1-10 presents results for all fishes in
which a measurement of oil content was performed. While the data are
sparse, it appears that such a simple bioconcentration model has validity.
The only difference between the simulations depicted in Figs 1-9 & 10 is
the biomass (wet weight vs. oil) and the corresponding BCF factor (in fish
flesh vs. oil). The uptake and depuration rates remain constant.
The results of Figs. 1-9 and 10 indicate that the average catch no
longer exceeds the FOA action level of 300 yg/kg residue. Uptake by fish
accounts for almost 10% of the inflow dieldrin loading, while ^42% of the
inflow undergoes sedimentation to the bottom of the reservoir, and 48% is
exported through the outflow. The partitioning of dieldrin in the water
column is 64% in the fish, 24% dissolved 1n the water, and less than 12%
adsorbed to suspended sol Ids. Sediment and fish (and fish oil) are essen-
tially 1n equilibrium with mean dissolved dieldrin concentrations. If
biouptake is ignored in the model or if depurated dieldrin is not metab-
olized but rather returned to the dissolved phase, then the transport in
the outflow is 54% and the net sedimentation 1s 46% of the total dieldrin
load.
Eight Compartment Model
Results of the eight compartment model are presented in Figs. 1-11 and
12. Field data were not available for each compartment. The field site of
Fig. 1-11 is downstream from the Reservoir at Iowa City and most closely
represents the hypolimmon compartment 6 and the epiUmmon compartment 3.
There appears to be good agreement between model results and field observa-
uions.
The addition of compartments to the model provides insight into the
dynamics of dieldrin transport, scour, and deposition. The only difference
between the two compartment and the eight compartment model was that a small
amount of scour was Introduced into the sediment compartment, resulting in
a lower net sedimentation rate. Compartmentalization of the system roughly
offset this effect. Resulting dieldrin concentrations in the dissolved,
particulate, and sediment phases all decreased proceeding downstream through
the Reservoir, a result of sedimentation from the water column of a progres-
sively lower particulate dieldrin concentration. A check on the mass balance
of solids 1n the reservoir model indicated that sedimentation was greater
than scour, which is also true of the prototype system. Scour of dieldrin
from the sediment to the water was a small but significant contribution to
the TGSS balance at the sediment-water Interface. Bulk dispersion coeffi-
cients in the vertical and longitudinal directions were 9.3 x 10"4 and
4.6 x 10"* m^/d, respectively, with a dispersive scour at the sediment-
water interface of 1.5 x 10"* m^/d.
The addition of compartments to the model is not an arbitrary choice.
It should reflect the physical geometry of the prototype and/or the
estimated dispersion pattern. Assuming a longitudinal dispersion coeffi-
cient of -v4 mi2/day, it 1s possible to estimate the proper number o*
13
-------
longitudinal compartments necessary to reflect this degree of dispersion.
For Coralvilie Reservoir, approximately 10 reactors in series would be
required disregarding any bulk dispersion between compartments. As you
increase the number of compartments, the model becomes more "plug-flow" in
nature. Since a greater amount of material will settle out in a "plug flow"
system, it would be necessary to decrease the sedimentation rate constant to
reflect the field data-
Unsteady Flow Simulations
Errors are generated in the annual average, steady flow simulations
(Figs. 1-4-12). Mass fluxes are underestimated by using annual average
flowrates and calibrating the model output with annual average water
concentrations. For this reason an unsteady flow simulation was performed
for dleldrin in Coralvilie Reservoir during 1976. Inflow and outflows
are shown in Fig. 1-13. Note that the reservoir volume was drawn-down from
February - May and subsequently refilled. Input of dieldrin was unmeasured,
so this calibration involved fitting the measured output dieldrin concen-
tration by adjusting the inflow concentration (Fig. 1-14). Mass flux to the
sediment (Fig. 1-15) is approximately 5% larger than that of the steady
flow results if they are run with comparable input loadings. Such errors
would be even larger for simulations with time variable loadings of suspend-
ed solids.
If the goal is to accurately reflect mass fluxes of sediment and
dieldrin, then one must use a fully time variable approach. If annual
average exposure concentrations are all that is required, then a steady flow
approach similar to Figs. 1-4-12 is warranted.
The mass flux to the sediment shown in Fig, 1-15 provides information
about seasona" variations that is not possible with annual average simula-
tions. It has been observed that fish, especially bottom feeding fish,
reach peak residue levels of dieldrin and other insecticides in June. This
is due to higher dieldrin concentrations in the water as well as more
recently contaminated sediment following spring runoff.
SUMMARY
Fate and transport of the pesticide dieldrin has been simulated in
Coralville Reservoir. From the dieldrin analysis it was determined that
40% of the dieldrin inflow to Coralville Reservoir is lost to the bottom
via sedimentation and 50% is released through the dam gates of this short
detention time Reservoir. Uptake by fish accounts for about 10% of the
dieldrin input due to the extremely large biomass of biota, 1000 Ib/acre.
The partitioning of dieldrin in the water column is 64% in the fish, 24%
dissolved in the water, and less than 12% adsorbed to suspended solids.
Mean residues in the edible tissue of bottom feeding fish declined below
the FDA guideline of 300 ppb. Fish and sediment concentrations are
essentially in equilibrium with mean dissolved dieldrin concentrations.
Under low flow conditions, the sediment becomes a net source for pesticide
in the Reservoir via desorption and pore water diffusion.
14
-------
It was determined that bottom feeding fish bioaccumulate dieldrin in
proportion to the oil content (petroleum ether extraction) of the fish
sample. Therefore averages or composites of very oily fish tended to be
higher than model predictions. The prospect for a continued decline in
dieldrin residues is good. Model projections indicate that by 1986, the
residues in bottom feeding fish flesh should average less than 100 ug/kg-
Research follows in the next chapter on the role of sediment water inter-
actions and food items on the bioaccumulation potential of the fishery.
15
-------
REFERENCES
1. Kellogg, R. L.; R. V. Bui key. Seasonal Concentrations of Dieldrin in
Water, Channel Catfish, and Catfish - Food Organisms, Des Moines River,
Iowa - 1971-73. Pesticides Monitoring Journal. 1976, 9, 186-194.
2. Smith, C. N.; R. A. Leonard; G. W. Langdale; G. M. Bailey. Transport
of Agricultural Chemicals from Small Upland Piedmont Watersheds.
EPA-600/3-78-056, U.S. Environmental Protection Agency, Washington, D.C.
1978, 1-364.
3. Donigian,A, S. Jr; N. H. Crawford. Modeling Nonpoint Pollution from
the Land Surface. EPA-600/3-76-083, U.S. Environmental Protection
Agency, Washington, D.C. 1976, 1-280.
4. Smith, J. H.; W. R. Mabey; N. Bohonos; B. R. Holt; S. S. Lee; T. W.
Chou; D. C. Bomberger; T. Mill. Environmental Pathways of Selected
Chemicals in Freshwater Systems, Part I: Background and Experimental
Procedures. EPA 600/7-77-113, U.S. Environmental Protection Agency
Washington, D.C., 1977, 1-81.
5. Lassiter, R. R., G. L. Baughman; L. A. Burns. Fate and Toxic Organic
Substances in the Aquatic Environment. In: State-of-the-Art in
Ecological Modelling, S. E Jorgensen, ed., International Society
of Ecological Modelling, Copenhagen, Denmark, 1979, 7, 211-246.
6. Karickhoff, S. W., D. S. Brown, T. A. Scott. Sorption of Hydrophobic
Pollutants on Natural Sediments. Water Research, 1979, 13, 241-248.
7. Dexter, R. N. Distribution Coefficients of Organic Pesticides in
Aquatic Ecosystems. Agreement B-62522-B-L, Battelle Pacific Northwest
Laboratories, Richland, Washington, 1979, 1-38.
8. Veith, G. D. Predicting the Bioaccumulation Potential of Organic
Chemicals. Abstracts, Third International Symposium on Aquatic
Pollutants, Jekyll Island, Georgia, October, 1979, 18.
9. Zepp, R. G., N. L. Wolfe, L. V. Azarraga; R. H. Cox; C. W. Pape.
Photochemical Transformation of the DDT and Methoxychlor Degradation
Products, DDE and DMDE, bu Sunlight. Archives of Environmental
Contamination and Toxicology, 1977, 6, 305-314.
10. Wolfe, N. L., R. G. Zepp; D. F. Paris; G. L. Baughman; R. C. Hollis.
Methoxychlor and DDT Degradation in Water; Rates and Products.
Environmental Science and Technology, 1977, 11, 1077-1081.
16
-------
11, Wolfe, N. I,; R. 6. Zepp, D. F. Paris. Carbaryl, Propham and
Chloropropham; A Comparison of the Rates of Hydrolysis and Photolysis
with the Rate of Biolysis. Water Research, 1978, 12, 565-571.
12. Steen, W. C.; D. F. Paris; G. L. Baughman. Effects of Sediment
Sorption on M1crob1al Degradation of Toxic Substances. Proceedings
of Symposium on Processes Involving Contaminants and Sediments,
American Chemical Society National Meeting, Honolulu, Hawaii, April,
1979.
13. Khan, S. U. Kinetics of Hydrolysis of Atrazlne In Aqueous Fulvlc Acid
Solution. Pesticide Science, 1978, 9, 39-43.
14. Zepp, R. G-; D. M. Cline. Rates of Direct Photolysis In Aquatic
Environment. Environmental Science and Technology, 1977, 11, 359-366.
,15, Spade, A.; J. L- Hamellnk. Dynamics of Trifluralln Accumulation in
River Fishes. Environmental Science and Technology, 1979, 13, 817-822.
16. Mackay, 0., P. J. Leinonen, Rate of Evaporation of Low Solubility
Contaminants from Water Bodies to Atmosphere. Environmental Science
and Technology, 1979, 9, 1178-1180.
17. Schooley, A. H. Evaporation in the Laboratory and at Sea. Journal
Marine Research, 1969, 27, 335-340.
18. Hartley, G. S. Evaporation of Pesticides. In: Pesticidal Formulations
Research, Physical, Collodial, Chemical Aspects, R. F. Gould, ed.,
Advanced Chemistry Series. 1969, 86, 115-134.
19. Ruiz Calzada, C. E. Pesticide Interactions in Iowa Surface Waters,
thesis presented to The University of Iowa, Iowa City, Iowa in 1979,
in parc.al fulfillment of the requirements for the degree of Master
of Science.
20. O'Connor, D. J., J. L. Schnoor. Steady State Analysis of Organic
Chemicals & Heavy Metals in Reservoirs and Lakes. Submitted to
Environmental, Science and Technology, 1980.
21. Mehta, S. C. The Limnological Factors Affecting Pesticide Residues in
the Iowa River and Coralville Reservoir, thesis presented to The
University of Iowa, Iowa City, Iowa, in 1969, in partial fulfillment
of the requirements for the degree of Master of Science.
22. Frietag, J. Fish Pesticide Residues in Coralville Reservoir, thesis
presented to The University of Iowa, Iowa City, Iowa, in 1978, in
partial fulfillment of the requirements for the degree of Master of
Science.
23. Johnson, L, G. Pesticides in Iowa Surface Waters. ISWRII-83, Iowa
State Water Resources Research Institute, Iowa State University, Ames,
la., March, 1977, 1-117.
17
-------
24. Thomann, R. V. Size Dependent Model of Hazardous Substances In
Aquatic Food Chain. EPA-600/3-78-036, U.S. Environmental Protection
Agency, Washington, D.C., 1978, 1-40.
18
-------
Ct Cl
MEPTACML.OR
Cl
CHLORDANE
DOE
CH,CH,
OVFON*TE
CH, CH,
CH,CH, 0
8
S - CH? - CH,,
THIMET
Table 1-1, Chemical Structures of Selected Iowa Pesticides
19
-------
CHj
CH
0CNHCH,
FURADAN
CH,
ATRAZINE
ICHJC1
'CHZCH, CHZ 0
0
I
CH,
LASSO
Cl
r\ /"'
CjHjHNIv^ ^NH C CS
CH,
BLADEX
CF,
TRIFLURALIN
Table 1-1. (Cont.)
20
-------
TABLE 1-2. PHOTOLYSIS, HYDROLYSIS, BIOLYSIS, AND SORPTION COEFFICIENTS FOR SELECTED IOWA PESTICIDES
OUKrlo
001
DIE
r«r,*n
»r'»"*lt. iMme
Cwlrr
4lr.,ln.
Cy.oMln,
1****
TreM.n
Hf»r Sui-flCf Alk»lln; .. . .
D\rtcl Ph»trly>1» Chem'ol Hyd"»lyM» '"
< t t >10.000
t t
07 1 -10'3 100» t t
0003 ^Ocf113 6"10'5 >l.,»f113 -ID'" -3CU]
I. -f * 10'4 >365 4-10' '" -9L12.
, ,s [13] ,,
9-l»'B »1.000 10' I0 7'2 -JO'1 35
>3*5 -10'" 35
ritt1 >365 -3"10'" 12
0 »3 22 f *
Sedlnent/HjO
P»rtH1»n Off
pay kg le> p>»>»Hf rr»cH»n i/f unknown rile
-------
SRI MODEL
MODIFICATIONS
POSSIBLE
Figure 1-1. Pesticide Fate and Transport
22
-------
-ZO km-
E '
*
cu
ti
f
f
10
6
J
50OO"-
A
1 / 1 1
">
o
2-COMPARTMENT POND
8-COMPARTMENT LAKE
p-7800.
r
2
» -
/
*T
tf
pTSQO-
/
>
3
OUTFLOW
I I WATER COMPARTMENT
JSM\ SEDIMENT COMPARTMENT
Figure 1-2. Physical Configurations of the Reservoir Model
23
-------
Input
Rapid
Sorptive
Equilibrium
Sedimentation
Metabolites
Zk
V Hydrolysis
Biolysis
Photolysis
Volatilization
Figure 1-3. Bioaccumulation Kinetics of the Pesticide Transport Model
24
-------
CORALVILLE OUTFLOW
«*0.164/yr
£/ ^ PART ICUL ATE
Uu1VJ 1
r%I f*+*. I ^^^ I -
0.0
68
Figure 1-4. Model Results for Dieldrin Concentration in the Coralville
Reservoir Outflow
25
-------
^006
o>
005
gO.04
O
O
§003
5 0 02
cr
o
UJ
001
O.OO
«=0.164/yr
o
33
0.3
1
0.2 ife
z
cr
o
YEAR
Figure 1-5. Input Loading Function to Coralville Reservoir
26
-------
o
5 01°
o
UJ
in
0 0.05
UJ
Q
CO
en
000
68I69'70I71I72I73I74I75I76I77'78I79I80I81I82I83I84I
YEAR
Figure 1-6. Calculated Mass Flux of Dieldrin to the Sediment
27
-------
CORALVILLE OUTFLOW
<»» 0.164/yr
s = 0,18/d
T= 14 d
KpM=0.50
KpMM.OO
68
Figure 1-7. Sensitivity Analysis of the Effects of the Partition
Coefficient, K
28
-------
CORALVILLE OUTFLOW
«=<0.164/yr
KPM -- 0.50
T-- 14 d
0.0
YEAR
Figure 1-8. Sensitivity Analysis of the Effect of the Sedimentation
Coefficient, Kc
29
-------
0.06 \-
CORALVILLE OUTFLOW
u"0.164/y»
k,'018/d
r 14 d
CT!»,' 0 05/ig/l
K,M»050
SEDIMENT COMPARTMENT
kr 0 005 AJ
FIELD DATA, Xi I.
rm MODEL RESULTS
1*00 |-
19 ' 79
BOTTOM FEEDING FISH
ki'0027/d
0083/d
300/tg/kg'
FDA ACTION LEVEL
Figure 1-9. Field Data and Model Results for Dieldrin in Coralville Reservoir.
Numbers represent the number of data points and percentages are
the percent oil or lipid content of the catch.
30
-------
° 16,000
o>
je
o»
« 12,OOO
(T
2
CO
^ 8,000
0
CO
UJ
or
? 4,000
CE
a
UJ
a
0
GAME* BOTTOM
A
' ^
X
4(
-
S
)
-
^ S
-
FISH
$ FIELD DATA, X±]s
MODEL RESULT
kT - 0.027/d
i
<
s
kd* 00083/d
B« 374 ^ 10" *
BCF*
}4
^^s^
^^
871,000
T
^^^
1 76
kg//
L_
i~~
i
68 ' 69 ' 70 ' 71 ' 72 ' 73 ' 74 ' 75 ' 76 ' 77 ' 78 ' 79 ' 8O '
YEAR
Figure 1-10. Field Data and Model Results for Dieldrin in Coralville
Reservoir Fish, Normalized on an Oil Basis,
31
-------
300400
O
0 0300
z
UJ
o
00200
ac
o
_i
UJ
O 00100
ooooo
CORAUVILLE WATER COLUMN
«u» 0.164/yr
k,« 018/d
CT,BO 005^g/| 1968
KPM = 0 50
OUTFLOW DATA, X + l$
MODEL RESULTS
EPILIMNION
HYPOLIMNION
I _. I.
-1 1 h
68 69 70 71 72 73 74 7S 76 77 78 79 80
YEAR
Figure 1-11. Eight Compartment Model Results for Dieldrin in the Water Column,
32
-------
1000 -
CORALVILLE SEDIMENT
KrM»200O
k2'0005/d $»d.
J FIELD DATA, X±l»
MODEL RESULTS
68 69 70 71 72 73 74 75 76 77 78 79
000
Figure 1-12, Eight Compartment Model Results for Adsorbed
Dieldrin on the Sediment
33
-------
O
O
15,000 -
VOLUME, INFLOW AND OUTFLOW
IN CORALVILLE LAKE, 1976
VOLUME
INFLOW
OUTFLOW
O
10,000 -
O
5,000 -
1 I ! I I 7
JAN1 FEB1 MAR1 APR! MAY1 JUN1 JUL1 AUG1 SEP1 OCT1 NOV1 DEC1
Figure 1-13. Input Data for Unsteady Flow Simulation
34
-------
n
2*0
X
CO
a. ,
20
UJ
o
O
o
a.
10
10
2 20
CD
a
DIELDRIN IN CORALVILLE LAKE,
1976
INPUT CONCENTRATION (MODEL)
LAKE CONCENTRATION (MODEL)
V LAKE CONCENTRATION
(OBSERVED)
ZK = 6*1O'S DAY''
K, 0-18 DAY'1
|- Kp ' 6250
M = 80 mg/jf
UJ
o
o
o
UJ
i i i r T
i i ri
JANl FEB1 MAR1 APftl MAY! JUN1 JUL1 AUG1 SEP1 OCT1 NOV1 DEC!
Figure 1-14, Model Results for Unsteady Flow
35
-------
DIELDRIN IN CORALVILLE LAKE, 1976
MASS FLUX TO SEDIMENT
t T t 1 1 I 1 1 1 1 1
JAN1 FEB1 MAR1 APR1 MAV1 JUN1 JUL1 AUG1 SEP1 OCT1 NOV1 DCC1
Figure 1-15. Unsteady Mass Flux to the Sediment
36
-------
CHAPTER II
SEDIMENT, WATER AND BIOTA INTERACTIONS
OF THE PESTICIDE DIELDRIN IN CORALVILLE RESERVOIR
INTRODUCTION
Dleldrin Inputs to Coralville Reservoir emanated as nonpoint source
runoff after the application and mlcrobial epoxldation of the insecticide
aldrin. Aldrin and dieldrin were banned by the U.S. Environmental Pro-
tection Agency (EPA) in 1975. As applications of aldrin decreased between
1965 and 1975 due to insect resistance, therewasa corresponding decrease
in total dieldrin 1n water samples(Fig. II-l). Peak dieldrin concentrations
in the water column generally correlated with high flows and turbidity
(suspended solids).
A hypothetical scenario for the "self-purification" of an impoundment
after pollution by a hydrophobic, persistent, toxic organic is depicted in
Fig. II-2. As with dieldrin in Fig. II-l, the concentration of toxin in the
water column decreases after the discharge decreases. For a period, fish
bioconcentrate the toxic from the dissolved phase -- they "track" the water
column with a slight time lag on the order of a few weeks to a few months.
The rate of sediment contamination depends on the rate of deposition of
suspended solids and the mixing characteristics of sediment and water. As
the water column concentration and fish residues decline, there is a period
when tht f-sh body burden is taken predominantly from sediment ingestion and
other food items. This occurs due to the low concentration in the water and
lagging sediment and biota residue levels.
Finally even the sediment declines in concentration due to desorption -
diffusion into the water column and due to sediment burial and mixing. It
is the last phase of the scenario of Fig. II-2 that best describes the cur-
rent situation 1n Coralville Reservoir for dieldrin, chlordane, DDE, and
heptachlor epoxides. All of these chemicals have been banned, yet all of
them continue to bioaccumulate to objectionable levels in fish despite
water concentrations below detection limits in most cases. One of the
chemicals, DDE, is apparently generated by dehydrochlorination in sediment
and -.s slow to decline. A more detailed analysis of the dieldrin field
data in Coralville Reservoir provides evidence of the importance of sedlment-
water-biota interactions.
FIELD OBSERVATIONS
Dieldrin is declining 1n the Coralville Reservoir outflow. Both the
annual average concentration, the annual maximum concentration, and the
coefficient of variation have declined since 1969 (Tab. II-l). From1965-1975,
37
-------
peak concentration:, occurred after spring application and runoff. Pre-
sently the peak annual dieldrin concentration is much smaller (-vO.010
vig/x.) and can occur any time throughout the year. Often peak concentra-
tions are still associated with high flows, but "background" concentrations
and baseflow concentrations are nearly as large as the annual peak. The
reservoir system 1s being "buffered" by inputs from the sediments during
moderate flow and low flow conditions.
Suspended solids in the outflow from Coralvllle Reservoir correlate
strongly with flow, as can be determined from the time series of Fig. II-3
This is due to higher shear stress near the bottom of the Reservoir during
high flows and due to increased sediment carrying capacity. Partheniades
[1] showed the relationship between average bottom shear stress and rate of
erosion of bed sediment (Fig. II-4). Unfortunately the power of the rela-
tionship of Fig. II-4 (n = 1 to n = 3) has been shown to vary widely among
various cohesive sediments and Investigators. It is virtually impossible
to predict the rate of scour a^ priori without very detailed knowledge of the
cohesive sediment properties (e.g. tensile strength, plasticity index, clay
content, compacted density, etc.)
