&EPA
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30613
EPA-600/9-84-026
December 1984
Research and Development
Prediction of Pesticide
Behavior in the
Environment:
Proceedings of
USA-USSR Symposium,
October 1981,
Yerevan, USSR
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EPA-600/9-84-026
December 1984
§
Nj
VJ
PREDICTION OF PESTICIDE BEHAVIOR IN THE ENVIRONMENT
Proceedings
of U.S.A-U.S.S.R. Symposium
October 1981
Yerevan, U.S.S.R.
°9 U.S.S.R. State Committee for Hydrometeorology
N and Control of Natural Environment
.U.S. Environmental Protection Agency
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30613
R;?!!"6"*3' *»**» Awsncy
Region 5, Library (Pi.. i2ji 7
/7 West Jackson Boufevaol i9fh n
Chicago, IL 60604-3590 ' h ftoor
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DISCLAIMER
The information in this document has been funded in part by the United
States Environmental Protection Agency. Papers describing EPA-sponsored
research have been subject to the Agency's peer and administrative review,
and the proceedings have been approved for publication as an EPA document.
Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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FOREWORD
Cooperation and exchange of scientific information under the US-USSR
Agreement on Cooperation in the Field of Environmental Protection has helped
both countries in their efforts to control environmental pollution. Among
several working groups and projects established under this Agreement is
Project 02.03-31, "Forms and Mechanisms by Which Pesticides and Chemicals
are Transported in Soil, Water, and Biota." Members of this project and
invited experts from government research organizations, academia, and
private industry from the two countries have exchanged visits on several
occasions. This document presents the proceedings of a project symposium
held on October 21-27, 1981, in Yerevan, USSR.
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, Georgia
iii
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PREFACE
Under the USA-USSR Agreement on Cooperation in the Field of
Environmental Protection, Soviet and American scientists, begin-
ing in 1974, are carrying on the work within the framework of the
project 02.03-31 "Forms and Mechanisms by Which Pesticides and
Chemicals Are Transported". Co-Chairmen of the project are George
Baughman, US Side and Vladimir Borzilov, USSR Side (until 1982,
David Duttweiler, US Side and Spartak Malakhov, USSR Side). The
Institute of Experimental Meteorology is the head Soviet institu-
tion under the project.
The main purpose of the project is to develop prediction
models of the behavior of pesticides and other chemicals in soil
and water.
The first stage of cooperation, i.e. familiarization with
research works under the project in both countries, was completed
for the most part in 1976 when a 7-day symposium on the Environ-
mental Transport and Transformation of Pesticides was held. Be-
ginning in 1977, the studies are being carried out under the pro-
ject on the agreed programs with the aim of developing and im-
proving the mathematical models, establishing the dependences of
their parameters on environmental characteristics, and testing
the models under laboratory and field conditions. Part of the
work has been performed independently in each country, and part -
during the exchange visits of scientists to the United States
and the Soviet Union. The results of the cooperative work were
appraised in the Protocol of the eleventh meeting of the Soviet
and American scientists engaged in the project (October 1981,
USSR) as follows: "The work carried out under the project and
planned for the future is very important from the practical
point of view and beneficial for both sides because it provides
results that cannpt be obtained by one side alone".
Some scientific outcomes of the work were reviewed at the
second symposium on the Prediction of Pesticide Behavior in the
Environment, which was hosted by the Armenian Hydrometeorologi-
cal Administration at Yerevan, October 21-27, 1981. Eleven Ame-
rican scientists (head of the delegation G. Bailey) and more
than thirty Soviet scientists (head of the delegation V. Volos-
chuk) heard ten papers from the American side, sixteen papers
from the Soviet side and one joint paper.
Based on the Synopsis of the symposium that recommended to
develop physical-Bfetktaalical models of chemicals behavior and
to test them, the program of future cooperation was worked out.
IV
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It includes continuation of joint research on:
- role of the components of natural waters in the kinetics
of pollutant transformation;
- improvement of the technique for predicting the kinetics
of microbial degradation;
- prediction of the kinetics of pesticide degradation in
soil;
- improvement and field testing of the ARM, EXAMS and sim-
ilar models for predicting the behavior of pollutants in soil
and water.
V.M. Voloschuk, V.A. Borzilov
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ABSTRACT
Under the USA-USSR Agreement on Cooperation in the Field of Environmental
Protection, a joint project committee on forms and mechanisms by which pesti-
cides and chemicals are transported sponsored a symposium on October 21-27,
1981, in Yerevan, USSR. Papers were presented by American and Soviet
scientists on predicting pesticide behavior in soil and water. Twenty-six
papers encompassed reviews of the state of the art in each country and re-
sults of research on particular aspects of the topics.
vi
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CONTENTS
Page
Foreword iii
Preface iv
Abstract vi
Present Requirements Imposed upon Pesticides in the Soviet Union ... 1
N.N. Melnikov, All-Union Research Institute-of Chemical
Means for Plant Protection
I. PREDICTION OF THE PESTICIDES BEHAVIOR IN SOIL
Metabolic Fate of Pesticides in Soil 15
J.J. Menn and G.B. Quistad, Zoecon Corporation
On the Possibility of Predicting Pesticide Behavior in Soil 33
M.S. Sokolov, Institute of Agrochemistry and Soil Science
Forecasting Pesticide Mobility in Soils: Dispersion and Adsorption
Considerations 42
R.E. Green, University of Hawaii
Adsorption of Atrazine by Soil Adsorbents 72
M.V. Khlebnikova and V.A. Konchitz, Timiryazev Academy
of Agriculture
Study on Pesticide Sorption under Irrigation to Predict and Regulate
the Processes of Their Migration in the Soil-Water System ... 82
A.I. Yiirchenko, V.G. Kovton and A.A. Vernichenko,
All-Union Research Institute of Water Protection
Characteristics of Soil Degradation Studies for Predicting Pesticide
Behavior 90
D.A. Laskowski, R.L. Swann, P.O. McCall, H.D. Bidlack and
H.J. Dishburger, The Dow Chemical Company
Translocation and Transfdonation of Pesticides in Soils and Plants . 102
K.V. Novozhilov, T.M. Petrova and Yu.B. Andreev,
All-Union Research Institute for Plant Protection
vii
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CONTENTS (continued)
Soil Processes and Their Use in Predicting Volatilization of
Pesticides from Soil 110
W.J. Farmer, W.F. Spencer and W.A. Jury, University
of California, Riverside
Modeling Transport and Degradation of Pesticides in the Soil
and Surface Water Environments 124
R.C. Johanson, University of the Pacific; A.S. Donigian, Jr.,
Anderson-Nichols, Inc.; and T.O. Barnwell, U.S. Environmental
Protection Agency
Modeling the Behavior of Pesticides Using the ARM Model 157
V.A. Borzilov, Ts. I. Bobovnikova, I.V. Dragolubova, Institute
of Experimental Meteorology; A^D. Fokin, V.V. Rachinsky,
Timiryazev Academy of Agriculture
Empirical Prediction of Space Redistribution of Pollutants in Soil
on the Basis of Field Tests 171
A.D. Fokin, Timiryazev Academy of Agriculture
Predicting the Behavior of Pesticides in Soil 178