Because of scour and resuspension of bed sediment, bank erosion, and
increased sediment carrying capacity, dleldrin loading and suspended solids
loadings are highly correlated as shown 1n Fig. II-5. It is interesting to
note that during the 100-year drought of 1976-1977, stream flows carried
negligible solids, but the dieldrin loading was nevertheless significant
(Fig. II-5). This is even more evident in Fig. II-6, showing the relation-
ship between suspended solids loads and total dieldrin concentration.
During the drought years, 1976-77, the dieldrin concentration was quite
significant. It was predominantly dissolved dieldrin from sediment desorp-
tion and dissolution. This illustrates the buffering effect that sediments
can have on the water column years after the primary source of dieldrin was
curtailed. While it does not amount to a large mass of dieldrin, it is a
significant exposure concentration for fish bioconcentration.
Sediments are historically contaminated with dieldrin, but the degree
of contamination has decreased since the ban 1n 1975. A typical upstream
sediment core 1s shown 1n Figs. II-7 and II-8.
In sandy substrates, such as the inflow delta, finer material is
deposited near the surface of the sediment under low flow, ice-covered
conditions. This fine silt is characterized by high organic content and
volatile solids greater than 10X. In these locations one observes a maxi-
mum dieldrin concentration in surficial sediments(Fig. II-8a). The bed is
continuously being reworked each year, and the fine silts will be subse-
quently scoured, resuspended, and deposited downstream. An interesting
feature of Fig. II-8b is the relatively constant sediment concentration with
depth on a volatile solids basis. There are some indications that the
sediment is disturbed enough to create rather uniform concentration profiles
on a volatile solids basis. This could indicate thermodynamic equilibrium
is achieved in mixed sediments.
38
-------
The site of Figs. II-7 and 8 Is characterized as one of intermediate
deposition located near the inflow to Coralville Reservoir. Bed surfaces
would be subject to scour and resuspension. A low density of nematodes and
Chironomid sp. were observed in the core samples. However bioturbation of
these areas is expected primarily by carp and other bottom feeding fish
which are 1n abundance. Carp, carpsucker, red horse, and buffalo are known
to work the sediments 1n this area. Model results indicate that bioturba-
tion of this sort can serve to increase the sediment flux of contaminants by
several fold.
A point in contrast is the sediment concentration profiles in highly
depositing basins of the reservoir. In Coralville Reservoir, the annual
average deposition is 5.8 cm/year. In deep, highly depositional zones, the
high rate of sedimentation of cohesive sediments precludes a great degree
of mixing. Field data, although it is sparse, supports the conclusion that
some degradation of dieldrln is occurring 1n the sediment otherwise
sediment concentration profiles would be considerably larger. Interstitial
waters track the sediment concentration very well. Diffusion and sorption
of dissolved dieldrin to the sediment from the water column may occur if
sediment degradation reactions decrease sediment and Interstitial concen-
trations as shown. Otherwise the sediments serve as a source of dieldrin to
the water column by scour/resuspension, and desorption/diffusion.
MODELING SEDIMENT-WATER INTERACTIONS
Compartmentalization
The model of Schnoor [2] and Schnoor and McAvoy [3] was further
developed to include a variable sediment volume (sediment burial) and was
extended to 25 compartments. Fig. II-9 is a schematic of the compart-
ment. Mzation.
The average longitudinal dispersion coefficient in Coralville
Reservoir was estimated from the formula by Liu [4] to be approximately
4 mi2/day. Twenty-five compartments would yield a numerical dispersion of
this same magnitude according to Equation 18;
c UAX /, UAt \ ,,_.
Ex *~r I1--AT) 08)
where E * longitudinal dispersion coefficient, L2/T
u = mean velocity, L/T
AX = compartment length, L
At s time step, T
Therefore, no bulk longitudinal dispersion was required in the model since
compartment volumes were chosen such that the artificial dispersion would
approximate the 1n-situ estimated dispersion.
39
-------
Solids Balance
Vertical bulk dispersion was required to scour and resuspend solids
from the soil compartment to the hypolimnion and epilimnion compartments.
The vertical dispersion coefficient (Ez = 1.0 x 1Q"1* m2/day) and the sus-
pended solids sedimentation rate constant (ks - 0.4/day) were determined
from a solids balance that was performed prior to the dieldrin simulations
using the program TOXIC, The Appendix provides the user documentation for
TOXIC. (Note: it is possible to perform the mass balance on suspended
solids and sediment using the model TOXIC by assigning a high partition
coefficient (1^1,000,000) and zero degradation rate (ik = 0). Thus it is
possible to determine the appropriate suspended sol Ids concentrations,
sedimentation rate constants, and vertical dispersion rates provided that
one knows the total sediment loadings and net deposition rates or trap
efficiency.) It 1s Imperative to have accurate knowledge of the solids
mass balance in order to simulate a hydrophobic toxic,,je.g. dieldrin. Mass
must be conserved.
The solids balance for the simulations 1n Chapter I utilized a bi-
weekly average inflow concentration of suspended solids of 241 ug/x,, a trap
efficiency of 72%, and consequently an outflow concentration of 79 yg/£.
The required sedimentation rate constant was determined from the solids
balance to be 0.18/day.
The capacity of Coralvilie Reservoir was resurveyed in 1958, 1967, and
1975. From the most recent sediment survey data and from daily, flow-
weighted suspended solids loadings at the inflow to Coralville Reservoir
(at Marengo, Iowa), it was determined that the true solids trapped were
i<4 x 106 kg/day with an average annual inflow concentration of 1£20 mg/x,.
Large errors can result in the mass flux of both solids and toxics if
loadings are averaged from infrequent measurements.
The modeling application here is quasi-dynamic-- the approach utilizes:
1) steady, annual average flow (from daily measurements) for long-term
simulations, 2) steady, annual average, flow-weighted solids (from daily
suspended solids measurements), and 3) time-variable toxicant loadings.
Researchers must choose whether to simulate annual average concentrations
of solids and toxics (to assess biological exposure) or whether to simulate
accurately the mass fluxes to the sediment and outflow. If it is important
to know both the mass flux and the exposure concentration accurately, then
one should use a fully time variable model with daily (or more frequent)
inputs.
In addition to utilizing the model TOXIC for solids balances, a series
of simple interactive programs were developed for performing solids
balances in two-compartment (sediment and water) reservoirs:
1) TWOCOMP - a constant volume compartment model for sol Ids which
interactively calls for desired simulation time, input suspended
solids concentration, sedimentation concentration, and a vertical
bulk dispersion coefficient
40
-------
2) TWOCOMP1 - a variable volume compartment model for solids which
interactively calls for the above information plus the "volume
increase factor" of the aggrading sediment comparmtent (a volume
increase factor of 2 will double the sediment volume linearly
during the duration of the simulation)
3) PEST! - a two-compartment pesticide model which utilizes the sus-
pended solids concentrations generated by the model TWOCOMP or
TWOCOMP1
Examples of these models appear 1n the Appendix.
Pesticide Balance
The final results for the PEST1 or TOXIC two-compartment model are
presented in Tab. 11-2, In this simulation, the volume of the sediment
compartment Increased at an annual rate of 5.8 cm/yr, the average rate
computed from the sediment resurvey of Coralville Reservoir by the U.S.
Army Engineers, Rock Island District. Results presented in Tab. II-2 are
quite similar to previous results of the eight compartment model from
Chapter I. Model results of Tab. II-2 are in general agreement with field
observations except for the sediment residue levels (yg/kg) which are low
by a factor of 2-4. This is easily corrected 1f we decrease the rate of
degradation in the sediment to zk = 0.0026/day. Since the rates of degrada-
tion Qf sorbed toxics are often uncertain, £k becomes a sensitive para-
meter about which more needs to be known.
Tab. II-3 details the results of the 25 compartment model with a sedi-
ment degradation of Ik = 0.0026/day and a three-fold larger inflow concen-
+ration than used in Tab. II-2. The larger dieldrin loading was used as an
annual average mass flux into the reservoir consistent with the updated
solias balance. Results of Tab. II-3 comprise the best estimate of what has
actually t2'<:n place in Coralville Reservoir on a mass flux basis
To be certain, there are diminishing returns to simulations with
greater and greater mechanistic realism and complexity. The 25 compartment
simulation of Tab. II-3 did not provide significant additional insight. How-
ever 1t represents the best estimate of the actual dieldrin dynamics. If
a management decision required quite accurate information, a model with
time variable inputs may be warranted. Model complexity should fit the
user's need.
A simulation was performed to determine the relative contribution of
the sediment desorption and scour to the mass of dieldrin in the water
column. The present Internal recycle of dieldrin from the sediments is
calculated as 0.003 kg/day with external loadings from inflows of 0.023
kg/day. About 12% of the dieldrin in the water comes from the sediment.
If there was a total cutoff of external loadings, the 25-compartment model
indicates there would be a sharp decline in dieldrin concentrations. How-
ever if this loading curtailment was in conjunction with a drought and
longer detention time, then the sediment could produce as much as 6 ng/2, of
dieldrin for a few months and 1.2 ng/x, for more than 2 years after the
41
-------
cutoff In load. Clearly the model is consistent with the record of
Fig. II-6.
Burial was simulated by increasing the volume of the sediment layer
in a 20 year simulation. Another option was to continually add new sedi-
ment compartments of M> cm depth each year to simulate the net deposition
in Coralvilie Reservoir. This proved to be rather costly in terms of
computation time since a 20 year simulation would generate a total of
200 sediment compartments. The top sediment compartment of 6 cm would
serve as the "active layer" while the others would be deep sediment.
The best estimate of dieldrin in sediments of highly depositional
zones is presented 1nFig. 11-10 using a partition coefficient of 1500, In
the absence of sediment degradation reactions and vertical transport, the
sediment profile resembles the dieldrin loading scenario. However there has
been a vertical flux of dieldrin from the sediment to the water due to re-
suspension and scour and a vertical migration within the sediment due to
diffusion. Furthermore degradation of adsorped dieldrin results in the two
profiles for the rate constants sk = 0.07/year and 0.95/year (half lives
of 10 years and 265 days, respectively). The profile with the large
sediment degradation rate of 0.95/year (0.0026/day) most closely reflects
field data. Sediment cores of 100 cm can be collected at Mahaffey Bridge
1n the upper portion of Coralville Reservoir. Core analyses indicate no
detectable dieldrin except in surficial sediment samples. The high organic
content of these cohesive, mucky samples does make extraction and recovery
of dieldrin difficult.
BIOTIC UPTAKE
Bioconcentrat-yon
Fish bioconcentrate dieldrin directly from dissolved residues in the
water by mass transfer at soft, epithelial tissue (gills). Also there
exists a strong correlation between insecticide residue levels in fish and
the percent oil or fat (petroleum ether extraction). To demonstrate that
the residue level of fish is directly related to their fat content, Fig. II-
11 was plotted with 1979 data for a variety of fish species and seasons.
Fish samples were analyzed according to US Food and Drug Administration
(FDA) procedures by the University of Iowa Hygienic Lab. Fig. 11-11
demonstrates that oil content removes 60% of the variance (r2) in fish
residue samples regardless of fish species. Autumn (October-November)
residues in fish are lower than summer (June and August) samples, primarily
due to a difference in exposure concentrations from agricultural runoff.
The slope of the line in Fig. 11-11 gives the residue concentration in
fish oil at 1979 exposure levels. The annual average insecticide exposure
concentrations during this period were 0.005 ug/1 dieldrin, -\-0.002 ug/1
chlordane (cis-chlordane plus trans-chlordane plus nonachlor),^ 0.001 ug/1
DDE, and'v 0.001 ug/1 heptachlor epoxide. Based on this residue and ex-
posure data, the oil-normalized bioconcentration factors (BCF-oil) ex-
pressed as log-|Q BCF ug/kg oil per ug/1 are 5.6, 6.0, 5.9, and 5.8 for
dieldrin, chlordane, DDE, and heptachlor epoxide respectively. Fig, 11-12
42
-------
shows the log BCF-oil plotted for a number of insecticides in Iowa waters.
Oil-normalized BCFs correlate roughly with octanol water partition coeffi-
cients.
The total storage of dieldrln in Coralville Reservoir is largely in the
sediment (^45 kg). Bottom feeding fish contain perhaps 0.5 kg while the
water column contains only ^0.2 kg and particulate suspended material ~0.1
kg. All other elements of the ecosystem contain very little dieldrin.
Apparently biomagniflcation is not a large problem since bottom feeding
fish have the highest blomass and residue levels (Fig. 11-13).
Bioaccumulation
The kinetics of bioaccumulatlon are shown in Fig. 11-14. Fish can lose
unmetaboHzed toxics via biliary excretion or "desorptlon" through the gill.
One the other hand, toxic organics can undergo biotransformations and be
eliminated as metabolic products. The rate constant, k2, includes total
depuration (both excretion of unmetabolized toxics, k2', and elimination of
metabolites, k2"). Only a fraction of this elimination is returned to the
water column as dissolved parent compound, designated as k2' in Fig. 11-14.
(Note: k2 = k2' + k2").
The kinetics of bioaccumulation are presented in Equation 19.
|£- e^C/B -k2F + k3rb + k4rf + k5rw
in which
e = efficiency of toxic absorption at the gill
kn = biouptake rate from the dissolved phase
. fi filtered\ v /kg fish\
" \kg fish-day^ x \ s. }
k2 = depuration rate constant including excretion and clearance
of metabolites, day"'
C = dissolved toxic organic, yg/x.
B - fish biomass, kg/2,
F = fish residue (whole body), yg/kg
r.,r.,r * whole body residues in bed sediment, fish prey, and worms,
respectively, yg/kg
k^ = uptake rate of bed sediment for detrivores, day'
k^ - uptake rate of fish predation, day"
43
-------
kg = uptake of worms and benthos, day"
The uptake rate constant from dissolved toxicant in the water, ki , is a
product of the fish respiration rate and the biomass of fish in the system.
At steady state, Equation (19) admits the following solution:
This is a good form of the equation 1n which to evaluate the relative
contribution of each term: 1) uptake of dissolved organic through the gill,
2) uptake of bed sediment, 3) uptake of fish prey, and 4) uptake of worms
and benthic organisms. Figs. 11-15 and 16 give the results for dieldrln
uptake by gamefish and bottom fish in Coralville Reservoir. More than 50
percent of the uptake is from the water in both cases, but game fish get
their second-most contribution from prey fish and bottom feeders get theirs
from sediment ingestlon. Results are based on field and microcosm studies
and literature values of feeding rates.
Often in the case of small fish and detritivores, the predominant path
of bioaccumulation 1s through the gill membranes. In this case bioconcen-
tration is described by only the first two terms of equation (20). The
steady state solution is then:
ek,C
in which
ek
k2
,
- the bioconcentration factor,
Bioconcentration factors normalized on a lipid basis, are especially
useful since often the kinetics of uptake and depuration are rapid compared
to transport and other reactions. In Coralville Reservoir, fish were at
near-equilibrium with dissolved dieldrin concentrations. The response time
of the fish to a step function change in dieldrin concentration is on the
order of three to ten weeks, based on field data and laboratory microcosm
testing. This means that fish would respond relatively rapidly to "cleanup"
of an ecosystem which had been contaminated by dieldrin.
SUMMARY
Physical, chemical and biological processes are all important in the
fate and transport of dieldrin in Coralville Reservoir. A good water budget
and sol Ids balance are necessary for an accurate mass balance on pesticide.
44
-------
During high flow periods, dieldrin continues to enter the Iowa River with
agricultural runoff. To a lesser extent, it is scoured from contaminated
sediments. Under low flow regimes, dieldrin desorbs and diffuses from
contaminated bed sediments to the water column. Thus dieldrin is decreasing
in the aquatic ecosystem due to washout, burial by less contaminated
sediment, and possibly by degradation reactions in the bed.
Fish bioconcentrate dissolved dieldrin and, to some extent, bio-
accumulate dieldrin from sediment ingestion and prey items. Since "hot
spots" of dieldrin do not exist in this case, sediment and benthos ingestion
are of less importance. Fish will respond to changes in dieldrin concen-
trations because the kinetics of bioconcentration are relatively rapid.
REFERENCES
1, Partheniades, E., "Erosion and Deposition of Cohesive Soils,"
Journal of the Hydraulics Division, ASCE, Vol. 91, No. HY1, Proc.
Paper 4202, Jan., 1965, pp. 105-139.
2, Schnoor, J.L., "Fate and Transport of Dieldrin in Coralville Reservoir:
Residues in Fish and Water Following a Pesticide Ban," Science, Vol.
211, 20 Feb., 1981, pp. 840-842.
3 Schnoor, J.L. and McAvoy, D.C., "A Pesticide Transport and Bioconcen-
tration Model," J. Environ. Engr. Div., ASCE, Vol. 107, No. EE6,
Dec. 1981.
4, Liu, H., "Predicting Dispersion Coefficient of Streams," J. Environ.
Engr. Div., ASCE, Vol. 103, No. EE1, Feb., 1977, pp. 59-69.
45
-------
Table II-l. Annual Total Dieldrin Concentration Statistics from Below
Coralville Reservoir at Iowa City, 1969-1979
YEAR
MEAN
CONC.
0.022
0.019
0.016
0.011
0.009
0.005
0.007
0.010
0.005
0.008
STD. DEV.
yg/i
0.019
0.010
0.009
0.007
0.005
0.005
0.005
0.005
0.003
0.001
%CV
COEFF.
VAR.
86.4
51.1
56.4
61.8
53.8
94.4
66.6
53.3
64.5
12.5
MAX
CONC.
0.
0.
0.
0.
0.
.052
.035
.031
0.026
0.020
.016
.018
0.017
0.009
0.010
N.
NO- OBS.
7
7
9
12
12
12
12
11
10
9
46
-------
Table II-2, Two-Compartment Model Results with Aggrading Sediment,
Coralville Reservoir
Parameters: K M = 0.6
water column partitioning
KM = 3210.
Tine
jdays)
0
600
1200
1800
2400
3000
3600
ks = o.
Ez = 0.
zk = 0.
zk = 0.
t0 - 14
cin,o
0. = 0.1
4/day
0001 m2/day
000325/day
0052/day
days
0.050 pg/Jl
64 yr.
DIELDRIN
Water, CT Sediment
ug/z yg/£
0.0250
0.0131
0.0100
0.0076
0.0058
0.0044
0.0034
2.25
2.91
2.27
1.74
1.33
1.01
0.77
sediment partitioning
sedimentation rate
vertical dispersion
dissolved reactions
sediment reactions
detention time
constant
initial inflow concentration
exponential decline
RESIDUES
Suspended
, CT Sediment, r
yg/kg
15.6
8.18
6.25
4.77
3.64
2.78
2.12
Bed
Sediment, r
ug/kg
0.70
0.91
0.71
0.54
0.41
0.32
0.24
47
-------
Table II-3. Results of 25 Compartment Simulation for Dieldrin in
Coralville Reservoir
TOTAL DIELDRIN CONCENTRATION, (^(pg/4)
COMPARTMENT
Water 1
2
3
4
5
6
7
8
9
10
11
12
13
14
II
Sediment 16
17
18
19
20
21
22
23
24
25
2.25
day 1200
0.0687
0.0544
0.0435
0.0351
0.0286
0.0232
0.0235
0.0176
0.0203
0.0144
0.0168
0.0124
0.0136
0.0112
0.0115
18.2
13.8
10.6
8.08
6.23
5,02
4.27
3,28
2.44
1.49
day 2400
0.0401
0.0318
0.0255
0.0206
0.0169
0.0137
0.0139
0.0103
0.0120
0.00846
0.00908
0.00732
0.00807
0.00663
0.00681
11,1
8.48
50
99
85
13
67
05
52
0.908
day 3600
0.0234
0.0186
0.0149
0.0120
0.00983
0.00798
0.00811
0.00603
0.00702
0.00494
0.00578
0.00427
0.00471
0.00387
0.00397
48
95
80
92
26
83
56
20
0.893
0.532
48
-------
Table II-3. Results of 25 Compartment Simulation for Dieldrin in
Coralville Reservoir (continued)
MASS OF DIELDRIN ON SOLIDS, r(ug/kg)
COMPARTMENT
Water 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sediment T₯
17
18
19
20
21
22
"3
£H
10.2
11.2
12.3
13.6
14.9
18.3
15.3
23.4
15.6
27.6
17.0
30.6
18.8
33.0
24,3
0.97
1.15
1.38
1.64
1.95
2.09
2.20
2.55
3.05
day 1200
28.0
24.4
21.5
19.1
1
17
17.0
14.4
16.5
12.6
15.8
.4
.2
.2
11
15.
10.
14.8
11.2
7.82
,10
.48
.90
.40
4.66
4.17
.72
.32
25
5.09
3.
3.
3.37
day 2400
16,4
14.3
12.6
11.2
10.0
10.0
8.50
9.70
7.49
9.34
6.76
8.97
6.08
8.76
6.64
4.77
4.35
.98
.64
.34
.90
.61
.32
.07
3.
3.
3.
2.
2.
2.
2.
day 3600
9.56
8.34
7.33
6.55
5.86
5.84
96
66
37
44
94
23
55
11
87
78
54
33
2.13
96
70
53
36
21
2.06
1.20
49
-------
Table II-3, Results of 25 Compartment Simulation for Dieldrin in
Coralvilie Reservoir (continued)
KM = 0.22 - 2,6
KM = 660 - 3,450
Ks -- 0,4
E = 0,0001 m2/day
zk * 0.000325/day
zk = 0.0026/day
t0 = 14 days
Cin,o ' °-150
u> = 0.164/yr
K = 1500
PARAMETER LIST:
water column partitioning
sediment partitioning
sedimentation rate constant
vertical dispersion coefficient
longitudinal dispersion
dissolved reactions
sediment reactions
detention time
initial inflow concentration
exponential decline
sediment: water partition
coefficient
50
-------
80
60
« 40
=>
Z
g 2-0
-------
o
cc
111
o
X
p
decreasing
o
x
_fi»h tr«k_
v»»ter
fish track
sediment"
u
2
O
ui
to
o
g
sediment response*
dvpends on r»t*
»f deposition 8
mumg
sediment response
depends on depositwn-
mixing
TIME IN YEARS
Figure 11-2. Hypothetical Scenario for the Self-Purification of an
Impoundment After the Introduction of a Hydrophobic Toxic
Organic
52
-------
IfOOO
in
CO
196»
74 75
TIME, y»»>s
Figure 11-3. Solids Loading and Flow Below Coralville Reservoir at Iowa City
-------
10.O
5.0
cv/ 2.0
g
tn
o
ir
ui
o
U)
10
0.5
02
.001 002 .005 .01 02 05 10
V AVG BOTTOM SHEAR STRESS, dynes/cm2
Figure II-4. Rate of Erosion versus Bottom Shear Stresses in dynes/cm
(calculated fron data of Parthemades, 1965)
54
-------
18000
»1SOOO
I
§12000
5 9000
§
~i 6000
-------
196» 70
74 73 76
TIME. y»»'»
80
Figure II-6. Solids Loading and Total Dieldrin Concentration Below
Coralville Reservoir at Iowa City (note, no dieldrin
record exists for 1978)
56
-------
CORALV1LLE RESERVOIR
SEDIMENT CORE AT
HWY 0, 16 FEB 1981
0 2 4 6 8 10
% VOLATILE SOLIDS
L_
n^
z
LU
2
O
UJ
CO
0>
JC
^^
^s
o>
4.
cr
o
_i
UJ
o
4
3
2
1
n
O
-
o
0
024 6 8 10
% VOLATILE SOLIDS
Figure II-7. Sediment Core Analyses for Percent Volatile Solids and
Dieldrin Concentration Near the Inflow to Coralville
Reservoir
57
-------
CORALVILLE RESERVOIR
HWY 0, 16 FEB 1981
01234
SEDIMENT
DIELDRIN /^g/kg dry
E
°_
x~
H
O.