E.I. Spynu, E.G. Molozhanova, P.E. Sova, USSR Ministry of
Health; V.S. Kikot1, Ukrainian Academy of Sciences
Biotic Responses to Pesticide Pollution of Natural Ecosystems
(Predictive Aspects) 192
L.D. Voronova and A.V. Denisova, USSR State Committee for
Hydrometeorology and Control of Natural Environment and
USSR Academy of Sciences
II. PREDICTION OF THE PESTICIDES BEHAVIOR IN WATER
Progress in Predicting the Processes that Determine Pesticide
Concentrations in Aquatic System 198
G.L. Baughman,, S.W. Karickhoff, D.F. Paris, N.L. Wolfe
and W.C. Steen, U.S. Envronmental Protection Agency
Prediction of Pesticide Behavior in Water 210
V.T. Kaplin and T.P. Likhovidova, USSR State Committee
for Hydrometeorology and Control of Natural Environment
Influence of Natural Substances on the Photoreactivity of Pesticides
in the Aquatic Environment 230
R.G. Zepp, P.F. Schlotzhauer and G.C. Miller, U.S. Environmental
Protection Agency
viii
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CONTENTS (continued)
Page
Approaches to the Study of the Kinetics of Liquid-Phase Pesticide
Transformation 251
Yu.I. Skurlatov, Institute of Chemical Physics; L.S. Ernestova
and T.V. Shpotova, Institute of Experimental Meteorology
Effect of Some Ecofactors on 3,4-Dichloroaniline Degradation in
Natural Water 261
G.K. VasiTyeva, N.D. Anan'eva and M.S. Sokolov, USSR
Academy of Sciences
Transport of Pesticides and Related Chemicals Across Air-Water
Interfaces 268
L.J. Thibodeaux, University of Arkansas
Modeling Herbicide Residue Behavior in Aquatic Ecosystems, Using
3,4-Dichloroaniline as an Example 290
V.A. Borzilov, L.S. Ernestova, N.I. Troyanova, Institute of
Experimental Meteorology; M.S. Sokolov, USSR Academy of
Sciences; G.L. Baughman, D.L. Brockway, D.S. Brown,
R.R. Lassiter and W.C. Steen, U.S. Environmental Protection
Agency
Rationale and Results of Testing a Chemical Fate Model in an
Experimental Ecosystem 291
R.R. Lassiter, U.S. Environmental Protection Agency
Verification of a Toxics Fate and Transport Model 304
J.L. Schnoor, University of Iowa
Technique for Predicting River Water Pollution by DDT and ft-BHC
Residues during Spring Floods 340
Z.L. Sinitsyna, USSR State Committee for Hydrometeorology
and Control of the Natural Environment
Regularities of Pesticide Accumulation and Migration in the Ecosystems
of Lowland Reservoirs • 346
L.P. Braginsky, F.Ya. Komarovsky and A.Ya. Malyarevskaya,
Ukrainian Academy of Sciences
Mathematical Model of Pesticide Effects on Aquatic Ecosystems .... 352
V.V. Alekseev, Moscow State University
IX
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PRESENT REQUIREMENTS IMPOSED UPON PESTICIDES
IN THE SOVIET UNION
N.N.Melnikov
Ail-Union Research Institute of Chemical Means
for Plant Protection,USSR
Ministry of Chemical Industry,
Moscow
It is now generally acknowledged that all chemical compo-
unds circulate in the environment (air, soil,plants,hydrosphere,
hydrobionts,animals and humans). The duration of circulation
is different for various compounds, and some unstable substan-
ces do not undergo all stages of the circulation. However,
persistent substances and particularly those containing mercury,
arsenic,lead,selenium,cadmium and other elements are capable of
accumulating in particular environmental objects,thus adversely
affecting them( 2,9,10,19,20) . Such elements as cadmium,mer-
cury, lead and arsenic have accumulated in the World ocean in
fairly dangerous concentrations due to their bioconcentration
in hydrobionts( 10) .
The circulation of pesticides in the environment occurs
in a similar way( 10,26) . Substances with high volatility and
low chemical persistence undergo relatively rapid degradation
and do not accumulate in environmental objects, while rather
stable and lipophilic substances accumulate most intensively in
various hydrobionts (2,42) .For example, the coefficient of
bioconcentration for rainbow trout is 124 for DDT; 5f360 for
pentachlorophenol; 947 for carbaryl; and 10,000 for 2,5-dichlo-
ro-4-nitrosalicyl anilide( 37) . This high bioconcentration of
organochlorine compounds in hydrobionts and other environmental
objects results in their accumulation in foodstuffs and even
in their entry into human milk (29) , which is rather dangerous
to infants.
From the above it follows that major requirements on pes-
ticides are determined by their behavior in environmental ob-
jects. Therefore the problem of pesticide transformation in va-
-------
rious environmental objects should be considered in greater
detail.
It is common knowledge that as a simplification, by the
environment is meant the crust of the Earth with the biosphere
and outer space surrounding the globe and affecting the life
processes. The biosphere comprises all living organisms includ-
ing animals,microorganisms and plants, as well as residues of
these organisms, both dead and undergoing various stages of de-
composition and transformation to simple organic and inorganic
compounds. Included are also all combinations of living and
nonliving matter (atmosphere,hydrosphere and soil).
Atmosphere. Pesticides may enter the atmosphere directly
when applied by spraying or dusting,using ground-based facili-
ties and particularly aircraft, and also as a result of evapo-
ration from the soil or water surfaces. Clearly, the largest
amounts of pesticides enter the atmosphere when being applied,
since pesticide evaporation from soil occurs much slower due
to their partial adsorption by soil colloids. Therefore,one and
the same compound will evaporate from the surface of soil with
different composition at different rates. Besides,the rate of
pesticide evaporation from soil is greatly affected by the na-
ture of a substance and its ability to be adsorbed by soil col-
loids, as well as by the temperature and velocity of air motion
over the soil surface (48) .
Among the major processes determining the fate of pestici-
des in the atmosphere are their diffusion to the upper layers,
deposition to soil,sedimentation to water bodies,photochemical
degradation,hydrolysis and oxidation by oxygen and ozone. Of
particular interest are chemical transformations of pesticides,
in most cases resulting in less toxic products compared to
parent compounds. Among these are primarily reactions of water
vapor hydrolysis and oxidation by oxygen and ozone (28) .Some-
times photochemical transformations of pesticides result in
the formation of rather stable substances capable of persisting
in the ^environment for a long time. For example, polychlorodi-
phenyls (28) and isomers I-chloro-2-(4-chlorophenyl)-2(2,4-dich-
lorophenyl)-ethylenes and 3,6,9»IO-tetrachlorophenanthrene( 34)
were found in the photochemical transformation of DDT and DDE.
Photolysis of insecticidal derivatives of carbamic acid pro-
ceeds more easily yielding corresponding phenols and their
derivatives (4-7) * wheras photolysis of pentachlorophenol re-
sults in almost complete degradation of the molecule (28) . As
to 2,3,7,8-tetrachlorodibenzodioxin ( 28) ,it is partially
dechlorinated in a solution of methanol to form a correspond-
ing trichloro derivative.
Pesticide photolysis is a major pathway of their transfor-
mation in the atmosphere, along with dispersion in the upper
atmospheric layers. Thus, natural pyrethrins are not used ex-
tensively for plant protection because of their low photoche-
mical stability.
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The second place is occupied by reactions of hydrolysis
and oxidation which appear to be of prime importance for de-
gradation of organophosphorus compounds in the atmosphere 08)
Sometimes even more complicated processes occur, as in the
case of photolysis of trifluralin,where azo-.and azoxy-compo-
unds were found to be formed( 50) . Other forms of photochemi-
cal transformations of various classes of pesticides were also
observed ( 46 ) .
Clearly,those pesticides should be preferable for practi-
cal application (other factors being equal) which decompose
most rapidly to form nontoxic products.
Hydrosphere. Pesticides and other chemical compounds may
enter water bodies either directly or from the atmosphere and
soil,or else in the form of products of human and animal vital
activity. Pesticides may arrive from the atmosphere with rain-
fall or through direct deposition in the form of drops and so-
lid particles, as a result of wind drift from aerial and even
ground spray or dust application to plants, and when applied
directly to water bodies to control mosquitos and other harm-
ful insects. After entering water bodies,pesticides undergo
chemical (hydrolysis,oxidation and photochemical decomposition)
and biochemical (uptake by hydrobionts and metabolic decompo-
sition) transformations.
Toxicity to hydrobionts and ability for bioconcentration
in plankton, vertebrate and invertebrate hydrobionts (1,12,42)
are important criteria to be taken into account when choosing
pesticides,especially persistent ones which eventually can
cause lethal effects if accumulated in the organism of hydro-
bionts. Of all insecticides, organochlorine chemicals accumu-
late most rapidly,whereas accumulation of organophosphorus
compounds in. fish and water is insignificant (29) • This has
been pointed out both in the early (2,10,26,4-2) and recent
studies, resulting in a considerably decreased use of organo-
chlorine insecticides, and in bans on the use of DDT and seve-
ral other organochlorine compounds in the Soviet Union. Along
with pesticide bioconcentration of great importance are path-
ways of metabolism in various hydrobionts and its rate (6)
related directly to pesticide accumulation in the hydrobionts
and the rate of their removal. These factors can be determined
using the method suggested by R.L.Metcalf and coworkers (26)
for an artificial ecosystem.
Microbial degradation of pesticides occurs in bottom
sediments as well. As a rule,the most rapid is metabolism of
those pesticides which form hydrophilous products of metabolic
transformations.
Based on the presently available data, all pesticides may
be subdivided into the following six groups,as to their persis-
tence in the hydrosphere and other environmental objects:
I — pesticides with a period of complete degradation over
18 months;
2 - pesticides with a degradation period up to 18 months;
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3 - pesticides with a degradation period not over
up to 12 months;
4 - pesticides with a degradation period of no longer
than 6 months;
5 - pesticides with a degradation period of 3 months;
6 - pesticides with a degradation period under 3 months;
By degradation is generally meant the breakdown of a chemical
under the influence of various chemical and biochemical factors
of the biosphere yielding nontoxic products.
It should be noted that the degradation period of one
and the same pesticide depends to a large extent on meteorolo-
gical conditions, type of ecosystem, population of water body,
composition and properties of bottom sediments. Ambient tempe-
rature and solar intensity also play an important part: the
higher they are, the more rapidly the degradation.processes
occur for most pesticides in aquatic and other ecosystems. All
the above factors should be taken into account when choosing
pesticides for a given climatic zone. Clearly, those chemicals
should be preferable which have a degradation period under
3 months and are incapable of accumulating in water and hydro-
bionts ( 6 ) .
As an illustration, Table I shows the data on the degra-
dation rates of various pesticides in river water ( 32 ) .Organo-
chlorine insecticides are most persistent in water,whereas
derivatives of carbamic acid and organophosphorus compounds
are hydrolyzed rather rapidly. A similar situation is observed
when studying pesticide persistence in fish ( 42 ) . The data
on pesticide persistence in fish are given in Table 2.
It should be noted that organochlorine pesticides have
much higher toxicity to fish compared to organophosphorus
compounds and derivatives of carbamic acid ( 26 ) . The data on
the toxicity of organochlorine insecticides to freshwater and
marine fish are given in Table 3, and those of organophospho-
rus and carbamate insecticides in Tables 4 and 5>respectively.
Toxicity of some herbicides to fish ( 8 ) is shown in Table 6
It is seen from the tables that biological activity of
various groups of compounds is strongly dependent on their
structure and therefore, on the pathways of their metabolic
transformations in the organism of hydrobionts and the eco-
system as a whole.
Naturally,the values given in Tables I to 6 are not ab-
solute, since they are strongly dependent on experimental con-
ditions and can vary over a wide range. Their relative characJ-
ter,however, is retained and may serve as a guide for compar-
ing chemicals.
Soil. A most important environmental object is soil which
forms a peculiar biogeochemical envelope, an essential compo-
nent of the biosphere,where a great number and variety of li-
ving organisms are concentrated, as well as the products of
their metabolism and die-off.Soil with soil-dwelling organisms
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Table I. Pesticide degradation rates in river water
Pesticide % of the initial amount
After 2 weeks After 4- weeks
Hexachl oro eye lohexane
(BHC) 100
Dieldrin 100
DDT 100
Endrin 100
Endosulfan 30
Heptachlor epoxide 100
Phosphamide /Dimethoate/ 100
Fenthion 50
Thiofos /Parathion/ 50
Carbofos /Malathion/ 25
Aminocarb 60
Propoxur 50
Carbaryl 5
Fenuron 60
Monuron 4-0
100
100
100
100
5
100
85
10
30
20
10
30
0
20
30
Table 2. Persistence of pesticides in fish
Pesticide Persistence
Diquate < 3 weeks
Simazine <72 hours
Sodium arsenite >I6 weeks
Diazinon <^ I week
Azinphos-methyl < I week
Thiofos/Parathion/CI week
Methoxychlor I week
Dieldrin I month
DDD > 6 months
Pesticide
Endothal
2,4 D
Dichlorbenil
Dursban
Carbofos
/Malathion/
Lindaae
Heptachlor
Toxaphene
DDT
Persistence
< 3 weeks
< I week
< 2 weeks
< I week
<24 hours
<4-8 hours
I month
> 6 months
> 6 months
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Table 5» Toxicity of organochlorine insecticides to fish
Insecticide
Freshwater fish
mg/1
Marine fish
Aldrin
Heptachlor
DDT
Dieldrin
Lindane
Methoxychlor
0.0052-0.0082
0.008-0.019
0.0016-0.005
0.0014-0.0028
0.027-0.087
0.05-0.075
5
Mirex
Penthachlorophenol 0.052-0. II
(PGP)
Perthane 0.005
Toxaphene O.OII-O.OI8
Chlordane 0 . 0078-0 .04
Endosulfan 0.012
Endrin 0.0004-0.0086
0.0028
0.0055-0.025
0.0004-0.002
0.0055-0.0071
0.05
0.055
2
0.00001-0.0055
0 . 0055
0.0006
0.0006-0.0026
is an universal biological adsorbent and neutralizer of orga-
nic compounds, and the result is decomposition of most waste
material arising from man's economic activities and its use
as a source of carbon and other elements essential for vital
activity of organisms (5,10,15t14,18,26) . Large concentra-
tions in soil of various chemical substances with high biolo-
gical activity can adversely affect the vital activity of soil
organisms and have a detrimental effect on self-purification
capacity of the biosphere( 5|I3|I8 ) .
Pesticides are transformed most intensively by various
soil microorganisms( 4,5,10,11,15-17,25,24,26,27,51.55,56,44,
4-5151) which in most cases use them as a source of carbon.
Pesticide degradation in soil can proceed both by the oxida-
tion and reduction mechanisms, depending on conditions. Under
aerobic conditions pesticide degradation occurs in most cases
as a result of oxidation,whereas reduction reactions, such as
transformation of DDT to DDD are possible only under anaerobic
conditions.