UJ
Q
5
10
15
20
OR
O
-
o
-
o
o
-
10 20 30 40 50
SEDIMENT
DIELDRIN /tg/kg VS
(*) (b)
Figure II-8. Sediment Core Analyses for Dieldnn Concentration on a Dry
Weight Basis and on a Volatile Solids (VS) Basis
58
-------
-2700m-»
/ / / / / / /
Figure 11-9. Compartmentalized Approach to Sediment - Water Interactions
in Coralvilie Reservoir
59
-------
20
SEDIMENT CONC-Uf/k*
Q 20
[1
i a.
g
INT£*STITI»L CONC "9/1
0 i '0 20
§
I »
no
Figure 11-10, Estimated Dieldrin Sediment Concentrations and Loadinn
Scenario in Highly Depositional Zones of Coralville
Reservoir
60
-------
1-500
LJ
>400
UJ300
(rt
SUMMER
» BUFFALO
A CATFISH
7 CARP
9 BASS
_ AUTUMN
SHADED
a
.1=0
56789
% OIL IN EDIBLE FISH
10 n
12 13 14 15
Figure 11-11. Dieldrin Residues in Fish vs. Percent Oil Content,
Coralville Reservoir, 1979 Field Data. Equation for
Least squares regression Y * 2640 X -10.8, r * 0.77,
61
-------
7 '
U_
O
CD
CD
o
'HE
DIELD
log
Figure 11-12. Iowa Field Bioconcentration Factors in Fish Oil vs.
Octanol/Water Partition Coefficients
62
-------
PISCIVOROUS
FISH
i- 5 mg/1 _,
30 ug/kg wet .
0.015 Ib (0 0067 kg)
J_
SMALL FISH
& MINNOWS
^ 5 mg/1* ,
30 ug/kg wet .
0 015 Ib (0 0067 kg)
ZOOPLANKTON
INSECTS
negligible
ALGAE*
2 ng/1,
5 ug/kg 4
0 001 Ib (0.0004 kg)
BOTTOM
FEEDING FISH
47 mg/1* 4
225 ug/kg wet
11 Ib (05 kg)1
t
IZOOBENTHOS
< 1 mg/1*
5 Xg/kg .,
< 0 01 Ib ( 0 004 kg)
HATER
0.004 ug/l"" ,
0 42 Ib (0-19 kg)
11
SEDIMENT4
1.3 kg/1
2 ug/kg4
lOOlb (45 kg)
BIOMASS
'DIELDRIN CONCENTRATION
1DiaDRIN MASS
Figure 11-13, Ecosystem Contamination Levels Based on Field
Observations and Laboratory Microcosm Experiments
63
-------
Input Input
Rapid
Sorptive
Equilibrium
Food Items
Porticulate
Sedimentation
Dissolved
Zk
Biouptake
Sediment
fish
worms
k3, k4, ks
Fish
Hydrolysis
Biolysis
Photolysis
Volatilization
Oxidation
Excretion
Metabolites
Figure 11-14. Bioaccumulation Model Schematic
64
-------
AQUATIC EXPOSURE MODEL
GAME FISH FOOD ITEMS
K, '00135/doy
K, ' 0.04W/doy
iILL + SEDIMENT + PREOATION * BENTHOS
ilLL+SE01MENT*Pf»EDAT10N
1968 70 72 74 76 78 80 83 84 86
TIME (year*)
Figure 11-15, Dieldrin Residues in Game Fish Simulation
for Coralvilie Reservoir
65
-------
AQUATIC EXPOSURE MODEL
BOTTOM FISH FOOD ITEMS
K, '00133/dfy
K2 »0.00413/d«y
GILL + SEDIMENT * PREDATION
GILL* SEDIMENT*
PREDATION+BENTHOS
1966 70 72 74 76 78 80 82 84 86
TIME (ytors)
Figure 11-16. Dieldnn Residues in Bottom - Feeding Fish for
Coralvilie Reservoir
66
-------
CHAPTER III
A DYNAMIC SIMULATION MODEL FOR ALACHLOR AND ATRAZINE:
FIELD VALIDATION USING MICROCOSM AND LABORATORY STUDIES
INTRODUCTION
One of the most widely used herbicides in the U.S. and Iowa is the
soluble, acetarrilide alachlor (Lasso") It is applied at 1.1 kg/ha as a
preemergent for grass control in corn during the period from mid-April to
mid-May (see Tab, 1-1 for structure). Concentration in Iowa waters range from
10-100 yg/i in headwater streams with dilution and biodegradation accounting
for lower concentrations M yg/fc) in large rivers directly after applica-
tion. It is a very water soluble herbicide (242 ppm) and has moderately
low vapor pressure (2.2 x 10~5mm Hg).
Other widely used herbicides in Iowa for broadleaf control are
atrazine (Aatrex10) and cyanazine (Bladex0), both triazine herbicides. These
herbicides are also quite soluble in water (33 and 150 ppm, respectively)
and have low vapor pressures. Atrazine is applied at 0.5-0.8 kg/ha (active
ingredient) from mid-April to mid-May. Concentrations in the Iowa River at
Iowa City are 1-2 ug/n during May-August. Concentrations of atrazine and
cyanazine in the headwaters of the East Soldier River in Iowa were 54 and
130 ug/x, on 13 June, 1979,
The p^vpose of this study was to further test and validate the toxics
model. A field validation was undertaken, and microcosm and laboratory
studies were used to supplement the field data collection under spring run-
off (time variable) conditions. One objective of the research was to
determine if laboratory-derived rate constants could be used directly in a
field validation effort. Soluble, biodegradeable chemicals were investi-
gated to complement previous studies with dieldrin.
Field data on alachlor was gathered on Lake Rathbun, south-central
Iowa reservoir on the Chariton River, by the University of Iowa Hygienic
Laboratory [1]. The study was conducted during a high flow runoff period.
Under these conditions, Lake Rathbun had a mean depth of approximately 29
feet, a mean hydraulic detention time of 162 days, and a mean volume of
351,000 acre-ft. Rathbun's 535 square mile watershed is primarily in row
crop agriculture and pasture land.
MODEL DEVELOPMENT
The model uses a completely mixed flow-through system of one compart-
ment. Equations 22 and 23 must be solved with a time variable mass loading
67
-------
W(t) and detention time. The herbicides are approximately 99/6 in tie
dissolved phase such that the last term of Equation 23 is of little con-
sequence. The time variable equation becomes a coupled set of ordinary
differential equations shown below which were solved via a variable step
size fourth order Runge-Kutta numerical technique.
IT - Qin -
d(VCT)
TT1 = W(t) ' Qout CT ' skCV ' ksCPV (23)
t - time, days
V = volume, a
Q = flowrate, Ji/day
CT = total herbicide concentration, ug/n
W = herbicide loading in mass per time, pg/day
k = sum of the pseudo- first order reaction rate constants in the
dissolved phase, n/day
k = sedimentation rate constant, £/day
C = particulate herbicide concentration = K CM,
C = dissolved herbicide concentration,
Equation 23 must be differentiated by the chain rule:
d (VCT) dCT ,v
^T1 =V
The final equation 1s:
=
THT = in Cin ' ^out CT " «<*<*> 'KsCP
r dV\ 1
" CT dt / vTtl
Relationships between the total, particulate, and dissolved pesticide
concentrations can be calculated assuming local equilibrium:
68
-------
c -
KM
Cp '
where M - suspended solids concentration, kg/£
K - solids/water partition coefficient, yg/kg per
GASP-IV Simulation Language
Ordinary differential equations can be easily coded into the simula-
tion language GASP-IV. GASP-IV is a simulation language developed for
handling discrete, continuous, and continuous/discrete time systems. The
integration method provided by GASP-IV is a fourth order, variable step
size Runge-Kutta routine for integrating systems of first-order, ordinary
differential equations with initial values.
Equations 22 and 25 can be coded directly into GASP-IV as:
DD(1) = QIN- QOUT
DD(2) = (QIN*CIN- QOUT*SS(2) - SS(2)*DD(1))/SS(1 )
- (KP*M/(1 + KP*M))*SS(2)*KS
- (1/(1 + KP*M))*SS(2)*K
where bSil) = V ; DD(1) E dV/dt
SS(2) ; CT , DD(2) E dCT/dt
These equations are coded in subroutine STATE of GASP-IV. The initial
conditions and the input values are set in subroutine INTLC. The values
QIN, QOUT and CIN are obtained from a user-written FORTRAN function which
looks up the values from a table with independent variable TNOW(=simu1ation
time). Simulation for several pesticides can be performed simultaneously.
(Their concentrations will be represented by SS(3), SS(4), etc.) Outputs
are obtained in the form of a table and/or plot (with user-specified scale).
As an example of model validation, consider the herbicides atrazine
and alachlor in an Iowa reservoir, Lake Rathbun. In order to assess the
herbicide dynamics, a three step effort was undertaken including 1) bio-
transformation tests 1n natural waters, 2) microcosm testing, and 3) field
data collection.
69
-------
MATERIALS AND METHODS
Blotransformatlon Tests
Biodegradation tests were performed In unfiltered Iowa River water
with spiked additions of alachlor to 100-300 yg/2, under the following
conditions: with and without bacterial seed from the Iowa River, with and
without bacterial seed from treated soil, with and without nutrient addi-
tions, and during and after rainfall runoff events in the Iowa River,
Controls were used to determine the extent of gas stripping, chemical
hydrolysis, and sorption to glassware. Samples were aerated in the dark at
*23'C,
A study of the microbial community was also performed during the
degradation tests. Samples were spread-plated, incubated at 20*C for 7
days, counted at lOx magnification using a Quebec counter, and colony
morphologies were recorded. Colonies were transferred to nutrient broth
for 3 days at 20*C, observed and gram-strained and all morphologies were
noted. In addition samples were streaked on BBL-brand MFC, M-Enterococcus,
and M-Endo agars to test for fecal coliforms, fecal streptococci, and total
coliform.
Mlcroc&sm Test
Fish used in this study were sunfish (Lepomis sp. ) collected by
shocking from the Coralville Reservoir on the Iowa River in early November,
1979. Algal growth was mostly Micr?sp?ra tp. indigenous to the Iowa River.
The alga became a test parameter by default after it choked out the
macrophyte potamegetcm orginally intended for use. Clams used w»re
Amblema sp. collected by hand from a bed south of Fort Madison, Iowa, in
Pool 19, Mississippi River during November, 1979, Sediment was collected
from the same clam bed and used to cover the bottoms of the aquaria to a
depth of about 4 inches. Water for the study was withdrawn from Iowa River
influent to the University of Iowa Water Plant. Water quality varied
greatly over the course of the two experimental phases. All equipment and
organisms were arranged 1n a manner to simulate productive areas of the
Mississippi River. Thirty fish and thirty clams were placed in each
aquarium. Organisms were allowed to acclimate to the aquaria for a period
of 2 weeks prior to beginning the herbicide experiment. Fish were fed a
combination of live mealworms and commercial pellets as a supplement to
natural food organisms present in the microcosm.
For the microcosm studies, Iowa River water was drawn into a 100
gallon cylindrical Nalgene tank and contaminants mixed in a batch process.
Delivery to the aquaria was accomplished by means of a Masterflex
peristaltic pump at 132 ml/min (^ 50 gal/day). Aquaria were 55 gallon all-
glass rectangular tanks (18"x48"x12") fitted with glass tubing at one end
1.5 inches from the top to allow positive overflow into a drainage system.
Total water volume of the prepared aquaria was approximately 5 cubic feet
or 37-40 gallons (140-150 liters) with a depth of 16 inches. Stable water
temperature was maintained by two 200 watt aquarium heaters per tank.
During the colder months, a temperature gradient resulting from the flow of
70
-------
cold influent into the warmer tank water was evident. Mixing was
accomplished by placement of an airstone near the center of the tank, The
photoperiod was automatically controlled with a 16 hour light and 8 hour
dark cycle. Illumination was provided by two 40-watt fluorescent bulbs per
aquaria which were suspended 5 cm (2-1/4") above the water surface.
Alachlor was added as Lasso herbicide, the emulsifiable liquid, diluted in
a carrier of acetone. Inflow concentrations averaged 8--10vg/l but varied
due to mixing conditions in the feed tank.
Field Tests
Samples were prepared, collected, and preserved according to Standard
Methods for the Examination of Water and Wastewater, 14th edition and the
Criteria for the National Pollutant Discharge Elimination System (NPDES).
Grab samples were collected from four locations representing the two
principal inflows from the Chariton River and South Chan ton River, a
depth-composite sample from Lake Rathbun, and a downstream sample. Weekly
samples of water, fish, and sediment were analyzed for the herbicides
alachlor, atrazine, and the insecticide dieldrin. The sampling period con-
sisted of a series of runoff events from May-July, 1978. Flow was from
guage records Of the U.S. Geological Survey, and samples were analyzed by
the University of Iowa Hygienic Laboratory,
RESULTS AND DISCUSSION
Biotransformation Test
Loss of alachlor by gas stripping proved to be a relatively small but
detectable loss mechanism. The degradation rate was surprisingly indepen-
dent of the source of inoculum, substrate pr nutrient addition, and water
quality. Zero order and first order kinetics fit the laboratory data well
with -ate constants of 6.4 yg/£-day and 0.055/day, respectively. Second
order ki.u'KS did not hold very well since 100 fold increases in viable
cells (as measured by the spread-plate technique in petri plates filled
with BBL-brand nutrient agar) did not result in increased degradation
rates Paris et al. [2,3,4] have noted second order kinetics for biode-
gradation, first order in total cell count and first order in pesticide
concentration.
Results are shown in Tab. III-l from all of the biotransformation studies.
The first order rate constants are in good agreement with those calcualted
from Beestman and Deming [5] and those of Rao [6] for alachlor degradation
in soil. An average half life of 12.6 days can be computed. The second
order rate constant is quite variable, and is an order of magnitude larger
than that determined in laboratory protocol measurements by Baughman and
Paris at the USEPA Environmental Research Laboratory, Athens, Georgia
(personal communication, 1980). However the procedures differ considerably
and the USEPA measurement was a much shorter duration test (3-5 days).
A typical degradation curve (a semilog plot of concentration versus
time) is shown in Fig. III-l. Usually a lag time of approximately 5-8 days
was evident in which the degradation rate was low, followed by a more rapid
71
-------
and linear phase. Zero order kinetics also fit the experimental data quite
well as reported by Fitter [7].
Results of the microbial community study indicated that spiked
additions of alachlor, dextrose, or nutrients did promote significant
changes 1n community structure, and that succession caused large changes
in the taxa of controls during the course of the degradation experiments.
This technique also demonstrated a source of bacterial contamination coming
through the filtered air supply [8].
Microcosm Test
Results from the microcosm experiment are shown in Fig. III-2 and 3 [9].
Alachlor and atrazine were not detected in fish, sediments, clams, or algae
probably due to their low octanol/water partitioning and rapid metabolic
transformations. After 30 days, the feed solution was changed to settled
river water, and washout of herbicides was observed from day 30-60. The
percent of alachlor removed during the experiment was approximately 35%
(Fig. III-2). At near steady state (days 15-30) the removal was also <35%.
Therefore 1t is possible to estimate the degradation rate for a system with
a 0.8 day dententlon time to be 0.67/day. This is much greater than the
estimated 0.055/day from the biotransformation study. This points up the
need for similarity 1n laboratory microcosms. Microcosms should have the
same ratios of biomass-to-volume and production-to-biomass ratios in order
to mimic the ecosystem. In this case, the large mass of algae which
developed and the large biomass was probably responsible for the rapid loss
of herbicides due to sorption and biotransformations. Algae are known to
biodegrade many organics and herbicides. Atrazine was ^40% removed in the
microcosm study, also indicating sorption and possible biotransformation
(Fig, III-3).
Field Tests
Modeling efforts on Rathbun Reservoir in Iowa for the herbicides
alachlor and atrazine are much different than for hydrophobic pollutants.
Being quite soluble, these herbicides are shown to undergo negligible
sedimentation but to biologically degrade and to be transported out of the
Reservoirs via the outflow. Figs. III-4-7arethe results for alachlor in
Rathbun Reservoir. Time-variable loadings and flow were required to
accurately model in-situ and outflow concentration data. The rate
of degradation in Fig, III-7 was significant, and the pseudo-first
order biodegradaton rate constant was 0,04 per day, in relative agreement
with laboratory biotransformation measurements. Equations 22 and 25 were
solved simultaneously to olot the results shown in Fig. III-7. At least for
the case of alachlor in Rathbun Reservoir, the use of a laboratory-derived
biotransformation rate would have been appropriate for modeling purposes.
The results for atrazine in Lake Rathbun are less satisfying.
Literature values for atrazine degradation in soils and waters -ange from
0.01-0.05/day. Using the lowest of these literature values (0.01/day) and
the measured input concentrations of Fig. III-8, the calculated concentration
is less than the 0.5-1.2 ug/i measured Lake concentration (Fig. III-9). The
72
-------
model results of Fig, III-9 use double the measured inputs in order to fit
the field data. The reason that model results do not reflect the field data
cannot immediately be attributed to channeling or incomplete mixing because
the alachlor mass balance worked nicely (Fig, III-7). Possible other reasons
include: 1) an unmeasured source of atrazine such as overland runoff or
bed sediment, and 2) analytical chemistry problems at these very low con-
centrations, near the detection limit of 0.2
For comparison a final simulation was performed for dieldrin in Lake
Rathbun, Measured inflow concentrations used as model input (Fig, 111-10)
and results are plotted in Fig, III-ll, Once again model results are lower
than measured values. However these concentrations range from 0.4-1.0 ng/j,
and are extremely low.
An observation is that one should not search for the biological effects
of pesticides in large rivers and reservoirs. It is the headwaters and
small tributaries which have 50-100 fold higher exposure concentrations.
It is also the tributaries which often have the most habitat diversity
and hence species diversity and which provide spawning areas for fish.
Nevertheless, the pesticide concentrations found in Iowa during the tenure
of this research are well below all acute toxic thresholds and LD5Q values.
This field validation demonstrates the use of laboratory measurements
in a modeling framework. A further improvement would be to track the
daughter products of herbicide biodegradation as well as the parent com-
pound. It is worth noting that approximately 75-90% of the transport
occurs over less than 20% of the time. Due to the transient nature of
runoff events, it is necessary to perform time variable simulations in
order to accurately represent the mass fluxes as well as concentrations.
Annual average or steady state representations are not accurate in these
cases.
SUMMARY
Laboratory biotransformation and microcosm studies were helpful in
predicting the dynamics of alachlor and atrazine in Lake Rathbun. Model
predictions were within a factor of five for atrazine, a factor of two for
dieldrin, and a factor of one for alachlor. For most purposes, these would
be acceptable simulations and the model should be considered as field-
validated. In these simulations, there was no adjustment of the rate
constants made to fit the field data-- there was no calibration or model
tuning. Laboratory and literature rate constants were used directly in a
comparison with field observations.
73
-------
REFERENCES
1. Kennedy, J.O. Lake Rathbun 1978 Pesticide Study. Final Report to
the U.S. Army Corps of Engineers, Kansas City District, Contract No.
DACW-41-78-M-1097, October, 1978, 44 pp.
2. Paris, O.F., Lewis, A.L., and Wolfe, N.L. Rates of Degradation of
Malathion by Bacteria Isolated from Aquatic System, Env. Sci. and
Techno!., Vol. 9, No. 2, 1975, pp. 135-138.
3. Paris, D.F., Lewis, D.O-, Barnett, J.T., and Baughman, G.L,
Microbial Degradation and Accumulation of Pesticides 1n Aquatic
Systems. USEPA- EPA-660/3-75-007, 1975.
4. Paris, D.F., Steen, W.C., and Baughman, G.L. Prediction of Microbial
Transformation of Pesticides in Natural Waters. An unpublished paper
presented before the Division of Pesticide Chemistry, ACS, Anaheim, CA,
1978.
5. Beestman, G.B. and Deming, J.M. Dissipation of Acetanilide Herbicides
from Soils. Agronony Journal, Vol. 66, No. 2, 1974, pp. 308-311.
6. Rao, P.S-C. and Davidson, J.M. Adsorption and Movement of Selected
Pesticides at High Concentrations in Soi1s, Water Research, Vol. 13,
1979, pp. 375-380.
7. Pitter, P. Determination of Biological Degradability of Organic
Substances. Water Research, Vol. 10, 1976, pp. 231-235.
8, Cartwright, K.J. Microbial Degradation of Alachlor Using River Die-
Away Studies. M.S. thesis, University of Iowa, Iowa City, Iowa,
December, 1980, 126 pp.
9. Noll, R.M. Pesticides and Heavy Metals: Fate and Effects in a
Laboratory Microcosm. M.S. thesis, University of Iowa, Iowa City,
Iowa, December, 1980, 112 pp.
74
-------
Table III-l
KlritlTC BIODEGRADATION RATE CONSTANTS
Sample
After R»in, no nutrient*
After R*l», dextreae
After R»inf deatro**, N&P
Before Ral», dextrose
Alochlor
Initial
Cone, (pg/1)
226,4
226.4
226.4
218.7
Before Rol», dextreee, N&P 218.7
River W«ter
R**«rvolr W»t»r
Sediment /Re*. W»t»r
Activated Sludge /R.W.