When choosing pesticides,one should take into account
not only their degradation rate in soil and other environmen-
tal objects but also their toxicity to useful soil organisms,
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Table 4. Toxicity of some organophosphorus insecticides
to fish
Insecticide
Freshwater fish
Marine fish
Azinphos-methyl
Demeton
Diamidafos
Diazinon
DDVP
Dicrotophos
Dimethoate
Disulfoton
Carbofos
/Malathion/
Metafos
/Methyl parathion/
Monocrotophos
Naled
0.014-0.052
0.081
1-306
0.002-0.09
0.48
6.3
6-8.5
0.04
O.I03-O.I?
2.7-5.7
4.0-4.9
0.08-0.33
0.0055
0.55
I
0.25
0.55
I
I
0.74
0.57
I
I
0.55
(I,2-Dibromo-2,2-dichloroethyl-0,0-dimethylphosphate)
Parathion
Trichloronate
Phosphamidon
Fenotrothion
Chlorof os
/Dipterex/
Chlorpyrif os
Chlorpyrif os , methyl
Ethion
0.47-2
0.24
8.0
0.7
0.26-1.4
0.0033
0.014
0.23
0.065
0.32
I
I
1. 00
0.07
0.15
0.069
such as earthworms( 26 ). Other factors being equal,prefersnee
should be given to those chemicals that are rather innocuous
for the useful soil flora and fauna.
Along with studies on the behavior of pesticides in the
atmosphere, hydrosphere and soil, of great importance, are
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Table 5. Toxicity of some carbamates to fish
Carbamate ^50 * ms/1
Freshwater fish Marine fish
Aminocarb
Carbaryl
Methiocarb
Mexacarb
Propoxur
—
4.34 - II. 2
O.II-0.64
15
8.2
I
1.75
0.55
I
I
those in animals and plants that is essential to prevent their
entry into man's food chains. It is necessary primarily to
study a possibility of pesticide entry into the milk of domes-
tic animals that is an essential food product of man,along
with other milk products (21,22,25,30,40 ) . In some cases,
cats, rats, dogs,. rabbits and other animals (25), rather
than cows,are used as models for studying metabolism. Note
that sometimes pesticide metabolism in the organism of birds
goes through a somewhat different pathway than that of warm-
blooded animals. It should be pointed out that more or less
considerable amounts of organochlorine pesticides and polychlo-
rinated biphenyls ( 49 ) have been found in food products up
to the present time. A positive property of many pesticides is
their capability (or that of their transformation products) to
form in the organism of animals water—soluble conjugates which
can be easily excreted (41) . Many pesticides form conjugates
with plant substances as well. These conjugates,however, are
less mobile and in many cases are retained in a plant for a
long time. Naturally, the use of these chemicals in rather
considerable amounts is less advisable. It is interesting
that until very recently some amounts of pesticides have been
found in the human organism (43) •
Based on the foregoing, the following general requirements
on pesticides can be formulated:
— moderate persistence in a soil—cliHiatic zone;
- low toxicity to hydrobionts and other useful organisms
dwelling in water bodies and soil;
- rapid degradation in water and soil yielding products
which are harmless to useful organisms;
- the absence of cumulation in the organism of man and
animalst
- the absence of remote adverse effects on man,animals
8
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Table 6. Toxicity of some herbicides to fish
0 , mg/1
Herbicide ... . .
Freshwater fish Marine fish
Amitrole 50
Atrazine 12-26 1.0
Bensulide 0.81 0.32
Bromoxynile 0.05
Butylate 5.5 1.0
2,4-D, Butyl ester 0.39-2 5
Dalapon 87 50
Dinoseb 0.07-0.3
Diquat 10 I
Dichlone 0.12-0.34
Diuron 3-60 6.3
Molinate 0.46-1.3 I
Monuron 76 16.3
Picloram 21
ftopachlor 0.16
Simazine 25-100
2,4,5-T 12-50
(2,4,5-Trichlorophenoxyacetic acid)
Sodium trichloracetate 100 I
(TCA)
Trifluralin 0.01-0.09
and various useful organisms, when pesticides are used syste-
matically for a long time;
- a possibility to use alternately chemicals belonging
to various classes of compounds to prevent the occurrence of
resistant forms of harmful organisms and the accumulation of
the chemicals in the environment;
- maximum effectiveness against harmful organisms at lo-
west possible rates of application;
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- sufficient safety of application and impossibility of
acute poisoning;
- rather high economic efficiency of the use in agricul-
ture and other fields;
- convinient and safe form of application.
When using pesticides in agriculture,of great importance
is a correct determination of reentry period (time of waiting
after pesticide application), as well as an accurate regula-
tion of pesticide residues in food and forage crops to provide
a safe use of agricultural products of plant and animal origin.
As an example of searching for new organophosphorus pesti-
cides, we can mention two directions being developed in tne
Soviet Union.
First, synthesis of mixed ethers of phosphorus acids con-
taining a peptide bond in the ether radical (35):
R y
\ //
p
R^ XCHCOtfHCHCOOR4
1 1 U
R R3
As a result of metabolism in environmental objects,these
compounds form harmless amino acids and phosphoric acid which
can be assimilated by various living organisms.
Second, synthesis of organic compounds of phosphorus with
asymmetric carbon. Among these compounds are substances with
different physiological activities,including insecticides,
nematocides, fungicides and herbicides (7) ;
R X
\ //
A
Y Z
LITERATURE CITED
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pesticides. Publishing House of Moscow State University;
Moscow; I960, 120 p. /in Russian/.
5. Glazovskaya, M.A. On soils classification by their re-
sistance to chemical pollution. In "Methods and Problems
of ecotoxicological modeling and predicting1'. Pushchinos
1979,6-20 /in Russian/.
10
-------
4-. Golovleva, L.A. et al. Microbial metabolism of the
thiocarbamate herbicide ordram. Izv.AN SSSE (Transacti-
ons of the USSR Academy of Sciences), Biology Series,
1978, 44-51 /in Russian/.
5. Finkelstein, Z.I.; Golovleva,L.A.;Golovlev,E.L.;
Skryabin.G.K. Microbial degradation of the herbicide
alvison-8. Microbiologya 1976,4-5,879-883 /in Russian/.
6. Melnikov.N.N. Protection of water resources from pesti-
cide contamination. Khimiqa v selskom khozjaistye
/Chemistry in Agriculture/ 1978, 16,No.9,35-40 /in
Russian/ .
7. Melnikov, N.N. Promising directions of searching for
new pesticides. Vlllth International Congress of Plant
Protection. Papers presented at the sessions: Moscow;
1975,2,107-112 /in Russian/.
8. Melnikov,N.N. Pesticides in the integrated system of
plant protection.Khimi.la v selskom khoz.laistve I960,
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9. Melnikov,N.N. Pesticides and environmental protection.
Journal of the All-Union Mendeleev* Chemical Society
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and the environment. Publishing House "Khimija:"Moscow;
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II. Golovleva,L.A. et al. Microbial degradation of herbici-
des ordram and 2,4- D in a water body. Izv.AN SSSR,
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12. Metelev,V.V.; Kanaev,A.I.;Dzasokhova,N.G. Aquatic
toxicology. Publishing House "Kolos": Moscow; 1971,
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I4-. Rodin,L.E.iBazilevich,N.I. Dynamics of organic matter
and biological turnover of substances in the main
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Leningrad; 1965,253 p./in Russian/.
15. Skryabin,G.K. et al. A novel pathway of microbial degra-
dation of molinate. DAN SSSR /Papers of the USSR Aca-
demy of Sciences/ 1978,239,717-720 /in Russian/.
11
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16. Skryabin,G.K.; Golovleva,L.A. The use of microorga-
nisms in organic synthesis. Publishing House "Nauka";
Moscow; 1976,233 p./in Russian/
I?. Skryabin,G.K. et al. DDT degradation to phenylacetic
acid by Pseudomonas Sp.64 ox. DAN SSSE 1977,237,1212-
1215 /in Russian/.
18, Sokolov,M.S.; Strekozov,B.P. Sequence and some prin-
ciples of pesticide regulation in soil. Khimi.la v
selskom khoz.laistve I975*13fNo.7,63-67 /in Russian/.
I9» Spynu,E.I.; Ivanova,L.N. Mathematical prediction and
prevention of the environmental pollution by- pestici-
des. Publishing House "Meditsina": Moscow; 1977,166 p.
/in Russian/.
20. Trunova,O.N. Biological factors of water body and se-
wage water self-purification. Publishing House "Naukal'
Leningrad; I979fHO p. /in Russian/.
21. Akhtar,M.H.j Foster,T.S. Metabolism and excretion of
tetrachlorovinphos in dairy cows. J.Agr.Food.Chem.
1980,28,698-704-.
22. Attalah,Y.H., Yu,C.C., Whitacre,D.M. Metabolic fate
of the herbicide buthadiazol in lactating cows and
laying hens. J.ART.Food Chem. 1980,28,278-286.
23. Bjjilasco,I. J., .Harvey , J. Jr. In vitro metabolism of
C - labeled oxamyl and selected metabolites of oxa^-
myl. J.Agr.Food Chem. 1980,28,689-692.
24. Bingham,S.W.; Shaver.R.L.j Guyton,C.L. Peanut uptake
and metabolism of (14C) oxadisoa from soil. J.Agr.
Food Ghem. 1980,28,735-740. —
25. Borsetti,A,P. Determination of pentachlorophenol in
milk and blood of dairy cattle. J.Agr.Food Chem.
1980,28,710-714.
26. Brown,A.W,A. Ecology of pesticides. New York.Wiley;
1978, 525 P.
27. Bull D.L. j Shaver,T.N. Fate of potassium 3,4-dichloro-
5-isothiazolcarboxylate in soil. J.Agr.Food Chem.
1980,28,982-985.
28. .Crosby,D.G. The significance of light-induced pestici-
de transformations. In "Advances in Pesticide Science",
Geissbuhler, H.,Ed., 1979,3,568-576.
12
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29. Davies, J.M.D. J. ;Miles,W. Traces of mirex in some Cana-
dian human milk samples. Bull .Environ. Contain. Toxicp]..
1978,19,564-570.
30. Gaughman,L.C. i Ackerman,M.E. $Unai,T. $ Casida J.E.
Distribution and metabolism of trans-and-cos-permeth-
rin in lactating Jersey cows. J.Agr.ffood Chem. 1978,
26,613-615.
31. Echols.G.W. ;Lichtenstein,E.P. Movement and metabolism
of (^C) phorate in flooded soil system. J.Agr.Eood
Chem. 1978,26,599-604.
32. Eichelberger, J.W.iLichtenbergjJ. J. Persistence of
pesticides in river water. Env. Sci . Technol. 1971, 5,
541-544.
33* Golovleva,L.A. et al. Microbiological transformation
of xenobiotics by Nocardia. Proceedings of the Int.
Symposium on Nocardia and Streptomices, Warsaw, 4-8
October , 1976 , 269-283.
34. Gothe,R. et al.Photo-isomerization and photo-degrada-
tion of DDE. Tetrahedron Letters 1976,4501-4504.
35. Kabachnik,M.I.;Mastryukova,T.A. Synthesis and selecti-
vity of action of some new thiophosphoro-organic insec-
ticides. In n Advances in Pesticide Science", Pergamon
Press: ±979, 2,120-129.
36. Kimber,R.W.L. An evaluation of the persistence in
soil of two non-chlorinated insecticides analogous
Pest. Sci. 1980,11,533-545.
37. Lech,J. J. i Bend,J.R. Relationship between biotrans-
fonnation and the toxicity and fate of xenobiotic
chemicals in fish. Environ. Health Perspectives I960,
34,115-131.
38. Mikami,N.; Ohkawa,H.j Miyamoto, J. Photodecomposition
of salithion and phenthioate. J.Pestic.Sci. I977»2,
279-290.
39. Miles, J.R.W.; Harris, C.R. Insecticide residues in
water sediment and fish of drainage system of the
Holland Marsh, Ontario, Canada 1972-75. J.Econ.Enfcimol
1978, 71, I25-I3I.
40. Oehler,D.D.$ Ivie,G.W. Metabolic fate of the herbi-
cide dicamba in a lactating cow. J.Agr.Food Chem.
I960, 28, 685-689.
13
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Paulson,C.D. Conjugation of foreign chemicals by ani-
mals. Res.Rev. 1980,76,31-72,
42. Pesticides in aquatic environments; Khan,M.A.Q. ,Ed.;
Plenum Press: New York,1977} 257 p.
43. Report of the government chemist *979; London; 1980;
197 p.
44. Rosenberg, A; Alexander, M. Microbial metabolism of
2,4,5-trichlorophenoxyacetic acid in soil, soil sus-
pension, and axenic culture. J.Agr.Food Chem. 1980,
28, 297-302.
45. Rosenberg, A: Alexander, M. 2,4,5-trichlorophenoxy-
acetic acid (2,4,5-T) decomposition in tropical soil
and its cometabolism by bacteria in vitro. J.Agr.
Food Chem. 1980, 28,705-709.
46. Saleh,M.A.; Casida,J.E. Reductive .dechlorination of
the toxaphene component 2,2,5-endo, 6-oxo, 8,9,10-
heptachlorbornane in various chemical,photochemical
and metabolic systems. J.Agr .Food Chem. 1978,26,583.
47. Slik,P.J.j Semiluk, C.P.; Unger,L. The photoreaction
of carbamate insecticides. Phytoparasitica 1976,4,
51-63. ^
48. Spencer. W.F.; Farmer, W.J. $ Cliath, M.M. Pesticide
volatilization. Res.Rev.I973. 49, 1-47.
49. Sullivan, J.H. Pesticide residues in imported spices.
J.Agr.Food Chem. 1980,28,1031-1034.
50. Sullivan,R.G.; Knoche,H.W.; Markle.J.C. Photolysis of
trifluoralin. J.Agr.Food Chem. I960, 28, 746-755.
51. Bromilov,R.H.;Baker, R.J.; Freman,M.A.H.; Gb*rb*g,K.
The degradation of aldicarb and oxamyl in soil. Pest.
Sci. 1980, II, 371-378.
14
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METABOLIC FATE OF PESTICIDES IN SOIL
by
Julius J. Menn and Gary B. Quistad
Zoecon Corporation
Agrichemicals Research Department
975 California Ave.
Palo Alto, CA 94304
The soil serves as a massive reservoir for chemicals which reach it as
unintentional and intentional contaminants. In order to predict the impact
of those chemicals on the soil environment it is important to determine
their degradative fate, establish metabolic pathways, and assess the ecotox-
icological significance of the terminal degradation products remaining in
the soil.