Soil 2/Rlver W«ter
Soil I/River Water
1/8 Inoculum/Rlv, Water
3/4 Inoculum/Rlv, Water
Mean
Coefficient of Variation
Geometric Mean
244.5
213.5
247.1
277.8
264.4
144.6
128,7
114.2
Zero Order
k"
(xg/l-day) r
8,10 -0.99
7.83 -0.98
7,91 -0,99
6,20 -0,97
6.12 -0.99
6,88 -0.99
3.68 -0.98
8.01 -0.98
7.20 -0,97
7.94 -0.99
4.20 -0.99
5.26 -0-99
3-98 -0.97
6.41 - 1.65
(X100) 25 7
6.11
lat
k'
(day"1)
0.071
0.062
0.048
0.023
0.044
0.030
0.021
0.074
0,046
0,060
0.062
0.083
0.099
0,055
41
Order
r
-0.99
-0.97
-0.99
-0.96
-0.99
-0,97
-0,97
-0.99
-0,98
-0.98
-0.98
-0.99
-0.99
t 0.023
.8
n.nsi
2nd Order
Rat* (XHT10)
(l/org.-day)8
5-3
15.4
2-5
3-5
67
64
20.7
8.1
5.7
11-3
0.1
11.3
3-5
Microbe
Cone. (X10*)
(org../l)b
135 t 104
40 - 39
195 * 121
69 * 70
66 * 39
45 * 40
10 * 11
91 * 49
79 * 41
53 * 32
5000 *1908
73 * 68
280 * 176
7 7± 4,4
56.4
5-07
Concentration lo «ver*ged for line IT nortlrm of degradation over the time period for vhlcb l*t order
calculation* were made
-------
-J
en
fll*chl»r Out, (ppb)
fllachlor Degradation Rates
flctivatvd Sludg* S»»d Riv»r Uat»r
112
1E1
6
T-32'
T-24'C
r
k'-0,020 X 2,303 - 0,046/d«y
r 0,98
I
20
(Days)
38
Figure III-l. Alachlor Degradation in Iowa River Water with an Activated Sludae Innoculum
-------
WATER, LASSO CONCENTRATIONS
TIME, days
Legend
A INKLUKNT
x EKI-'LUIiNT
vure or-* Hl-2. Alachlor Microcosm»tioc;ntratipns In
Water Samples
-------
WATER, ATRAZINE CONCENTRATIONS
00
Legend
x EFFLUXNT
TIME, days
Yne «ic*trazine >rrrp~C(X'tlons"'i" Filti»ri»d ^Vjltered Water Samples
-------
INFLOW HYDROGRAPH
STATION 11: CHARITON RIVER
1000
800
V)
u.
o
600
U.
400
200
0
t
MAY 18,
1978
r
10 20 30 40 SO 60
"TIME, DAYS
70
Figure III-4. Model Inflow to Lake Rathbun
79
-------
INFLOW HYDROGRAPH
STATION 12: SOUTH CHARITON RIVER
£2
o
1000
800
600
400
200
(
t
MA
IS
-
-
-
-
-
, I- -
3 10 20 30
)78
J
I
I^BMH^^MH
1
40 50 60 TO
DAYS
4
Fiaure III-5. Model Inflow to Lake Pathbun
80
-------
LASSO IN LAKE RATHBUN
INPUT CONCENTRATIONS
20
a:
i-
8
10
-O STATION 11, FIELD DATA
-O-- STATION 12, FIELD DATA
70
TIME, DAYS
Figure III-6. Measured Alachlor Inflow Concentrations and Model Input
81
-------
LASSO IN LAKE RATHBUN
04
o>
. 03
02
ui
o
UJ
0
t
MAY 18,
1978
LAKE CONCENTRATION (MODEL)
LAKE CONCENTRATION (OBSERVED)
SK -- .04 DAY"1
K» * .03 DAY"1
Kp ~- 260
M * 39 mg/t
10
20
30
40
»TIME, DAYS
50
60
70
80
Figure III-7, Model Results and Measured Alachlor Concentrations
in Lake Rathbun, 1978
82
-------
CD
a,
a.
O
15
ATRAZINE IN LAKE RATHBUN
INPUT CONCENTRATIONS
a STATION 1 1
--- STATION 12
9
i
MAY 18.
1978
20
30 40
»TIME. DAYS
50
60
70
Figure III-8. Measured Atrazine Concentrations and Model
Input
83
-------
CO
Q.
Q.
a:
z
tu
o
o
o
06
03
ATRAZINE IN LAKE RATHBUN
YODEL RESULTS (USING DOUBLE OF ACTUAL INPUTS)
» CONCENTRATION IN OUTFLOW
r CONCENTRATION IN LAKE
T
EK - 0 01 DAV1 *
0
MAY 18.
1978
30 40
TIME. DAYS
6O
Figure III-9. Model Results and Measured Atrazine Concentrations
84
-------
0, .
a.
g
DIELDRIN IN LAKE RATHBUN
INPUT CONCENTRATIONS
O STATION 11
O STATION 12
t
MAY 18.
1978
10
20
30 40
>TIME DAYS
50
60
70
Figure 111-10. Measured Dieldrin Concentrations and Model Input
85
-------
10
H-
Q.
0.
2
2 08
x.
t-
UJ
- K0
06
O
o
DIELDRIN IN LAKE RATHBUN
SK' 6*10-5 DAY'1
K, * 03 DAY'1
7000
39 mg
MODEL RESULTS
(USING ACTUAL INPUT)
MODEL RESULTS ( USING
DOUBLE OF ACTUAL INPUT)
9 CONCENTRATION IN OUTFLOW
T CONCENTRATION IN LAKE
0
t
MAY 18,
1978
10
20
30 40
»TIME. DAYS
50
60
70
Figure 111-11. Model Results and Measured Dieldrin Concentrations
86
-------
CHAPTER IV
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
The model TOXIC (and versions thereof) has been calibrated with Iowa
reservoir data for the insecticide dleldrln and the herbicides alachlor and
atrazine. Steady state analyses and quasi-dynamic simulations with time-
variable flows and loadings have been accomplished. Model coefficients
were derived from laboratory data and 1n some cases literature data.
For the herbicide alachlor, laboratory protocol measurements were used
directly in mode! simulations with excellent agreement between model pre-
dictions and measured concentrations. Thus the model may be considered
field-validated. Laboratory measurements were used 1n simulations of
alachlor, atrazine and dieldrin, and results were generally within an order-
of-magnitude of field data.
Of course the toxic substance model described in this report can still
be improved. It can certainly be made more mechanistic and complicated,
but the lawof diminishing returns will apply in further endeavors. It is
romewhaf difficult to speak of a validated model code--we gain confidence in
the model structure and code with each new and successful application. A
new calibration and verification is necessary for each new chemical which
tests each v j feature of the model TOXIC. In this research the model has
been successfully tested for three toxic chemicals under a variety of con-
ditions and locations.
One of the most important aspects of the dleldrln simulations in
Chapters II and III was the choice of time scale and space scale. Especially
in quasi-dynamic applications, where one variable varies in time and space
while another 1s held constant, averaging problems arise. To simulate both
the exposure concentration and mass fluxes accurately, one must use a fully
dynamic, spatially variable model 1n which flow, suspended solids, toxicant
concentration, pH, and other state variables are functions of time and
space. Fortunately most applications do not require such accuracy, and we
may settle for steady state or quasi-dynamicmodels such as TOXIC. TOXIC
was utilized in a management decision by the Iowa Conservation Commission to
11ft the ban on commerical fishing in Coralville Reservoir in 1979.
Sediment has been a small net source of dieldrin to the water column of
Coralville Reservoir, especially important under low flow conditions.
Coralvilie Reservoir contains approximately 100 Ibs of dieldrin in the
sediment, It will take 6-10 more years to achieve less than detectable
87
-------
(<0,002 yg/i) dieldrin in the water column, primarily due to continued in-
flow as well as sediment desorption.
Lastly, field bioconcentration factors when normalized on a lipid basis
were approximately equal to laboratory - derived bioconcentration factors
similarly weighted. The bioconcentration factors were also proportional to
the octanol water partition coefficient. Bioconcentration is the primary
mode of pesticide uptake in Coralville Reservoir and food items are
generally of less importance.
RECOMMENDATIONS
The following recommendations are offered
1) Reactions in the sediment phase, especially biotransformations and
sediment catalyzed reactions, are critical to model results. This
is an area of future research needs.
2) Most hydrophoblc toxics are Intimately connected with the solids
balance. More research is needed in the area of sediment transport,
especially of cohesive sediments.
3) Chronic biological effects should be examined in headwater streams
where nonpoint source runoff concentrations are large and diversity
is high. Modeling will require a fully dynamic approach.
The key to modeling toxic chemicals is not unlike that for the con-
ventional pollutants; one needs a tight water budget, a knowledge of
mixing characteristics, a good solids balance, and accurate reaction
kinetics.
88
-------
APPENDIX
TOXIC Documentation
Program Codes
Sample Input
Sample Outputs
89
-------
TOXIC, A multicompartment pesticide simulation model
TOXIC is a 30 compartment pesticide simulation model. It is mainly
intended for simulating one pesticide in a reservoir. TOXIC considers the
aquatic system being simulated as being divided into a number of compart-
ments, not exceeding 30. Each compartment is considered to be a completely
mixed system. The concentration of pesticide through time 1s described by
a set of ordinary differential equations, one for each compartment. The
equations are integrated by the Runge-Kutta fourth order method to result
in a simulation.
The inputs to the model can be classified under the following
categories:
1. Geometric properties, such as volumes of compartments, distances
between them, surface areas, and locations with respect to other
compartments
2. Flows between compartments, and between each compartment and the
outside of the system
3. Reaction rates, settling rate constants, and partition coeffi-
cients
4. Solids concentrations in each compartment
5. Bulk dispersion coefficients between compartments
6. Simulation parameters such as step size and time of simulation.
The solids concentration in each compartment is obtained from another
model called SOLIDS. SOLIDS is similar to TOXIC in every way except that
only solids are simulated. The input for the solids model are 1,2,5 and 6
above.
The subroutines involved in TOXIC are:
1. The main program.
The main program reads in all the input data. All reaction rates and
flows are initialized to zero. The input is in free format, therefore
only the non zero elements need to specified. The main program also
checks for a flow balance on each compartment within an error of 5%,
If the flows do not balance, it calls subroutine ERROR which prints out
an error message. If errors are found in the input data, simulation is
not attempted. If no errors are found, the main program calls sub-
routine SOLVE which performs the integration. The main program also
echo checks the reaction rates and prints the fractions in the dis-
solved and suspended phases for each compartment. Other input data is
echo checked on request.
2. Subroutine ERROR.
This subroutine prints out error messages and keeps a count on flow
balance errors.
3. Subroutine PRINT.
This subroutine echo checks the input data on request.
90
-------
4. Subroutine SOLVE.
This is the executive routine which performs the integration.
5. Subroutine DIFFEQ.
This subroutine sets up the differential equations for each compart-
ment. It is called by SOLVE several times during each integration step.
6. Subroutine INPUTS.
This subroutine is called by subroutine SOLVE at the beginning of each
integration step. INPUTS has to be provided by the user, and contains
any exogenous inputs to the sytem such as concentrations in inflows.
COMMON BLOCKS
The common blocks used 1n TOXIC are:
COMMON /GEN/ NUM,H,OUTDEL,TFIN,TIM,NERR,NOUT,INTER
COMMON /FLOWS/ V(30),QIN(30),QOUT(30),Q(30,30),IPOS(30,30),
1ED(30,30),EP(30,30),SA(30,30),AL(30,30),DISP(30,30),DISD(30,30)
COMMON /SOLIDS/ AM(30),AMIN(30),DAM(30),AKS(30),PC(30)
COMMON /CONC/ C(30),CIN(30),DC(30),AF1(30),AF2(30),F(30,10)
COMMON /REACT/ AQY(30),AKI(30),AKP(30),OXK(30),R02(30),AKO(30),
1BKB(30),AKA(30),AKN(30),PH(30),AKH(30),AKV(30),GRM(30),YLK(30),
2HKS(30),XMB(30),AKB(30)
COMMON /REAC2/ OXKP(30)fR02P(30),AKOP(30),BKBP(30),AKNP(30),
1PHP(30),AKPH(30),GRMP(30),YLKP(30),HKSP(30),XMBP(30),AKBP(30)
Each of the variables in these common blocks will be explained now.
91
-------
COMMON /GEN/ NUM,H,OUTDEL,TFIN,TIMNERR,NOUT,INTER
Variable Description Provided by:
NUM Number of compartments in system. User
H Step size for integration, days. User
OUTDEL Time between storage of output, User
days.
TFIN Time of simulation, days. User
TIM Current simulation time, days. Computed
NERR Number of errors in input days. Computed
NOUT Number of output points. Computed
INTER Indicator for printing fluxes User
between compartments.
= 1 ; print fluxes
= 2 : do not print fluxes
This common block is used to specify general simulation parameters.
92
-------
COMMON /FLOWS/ V(30),QIN(30),QOUT(30),Q(30),IPOS(30,30),
1ED(30,30),EP(30,30),SA(30,30),AL(30,30),DISD(30,30),DISP(30,30)
Variable
QIN(I)
QOUT(I)
Q(I,J)
IPOS(I,J)
£0(1, J)
EP(I,J)
SA(I,J)
AL(I,J)
DISD(I,J)
DISP(I,J)
Description
Volume of compartment I,
Inflow to compartment I
from outside the system,
Outflow from compartment I
to outside the system,
Flow from compartment I
to compartment J,
Mm"
MmVday
Mm3/day
MnT/day
=1; compartment I 1s above comp. J
=0; compartment I not above comp. J
Dispersion coefficient for dissolved
phase between compartment I and-
compartment J, m /day
Dispersion coefficient for partlculate
phase between compartment I ~
and compartment J, m /day
Surface area between compartmentjl
and compartment J, km
Mean distance between compartment I
and compartment J, m
ED(I,J)*SA(I,J)/AL(I,J)
EP(I,J)*SA(I,J)/AL(I,J)
Provided by:
User
User
User
User
User
User
User
User
User
Computed
Computed
*Mm3 - 106 m3
93
-------
COMMON /SOLIDS/ AM(30),AMIN(30),DAM(30),AKS(30),PC(30)
Variable Description Provided by:
AM(I) Mass of solids in compartment I kg/1 User
AMIN(I)* Mass of solids in inflow for User
compartment I kg/1
DAM(I)* Derivative of AM(I) kg/1-day Computed
AKS(I) Settling rate constant User
for compartment I I/day
PC(I) Partition coefficient for User
compartment I i/kg
*Used only for the solids balance model.
94
-------
COMMON /CONC/ C(30),CIN(30),DC(30),AF1 (30),AF2(30),F(30,10)
Variable
C(D
CIN(I)
DC(I)
API(I)
AF2(I}
Description
Concentration of pesticide in
compartment I, ug/1
Concentration of pesticide
in inflow to compartment I, vg/1
Derivative of C(I) ug/l/day
Fraction of pesticide 1n dissolved
phase in compartment I
Fraction of pesticide in participate
phase in compartment I
Net reaction rate of type J in
compartment I I/day
J Type
1 Photolysis in dissolved phase
2 Oxidation in dissolved phase
3 Hydrolysis in dissolved phase
4 Volatilization in dissolved phase
5 Biolysis in dissolved phase
6 Settling in participate phase
7 Oxidation in participate phase
8 Hydrolysis in participate phase
9 Biolysis in participate phase
Provided by:
Computed
(Initial
values are
user pro-
vided)
User
(through sub-
routine
INPUTS)
Computed
Computed
Computed
Computed
95
-------
COMMON /REACT/ AQY(30),AKI(30),AKP(30),OXK(30),R02(30),AKO(30),
1BKB(30),AKA(30),AKN(30),PH(30),AKH(30),AKV(30),GRM(30),YLD(30),
2HKS(30),XMB(30),AKB(30)
Variable
AQY(I)
AKI(I)
AKP(I)
OXK(I)
R02(I)
AKO(I)
BKB(I)
AKA(I)
AKN(I)
PH(I)
AKH(I)
AKV(I)
GRM(I)
YLD(I)
Description Provided by:
Quantum yield for photolysis in User
dissolved phase
Suirenation of the molar extinction User
coefficient times the light intensity, I/day
Photolysis rate constant for the Computed
dissolved phase = AQY(I)*AKI(I) I/day
Oxidation rate constant for the User
dissolved phase 1/mole-day
Concentration of R02 in the User
dissolved phase mole/1
Oxidation rate constant in the Computed
dissolved phase = OXK(I)*R02(I)
Base catalyzed hydrolysis rate constant User
in dissolved phase I/mole-day
Acid catalysed hydrolysis rate constant User
in dissolved phase I/mole-day
Neutral hydrolysis rate constant User
in dissolved phase I/day
Hydrogen ion concentration in User
dissolved phase (cannot equal zero) mole/1
Hydrolysis rate constant in dissolved
phase
= BKB(I)*10**(-14)/PH(I) + AKA(I)*PH(I) + AKN(I)
Volatilization rate constant 1n
dissolved phase I/day
Maximum growth of the bugs in the
dissolved phase I/day
Cell yield in the dissolved phase
(cannot equal zero) ug/yg
Computed
User
User
User
(continued on next page)
96
-------
Variable Description Provided by;
HKS(I) Half saturation concentration in the User
dissolved phase yg/1
XMB(I) Concentration of blomass ug/1 User
AKB(I) Biolysis rate constant in the Computed
dissolved phase I/day
= GRM(I)*XMB(I)/YLD(I)/HKS{I)
Note: I refers to compartment number
97
-------
COMMON /REAC2/ OXKP(30),R02P(30),AKOP(30),BKBP(30),AKAP(30),AKNP(30),
1PHP(30),AKHP(30),GRMP(30),YLDP(30),HKSP(30),XMBP(30),AKBP(30)
Variable
OXKP(I)
R02P(I)
AKOP(I)
BKBP(I)
AKAP(I)
AKNP(I)
PHP(I)
AKHP(I)
GRMP(I)
YLDP(I)
HKSP(I)
XMBP(I)
AKBP(I)
Description
Oxidation rate in the particulate
phase 1 /mole- day
Concentration of R02 in the
particulate phase mole/1
Oxidation rate constant in
particulate phase I/day
= OXKP(I)*R02P(I)
Base catalyzed hydrolysis rate
constant in particulate phase I/mole-day
Acid catalyzed hydrolysis rate
constant in particulate phase I/mole-day
Neutral hydrolysis rate constant
in particulate phase I/day
Hydrogen ion concentration in
particulate phase (cannot equal zero) mole/1
Hydrolysis rate constant in
particulate phase
= BKBP(I)*10**(-14)/PHP(I) + AKAP(n*PHP(I)
+ AKNP(I)
Maximum growth rate of bugs in
particulate phase I/day
Cell yield in particulate phase
(cannot equal zero) g/g
Half saturation concentration in
particulate phase (cannot equal zero) g/1
Concentration of biomass in
particulate phase g/1
Biolysis rate constant in
particulate phase I/day
Provided by
User
User
Computed
User
User
User
User
Computed
User
User
User
User
Computed
GRMP(I)*XMBP(I)/YLDP(I)/HKSP(I)
Note: I refers to compartment number
98
-------
INSTRUCTIONS FOR RUNNING 'TOXIC'
TOXIC is a 30-compartment model for simulating toxic organics in
reservoirs. The data for TOXIC is contained in the file DATA. The file
DATA is assigned logical unit #5 in TOXIC (corresponding to PRIME file
unit #1).
In order to run TOXIC, the file DATA must first be opened. This is
done by the command:
0 DATA 1 1
To run TOXIC, type the command:
SEG fTOXIC
The output from TOXIC will appear on the terminal. If you wish to
store the output 1n a file, use the COMO command thus:
COMO filename
COMO -E
Whate1 c" appears on the terminal between the commands COMO filename
and COMO -E will be stored in the file specified by filename. This file
can then be spooled or edited as desired.
The following example illustrates the procedure just described. The
output from TOXIC is stored in the file OUTPUT. (OK is the system prompt).
OK,0 DATA 1 1
OK,COMO OUTPUT
OK,SEG #TOXIC
Output from TOXIC appears on the terminal
99
-------
OK,COMO -E
The file OUTPUT now contains the output from TOXIC. When not required
any more, it should be deleted.
100
-------
PROGRAM CODES
TOXIC
TOXIC - 30 compartment version
TWOCOMP
TWOCOMP1
101
-------
NULL,
C
C
COMMON NUMrHfOim'ETLrTFIN'NERRrNOUT
COMMON QIN(20)rQOUr(20) , Q ( 20 < 2Q ) , IPOS ( 20 , 20 ) , DIS ( 20 , 20 )
COMMON C(20)rDC(20)rF(20riO)rCIN(20)»V(20)rAFK20)rAF2<20)
COMMON AM(20)
DIMENSION
DIMENSION
DIMENSION
DIMENSION
DIMENSION
DIMENSION
DIMENSION
DIMENSION
DIMENSION
o
PO
C
C
C
C*****INITIALIZE VARIABLES
C
PO 100 I=lr20
QIN(I)=0.0
GOUT'SA<20>20>rAL(20r20)rHMD(20)
AQY(20)'AKIi20) rAKF (20)
OXk(20)rR02(20) r AKO ( 20 ) r BKB ( 20 )
AKA(20) rAKN(20) > f H( 20) f AKH( 20 )
AKV(20) »AKS(20)
GRM(20) r YLD ( 20) r HKS( 20) r XMB( 20) f AkB(20)
OXKF(20>»RO:F ( 20 ) - AKOF ( 20) > BKBF ( 20 )
PHP ( 20 ) , AKHF ( 20 )
HKSF (20) < XMBF ( 20 ) * AKBF ( 20 )
AKAF(20) -AKNF(20)
GRMF (20) 'YLI'F(20)
DO 110 J=lr20
IFOS(IrJ)=0
Q ( I f J ) - 0 . 0
E ( I r J ) = 0 . 0
SA(I- J)=0,0
0
100 CONTINUE
C*****S£T ALL REACTION RATES TO ZERO
C
DO 120 1=1
AQY(I)=0.0
AM (I>=0,
OXI\(I)-0,
ro2(i)-o,
HKB(I)=0
AhA(1)^0,
AKN(I)=0.