Physical modeling of pesticide degradation under controlled laboratory
conditions is of immeasurable value in predicting behavior and fate under
actual field conditions. Useful models for laboratory studies have been
reported by several investigators, including: Bartha and Pramer (1965) (1);
Lichtenstein and Fuhremann (1974) (2); Best and Weber (1974) (3); Bromilow
and Leistra (1980) (4); Rhodes (1980) (5); Liang and Lichtenstein (1980)
(6); Yockim et. al. (1980) (7); Guth (1980) (8) and 1981 (9); and Koeppe
and Lichtenstein~Tl982) (10).
The foregoing cited studies have provided key information important in:
1. Predicting environmental impact of terminal residues.
2. Predicting field behavior from controlled laboratory studies.
3. Determination of extent of bound residues and binding capacity of
soil.
4. Determining whether unique, toxicologically significant terminal'
residues are formed in the soil.
5. Assessing hazard of terminal residues by comparing their occurrence
and magnitude in animals, plants, and soil.
The latter (i.e. 5.) is useful in situations where unusual degradation
products can be identified in the soil. In such situations further
toxicological studies may be needed to assess the potential hazard of
terminal residues.
15
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Predicting nonmetabolic disposition rates and routes for pesticides in
the soil environment has been reported in this volume by other participants
in the symposium. Our primary concern is with degradation associated with
xenobiotic metabolism of microbial, fungal or exoenzymic origin.
The various factors governing degradation and dissipation of pesticides
in soil can be identified as follows:
1. Chemical nature of the pesticide
2. Formulation and delivery system
3. Physical parameters - volatilization, sorption, leaching
4. Chemical parameters - hydrolysis, photolysis
5. Metabolism by microflora
6. Uptake by plants
7. Runoff in water or eroded soil
8. Edaphic parameters: soil type, pH, temperature, moisture
Of all parameters listed, degradation resulting from microorganismal
metabolism is mostly responsible for dissipation of the parent pesticide in
soil (11). Soil microflora account for less than 0.1^ of soil by volume,
yet population density reaches 109 ogranisms/g soil and fungal hyphae may
reach several 1000 m/g soil and the biomass of microorganisms per hectare
soil approaches several tons (11).
The major transformation reactions associated with microbial action in
the soil are shown in Table 1. These reactions are largely of the same type
involved in metabolism of xenobiotics in animals and plants (12). Generally
these reactions include various hydrolyses, oxidations, reductions,
desalKylation, decarboxylation and isomerizations. While in animals and
plants these transformations facilitate further conversion into excretable
or stored conjugates, these conjugation capacities are apparently lacking
in soil microorganisms. However, metabolic capabilities of higher organisms
are often modest by comparison with soil inhabiting heterotrophic bacteria
and fungi (13). This can be illustrated by the ability of soil micro-
organisms to cleave aromatic rings via action of dioxygenases, eventually
leading to total mineralization of aromatic xenobiotics (14). Indeed some
aromatic herbicides are metabolized to compounds with functional substitu-
tions such as: -NH2, -OH, and -COOH. These products can be further
incorporated by polymerization and esterification reactions into humic
substances, thus remaining bound to components of the soil (15).
Metabolism of pesticides in the soil is also aided by cometabolism or
cooxidation without concomitant, sustained growth of a microbial population
which is growing on another substrate (16, 13). Golovleva et. a!. (17) have
demonstrated the foregoing cometabolism phenomenon in metab"bTic studies with
the rice herbicide, molinate. The metabolism of molinate b^ Mycococcus sp.
104 was greatly accelerated in the presence of sucrose (0.1^) as a cosub-
strate. Similarly, addition of ethanol (0.5%) accelerated the metabolism
of molinate by Pseudomonas sj). 99. Further examples of microbial cometabo-
lism of xenobiotics in the presence of various cosubstrates were given by
Skryabin et. al. (18). As stated previously, the ability of soil micro-
organisms to metabolize xenobiotics in the soil is a major contributing
16
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Table 1. Major Transformations of Pesticides in Soil
Reaction
Example
1. Hydrolysis
Ester
Ami de
CsN -> CONH,
COOH
Many
Propanil (Stam)
Fenvalerate
2. Oxidation
Hydroxylation (Aliphatic, Aromatic)
Epoxidation
P=S -»• P=0
Sulfoxidation
Carbofuran
Aldrin
Parathion
Phorate
3. Reduction
Parathion
4. Involving Halogen
Cl -> OH
01 -»• H
Denydrohalogenati on
Atrazine
DDT, Lindane
DDT
5. DealKylation
0 -
N -
Methoprene
Trifluralin, Triazines
6. Isomerization Cis + Trans
Permethrin
7. Decarboxylation
Many
17
-------
factor to reduction of the pesticide load in the environment. Several
recent reports have shown that the soil microbial population can adapt and
be induced to metabolize certain biodegradable pesticides at an
accelerating rate, in some instances rendering the pesticide less useful.
Felsot^t. al. (19) have reported evidence suggestive of an inductive
selection process in Pseudomonas sp. in Illinois cornfields, resulting in
accelerated degradation of the carbamate insecticide carbofuran and
reduction in corn rootworm control. Reduced weed control in corn in certain
soils in New Zealand was apparently due to accelerated microbial degradation
of the herbicide EPIC plus antidote [Rahman et al., (20)]. Similar reports
of accelerated degradation of EPIC plus an antfHote (R-25788) in soil
resulting in reduced weed control were attributed to microbial degradation
in certain midwestern soils in the U.S. Degradation was retarded by
coapplying an extender, Stauffer R-33865, which most likely inhibited
microbial oxidation or hydrolysis of the thiocarbamate herbicide [Harvey and
Schuman, (21); Gunsolus and Fawcett, (22)].
The U.S. Environmental Protection Agency (EPA) has recognized the impor-
tance of soil degradation studies as being highly important in incremental
risk assessment associated with the registered use of pesticide chemicals.
Relevant to this, the EPA has developed detailed guidelines for soil
metabolism studies which are summarized in Table 2. These requirements are
based on the Federal Insecticide, Fungicide, and Rodenticide Act as amended
in 1972, 1975, 1978, and 1980, and on environmental chemistry guidelines
issued by the EPA in 1975, 1978, and 1980.
Many of the required studies can be conducted in soil biometer flasks
developed by Bartha and Pramer (1) and improved by Laskowski et. a±. (23).
An essential feature in these studies is the requirement for compound
radiolabeling in one or more positions, usually using l^C an(j occasionally
35S, 32P, 3H and 36C1 as a means of tracing the fate of the molecule
and establishing a material radiobalance in the course of the study. Soil
studies in biometer flasks should be conducted with fresh or "viable" soil
to preserve microbial activity and soil structure. Under such conditions
closer simulation of actual conditions may be achieved. Guth (8) has
concluded from laboratory and field studies with 12.pesticides that no
substantial differences existed in the pattern of metabolism of these
compounds as a function of locale. However, the small number of published,
comparative laboratory and field studies suggests that more research is
needed in this area. Laboratory studies with a variety of soils has also
established that soil type had minor impact on the metabolic pathway of
pesticides studied (8).
To illustrate the major points discussed here as related to biodegrad-
ation of pesticides in soil, the following review of metabolism studies
with several insecticides (fenitrothion, diflubenzuron and pyrethroids) and
the herbicide molinate provides further details on these studies, their
significance and environmental impact.
18
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Table 2. Summary of U.S. EPAl/ Guidelines for Soil Metabolism Studies
Aerobic Soil
Compound radiolabeled in one or more positions
Material balance; rate, type, and degree of metabolism
Identification of residues (more than 10% applied dose or 0.01 ppm)
Three or more soils for product with broad uses
Maintenance at 18-30°C and 75% of 0.33 bar moisture
Data collection until 90% loss of pesticide (up to 1 year)
Anaerobic Soil
Radiolabeled compound optional
Unnecessary if anaerobic aquatic metabolism study done
Pesticide first aged aerobically for 1 month
Anaerobicity (by water logging or purging with inert gas)
I/ Extracted from: Federal Register. Monday July 10, 1978, Part II
FENITROTHION
A comprehensive laboratory study describing the degradation of the
organophosphorus ester insecticide, fenitrothion, was reported by Spillner
et£K (24), who studied its fate in two forest soils using [ring-^C]-
fenitrothion (20mCi/g). The methodology and results used in this study are
described here as an illustrative model for metabolic fate studied in the
soil. This compound has been extensively studied in plants, animals and
components of the environment [Ohkawa et al. (25); Greenhalgh and Marshall
(26); Miyamoto et al_. (27); Takimoto aTid" Miyamoto (28); Loos et al. (29).].
Studies reported here were conducted in two forest soils, an organic
soil and a sandy loam whose physical properties were described in detail by
Spillner et al. (24). The soils were evenly incorporated with 7.4 ppm
[ring-^ClTenTtrothion as an emulsion and 50-g aliquots were placed in
modTfied metabolism flasks described by Bartha and Pramer (1), and held at
30 ±_ 1°C for the duration of the experiment. The system has been designed
for a total material balance study including provisions for monitoring of
14C02 and total C02. Soils were analyzed for fenitrothion degradation
products periodically during the 56-day incubation period.
19
-------
Soil microorganisms appear to play the key role in degradation of feni-
trothion since over 90X of the applied dose was recovered intact after
30 days incubation in sterilized soils. In the viable soils degradation of
fenitrothion was relatively rapid; the time for 90% degradation was 29
days in the organic soil and 15 days in the sandy loam. The proposed
aerobic metabolic pathway for fenitrothion in both forest soils is shown in
Figure 1.
Degradation products were identical in both soils. These were:
3-methyl-4-nitrophenol (MNP), 3-methyl-4-nitroanisole (MNA), C02 and a
soil-bound fraction. Identity was established by two-dimensional, multiple
cochromatography with authentic standards and by GC-coupled to a radio-
activity monitor. A similar pattern of products was found in agricultural
soils by Takimoto and Miyamoto (28) but reduced products such as amino-
fenitrothion, 3-methyl-4-aminophenol (MAP) and desmethyl-fenitrothion were
detected in flooded soil (anaerobic) or from mixed culture isolates from
soil (28).
The occurence of MNA (5% after 50 days incubation) in the soil is
an unusual example of phenolic ^-methylation in the soil. Previously Loos
et al. (29) reported the methylation of 2,4-dichlorophenol to yield
"2"74-dichloroanisole in Athrobacter sp. MNP is the major hydrolytic product
of fenitrothion. It.is rapidly formed at alkaline pH's and by action of
esterases, most likely arylesterases of group II (30). MNPoaccounted for a
high value of 40% of applied 14C in sandy loam soil and 32% in organic
soil 8 days after treatment.
(CH30)2P(S) 0
FENITROTHION
anaerobic
(CH30)2P(S)0-^^-NH
CH3 J
AMINOFENITROTHION
CK
3 -I
'3 -J
MAP
r
i
BOUND
MHO.
CO,
Figure 1. Aerobic and anaerobic metabolic pathway of fenitrothion in
forest soils. From Spillner £t. al. (24). Reprinted with
permission fron ref. 24. CopyrigEt 1982 American Chemical Society
20
-------
The transient occurrence of 2-methylhydroquinone (MHQ) is implied since
it is a possible precursor to the formation of ^O^ which results from
complete degradation of the phenyl ring. Very likely MHQ undergoes
additional oxidation of the methyl group, followed by ortho ring scission
and evolution of C02- The latter is also a major metabolic product as
shown in Fig. 1. By analogy hydroquinone (HQ) has been observed in micro-
bial degradation of phenolic soil residues leading to evolution of C02 as
the terminal residue of aromatic ring cleavage by microorganisms (24).
The bound fraction represents a major proportion of the radiocarbon in
the treated soil; 30-44% was incorporated into humic acid, 12-34% into
fulvic acid, 20-29X was incorporated into a more insoluble fraction
remaining after alkaline extraction, and 10-12X into organosoluble
acidic compounds isolated from the fulvic acid fraction. Considering the
mechanism of binding under aerobic conditions, MHQ is suggested as the
precursor leading to binding. Most likely, MHQ polymerizes via free
radicals which readily lead to binding to humic substances (24). Under
anaerobic conditions the reduced (amino moiety) products of fenitrothion,
in part, bind to the soil-bound fraction; by analogy, Katan et aK (31) and
Katan and Liechtenstein (32) reported similar findings involving reductive
soil degradation of parathion.
Neither fenitrothion nor its identified metabolites MNP and MNA had an
observable detrimental effect on soil bacteria, actinomycetes, fungi and
yeasts as determined by C02 evolution (respiration) and plate counting
techniques at the elevated treatment rate described in the foregoing study.
The degradation curves of fenitrothion in an organic soil (OS) and a
sandy loam soil (SL) (Fig. 2) were calculated using data generated in the
study by Spillner jet. al . (24) and from the two -compartment model derived
by Hamaker and Goring T3~3) for pesticide degradation in soil.
In this model (Fig. 2) K is the degradation rate constant; K^ the rate
constant describing the transport of the pesticide from the labile to the
unavailable compartment; and K_i the rate constant for transport of pesti-
cide from the unavailable to the labile compartment. The total observed
concentration of the parent compound obtained through extraction and
analysis is C = Ci + 62, where GI and C2 denote the concentration of the
pesticide in the labile and unavailable compartments, respectively. The
differential equations which describe degradation of the pesticide are:
-— = (fe + ki)
at
dc2
-— = /?_,c2
at
The K values are similar for both soils (Fig. 2). This suggests that soil
type is not a major critical factor governing degradation, a finding in
21
-------
concert with the conclusions reached by Guth (8). The time for 90%
degradation (To.go) was reached faster in SL than in OS, most likely due to
contribution of'Ki. The degradation rate for fenithrothion in both forest
soils was faster than that reported in agricultural soils. The faster rate
could have resulted from the higher temperature at which this study was
conducted (24).
DIFLUBENZURON
The chlorophenylbenzoyl urea insecticide, diflubenzuron, represents a
novel insecticidal mode of action by virtue of inhibiting chitin deposition,
and having ovicidal and cnemosterilant action in important target insects.
Furthermore it belongs to a chemical class which was not previously studied
in the soil, and other components of the environment.
PRODUCTS
UNAVAILABLE
CALCULATED PARAMETERS OF
FENITROTHION DEGRADATION
ORGANIC
0.216
0.0117
0.0300
3.3 DAYS
28-5 DAYS
SANDY LOAM
0.207
0.0209
0.0327
3.3 DAYS
15.0 DAYS
10 20 30
DAYS AFTER TREATMENT
40
SO
Figure 2.