.0
.0
.0
.0
.0
.0
Table 1, TOXIC code in Extended Fortran for PRIME 750 computer
-------
I Hi 1)
AKy=0
GF«M(I>=0
XMB = 0.0
CKBF = 1.0
HKSP(I)=1.0
120 CONTINUE
LOGICAL UNIT NUMBERS FOR INPUT AND OUTPUT
o
CO
C:*****SET
ic
INFUT=5
NOUT=1
|vEAr(INFUT-200) CHEM
200 FORMAT(AIO)
URITE(NOUTr210) CHEM
210 FORMAT(///lXr'INPUT DATA FOR'rlXrAlO)
REAP (INPUT'*) NUM'H'nUTI'ELrTFIN
URITE(NOUTf230) MUM ' H * OUTE'EL ' TFIN
230 FORMATdXr'NUMBER OF COMFARTMENTS= ' r I5/1X r
1 'STEP SIZE FOR CALCULATIONS^'rF10.2f
2 'TIME BETWEEN OUTPUT fOINTS='rF10.2,
3 'TIME FOR SIMULATION ='rF10.2r
DAYS'
DAYS'
DAYS'
PEAD
L'O ?70 1 = 1.NUM
270 WRITE
-------
E'O 281 I=1'NUM
281 KEADdNFUTr*) ( Q < I r J) - J= 1 - N'UM )
WRITE
285 FORMAT(/20Xf'INTERFLOW MATRIX FOR COMPARTMENTS')
DO 290 I=lrNUM
290 WRITE>
C
C*****CHECt\ FOR A FLOW BALANCE ON EACH COMPARTMENT
C
DO 301 I=lfNUM
FIN=QIN(I)
FOUT=QOUT(I)
DO 302 J=lfNUM
FIN=FIN+G(JfI)
302 FOUT=FOUT + Qdf J)
IF (FIN.NE.FOUT) CALL ERRORd'I)
301 CONTINUE
READdNFUTf*) NUM )
READdNFUTf*) (HMD (I) f I- IrNUM'
C
C
URITE(NOUTf350)
350 FORMAT(/23X'DISFERSIOH COEFFICIENTS')
WRITE(NOUI f 360) ( (Ed-J) f J=1-NUM) f 1=1 f NUM)
360 FOF.'MAl
-------
370 FORMAT(/2^X'INTERFACE AREAS')
URITE(NOUTr380) ((SAd-J)fJ=1'NUM>- 1 = 1fNUM)
300 FORMAT(/9F8.3)
WRITE(NOUT'3»0>
390 FORMAT(/20X'MEAN LENGTH BETWEEN COMPARTMENTS')
URITE(NOUT'400) ((AL(1>J),J-1,NUM),I=1rNUM)
400 FORMAT(/1X'9E9.3)
r
DO 402 I=lrNUM
DO 403 J=lfNUM
403 DISC If .)) = <£(!-J)*SA( I-.I) )/AL(I-J)
402 CONTINUE
C
C***#*READ IN REACTION RATES
C
F>E AD (INPUT'*)
C
READdNFUTr*) ( AKI (I ) » I = 1 r NUM )
C
DO 430 I=lfNUM
AKF(I)=AQY(I)*AKI(I)
430 CONTINUE
C
C***)K*AKP IS THE PHOTOLYSIS RATE CONSTANT (PER DAY)
C
I'D 440 I = lrNUM
440 F(Irl) = AFl(I>*AI\F(I>
C
C*****OXIDATION FOR THE DISSOLVED PHASE
C
READdNFUT-*) ( OXK (I) , 1 = 1 , NUM >
C
READdNFUTr*) (R02(I) -1 = 1. NUM)
C
I'D 470 I = 1'NUM
470 ANO(I)=OXK(I)*R02(I)
C
C*****AKO IS THE OXIDATION RATE CONSTANT (PER DAY)
C
DO 480 I=1'NUM
480 F(lr2) = AFld)*AKOd)
C
READdNPUT'*) (PKBd)-I = 1'NUM)
C
READ (INPUT-*) (AKAd) > I-If NUM)
C
RtLADdNPUTf *) (ANNd) -T-1'NUM)
KLAD(INPUTf*) fI- 1
-------
c
I'D 500 I=lrNUM
000 ANHm = BI\B*PH
C
C*****VOLATILIZATION FOR THE DISSOLVED PHASE
C
READUNPUTr*) (AKVU) r I = lrNUM>
C
I'O 520 I=lrNUM
520 F(If4)=AFl(I)*AKV(I)
C*****BIODEGRADATION FOR THE DISSOLVED PHASE
C
READdNFUTr*) < GRM (I) r 1 = 1 , NUM >
READ(INFUTf*) (YLD(I)r1=1,NUM)
C
READdNFUTr*) (HKP< I) ^ 1-1 ^NUM)
C
READUNFUTr*) ( XMB (I) r 1 = 1' NUM )
C
DO 540 I=lrNUM
540 AKB(I)=6RM(I)*XMB(I)/YLD(I)/HKS(I)
C
DO 550 I=lfNUM
550 F(If5)=AFl(I)*AKB(I)
C
C*****SETTLIN6 FOR THE PARTICIPATE PHASE
C
READdNFUTr*) ( AKS (I ) < 1 = 1 - NUM )
URITE*AKS9I4)
-------
c
r*****
c**********************************
C***** REACTION RATES FOR THE *****
C***** FARTICUL.MTE PHASE *****
C**********************************
r*****
C
C*****OXIDATION
C
REAPdNFUTr*) (OXKF(I) r I-lrNUM)
C
REAI'dNFUTr*)
C
DO 600 I=1,NUM
600 AKOFd)=OXKFd)*R02Fd)
C
DO 610 1=1, NUM
610 Fdr7>-AF2d)*AKOPd)
C
C#****HYPROLYSIS
C
REAPdNFUTr*) (BKBF (I) * 1=1, NUM)
C
REAPdNFUTr*) (AKAP(I)'I = lfNUM)
C
REAI'dNFUTr*) ( AKNP '. I ) - 1 = 1 r NUM)
READdNPUTr *) (FHFd) r 1=1 r NUM)
C
DO 620 I=lrNUM
620 AKHFd) = BKBF(I)*10**(-14)/FHP(I) + AKAF( I )*PHP( I ) 4 AKNFd)
I'D 630 1 = If NUM
630 Fd'8)=AF2d)*AKHFd>
C
C*****BIOPEGRADATION
C
RTADdNFUTr*) ( CF.MF ( I ) ' I - 1 NUM )
REAP (INPUT r *) (YLPK
-------
UKITE(NOUT'680)
680 FQRMAT(//llXf'FSEUDO-FIRST-ORDER RATE CONSTANTS (PER PAY)'/
121Xr'IN THE DISSOLVED PHASE')
URITE(NOUTf68S>
685 FORMAT(//3Xr'CMFN'r2X,'PHOTOLYSIS OXIDATION HYDROLYSIS BIOLYSI
IS VOLATILIZATION')
DO 690 I=lfNUM
690 WRITE(NOUTr700) I ' AI\F ( I ) , Al\0< I ) ' AKH (I ) ' AKB (I ) > AKV (I )
700 FORMATUXr I5r5F12. 7)
C
WRITE(NOUT'710>
710 FOF:MAT(//llXr ' PSEUI'O-FIRST-ORDER RATE CONSTANTS AKOP GO TO 800
C
URITE 20 ) r DIS ( 20 , 20 )
COMMON C(20) r DC (20) -F^O- 10 / -CIN<20) rV(20) r AFK20) ' AF2(20)
COMMON AM(20)
C
C
DIMENSION TOUT (50) r CL ' ,70 ) r A 1 ( 20 > r A2 ( 20 ) , A3 ( 20 ) r A4 ( 20 )
DIMENSION CT(50f 20) rfD(L-Or20> 'CF(DOf 20) rCM(50f 20)
DIMENSION Rl (20) fR2'- ,T) < F'3 ( ?'." ) f R4 ( 20 ) r R5 ( 20 )
DIMENSION EXT<20)'FNEr(20>
-------
C
URITEU'lll)
111 Ff)RMAT TO EACH COMPARTMENT')
WKITE(l-222)
222 FORMAT+DTHLF*A1(I>
CALL DIFFEO (Rl r R2 f R.7 - R4 ' Rf. - EXT r FNET )
DO 120 1=1
A2(I)=DC(I)
120 C(I)-CL(I)-fDTHLF*A2(I)
CALL DIFFER(RlrR2-R3-R4'R5'EXT-FNET)
DO 130 I=1'NUM
A3(I)=DC
-------
TOUT(IOUT)=TIM
CFdOUTr I)-C(I)
CFdOUTr I)=AF2(I)*C(I)
CM< TOUTr I) = CF(IOUTr I)/AMd)
160 CONTINUE
C
WRITE(lr401) TIM
401 FORMAT < ///30X , 'TIME=' -F8- 1//'CMFN' r 5X r ' Rl ' , 14X r ' R2 ' , 14X »
1 'R3' r 13Xr 'R4' r!3Xr 'R5 r!2Xr 'EXT' r!2Xr 'FNET'/)
DO 402 I=lrNUM
402 URITE(lr403) I ' Rl ( I ) - R2 (I ) » R3d ) r R4 (I ) r R5 ( I ) r EXT (I ) r FNET d )
403 FORMAT(I3r7(3XrE12,6) )
IF(TIM.GE.TFIN) CO TO 170
IOUT=IOUT-fl
TSAV=TSAV+OUTPEL
GO TO 50
C
170 URITEtNOUTr 180) TFIN
180 FORMAT(//17X SIMULATION HALTED AT TIME = 'rF7.D
C
C*****FRINT RESULTS
C
URITE
200 FORMAT(///10Xr 'TOTAL CONCENTRATION OF PESTICIDE OVER TIME (FFB)'
l//lXr TIME (DAYS)' rl&Xr 'COMPARTMENT NUMBER' )
URITE(NOUTr205) drI=lrNUM>
205 FORMAT(/'9X-9I7/)
DO 210 L=1»IOUT
210 URITE 3 S> >
DO 270 L=1'IOUT
270 WRITE(NOUT-220) TOUT < L ) - ( CF ( L r I ) > 1= 1 , NUM )
-------
UFITE'COMPARTMENT NUMBER >
DO 290 L=1'IOUT
290 URITE(NOUT-300> TOUTC >r(CM- I = 1'NUM>
300 FORMATUXrFS. lr9(lXr! 7.2»
C
RETURN
END
C
SUBROUTINE DIFFECMRl'R2rR3rR4rR5.EXTfFNET>
C
C
COMMON NUMrH.OUTDFL'TFINrNF.RRrNOUT
COMMON QINC20)rQOUT<20)-Q(20r20).IF OS<20r20)>DIS<20'20)
COMMON C(20) 'DC(20> 'F(20'10) rCIN(20) rV(20) rAFK20) f AF2(20)
COMMON AMC20)
C
DIMENSION Rl(20)rR2(20).R3(20)'R4(20)rR5<20)
DIMENSION EXT(20)rFNET(20)
C
C
L'0 100 I = lrNUM
C
C*****INITIALI2E NET FLUXES
C
R1(I)=0-0
R2 = 0 . 0
C
C*****MA5S FLUX DUE TO INFLOWS FROM OTHER COMPARTMENTS
I'D 110 J=lrNUM
110 Rl(I)=Rl(I)+Q(JfI)*C
-------
DO 130 K=lr9
130 R3(I)=R3-fF
C
C*****MASS FLUX DUE TO DISPERSION
C
DO 140 J=lrNUM
140 R4(I>=R4+DIS(I-J>*>
C*****MASS FLUX DUE TO SETTLING FROM ABOVE
C
DO 150 J=lrNUM
150 R5(I)=R5(I)+F
C
C*****SET UF DIFFERENTIAL EQUATION FOR EACH COMPARTMENT
C
EXT(I)=QIN(I)#CIN(I) - QOUT(I)#C(I)
FNET(I)=EXT(I) + Rl(I) - R2(I) - R3(I) + R4(I) +
DC(I)=FNET(I)/V(I)
C
100 CONTINUE
RETURN
END
BOTTOM
Q
-------
NULL-
C
C*****FROGRAM FOR PESTICIDE SIMULATION IN AQUATIC SYSTEMS
C*****30 COMPARTMENT MODEL
C#****AUTHQK ! NARASINGA B. RAO
C
COMMON /CEN/ NUM,HrOUTDEl - TIM,TFIN,NERR,NOUT- INTER
COMMON /FLOWS/ V ( 30 ) r QIN (^0 ) r QOIJT < 30 ) , Q ( 30 ,10 )
1 . IFOS(30r30) f ED(30r30)rE.F (30f 30) » SA(30f 30)f AL(30r30)
2rDISD(30f30)fDISF(30.30)
COMMON /SOLIDS/ AM<30)-AMIN(30),DAM(30),AKS(30)rPC(30)
COMMON /CONC/ C(30),CIN(30)- DC<30 ) ,AF1(30)rAF2(30)rF(30r10)
C
INTEGER** CHEMC3)
COMMON /RE AC I/ AQY<30) -AM (30) rAI\F(30) 'OXK(SO) f R02 ( 30 ) f AKO ( 30 ) ,
16KB(30)rAKA(30)fANN(30).FH(30)>AKH(30> rAKV(30)rGRM(30)rYLD(30)r
2HKS(30)rXMB(30)-AKB(30)
COMMON /REAC2/ OXKF(30),RO?F(30),AKOF(30),BKBP(30)>AKAF(30) r
1AKNF(30)rFHF(30)rAMHF<30>'GRMP(30)fYLDF(30)fHKSP(30)rXMBP(30),
2AKBF(30)
C
C*****INITIALIZE VARIABLES
C
DO 100 1-1.30
QIN(I)=0.0
QOUT(I)=0.0
CIN(I)=0.0
DO 110 J=lf30
IFOSd- J)=0
Q(IfJ)=0-0
£ D (I r J) = 0 . 0
l~f (I- J) = 0.0
SAdf J) = 0.0
Al(IrJ)=l,0
110 CONTINUE
100 CONTINUE
Cf<*)lt**SEr ALL REACTION RATES TO ZERO
C
DO 120 I=lr30
AQY(I)=0,0
AM(I) = 0.0
OXK(I)=0-0
f>'0?(I)-1.0
BKB(I>=0-0
ANA(I)^0.0
Table 2. 30 - compartment TOXIC code in Extended Fortran for PRIME 750 computer
-------
AKN(I)=0.0
FHd) = 1.0E-07
AKV(I)=0-0
ORhd>--0.0
YLDd)-l -0
HKS(I)=1 .0
R02Pd) = 1.0
EKBF d) = 0-0
AKAF d>=0.0
AKNF d> = 0-0
PHFU> = 1.0E-07
GRMF d)=0.0
XMBFd>=0.0
YLPPd) = l .0
HI\SPd) = l ,0
C
120 CONTINUE
C
NERR=0
C
L#>K!((*!KSET LOGICAL UNIT NUMBERS FOR INPUT AND OUTPUT
NOUT=1
READ(INFUTF200) CHEM
200 FORMAT<3A4)
URITE(NOUT'210) CHEM
210 FORHAT(///1X' ' INPUT DATA FOR'flX'3A4)
REAI'dNFUTr*) NUMfH'OUTI'ELrTFIN
WRITE(NOUTr230) NUM r H ' OUT PEL * TFTN
1'30 FORMATdX' 'NUMBER OF COHF ARTMENTS= ' ' I5/1X *
I 'STEP SIZE FOR CALCULATIONS= ' r F10 - 2 ' ' I'AYS'r/lX.
2 'TIME BETUELN OUTPUT F OINTS= ' , F10 , 2 r ' PAYS' »/lX.
3 'TIME FOR SIMULATION ='rF10.2r' DAYS'r/)
REAI'dNFUTr*)
-------
DO 302 J=lrNUM
FIN=FIN + C)< J' I)
302 FOUT-FOUT + Qdr J)
C
PIFF=ABS
TOL=0-05*FIN
C
IF CALL ER ORd-I)
301 CONTINUE
C
REAPdNFUTf*) ( C (I) r 1= 1 , NUM )
READdNFUT'*) ( AM ( I) , I = 1 ' NUM )
READdNFUT.*) < F C (I) , 1 = 1 , NUM )
DO 310 1=IfNUM
AF1(I) = 1.0/(1.0 + AMd)*FCd)>
310 AF2d) = 1.0 - AFld)
C
URITE(NOUT'320>
320 FORMAT(/'COMPARTMENT 5X, 'INITIAL' 1 OXr 'MASS OF' rlOXr PARTITION' »
17Xr FRACTION IN r5Xr FRACTION IN ,/12Xf'CONCENTRATION',4X'SUSFENDE
2D SOLIDS'r4Xr'COEFFICIENT MXr DISSOLVED PHASE'rIX< 'SUSPENDED FHAS
3E' )
C
DO 330 1=1.NUM
330 URITEAM(I ) -FC(I) 10X , F6 . 4 r 1 OX - F9 . 3 r 10X ' F6 . 0 r ?. (10X r F5 . 3 ) )
C
NUML=NUM-1
C
DO 341 I=1»NUML
11=1+1
341 REAPdNFUT-*) ( EF (I» J ) < J= 11 - NUM )
DO 342 I=lfNUML
11=1+1
342 REAP (INF UTf*) ( ED (I - J ) . J--11, NUM )
DO 343 I=lfNUML
II = H1
343 REAIKINFUT'*) ( SA (I r J ) < J^ 11 - NUM )
DO 344 I=lrNUML
11^11 1
REAPdNFUT'*) ( AL d » J ) r J= 11 ' NUM )
PO 345 I=1-NUML
11=1 H
PO 34J- J=IIrNUM
FP( J. I) = EDdr J)
If (J'I)=EP(I»J)
!JA( J' I)=SA(I«.J)
t>\.(.),l) - ALd-J)
-------
DO 402 I=1»NUM
I'D 403 J=1>NUM
DISPdr J) =
403 I'ISPdr J) = (EP(If J)*SA< Ir J) >/AL(IrJ)
402 CONTINUE
C
C*****REAP IN REACTION RATE5
C READCINFUTf*) (AQY (I) , 1 = 1. NUM )
C
REAP*R02(I)
C
C*****AKO IS THE OXIDATION RATE CONSTANT (FER DAY)
C
DO 480 I=lfNUM
480 F(Ir2)=AFKI)*AKO(I)
C
READ
-------
c
PO 520 1=1. NUM
520 Fd'4)=AFl(I)*AKV(I)
C
C*****BIOPEGRAPATION FOR THE PISSOLVEP PHASE
C
REAPdNFUT.*) (GRM(I)fI-J-NUM)
C
READ (INPUT,*) (YLDd). 1= IrNUM)
C
REAPdNFUTr*) (HKS(I). I IrNUM)
C
KEAPdNFUTf*) (XMBd) - I-lrNUM>
C
I'D 540 I = lrNUM
tJ40 AKB(I) = BRM(I)*XMB(I)/YLP(I)/HKS(I)
C
I'D 550 I = lrNUM
550 Fdr5) = AFl(I)*AKB(I)
C
C*****SETTLING FOR THE FARTICULATE PHASE
C
REAPdNFUTf *) J ) , J= 1 > NUM )
C
C
C*****
C********#***#*********************
C***** REACTION RATES FOR THE *****
C***** FARTICULATE PHASE *****
C*************tt*************#******
C*****
C
C*****OXIPATION
C
REAP (INPUT'*) (OXKFd) f 1 = 1 fNUM)
C
REAP(INPUTf*) (R02F(I),I=l»NUM)
C
PO 600 I-lrNUM
600 AI>OF(I)=OXKF (I)*R02F(I)
C
PO 610 I=lrNUM
610 Fd,7)-AF2(I)*ANOFd)
-------
C*****HYDROLYSIS
C
C
C
READ(
READ(
READ(
READ(
INFUTf
INFUTf
INFUTf
INFUTf
no
*)
*)
*)
READ/HKSF AKF (I ) C'KO (I ) , AKH (I ) f AKB (I ) f AKV< I )
700 FORMATdXr I5-5F12.7)
C
URITE(NOUTf710)
710 FOF>MAT
-------
c .
C*****CHECN FOR INPUT ERRORS
C
IF (NLRR.EQ.O) GO TO BOO
C
URITE(NOUT.750) NERR
75-0 FORMAT(//1X.I3.' ERRORS FOUND IN INPUT PATA - SIMULATION NOT ATTEM
1FTEP')
GO TO 999
C
C
BOO CONTINUE
URITE(NOUT.810>
BIO FORMAT(/1X. 'DO YOU WANT AN ECHO CHECK OF THE INPUT DATA?'.IX.
REAPd.*) IECHO
IFdECHO.EQ. 1) CALL PRINT
C
WRITE(NOUT.820)
820 FORMAT(/'PO YOU WANT INTCRMEPIATE OUTPUT (1 = YES) . (2=>NO) 7/)
REAPd.*) INTER
CALL SOLVE
C
999 CALL EXIT
END
SUBROUTINE PRINT
C
COMMON /GEN/ NUM.H.OUTPEL.TIM,TFIN.NERR.NOUT.INTER
COMMON /FLOWS/ V(30).QIN(30).QOUT(30).Q(30.30)
1.IF OS(30.30).EP(30.30).FF(30.30).SA(30.30).AL(30.30)
2.PISP(30.30).PISP(30i30)
COMMON /SOLIPS/ AM(30).AMIN(30).PAM<30).AKS<30>.PC(30)
COMMON /CONC/ C(30).CIN(30).PC(30).API(30).AF2(30).F(30.10)
C
INTEGER** CHEM(3)
IOMMON /RFAC1/ AQY(30).AM(30).AKF(30).OXK(30).R02(30) .AKO(30).
IBM'(30) .AKA(30) .AKN(30) . I H(30) . rtl\H< 30 ) . AKV ( 30 ) r GRM ( 30 ) . YLD(30) .
2HKG(30).XMB(30).AKB(30)
COMMON /REAC2/ OXKF(30) , R02F ( 30 ) . AKOF ( 30 ) -BKBF(30) .AKAF (30) .
1AKNF(30).PHP(30).AKHF(30)-GRMF(30).YLDP(30).HKSP(30).XMBF(30).
2AKBF(30)
URITE(NOUT.260)
260 FORMA! (// 'COMPARTMENT' . :>X. ' VOLUME r MILL ION CU.M' -5X.
1'INFLOU. MILLION CU.M 5X. OUFFLOU. MILLION CU-M')
I'D ?70 1=1.NUM
270 WRTTE(NOUT.280) I.V(I)OINU)OOUT(I)
280 FORMAr
-------
URITEOIOUT-285)
285 FORMAT
290 (JRITFJ)NUM )
URITE(NOUTr390)
390 FORMAT(/20X'MEAN LENGTH HETWEEN COMPARTMENTS')
DO 4003 I=lrNUM
WRITE(NOUTf283) I
4003 URITE(NOUTr300) (AL(IrJ),J=1,NUM)
c
URITE(NOUT'562) (AKS1=1rNUM)
562 FORMAT
^85 FORMAT(//20Xf'COMPARTMENT POSITION MATRIX')
DO 600 I=lrNUM
600 URITE(NOUTr610) (IPOS
-------
c
c
COMMON /CEN/ HUMrHrOUTDEL.TIM, >FIN»NERRrNOUTrINTER
COMMON /FLOWS/ V(30)-QIN(30)rQOUT(30)>Q<30r30) '
lrIFOS(30'30)rED(30'30)-ET <30>30)rSA<30»30)rAL<30r36>
2rDISD<30r30)rDISF<30'30>
COMMON /SOLIDS/ AM(30)'AMTN(30>- DAM(30),AKS(30)
COMMON /CONC/ C(30)rCIN(30'rDC(30)rAF1(30)rAF2030)rF(30r10)
COMMON /FLUX/ Rl ( 30 ) ' R2 (30 > r R3( 30) r R4 ( 30) r RM30 ), EXT ( 30) > FNET (30 )
DIMENSION
DIMENSION
TOUT(50)-CL(30
CT rC
Al(30)-A2(30)rA3(30)rA4(30)
30)fCF(50>30)rCM(50f30)
IFUNTER.GT. 1) CO TO 223
311 FORMAT(//20X
1//20X''R2 =
2//20X' R3 =
3//20X>'R4 =
4//20X''RS =
5//20X-'EXT =
6//20Xr FNET=
WRITE(lf222)
222 FORMAT(//20X
223 CONTINUE
- 'Rl = FLUX
FLUX DUE TO
FLUX DUE TO
FLUX DUE TO
FLUX DUE TO
FLUX DUE FL
NET FLUX TO
r 'ALL UNITS
DUE TO INFLOW FROM OTHER COMPARTMENTS'
OUTFLOW TO OTHER COMPARTMENTS'r
REACTION'r
DISPERSION (NET INFLOW) ',
SETTLING FROM ABOVE'r
JWS TO OR FROM OUTSIDE THE SYSTEM'r
EACH COMPARTMENT' )
IN KG/DAY7)
40
100
TIM=0.0
DTHLF-0-S*H
TSAV=OUTDEL
DO 40 I=lfNUM
TOUT(1)=TIM
CTdr I>=C(I)
CD
CONTINUE
IOUP'2
DO 100 I = lfNUM
CL(I)=C
CALL DIFFEO
DO 120 T=lrNUM
120 C(I)=CL(I> fDTHLF*A2(I)
-------
ro
ro
CALL DIFFEQ
DO 130 I=lrNUM
A3+2.*A3/6.
v
TIM=TIM+H
TNEX=AMINO(TSAVrTFIN)
IF(TIM.GE.TNEX) GO TO 150
GO TO 50
150 DO 160 I=lrNUM
TOUT(IOUT)=TIM
CT
CMdOUTr I) = CF(IOUTr I)/AM(I)
160 CONTINUE
UKITE NUM)
^1602 FORMAT<5(2XrE12,5> )
WRITE(NOUTr 1603) V( 16 ) r AL ( 16 r 1 )
1603 FORMAT (/lOXr ' VOL=' rF10.2'10Xr AL= ' rF10-3r//)
IFdNTER.GT. 1) CO TO 444
URITE(lf401> TIM
401 FORMAT
C*****FRINT RESULTS
C
WRITE(NOUTr200)
200 FORMAT
-------
WRI1E(NOUT'205) I2'12XrI2Fl2X-I2rl2 rI2-12X'I2)
DO 210 L=1-IOUT
URITE(NOUT'220) TOUT(L)
220 FORMAT(//23Xr'TIME='-F7-\,' PAY&'//)
WRITE(NOUTr2201) (CT(Lr I)- I - 1rNUM)
2201 FORMAT<5(2XrE12.5»
210 CONTINUE
C U)RITE (IfI=lrNUM)
DO 240 L=lrIOUT
URITE(NOUT-220> TOUT
DO 290 L=1'IOUT
URITE
-------
R1(I)=0.0
R2(I)=0.0
K3< 1)^0.0
R 4 < I > - 0 . 0
C
C*****COMFUTE DISSOLVED AND FARTICULATE CONCENTRATIONS
C
Cr'(I)=AFl(I)*C(I)
CF=R2(I>-NJ(Ir J)*C(I)
E******CHANGE IN CONCENTRATION DUE TO REACTION
C
DO 130 K=lr9
130 R3*V+DISF (I. J)*(CF1-CF (I) )
C
C*****MASS FLUX DUE TO SETTLING FROM ABOVE
C
DO 150 J=lfNUM
J&O R5(I)=R5(I)+F( Jr6)*C( J)*IFOS( Jf I)*V( J)
C
C*****SET UP DIFFERENTIAL EQUATION FOR EACH COMPARTMENT
C
EXT(I)=QIN
l'C(I)
C
100 CONTINUF
RF. TURN
END
C
-------
SUBROUTINE INPUTS
C
COMMON /CEN/ NUMrHrOUTI'E' rTIM,TFINfNERRfNOUTrINTER
COMMON /CONC/ C(30 ) rCIN( 50)rDC<30)rAF1<30>rAF2<30) OUTPEL-C'O.
IF OUTPEL=600.
IF(TIM-GE.1800.0) GO TO 111
CIN<1)=0.15*EXF<-0.00045*TIM>
IF(TIM.NE.O.O) RETURN
URITE(NOUT'100)
100 FORM.U(/10Xf'INPUT: CIN(1) = 0.15*EXF(-0 -00045*TIM)'//)
RETURN
C
111 CONTINUE
CIN(1)=0.0
IF
-------
U1 IWOCOMF
EMI
f'?9V
NULL.
C
C*****TUO COMPARTMENT MODEL FOR MASS BALANCE
C*****CORALVILLE LAKE
C*****CONSTANT COMPARTMENT VOLUMES
C*****FROGRAMMER: RAO *FRIL 1931
c
COMMON TIM>GTAMIN'AKSrVl>V2rE'A'ALrAH(2) rDAM(2>
DIMENSION AML(2)
DIMENSION Al(2)rA2(2)rA3(2>-A4(2)
NUM = 2
TIM=0.0
H=l
AMIN<=O. 00024
AM(1)=0. 00005
TSAV-OUTDEL
0=3.3519
V2=5.952
A-19.836
AL=1.317
55^ FORMATt/'ENTER ENDING TIME FOR SIMULATION (DAYS)')
READUr*) TFIN
WRITEdf 888)
888 FORMAT
-------
FLUX=FLUX1-FLUX2
WRITEd-333) TIM
C -
50 CONTINUE
C
PO 200 I = lr2
200 AML+PTHLF*AKI>
CALL DIFFEM
DO 120 I=lrNUM
A2(I)=PAM(I)
120 AM(I)=AML(I)+DTHLF#A2
CALL DIFFEM
DO 130 I=lrNUM
A3(I)=DAM(I)
130 AM(I)=AHL(I)+H*A3(I)
CALL DIFFEM
DO 140 I=lrNUM
A4(I) = DAM(I)
J40 AM(I) = AML(I)+H*(Al(I)+2,*A2(I)4-2.*A3(I)+A4(I))/6.
TIM=TIM+H
TNEX=AMINO(TSAVrTFIN)
IF(TIM.GE.TNEX) GO TO 150
GO TO 50
150 CONTINUE,
UX2=(E*A*(AM(2)-^M(1) ) )/AL
FLUX2=FLUX2*1-E09
FLUX=FLUX1-FLUX2
URITE(lr333) TIMr AH -FLUX lrFLUX2r FLUX
333 FORMAT(/F5,Or 2Xr5(E12,4-2X) )
C
IF(TIM.GE-TFIN) GO TO 444
GO TO 50
444 CALL EXIT
EN1'
r.UE'ROUTINF DIFFEM
( UMMON TIMrR'AMINr AKS'Vl'V2fE'A'ALf AM(2) rDAM(2)
PAM(1)=0*(AMIN-AM<1)) - AKS*V1*AM ( 1 ) + E*A* ( AM< 2 ) - AM ( 1 ) > /Al
PAM<1)=PAM(1)/V1
DAM(2)r AKS*V1*AM( D/U2 + F*A* ( AM ( 1 ) - AM ( 2 ) ) / ( AL*V2 )
PC TURN
L'ND
BOTTOM
-------
ED rUOCOMFl
EDIT
F999
NULL-
C
C*****TWO COMPARTMENT MODEL FOR MASS BALANCE
C*****CORALVILLE LAKE
C*****VARIABLE COMPARTMENT VOLUMES
C*****FROGRAMMER: RAO APRIL 1981
c
COMMON TIMfQf AMINf AKS'VlfV2fErArAL'AM(2)rDAM<2)
DIMENSION AML<2)
DIMENSION AK2) -A2(2)rA3(2)rA4<2)
NUM = 2
TIM=0.0
H=l
AMIN=0. 00024
A«<1)=0. 00005
AM(2)=0-3
OUTDEL=600
TSAV=OUTDEL
0=3.3519
A-19,836
AL=1-317
555 FORMAT(/'ENTER ENDING TIME FOR SIMULATION (DAYS)')
READdr*) TFIN
URITE(lr888)
888 FORMAT(/'ENTER INFLOW SOLIDS CONC (KG/L)')
READU'*) AMIN
URITE(lr999)
999 FORMAT(/'ENTER KS (I/DAY) >
READ
-------
998 FORMAT(/'ENTER E ( MS *2/DAY ) ' )
R£ADd-*> E
UIRITEd'996)
996 FORMAT (/'ENTER VOLUME INCREASE FACTOR' >
READdr*) FAC
WRITEd'332)
332 FORMAT
-------
120 AM
CALL I'IFFEM
PO 140 I=lrNUM
A4(I)=I'AM
140 AM(I>=AML+2,*A2-l-2.*A3+A4
DAM(1)=Q*
-------
SAMPLE INPUT FOR TOXIC
131
-------
Ql\r ED I'ATA
EDIT
F799
NULL-
DIELDRIN
Vr1.0-600.Or3600 / NUMrHrOUTDELrTFIN
6.612r6.612r6.612.2.015rl3.224-13.224rl.0r2.015r2.
3.3519r/ INFLOWS FROM OUTSIDE THE SYSTEM
OrO-OrOrOr3.3519r/ OUTFLOWS TO OUTSIDE THE SYSTEM
Or3.3519r/FLOWS OUT OF COMPARTMENT 1
OrOrl,67595rO'l-67595'/ FLOWS OUT OF COMPARTMENT 2
0-OrO-OrOr1.67595r/ FLOWS OUT OF COMPARTMENT 3
Or/ FLOWS OUT OF COMPARTMENT A
OrO.O'OrOr1.67595r/ FLOWS OUT OF
Or/ FLOWS OUT OF COMPARTMENT 6
Or/ FLOWS OUT OF COMPARTMENT 7
Or/ FLOWS OUT OF COMPARTMENT 8
0-/ FLOWS OUT OF COMPARTMENT 9
025rO-025rO.025r2.25rO
015
COMPARTMENT
025rO.Or2.25r2.25 / INITIAL CONCENTRATIONS
0.
0.000226r 0.000126rO.000049-0-36'0.000150rO.000096r0.00005r0.577r0-424
6700-6700r6700-6700r6700r6700'6700r6700r6700 / PARTITION COEFFICIENTSr PC
O.Or1.0rO,Or0.00015r/ DISPERSION COEFFICIENTS
1-OrO.Orl.Or-OOlr/
0. Of 1 . Or Of Of Or . 001 r /
.00015r/
0.0-.001-0-0r0rl.0-0r.00015rO
OrOr.OOl-Orl.OrOrO-Or.00015
Or/
O'OrOrOr,00015r/
O'0-O-O-O-.00015-/
Or.00057rO-6.612r/ INTERFACE
00057rOr .00057rOr6.612r/
Or.00057rO.OrO-6.612-/
612r/
6.612rOrOrOr.00136-Or6.61,-'rO
Or6.612rOr.00136'0-0-0-6.612
SURFACE AREASr MILLION SO. M
0-0 - Or ft .
0'Or 0 -Oi
11641- - -
612-/
6.612-/
65./ DISTANCES
BETUFFN COMPARTMENTS' M
Table 5, DATA file for input to TOXIC
-------
11641' 1
,65r/
1 1 f 1 . 5
1 /
1 lr lr 1
1 If If .
/ AQ Y
/ AM
/ OXK
/ F. 0 2
/ BXB
/ AKA
/ ANN
/ FH
, 000325
/ GRM
/ YLD
/ HNS
/ XMB
0 r 0 r 0 r 0
Or Of Or 0
n X ' ft X
0-OrO-
1 'lr 1 - 1
/ PKBF
/ AKAF
/ AKNF
/ FHF
/ PRMF
/ YLDF
/ HK5F
/ XHBF
BOTTOM
rll641.lrl.5r/
r 1-15-/
,1,1 IS./
'i.f4'J.*J'/
305. 2 . 2r 1 r . 305f . 305
PHOTOSYNTHESIS
QUANTUM YIELD
OXIDATIONrDISSOLVED PHASE
BASE CATALYSED HYDROLYSIS
ACID CATALYSED HYDROLYSIS
NEUTRAL CATALYSED HYDROLYSIS
r - 000325 r . 000325 r .000325- - 000325 r . 000325 r Or , 000325 r ,000325
r/ 1 IS ABOVE 4
fir/ 2 IS ABOVE 5
rOrlr/ 3 IS ABOVE 6
'5\'5\'/\n i
0052- . - - .0052- .0052
r lr lr lr lr 1
-------
SAMPLE OUTPUTS
TWOCOMP
TWOCOMP1
PESTY2 - 2 compartment
TOXIC - 9 compartment
TOXIC - 25 compartment
134
-------
CO
en
ENTER ENDING TIME FOR SIMULA:ION (DAYS)
3600
ENTER INFLOW SOLIDS CONC (KG/L)
.00014
ENTER KS (I/DAY)
.4
ENTER E (M**2/DAY)
.0001
TIME
DAYS
0.
600.
1200.
1800.
2400.
3000.
3600.
M IN WATER
KG/L
O.SOOOE-04
0.4440E-04
0.4660E-04
0.4874E-04
O.S084E-04
0.5289E-04
0.5489E-04
M IN SEDIMENT
KG/L
o.
0.
0.
0.
0.
o.
0-
3000E
3327E
3646E
3958E
4262E
45:.9E
4B50E
00
00
00
00
00
00
00
TO
0,
0.
0-
0.
0.
0-
o.
FLUX
SEDIMENT
KG/DAY
9250E
8213E
8620E
9017E
9405E
9785E
1016E
06
06
06
06
06
06
07
FLUX
FROM SEDIMENT
KG/DAY
0.
0.
o.
0.
0.
0.
0.
4518E
5010E
5491E
5960E
6418E
6866E
7304E
06
06
06
06
06
06
06
NET H-UX
TO SEDIMENT
KG/PAY
0.
0.
0.
0.
0.
0.
0.
4732E 06
3203E 06
3129E 06
305'/t 06
2987E 06
2918E 06
2851E 06
Table 6, TWOCOMP output for hypothetical solids balance in Coralville Reservoir
-------
OK? r
ENTER ENDING TIME FOR SIMULATION (DAYS)
3600
ENTER INFLOW SOLIDS CONC (KC/L)
.00044
ENTER KS (I/DAY)
.4
ENTER E
-------
OKr O twort/lt 1 |
OK- sea #to.].
INPUT DATA FOR IN
NUMBER OF COMF ARTMENTS= 2
STEP SIZE FOR CALCULATIONS= '-00 DAYS
TIME BETWEEN OUTPUT FOINTS= 6' 0.00 DAYS
TIME FOR SIMULATION = 36CO-00 DAYS
COMPARTMENT VOLUME , MILL ION CU-M INFLOW' MILLION CU.M OUTFLOW- MILLION CU.M
1 46.2840 3-3519 3.3r-19
2 5.9520 0.0000 0.0000
INTERFLOW MATRIX FOR COMPARTMENTS
0.0000 0.0000
0,0000 0.
QUIT-
OK f c > lose
QUIT-
0 K 1 L 1 9 6 C it II
OKr 01 tuoddt 1 1
OK'
DO YOU WANT INTERMEDIATE OUTPUT < 1= YES ) » < 2=NO>
INPUT DATA FOR DIELDRIN
NUMBER OF COMFARTMENTS= 2
STEP SIZE FOR CALCULATIONS= 1-00 DAYS
TIME BETWEEN OUTPUT FOINTS= 600-00 DAYS
TIME FOR SIMULATION = 3^00.00 DAYS
COMPARTMENT VOLUME,MILLION CU.M INFLOW. MILLION CU,M OUTFLOWr MILLION CU.M
1 46.2840 3.3519 3.3519
2 5.9520 0.0000 0-0000
INTERFLOW MATRIX FOR COMPARTMENTS
0.0000 0.0000
0.0000 0,0000
Table 8, Sample output for PESTY2 or two - compartment TOXIC
-------
COMPARTMENT INITIAL MASS OF PARTITION FRALIION IN
CONCENTRATION SUSPENDED SOLIDS COEFFICIENT DISSOLVED PHASE
1 0,0250 0-600E-03 1000. 0.625
2 2.2500 0.321E 01 1000. 0.000
DISPERSION COEFFICIENTS
O.OE 00 0.1E-03 0.1E-03 O.OFJ 00
INTERFACE AREALi
0.000 19.836 19,836 0.000
MEAN LENGTH BETWEEN COMPARTMENTS
0.100E 010.132E 010.132E 010.100E 01
SETTLING RATE CONSTANTS
0.40 0.00
COMPARTMENT POSITION MATRIX
£ 0100
CO
FSEUDO-FIRST-ORDER RATE CONSTANTS (PER PAY)
IN THE DISSOLVED PHASE
CMFN PHOTOLYSIS OXIDATION HYDROLYSIS BIOLYSIS VOLATILIZATION
1 0.0000000 0.0000000 0.0000000 0,0000000 0.0003250
2 0.0000000 0.0000000 0.0000000 0,0000000 0-0003250
FSEUDO-FIRST-ORL'ER RATE CONSTANTS (PER PAY)
IN THE FARTICULATE PHASE.
CMFN OXIDATION HYDROLYSIS BIOLYSIS
1 0.0000000 0.0000000 0,0000000
2 0.0052000 0.0000000 0-0000000
SIMULATION HALTED AT TIME = 3600-0
Table 8. (continued)
-------
TOTAL CONCENTRATION OF PESTICIDE OVER TIME (FFB)
TIME(PAYS) COMPARTMENT NUMBER
1
o.
600.
1200.
1800.
2400-
3000-
3600-
0
0
0
0
0
0
0
0.
0.
0.
0.
0-
0.
0.
0250
0131
0100
0076
0058
0044
0034
T
>
*>
1
1
1
0
2500
9135
2720
7364
3256
0119
7725
DISSOLVED CONCENTRATION OF PESTICIDE OVER TIME
(FFB)
TIME(PAYS)
COMPARTMENT NUMBER
0.0
600.0
1200.0
1800.0
2400-0
3000.0
3600.0
0.0156
0.0082
0.0062
0.0048
0.0036
0.0028
0.0021
0.0007
0.0009
0.0007
0-0005
0.0004
0.0003
0.0002
00
ID
PARTICULATE CONCENTRATION OF PESTICIDE OVER TIME
(FFB)
TIME(PAYS)
COMPARTMENT NUHBER
0
600
1200
1800
2400
3000
3600
,0
.0
.0
.0
.0
.0
.0
0,
0.
0.
0,
0.
0.
0.
1
0094
0049
0037
0029
0022
0017
0013
2
2
2
1
1
1
0
-)
2493
9126
2713
7358
3252
0116
7722
MASS OF PESTICIDE C'N TIIF MASS OF SOLIDS (MICROGRAM PER MLOGKAM)
riME(DAYS)
COMPARTMENT NUMBER
0-0
600.0
1200.0
1800.0
2400.0
3000-0
3600.0
1
15-63
8. IB
6.25
4.77
3.64
2-78
2, 12
2
0 70
0.91
0.71
0.54
0.41
0.32
0.24
label 8. (con't)
-------
INPUT DATA FOR IN
NUMBER OF COMPARTMENTS^ 9
r.TETF SIZE FOR CALCULATIONS-
TIME BETWEEN OUTPUT FOINTS=
TIME FOR SIMULATION
1 .00 DAYS
600.00 PAYS
7200.00 PAYS
COMPARTMENT
1
->
3
4
5
6
7
8
9
VOLUME'MILLION CU.M
6.6120
6.6120
6.6120
2,0150
13.2240
13.2240
1,0000
2,0150
2.0150
INFLOWr MILLION CU.M
3.3519
0,0000
0.0000
0,0000
0.0000
0.0000
0-0000
0-0000
0,0000
OUTFLOW. MILLION CU-M
0.0000
0.0000
0.0000
0.0000
0.0000
3.3519
0.0000
0.0000
0.0000
INTERFLOW MATRIX FOR COMPARTMENTS
0
0
0
0
0
0
0
0
0
.0000
,0000
.0000
,0000
.0000
.0000
.0000
.0000
.0000
3-3519 0.0000
0.0000 1.6759
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0,0000 0.0000
o.oooo o.oooo
o.oooo o.oooo
0.0000 0.0000
0
0
0
0
0
0
0
0
0
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
COMPARTMENT INITIAL
1
3
.)
5
6
7
8
9
CONCENTRATION
0,0250
0,0250
0,0250
2.2500
0.0250
0.0250
0.0000
2.2500
2.2500
SUSP
0
0
0
0
0
0
0
0
V.1
o.oooo
1.6759
0.0000
0.0000
0-0000
0,0000
0,0000
0.0000
c.oooo
MnSS OF
0.
0.
1 .
0-
1 .
o.
0-
o.
0-
0000
0000
6759
0000
6759
0000
0000
0000
0000
0,0000
o.oooo
0,0000
0.0000
o.oooo
0.0000
o.oooo
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
PARTITION
ENDED SOLID
.226E-03
- 126E-03
.49011-04
-360E 00
- 150E-03
.960F-04
500E-04
.577E 00
. 424E 00
S
COEFFICIENT
6700,
6700,
6700,
6700,
6700.
6700-
6700.
6700.
6700,
o.oooo
0.0000
o.oooo
0.0000
o.oooo
0.0000
0.0000
o.oooo
0.0000
FRACTION
DISSOLVED
0,398
0,542
0,753
0.000
0.499
0.609
0.749
0.000
0.000
IN
PHASE
Table 9. Sample Output for the 9 - compartment TOXIC model
-------
DISPERSION COEFFICIENTS
O.OE 00 0.1E 01 O.OE 00 0-1E-03 O.Oil 00 O.OE 00 O.OE 00 O.OE 00 O.OE 00
0.1E 01 O.OE 00 0.1E 01 0.1E-02 0. CF 00 0. OE 00 O.OE 00 0. OE 00 0. OE 00
O.OE 00 0.1E 01 O.OE 00 O.OE 00 O.OE. 00 0.1E-02 O.OE'OO O.OE 00 O.OE 00
0.1E-03 O.OE 00 O.OE 00 O.OE CO O.OE 00 0-OE 00 O.OE 00 0-OE 00 O.OE 00
O.OE 00 0.1E-02 O.OE 00 O.OE 00 O.OE 00 0-1E 01. O.OE 00 0.1E-03 0 - OE 00
O.OE 00 O.OE 00 0.1E-02 0-OE 00 0.1E 01 0-OE 00 O.OE OQ O.OE 00 0-1E-03
O.OE 00 O.OE 00 O.OE 00 O.OE 00 O.OE 00 O.OE 00 O.OE 00 0,OE 00 O.OE 00
O.OE 00 O.OE 00 O.OE 00 O.OE 00 0.1E-03 O.OE 00 O.OE 00 O.OE 00 O.OE 00
O.OE 00 O.OE 00 O.OE 00 O.OE 00 O.OE 00 0.1E-03 O.OE 00 O.OE 00 O.OE 00
INTERFACE AREAS
0.000 0.001 0.000 6.»i: 0.000 0.000 0.000 0.000 0.000
0.001 0.000 0.001 0.000 6.612 0-000 0.000 0.000 0-000
0.000 0.001 0.000 0.000 0.000 6.612 0-000 0.000 0.000
6.612 0.000 0-000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 6.612 0.000 0-000 0-000 0.001 0.000 6.612 0,000
0.000 0,000 6.612 0-000 0-001 0-000 0-000 0.000 6.612
0,000 0.000 0.000 0-000 0.000 0.000 0.000 0.000 0.000
0.000 0-000 0.000 0-000 6-612 0.000 0.000 0.000 0.000
0-000 0.000 0.000 0.000 0-000 6.612 0,000 0,000 0,000
MEAN LENGTH tETUFEN COMPARTMENTS
0.100E 010.116E 050.100E 010,»OOE 000.100E 010.100E 010.100E 010.100E 010.100E 01
0.116E 000.100E 010.116E 050-100? 010.150E 010.100E 010.100E 010.100E 010.100E 01
0.100E 010.568E 030-100E 010.100E 010-100E 010-150E 010-100E 010.100E 010.100E 01
0.6SOE 000.100E 010.100E 010-100E 010-100E 010.100E 010.100E 010-100E 010-100E 01
0.100E 010.150E 010.100E 010.K-OE 010.100E 010.10QE 010-100E 010-115E 010.100E 01
-------
0.100E 010.100E 010.150E OlO.iOOE 010.11»E 050-100E 010-100E 010.100E 010-115E 01
0.100E OlO.iOOE OlO.iOOE OlO.iOOE OlO.iOOE OlO.iOOE OlO.iOOE 010-100E OlO.iOOE 01
0.100E OlO.iOOE 010-100E 010-100E 010-115E 010-100E OlO.iOOE OlO.iOOE OlO.iOOE 01
0.100E OlO.iOOE OlO.iOOE OlO.iOOE OlO.iOOE 010-115E OlO.iOOE OlO.iOOE OlO.iOOE 01
SETTLING Ri-vrE CONSTANTS
0.60 0.60 0.60 0,00 0.60 0.60 0.00 0.00 0,00
COMPARTMENT fOSITION MATRIX
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
FSEUPO-FIRST-ORDER RATE CONSTANTS (PER DAY)
IN THE DISSOLVED PHASE
CMFN
1
~>
3
4
5
6
7
8
9
PHOTOLYSIS
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
0,0000000
0,0000000
0,0000000
o.ooooooo
OXIDATION
0.0000000
0.0000000
o.ooooooo
0.0000000
o.ooooooo
0-0000000
0.0000000
0.0000000
0,0000000
HYDROLYSIS
o.ooooooo
0-0000000
o.ooooooo
0-0000000
0.0000000
0-0000000
o.ooooooo
o.ooooooo
0.0000000
BIOLYSIS
0.0000000
0.0000000
0.0000000
o.ooooooo
o.ooooooo
o.ooooooo
o.ooooooo
0.0000000
0.0000000
VOLATILIZATION
0,0003250
0. 0003250
0,0003250
0-0003250
0.0003250
0.0003250
0,0000000
0.0003250
0.0003250
fSEUPO-F-IRGT-ORDElR F M I £ CONSTANTS (PER DAY)
IN THE FAKFICULATE PHASE
-------
CMFN
1
3
4
A
8
9
OXIDATION
o.ooooooo
0.0000000
o.ooooooo
0.0052000
o.ooooooo
o.ooooooo
0.0000000
0.0052000
0.0052000
HYDROl YSIS
0.0000000
0 0000000
0.0000000
('.0000000
.'.0000000
o-ocooooo
0-0000000
o.ooooooo
0-0000000
BIOLYSIS
o.ooooooo
o.ooooooo
o.ooooooo
o.ooooooo
0.0000000
o.ooooooo
o.ooooooo
0-0000000
o.ooooooo
Rl = FLUX DUE TO INFLOW FROM OTHER COMPARTMENTS
R2 = FLUX DUE TO OUTFLOW TO OTHER COMPARTMENTS
R3 = FLUX DUE TO REACTION
R4 = FLUX DUE TO DISPERSION (NET INFLOW)
R5 = FLUX DUE 10 SETTLING FROM ABOVE
EXT => FLUX DUE FLOWS TO OR FROM OUTSIDE THE SYSTEM
FNET= NET FLUX TO EACH COMPARTMENT
ALL UNIFS IN KG/DA?
I 1ME =
600.0
CMFN Rl
1 0.OOOOOOE 00
2 0.791232E-01
3 0.256707E-01
4 0.OOOOOOE 00
5 0.254707E-01
6 0.330116E-01
7 0.OOOOOOE 00
8 0.OOOOOOE 00
9 0,OOOOOOE 00
ooooooooo
R2
791232E-
513415E-
161941E-
OOOOOOE
168175E.-
OOOOOOE
OOOOOOE
OOOOOOE
oooooc-t
01
01
01
00
01
00
00
00
00
ooooooooo
R3
564203E-01
278341E-01
949010E-02
518014E-01
399301E-01
213023E-01
OOOOOOE 00
38841»E-01
211412E-01
0.
0.
0.'
0.
R4
751043E-02
128968E-09
123785E-04
751043E-02
320812E-02
174716E-02
OOOOOOE 00
318916E-02
173478E-02
ooooooooo
R5
OOOOOOE 00
OOOOOOE 00
OOOOOOE 00
564001E-01
778162E-01
947447E-02
OOOOOOE 00
399086E-01
212844E-01
-------
T111E- 1200.0
LMFN
1
>
3
A
5
6
7
8
9
CMF-N
1
->
3
4
S
6
7
B
9
LMFN
1
3
4
IT
A
7
0
9
Rl
O.OOOOOOE 00
0.604994E-01
0. 196286E-01
O.OOOOOOE 00
0. 196286E-01
0.25?<4S7E-01
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
Rl
O.OOOOOOE oo
0.461874E-01
0.149852E-01
O.OOOOOOE oo
0. 149352E-01
0. 192898E-01
O.OOOOOOE 00
O.OOOOOOE oo
O.OOOOOOE oo
Rl
O.OOOOOOE 00
0.3S2D86E-01
0, 114394E-01
O.OOOOOOE 00
0. 114394E-01
0.1472D6E-01
O.OOOOOOE 00
O.OOOOOOF 00
O.OOOOOOE 00
R2
0.604994E-01
0.392573E-01
0.123828E-01
O.OOOOOOE 00
0. 128829E-01
O.OOOOOOE 00
o.ooooooi: oo
O.OOOOOOE oo
O.OOOOOOE oo
TIME =
R2
0.461874E-01
0.299704E-01
0-945349E-02
O.OOOOOOE 00
0.98363^E- 02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE oo
O.OOOOOOE oo
1IME =
R2
0.352C-86E-01
0.228788E-01
0.721662E-02
O.OOOOOOF OC
0.750894L 02
O.OOOOOOF 00
O.OOOOOOE oo
O.OOOOOOF 00
0.00000ft 00
R3
0.431403E-01
0.212828E-01
0.725659E-02
0.406939E-01
0.30&880E-01
0. 163079E-01
O.OOOOOOE 00
0.306493E-01
0. 163524E-01
1800,0
R3
0.329348E-01
0.162480E-01
0.553995E-02
0-311040E-01
0.233&46E-01
0. 124510E-01
O.OOOOOOE 00
0.234435E-01
0. 124958E-01
2400.0
R3
0.2^1418E-01
0- 124030E-01
0.422909E-02
0.237454E-01
0- 178286E-01
0-950495E-02
O.OOOOOOE 00
0. 178980E-01
0.95397L/E-02
R4
0.590076E-02
0.986114E-10
-.943812E-05
-.590076E-02
0.2S3113E-02
0.135132E-02
O.OOOOOOE 00
-.251670E-02
-.134187E-02
R4
0.451022E-02
0.752837E-10
-.720397E-05
-,451022E~02
0. 193»03E-02
0-103261E-02
O.OOOOOOE 00
--192502E-02
-. 102540E-02
R4
0.344319E-02
0-S74701E-10
-.S49930E-05
-.344319E-02
0. 147810E-02
0.788332E-03
O.OOOOOOE 00
-. 146970E-02
-.782830E-03
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0.431248E-01
0.212692E-01
0.724464E-02
O.OOOOOOE 00
0.305715E-01
0.162942E-01
R5
O.OOOOOOE 00
O.OOOOOOE oo
O.OOOOOOE 00
0.329230E-01
0. 162376E-01
0.^53082E-02
O.OOOOOOE 00
0.233420E-01
0. 124406E-01
RS
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0.251328E-01
0. 12395^>E-01
0.422213E-02
O.OOOOOOE 00
0.178190E-01
0.94969iE-02
-------
1IME= 3000-0
CMFN
1
^
3
4
5
A
7
g
9
CMFN
1
~»
3
4
5
A
7
8
9
LMFN
1
T
3
4
5
6
7
8
9
Rl
O.OOOOOOE 00
0.269157E-01
0.873262E-02
O.OOOOOOE 00
0.873262E-02
0. 112412E-01
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
Rl
O.OOOOOOE 00
0.20S469E-01
0.666630E-02
O.OOOOOOE 00
0.666630E-02
0.858130E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
M
O.OOOOOOE 00
0.156851E-01
0.503892E-02
O.OOOOOOE 00
0.508S92E-02
0.655078E-02
O.OOOOOOE. 00
O.OOOOOOE 00
O.OOOOOOE 00
R2
0.26915"7E 01
0.174652E-01
0.55090111-02
O.OOOOOOE 00
0.573217E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
TIME =
R2
0.205469E-01
0. 133326E-01
0.420547E-02
O.OOOOOOE 00
0.437582E 02
O.OOOOOOE: oo
O.OOOOOOE 00
O.OOOOOOE oo
O.OOOOOOE 00
TIME =
R2
0. 156851E-01
0.101778F 01
0.321037E-02
O.OOOOOOE 00
0.334041L 02
0-OOOOOOF 00
o.ooooooi: oo
o.oooooor oo
o.ooor-O'.ic oo
F3
0. 191927E-01
0.94A855E-02
0.327840E-02
0. 181269E-01
0. 136100E-01
0.725589E-02
O.OOOOOOE 00
0.136634E-01
0.728248E-02
3600.0
R3
0. 14A513E-01
0.722810E-02
0.246450E-02
0-138377E-01
0, 103896E-01
0.553900E-02
O.OOOOOOE 00
0. 104303E-01
0.555926E-02
4200.0
R3
0. 111845E-01
0-551778E-02
0. 188135E-02
0- 105633E-01
0. 793120E-02
0-422836E-02
O.OOOOOOE 00
0-79i234E-02
0. 4243S6E-02
R4
0.262848E-02
0-438715E-10
--419805E-05
-.262848E-02
0.11283AE-02
0- A01799E-03
O.OOOOOOE 00
-.112194E-02
-.597599E-03
R4
0.200652E-02
0.334907E-10
--320471E-05
-.200652E-02
0.861363E-03
0.459398E-03
O.OOOOOOE 00
-.8564AAE-03
-.45A192E-03
R4
0, 153173E-02
0.255660E-10
-.244641E-05
- .153173E-02
0.657551E-03
0-350698E-03
O.OOOOOOE 00
-. A53813E-03
- .348250E-03
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0. 1918S9E-01
0.946248E-02
0.322309E-02
O.OOOOOOE 00
0-136027E-01
0.724979E-02
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0. 146461E-01
0.722346E-02
0.246044E-02
O.OOOOOOE 00
0.103840t-01
0.553434E-02
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0.111805E-01
0.551424E-02
0. 187825E-02
O.OOOOOOE 00
0.7C2693E-02
0-422480E-02
-------
1IME> 4800-0
CMPN
1
">
3
4
5
6
7
8
9
CMFN
1
»
3
4
5
6
7
8
9
CMFN
1
2
4
5
6
7
3
9
F<1
O.OOOOOOE 00
0.119737E-01
0.388478E-02
O.OOOOOOE 00
0.388478E-02
O.S00073E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
Rl
O.OOOOOOE 00
0.914044E-02
0.296556E-02
O.OOOOOOE 00
0.29655&E-02
0.381746E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
Rl
O.OOOOOOE 00
0.697763E-02
0.226385E-02
O.OOOOOOE 00
0.22638SE-02
0.291417E-02
O.OOOOOOC 00
O.OOOOOOE 00
O.OOOOOOE oo
R2
0.119757E-01
0.7769f'C-E-02
0.24C*C~'-3
0.8C.380&E-02
0.421216E-02
0. 143618E-02
0.806388E-02
0.60^4D1E-02
0.322784E-02
O.OOOOOOE 00
0.607824E-02
0.323967E-02
5400.0
R3
0.6S1777E-02
0.321548E-02
0. 109635E-02
0-615579E-02
0.462189E-02
0.246407E-02
O.OOOOOOE 00
0.4A4003E-02
0.247309E-02
6000-0
R3
0.497S53E-02
0-245463E-02
0.83»932E-03
0-4£9921E-02
0.352826E-02
0.18B102F-02
O.OOOOOOE 00
0.3C.4210E-0?
-------
TIME= 6600.0
CMFN
1
O
3
4
5
6
7
8
9
CMFN
1
''y
3
4
5
6
7
B
9
Rl
O.OOOOOOE 00
0.^32658E-02
0. 172817E-02
O.OOOOOOE 00
0.172817E-02
0.22:'462E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
Rl
O.OOOOOOE 00
0.406620E-02
0.131925E-02
O.OOOOOOE 00
0. 131925E-02
0. 169823E-02
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
RL'
0.5326S8E-02
0-34563TE-02
0. 109023E-02
O.OOOOOOE 00
0. 11343VE-02
O.OOOOOOE: oo
O.OOOOOOE. 00
O.OOOOOOF 00
O.OOOOOOE: oo
TIME =
R2
0.406620E-02
0.263850F-02
0.832257E-03
O.OOOOOOE 00
0.865969E-03
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
R3
0.379822E-02
0.187381E-02
0.638897E-03
0-358728E-02
0.2693-10E-02
0- 143593E-02
O.OOOOOOE 00
0.270395E-02
0. 144119E-02
7200.0
R3
0.289948E-02
0. 143043E-02
0.487720E-03
0.273844E-02
0.205609E-02
0. 109616E-02
O.OOOOOOE 00
0.206416E-02
0.110018E-02
R4
0.520171E-03
0.868212E-11
-.830790E-06
- .520171E-03
0.223300E-03
0.119095E-03
O.OOOOOOE 00
-.222030E-03
-.118264E-03
R4
0.397086E-03
0.662773E-11
-.634207E-06
-.39708&E-03
0. 170464E-03
0-909150E-04
O.OOOOOOE 00
-.16949&E-03
-.902805E-04
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0.379686E-02
0.187261E-02
0.637844E-03
O.OOOOOOE 00
0.269195E-0?
0.143472E-02
R5
O.OOOOOOE 00
O.OOOOOOE 00
O.OOOOOOE 00
0.289844E-02
0. 142951E-02
0-486917E-03
O.OOOOOOE 00
0.20S498E-02
0.109524E-02
SIMULATION HALTED AT TIME = 7200.0
-------
TOTAL CONCENTRATION OF PESTICIDE OVER FIME
A'.
3.
>
l'.
1 -
1.
0.
0-
0-
o.
0.
\
2500
9457
8853
9697
7671
7307
3212
0085
7699
5877
4487
3425
2615
5
0-0250
0.0100
0.0077
0.0059
0.0045
0.0034
0-0026
0.0020
0-0015
0.0012
0.0009
0-0007
0-0005
6
0-0250
0.0069
0.0052
0.0040
0.0031
0.0023
0-0018
0.0014
0.0010
0,0008
0,0006
0.0005
0.0004
o.
0.
o.
0.
o.
o.
0-
0.
o.
o.
0.
o.
o.
7
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
8
2.2500
3.7079
2.9258
2.2379
1.7086
1.3043
0.9957
0.7601
0.5802
0.4429
0.3381
0.2581
0.1970
9
2.2500
2.0183
1.5612
1.1930
0.9108
0.6953
0.5307
0.4052
0.3093
0,2361
0,1802
0,1376
0.1050
DISSOLVED CONCENTRATION OF PESTICIDE OVER TIME
(PFB)
TIME(DAYS)
COMPARTMENT NUMBER
0.0
600,0
1200.0
1800.0
2400.0
3000.0
3600-0
4200.0
4E500.0
5400,0
6000,0
6600, 0
/L'OO.O
1
0,0099
0.0094
0-0072
0.0055
0.0042
0-0032
0.0024
0-0019
0.0014
0.0011
0.0008
0.0006
0.0005
*>
0.0136
0.0083
0.0064
0,0048
0,0037
0.0028
0,0022
0.0016
0.0013
0.0010
0.0007
0.0006
0-0004
0
0
0
0
0
0
0
0
0
0
0
0
0
-J
-0188
.0073
.0056
.0042
,0032
,0025
,0019
,0014
-00 11
. 0005
.0006
.0005
.0004
4
0-0009
0-0020
0-0016
0.0012
0.0009
0.0007
0-0005
0.0004
0-C>003
0-0002
0.0002
0-0001
o-ooot
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0125
0050
0038
0029
0022
0017
0013
0010
0008
0006
0004
0003
0003
6
0.0152
0.0042
0.0032
0.0024
0.0019
0-0014
0-0011
0.0008
0.0006
0.0005
0.0004
0.0003
0.0002
7
0.0000
0.0000
o.oooo
0-0000
o.oooo
o.oooo
o.oooo
o.oooo
o.oooo
0.0000
o.oooo
o.oooo
0-0000
8
0.0006
0,0010
0,0008
0,0006
0,0004
0-0003
0.0003
0.0002
0-0002
0.0001
0.0001
0.0001
0.0001
9
0.0008
0.0007
0.0005
0.0004
0.0003
0.0002
0.0002
0.0001
0.0001
0.0001
0,0001
0-0000
o.oooo
-------
PARTICULATE CONCENTRATION' OF PESTICIDE OVER TIME
TIME(DAYS) COMFARIh-NT NUMBER
(FFB)
0.
600.
1200.
1800.
2400-
3000.
J600-
4200.
4800.
5400.
6000.
6600.
7200.
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0, 0151
0,0142
0-0109
0.0083
0-0063
0.0048
0-0037
0,0028
0.0022
0,0016
0,0013
0,0010
0.0007
MASS OF
*>
0,0114
0,0070
0,0054
0,0041
0,0031
0.0024
0.0018
0.0014
0.0011
0.0008
0,0006
0,0005
0,0004
0.
0,
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
PESTICIDE
TIME(DAYS)
0,0
600,0
1200.0
1800.0
2400,0
3000,0
3600.0
4200.0
4800.0
5400,0
6000,0
6600,0
7200,0
66.62
62.91
48. 10
36-72
28.03
21 .40
16.34
12.47
9.52
7.27
5-55
4.14
3,23
90,83
55.64
42,55
32-48
24-80
18-93
14-45
11-03
8.42
6,43
4,91
3,75
2,86
3
006? ?
0024 4
0018 3
0014 2
0011 2
0008 1
0006 1
0005 1
0004 0
0003 0
0002 0
0002 0
0001 0
ON THE
4
-2491 0-
.9437 0-
-8836 0.
.9684 0-
-2662 0.
.7299 0.
.3206 0.
-0081 0.
.7696 0.
-5875 0-
-4485 0-
.3424 0.
.2613 0-
MASS OF
5
0125
0050
0039
0029
0022
0017
0013
0010
0008
0006
0004
0003
0003
6
0.0098 0
0.0027 0
0.0021 0
0.0016 0
0.0012 0
0.0009 0
0-0007 0
0-0005 0
0-0004 0
0-0003 0
0-0002 0
0-0002 0
0,0001 0
7
.0000
.0000
.0000
.0000
.0000
.0000
.0000-
.0000
.0000
.0000
.0000
.0000
,0000
SOLIDS CMICROGRAH
8
2,2494
3,7069
2,9251
2,2374
i-7082
1.3040
0-9954
0.7599
0.5801
0.4428
0-3380
0.2581
0.1970
9
2.2492
2.0176
1.5606
1.1926
0.9104
0.6950
0.5306
0.4050
0.3092
0.2360
0.1802
0-1375
0,1050
PER K-ILOGRAM)
COMPARTMENT NUMBER
1
2o. 10
48-74
37,27
28-45
21. 72
16.58
12-66
9.66
7-38
5. A3
4.30
3.28
2-50
6-25
13-73
10.79
8-25
6-29
4.81
3.67
2-80
2,14
1 -63
1.25.
0.95
0.73
83
33
25
19
14
11
8
6
5
3
^
~*
1
,54 101,
,53 27,
,69 21,
.61 16.
.97 12,
,43 9,
,73 7,
-66 5-
,08 4,
,88 3.
,96 2.
.26 1,
,73 1,
94
94
39
33
47
52
27
55
23
23
47
88
44
0.00
0.00
0.00
0-00
o.oo
0.00
0.00
0.00
0.00
0-00
0-00
0-00
0-00
3.90
6.42
5.07
3.88
2.96
2.26
1.73
1.32
1,01
0,77
0,59
0-45
0-34
f)Kr B(J
Al.O.it:? (27) LOGGEn OUT f>1 16"?5 M16B1
CONNECT^ 24 MINUTES- CPU= 2r>6 SECONDS. 1/0 =
5,30
4,76
3.68
2.81
2.15
1.64
1-25
0,96
0,73
0,56
0-42
0-3?
0,25
3 SECONDS
-------
INRIT DATA' FTJTTTTFUTjri R
NUMBER OF COMPARTMENTS:
STEP SIZE FCR CALCUL AT
TIME BEHrUEETK'OTTPUT
TIME FOR SIMULATION
25
l.ilC DAYS
6~OO.tiC 5 AYS
3600.00 DAYS
COMPARTMENT
1
. ; . z'
' . *.;'-'.
5
6
7
'-..,-' H
' / . 9..- : /
11
12
' 13 ,
15
16 ,
17
Ifl
19
21
' 22
\ 11
INITIAL
CO^CN'TRATIOM
0 . 0 2 i> 0
C.0250 ;
C.0250
C . 0 2 1> 0
0.0250
C.025Q
:0.0250
0,0250 ,
0 ,0250
C.0250
Q,02bO
C.0250 \
5.2500 '
Z .2500
2.2500
? .2500
2.2SOO ' .
"2.2500
r . 2 s a Q
I .2500
HAS^ OF
SUSPENDED COLIDS
"0.17HE-02
"' ; '" UiT^feE'- fl2 !
.... : ' .'o.i.^^r-o? ' .
'',' 0.117E -0? '
0 , 7 'J 0 E - 0 j
0 . 9 7 0 r - C .'
: u. ;'li.
1 , 0 C| 0
1 . 6 0 0
~T."i
-------
.' PSFUE-C-FIRST-O^fH RATE CONSTANTS
il
IN Till' PIS:
i>
'"C'MPN PHOTOLYSIS OXIDAT11N
' \ O.OOOOCGO o.ocoon.-'Q
, 2 O.OOOOOCO O.OOOOOCO
' ' 3
. 'J,
. 1
v ;
16
17
18
19
20
"'' 23
22
, 25
0.
0.
u.
o.
0.
'. ' 9
Q.
0.
ir;
0,
0000000
0000000
O'C'OG'CCO '
ocoocco
0000000
0 p 0 u u u o
OCOOCQO
ncoocoo
ff^OOD-O-O"
0000000
0
0
u
0
0
' 0
0
0
"*" 0
c
*
»
9
»
«
»
0
9
0
o o n o r
o c o r i"
en
on
Tnrrro o r D "
o b o o o n r.
o o o o r : o
DTJOO "
00000
o c1 n o o
u . t '-
o o f o :
r o
0.
n.
c.
. r
C.
J.
0000000
C " C 0 0 0 0
0000000
j 0 0 0 n o
""UFO^LTF
3 0 0 0 D P 0
) c u o o n o
1 " C 0 C 0 T~
CCOOOCQ
o.onooOoo
0.0000000
o. o ado ooo
o.oooopoc
o.oouoooo
0.0'JOOOOt
0. OOOOOOC
0, OOOOOOC
o.coooooo
0
0
u
0
0
0
n
0
,00032^0
.0003250 ^
.00032!>E T. i
.0007250 ';.' :
. 0 C 0 3 c ? 0
. o c o 3 r r- c
.l20Q32tQ' ,
-------
tn
ro
P5EUCC-
,;,-, CHP(V
1
2
3
' '> . ;.'' -;- i.
- V: - -.'.' *>?'
.' 8
" :f ' ' ' n
13
15
, :. : 16 :"
' ".; 17
', ''.-': _ 18
20
21
' ' ; 22
23
21
2^
FIRST-Ohrn- RAT! CONSTANTS tPFR DAY)'
IN' THE P.'.RTICULAT: PHAST '. \
OXIOATIC'J
d.OUUOUUU
0,0000000
0,0000000
u. uuuuuuu
.'tf.o oooco o
0»ooooouo
U . UOUUU J 0
^O.OQOOQPQ
0.0000000
' ff> OOOOCQO
0.0000000
.- p.. .00 000 CO
O.OOT30000
0.00000 DO
0,0000000
0. 002600 0
0.0026000
0.0026c 00
0,0026000
0 . 0 0 2 1 C 0 C1
0.0026001)
0,002? 'IOC
0,002fcfOn
HYDROLYSIS '
U'.yOOC'O'OD"
0. 0000000
0.0000000
. Q.UOO'JUOO
0.0000000
0. DO 0 CO 00
0 r QO UUO UU
0,0000000
O.OOOuOOO
0.3000000 '
o.aoooooo
0.0000000
0 . 0 0 0 0 '-1 0 0
0.3000000
o.aopoooo
o.oocooco
0.0000000
" 07(1000000""""
0.0000000
O.OC60000
0.3000000
O.OOOQOOO
o ". ox- c o~Q o a
'BIOLY.SJE?'";-'; , . .rf.^/^/t .
U.OOOOOtJO
0.0009000
0,0000000
-o.aooaoQO ,', v>. - ,.? ». . . .-;. ^ -
:0i..000fl000 '> .:',>"'' v-./;^^,
.o.-ooooooo. , , *s> // ^t^;-1, ';
U, OOUUUOU
0.0000000
0.0000000
o.ooooooo -> .;.-.-- ';"'
0,-aOOQOO'O '; ,.!" .'vv''^ - "
o.O'ftooop.O;'//.; '.'/i'^^-J^
0.0000000
0,0000000
0.0000000
0,0000000 . . ",-';*-:
o.ooooooo , '/ -:,.,..,
o.ooooooo . / '/'
0,0000,000 .
o.ooooboo
0,0000000 ' '''-','
o.ooooooo ' , "
o.oonooo'o' :
''']'' 2
' »,'
?'M
' " .' 1
' 'fl
' r
1
' l;'l
>->" J
*
DO YOU WANT AN ECHO CHECK ^ THE I^PUT OATA? (l=YES«?rNO)
)0 YCU UANT INTERMEDIATE CLT^IT (1- Y CS ) , ( ?.- NO )
INPUT: dim >=.'. it ?: yp <-r. 00015*
-------
TIME=
,0 DAY:.
0 DATA25A 1 1
Q.25359F-01 > 0 .18133E-01/' ,v"
~OTBT9T5 E~1n "0
Q,540£1E Gl CU46062E 01
0.18043E 01
tn
CO
". j : " VOL=
Q.68719r-01
0,232f<)F-Cl
o,:
0.
DTI 6T?3 f=TI BT
0.18207E 02
0.50240F 01
VOL,
0.
0.'
1,23
TIME= 1?OC.O
)t^(26F-Cl 0,
23546E-?! 0.
r?413E-'0"l" "0.
13M5E C? P.
»2736E t' 1 P.
1.23
AU
UAYS
A ? of:
''.-.'
-01
-01
-01
01
01
f- , . '9
. ' I ' ~- '_ _, ' a
;
A 5fiA^?F"»fl1^i' 3
'0.10359E-01 .;';. .-J
0". 11^92E-bl"t^~^1
0.62274E 01
O.lte^fF 01
L i
-------
:, 0,5;^E;C1
0.17899E- Cl
0.12928E-01
0.14421E C2
'/; 0.40253E Cl
£i>.;V"V':
0.136S1E-01
0.99076E-C2
';, 0.11097E 02
":)0. 31281 E 01
VOU
TIME: 1'r
0.41684E-C1
0.18170F- 01
0.95691E-02
0.11004E 02
0. 34437E 01
,;;:;;jfV=»'
ti,31844F-')l
0.1J909E-01
' 0.73232E-02
0.84789E 01
0.26701E 01
1.23
:)'&', 0" FAYS
0.3-"i3ft3L-
0"7T3ST6t~
0,10^>?7E-
0 . ft 4 r 6 1 E
AL= :
00.0 DAYS
0.10?48E-
; .!:«J!S!
AL-
oa.i DAYS
01
01
01
Cl
01
1
01
01
02
Oi
01 '
1
0.26972E-
0.86611E-
0.64607E
.-JSP"' -V^-'.V.
' ' i '' o ^ 4 ''-'.
0.20622E-
0.12032E-
0.66291E-
' .0.15229E
.OSft '
;
01, \,.0..22Q36E-01 \ <.'>,:*
Ol 0, 110£3£-01 ^
02 0.86906E-02 " '. '
0.1 0.49P72E 01
01 1"'1-o;-i1i>53f oi p"*
V' ' .
-^lll^til
01 0.168'5^>E-01 ^'- -
01 0.&4648E-02 - i, !
02 0.68120Er02 ' -V .'
0. 1 .",_; 0 . 3 8 5 5 2 E 0/1 t^.^i
;
: ,:;- ; .^>>:
,_
'
0.306?3E-C1
0.104h7E-01
a,75713E-02
0.84PC1E 01
0.22S74E Cl
TIME =
0.24314F. -
,0,55940t-
0.:0474T
"CO
Jl
01
ni
a.-)
o .
' ' 0.
0.
DAYS
1-H70E-
'1 9 7 4 9 E
IU7,?OF
01
Cl
01
0,
',\.
u .
Qr
0.
lb745E
91936r
50639C
3P201E
11683E
-01
-02
-02
01
01
0.12873E-
0.295?7F.
01 :
02
"? /
0.1 a
o o ! _-;_
1.23
At.?
1.050
-------
:: 3^00.0 DAYS
0 DATA25A 1 1
0.2J385E-01 0.18561E-UJ
0.12023L-01 0.9B285C-02
ui
en
'0.79877E-02
' .0.57814E-02
0.64.8 09 E Cl .;'
0.18JZ1E Cl
VOL^
i .
>
TOTAL
1
' ', ' 6 ' '
11
16 '
21
0.250COE-C1
0.250COF-C1
0.22?QOE Cl
Q.8U27E-0:
0.15649E 01
1.23
.
0 .ft0343F-02 '' 0»70i98E -OZ ' '! iO»-19'36!iE"-OS y'}\" ;' .3
1 0.47107E-02.''/'-,0»58$64E-.0.2' . 0 ."39748E- 0? ^\ 'A
C»S8001E 01 ,0»?S183E 01:\ 0»22560E 01 (<>jrf
G.12017E til 0.8^320E 00' 0.53175E 00 j
AL- 1.050 .
' '" ' - . ' ' " ' ' - ' . ' r>1
SIMULATION HALT no AT TIME - 36co.o " ]
CONCEMTRATIOM
COHF
S
7
12
17
0.250'OGr-r.i
0.225CLC C 1
OF PESTICIDE OVER 'TIME :- - -1- vj
"ARTMENT NU^fiER
^ A C,
' O . g ' ,, i ' .. :j Q i > '^1
1? 14 i«5 ''>,-'
18 19 20
C.H HAYS""
i: ,clr-r Jilt- 01 0.25000E-01 . Q.i500Cf-Pl
0 ."PS CC flE-'C'I 0^'2r'00 OF - 0 1 C .?50 D('E> 01 "
n.rsoocE-oi o.ebooot-oi o.^socnE-?}
0.??r^CF 01 0.22500F 01 0,22C--CUE 01
r
OT7 2 5 inr
-------
'F- fffO.O~CaYS
CI G,7C278£-M
0^29454E-Or~~~ 07295FEE-01
C2 0
,ll?8cE 02 Q.86935E 01 0.67113E I1! '*,
TT tmTTVEr? E "T1
0.68719E-C1
nuL-
0.51426F-01
0.182C7E C2
0.1241JF-C1
0.13845L 02
"DYT2736E~CI1
crsrs
0.13190E-01
Dr
01
02
0,35101£-01
ri"5F=Trr
0.11231E-01
0.80823E 01
;!.!?
0.28632O01
0 . 1 1 ^ ? E - 0 1 '
0,62271E 01
~~nf>r-~ iTna.Tr'UAYTT
84E-01 0
TT; 1817BT- o i ~u
0.14421E C2
"rr
0.26972E-01
C," Q.10'r?7E.-Gl 0.8661JE-02 0 . 88 9.0 6 E-0 ?/.''>./ V
0
' o i ~" cr,srK4T6r"niUTniBrnriJTorrrr75^r~n''-i;~"^
l» C CATAT5A 1 1
0.31P44F-CI
r-r j
0.11057F C2
OT". 0' '"'AYS
ir. -U2
C ./ bPL!.«j; 01 0,49H95£ 01
"0784-6-4"B'E-V:'-
-------
0.30673: -01
0.75717-E-02
, fl . 84864 E 01
U » 23S ft t Ll
; 0.a3?85F-01
0.57811E-C2
0.64809E 01
P'.lPJ^l.L tl
TIME- .'-CC*1. T'O'A YS' ' '
0.24M4E-C1 V,l' 47CO01
D"; 1 0 6'2 b'E - C 1 0 .79 C 3 !> E-"0 2
0.55940T-0? f.61tl:-tE-02
0.64667E 01 0.^75E-CI
0, 1136-4F-01
0 . t '44 1 r-> E - C 3
0.1391f5E-C2
COTTAR TMtNT NUHHt
2 ?
7 8 ' J
12 13
17 11-
22 ' "' ?'3
" l"inE= O'.'O OA'Y'S "
o.7i«eoE-c: o.'fr; 77E -02
""0'. 101831-01 c.r.icr^r-nr
0.^040eE-Cl 0 . 1 <' 'v.- 3 £ - 0 1
0.76936E-CJ C.CDilE-03
0.15749E-Q1 ,
0.50633E-02
0.3a201E.-pl
U.ll68Jt Ul
0.1202ZE-01
"u, 7tii9er'"02"
0.29183E 01
IDE OVFR TIME
\
y
!*>
' ,
0.9C744E-02
0.2?i12fE-01
0. 1C°32E' Oi
0.12873E-r,i
.0. 29^2;7F 01
U . ^> y b / 7 L OU
0.982,8.5E-02
U. 4*5^6? t-0?
0,39 74fe E- 02
0,22560E 01
' Q.'S3175r 00
-------
Ul
00
0,21014317E-Cl
9*K'IS 111 - ILJ '
0.95911E-0?
-------
TTCF- ; jrCUT~0'~0'FrS~~
in
us
0.10933F-01 0.95342E-C." P ..c ?fi76F-0?
0.67019E-.'?
- C'2"
-0^
0.31770E-02
7^-U2 o
C»?^'i'3f-02 . Q. 24286E-0?' .-'., (|.22g'.'
; u»iy.*M t- ui;
i
~0.11^791- 02 D1.13782F-02
="7innjT!rT5ATS"
-02 'y'Q.727951-02 n
,U,S101UE-U20.132BOL-U
i 0.34115E-C2 0,<(0665E-0?
, '0.2429SE-02 Or25181E-02
-02 0.338C2E-&2 !
_Q.2_ ^
- 7'6I5C; OT'DAYJT"
0.63719E-02 0.55572E-02
O.P6279E-02 0,
0.18551E-02 0.16911E-0?
0. J9080E-1'2
r OTJB^SFT-IR
0.34065E-02 OrSbglOF-.O?'^
O.H20RE-02 0.;30^7E-Oa ;|
lT7B'0?B'5-E^OT"aai
FARTIClLAtE COf-;CF NT? AT IOU CF PESTICIDE OVER TIME
- - cr-^'ARTMniT'l^UMPE'R ~
1
e
11
16
7
12
17
"2
1?
11
19
1':
-------
Ol
o
--- - ................. ._rr,rr_ -c^0 r?yr.._
0.1J»158f-01 0.1751r'r-'.'l "o.H77M:-n
0.12POTF=Tn O.TCB17r-ni Tl^-^nTE-tJ
OK» 0 DATAJ5A 11
-
0.13656E-01 0,
0.22494E 01 0.
iJ^a^tSfeE. 01 o.
. - /
: , . .
0.65013E-G1 0,
" U'.150E6E-C!1 0'.
. p.llJlDE-Cl 0.
0.19814C C2 0.
0 .54 OTBF Tl 0,
*
'.
' 0,45'3?4r-01 0.
O.ll'JiUL-l'l P,
0,°10^2E-C2 0,
0.182C2E 52 0,
7 0.5 02 14 E Ul ' 0,
n ,(J167f. C-02 ' 0,
0.7C~l'.'E-Crv 0.
0.14417E 12 Q,
n »i oifeE n o.
' > > , . '
4591. 'L-C,' Q.12437E-01
2249TC 'Jl 0.22't91E 01
2248rJE 01, C,?2483F 01
' . ' , ^
Tl^fi 500TO" DAY?'
492?CC-ni '.',^74^1f-01
175?4^-''l U,f<3326E'-02
28739E-D2 O^Jfc7"E-02
14969F C^ 0.11377E 02
4£032F 01 ' 5. T5M If 01
TTf'L'X TZn'P'.'C D'AYT"
38131P-U1 J.2^184E-0'l
j'Tnycif -u 1 , P'.K^H?.''1^-^?
22800!-!- 7 U.f 7^4^? -0"
1384 'jt r2 U.10ifc4E 0^
427 C^F : V" 0732773T D1-"
TI' ( - IfiCO. U FiAYfl
2 S 2 ""> (. - "! . C . ^ / .* t f\ L - 1 1
107 t?r -:|]" '~o, -o7.\'r-5r
I7f. 7fi - .)/ (>.' .:/. 71 "- or
1 1 0 0 1 C : 2 0 . 6 4 2 <. 1 F 1 1.
34414E '.I 0'^"V*£t 01
. . _. . '. /,. .- . i. .-\. .
"'.I' r. '' '-' '' ' '/ n'- ', ' '-' 'J'
I . t
7.
5 '
''' /
0,2973fer-02 0.87C62E-0^ i '
, 0.22485E 01 0.22487K 01 i
8. 2.246 Of Ol ,~> Or 224&6E OlF^ 1
- . /.... .. ' ^ i ',' '.. ' ,-. ./ ' , i . 'jj
' ' ^ ' ' { V i' >*
;.' v ' : \ i '.' . ..' > ^,\l
0.2P498E-01 0,218&5F-01 '
0 » 1 4 p3 1 E T 0 1 0.479f)BE-'021' -,1
0,16822E-Oil 0,502C-1E-G2 ,T
, &.8689.2E 01 . . 0.$7074E .01 'v.'( ..'';
0.27422E 01 0,18Cl^'r 01
1
'' "' ''.'."'. ' ^'-^
''-.- \ \.. ;:' ' tw-"'-^"
;0.22360£-01 ". 0 . 17247!: -.01 /i-'.S ?,>i
D , 1 1 88 61! - 0 1 ' 0 . ^ 8 0 1 0 f - I' r ' "*'
O.l^SSyE-O^ 0. 402S 6 F. -??.
0,8078:E 01 0.62-\'CF 01
" "'0. 2f427C' 01 \"BTl"*8t^r-"D'l'7:''"-'. '
. ." V
l"1
'
P,17ie2L-01 0.1J274t -ill
0. 1C 30 2i -02 0 ,31 17^'F. -It
C.£45'/f.1: OJ .-, .^-. fl.4^a44C. Ul^
O.iO^lJF fri ''".-O.liTJ^f 0?
. . .. ' ,-jfc.-' _> , k. ..iuV-'.. >... ^ ..-.,.-/.. v .
-------
0.22310E-01
4 0-C.TTD'AYS ~"
0»17111E-01
0.13137E-01
0.701271:^02^
Q.54041E-G2
0.110S4E C2
0.13451E-Q?
0.84760E 01
t'i
1
O.?38f47F 01
" L 0 (V
-------
OK, C DATA20A 1 1
PASS OF PEKTJCIDE ON THC MASS OF SOUJDS fHICROGRAw PER KILOGRAM)
''*'' . . .-'.' -;--. \ - '. : . '' -''^i ... v .
N * > '-'.' ,:,;;: iO->lCP.MPAfnr'E'\!T NUMBER ,V"v ''!'",
1 2 3 4
f I . 7 .ft ' . 9
11 12 13 14
:'; 16 .-.'. -17 - - 13 . . . ' i?. r -'
. '.21 \ >v 22 '/;.../ - .23"'' ; V, '-124.~ ~ 'Q"
TIME: C.O DAYS . ; ,
."' lu.zs; .""." "'.','" "ii .2-5 , T 12. 31 . :
''.'. ,: '' . 18.29' .'',".; ' ,'13.t'7 , - - 23 . 1 1 ,"'','. . 'r.-^
'.::,. 17. o:- -.- ': ,':,'< : - '}
TIKE= iri'.T.D DAYJ
-------
ll
h
V
" '.: ZF.
17.
11.
i/' "" ' 7V
I "'
ii
a
u
2 TV
. 13.
\ *'
I
o*
' '- 6.
?.
1
,
1
16.
10.
6.
1 ' *
2.
I
1
^
'
, - ~ IT .
»
05
01
?8
&Z
66
48
10
81
1 5
7E
40
02
76
7 7
9C
'?/
,
24
14
15
"~ J "' ""7
4
TIME-
Is
11
11
"~ 5
\' 3
TIME:
1
14
e
ft
4
2
TIKE:
I
ll
' ^ t
.39
.20
.1C '
.17
ieno.0
v 7 : ' "
.30
r / L
".61
.36
2 4 C' 0 . 0
. 50
O 7
» !* S
.61
i c r r , o
rCr-
2 r. 4 6
16.47 -
10.23
- - -f.l'p"'
3.72
DAYS
' IP .46 '
12.68
7.93
'-> .16 '' '
P. 99 .
DAYS
l^.'iH
9.70
ft. 08
' . y Vi
7.32
OAYi.
i .61
.....»- ^ g
-- .-'' " 12
14
U_, g
3
14
9
v 11
4
2
i
- '
. H
.f 4 '
.84
."9Q ""
.32
-
7T1?
.78
.45
.72
?66
11. ai
7
8
i
t
1
.49
.76 '
.64
.07
.-57
.. . TIT, ac
^- -,-\# ,' :, j/-,$4 L' . ;
- . 11.19 ' '<
_t_^0 -
3. 37
' ' -1' . / ': -"'
\ ' '- ''
" '" "13. 14 ' U1 :
1Z.20
O » DO
" "". - 4.3?
, 2.66 ,
^
'
""' "" 1Q. 0^"" "
9. 34
. 6.64
'' ' .',. 54 '
2. Ob
I
" ""' : ' 1
i
/.fc.8
f
-------
JOK» 0 QATA.25A
7.6e
e.ic
%6«r
>,22
2, 78"
1,70
JK.| LUFID -t
' ' ' ' ''"'
6.19
f. , a :.
TIKE= 360Q.O DAYS
' - , ' ' 7T. W "
7,^1
1.65
yrcrv
i \
'V' .
1,16
5.72
6.69
1 1
2,15
J.21
5.
r9~£
1, 20
. i -.. ..n- -i-,,r,T r V1"'^'/-^v~Tr '" ~"r>"J 1I-T"''-- -7,^--'--^. -'"^ -"'- -' T7 r|~~'-r "T1 t!%'~^'
Iij^k:;i^feiM$^i^^tejfi^ .£&
------- |