Degradation of fenitrothion in organic soil (OS) and sandy loam
(SL) and calculated parameters of degradation based on a
two-compartmental model for pesticides degradation in soil.
Adapted from Spillner et. a\_. (24).
22
-------
The metabolic and degradative fate of diflubenzuron has been reviewed by
Hammock and Quistad (34). The importance of formulation and physical state
of the chemical and delivery was indicated by Verloop and Terrell (35).
The authors noted that particle size was a key to the longevity of the
compound in soil. The half-life was reduced from 16 weeks to one week with
reduction of particle size from 10 microns to 2 microns. Apparently the
half-life is governed by the rate of dissolution, KI, or by the true rate
of degradation, K2- Apparently KI is rate determining because the shorter
half-life with smaller particle size approximates the disappearance rate of
the compound when present in true solution (35).
K! K2
Diflubenzuron suspension - » diflubenzuron solution - > metabolites.
In contrast, soil type had much less influence on the rate of degradation,
similar to the findings of Spillner et al. (24). In five agricultural soils
and three hydro soils, the range i n "FalT^l i ves was only two-fold.
The importance of microbial degradation was also reported by these
authors using [*4C-ani 1 i nojdif 1 ubenzuron applied at field rates to steam-
sterilized and viable sandy- loam soil. In the viable soil only 2X of the
parent compound remained whereas 94% of parent was recovered from the
sterile soil, thus indicating the importance of microorganisms in transforming
this compound in the soil environment.
The metabolic pathways of diflubenzuron in soil and their interrelation to
movement and transformation in plants and animal, is shown in Figure 3,
courtesy of Dr. G. G. Still, USDA, Washington, D.C. (1981). Primary degra-
dation in soil resulted from hydrolysis releasing p-chlorophenylurea as the
major product (20X of applied 14C after two weeksT and 2,6-difluorobenzoic
acid. Bound residues arise from binding via the amino moiety of p-chloro-
phenylurea; only small amounts of free £-chloroani line were foundfrom the
phenylurea precursor in soil.
Using the parent compound, independently labeled with C jn tne carbonyl
group of the benzoyl ring or ^H in the ring, it was shown that 2,6-difluoro-
benzoic acid was also a major metabolite with maximum concentrations reaching
20a/» of the applied dose and a half-life of less than four weeks. Further
transformation led to decarboxylation of the benzoic acid, releasing a major
^ 1^
portion of the C as C02. A minor route of soil metabolism cleaves
the parent compound, releasing small amounts of £-chloraniline which was
present in the bound residue.
Figure 3 summarizes the metabolism and degradation of diflubenzuron in
plants, soil and animals. It is interesting to note that while diflubenzuron
undergoes amidolysis and decarboxylation reactions leading to numerous
degradation products in the soil, several researchers have demonstrated the
refractory nature to metabolism of the parent compound when deposited on
plant surfaces. The compound is not systemic and dissipates largely
unchanged from plant surfaces with minor intervention due to
photodegradation (35).
23
-------
Man sage r jet a±. (36) have also shown the refractory nature of
diflubenzuron on and in cotton plants grown in soil treated with [14C]-
diflubenzuron labeled in both phenyl moieties. Only traces of 14C-labeled
chlorophenylurea were recovered from foliar tissue while root tissues
contained trace amounts of the parent compound and 2,6-difluorobenzoic acid.
Similar metabolites to those isolated from soil were identified in urine
and feces of rats orally dosed with [3H-benzoyl] and [14C-am'linp]-
diflubenzuron. In addition to 2,6-difluorobenzoic acid and p-chlorophenyl
urea (20% of recovered dose), major transformation reactions involved
ring hydroxylations of the intact parent molecule (80% of recovered dose)
as shown in Figure 3.
The three compartment physical model shown in Figure 3 demonstrates the
key role the soil plays in degradation of what otherwise might have been a
persistent pesticide. Furthermore soil microorganisms not only approximate
the action of liver microsomal enzymes, but often have greater capacity
than liver enzymes in their metabolic capacity, in transforming xenobiotics
to C02 and to formation of various biopolymers.
MOLINATE
Fenitrothion and diflubenzuron represent pesticides reaching the soil
milieu as unintentional contaminants. Preemergent herbicides represent a
class of pesticides which are applied directly and intentionally to the
soil. An appropriate example in this category is the selective
thiocarbamate herbicide, molinate, which has been extensively used in the
USA, Japan, USSR and throughout the rice growing regions of the world for
control of barnyard grass, Echinocloa sp., in rice culture. TJie use of this
herbicide in rice culture makes it an ideal model compound for studying its
impact and degradation in various components of the environment. The
metabolism and degradation of molinate have been studied extensively in rats
by DeBaun et aj_. (37), in fish by Lay et al. (38) and by Lay and Menn, (39),
in microorganisms by Golovleva et _al_. ,~Tl7F, in rice fields by Soderquist
el: jjl_. (40), and in the soil by~Thomas and Holt (41).
Thomas and Holt (41) have shown in controlled laboratory studies with
[ring-14C]molinate (8.2 mCi/mmol) that 50% of the applied dose was lost
after three weeks underoaerobic conditions, while under anaerobic (flooded)
conditions a loss of 50% of the applied dose was encountered after 10
weeks. Under anaerobic conditions little degradation of molinate occurred
and volatilization was the prime route of dissipation. Soderquist et al.
(40) have determined that under actual field conditions volatilization
accounted for 75-85% loss of molinate from the water phase of the
treated rice field within 48 hours after treatment. These researchers have
also shown that photolysis provided only a minor contribution to the
degradation of molinate. This is probably due to the absence of a signifi-
cant chromophore in the molecule and consequently molinate does not absorb
appreciable solar energy (42). However, certain pesticides can be degraded
more rapidly under anaerobic conditions. Sethunathan (43) reported that in
flooded soils the insecticides s-BHC, methoxychlor, DDT, heptachlor,
endrin, parathion and diazinon were degraded significantly more rapidly than
24
-------
under aerobic conditions. The rapid degradation under the anaerobic condi-
tions is most likely due to metabolism by anaerobic soil microorganisms.
The predominant reductive transformation reactions involving these
pesticides have been listed in Table 1.
Thomas and Holto(41) reported that under aerobic conditions, 32 weeks
after treatment, 30^ of the applied [^cjmolinate was present in the
bound fraction and only approximately 5X. in the organosoluble fraction.
DIFLUBENZURON
ANIMALS
Mammals Fish Insects
PLANTS
Shoots. Leaves, etc.
Figure 3. Comparative aspects of metabolism of diflubenzuron in soil,
plants, and animals. Courtesy of G. G. Still, 1981.
25
-------
In the latter fraction approximately one half of the radiocarbon was
recovered as the parent compound and the remaining radiocarbon was distri-
buted among the metabolites shown in Figure 4. The pathway incorporates the
comparative metabolic routes of molinate in soil under aerobic conditions,
metabolism in fish, the rat, and its photolytic degradation products.
Comparing the soil degradative products with those identified in rat
urine (37) reveals that major biotransformation in the rat involves
sulfoxidation and conjugation with glututhione (GSH), resulting in molinate
mercapturate as the major terminal urinary metabolite. Other terminal
products of metabolism involve 0-glucuronide conjugation of 3- and
4-hydroxymolinate. Hydrolysis of sulfoxidized molinate and its hydroxy
derivatives gave rise to hexamethyleneimine (HMD and 3- and 4-hydroxy-HMI;
these metabolites accounted for 87.4^o of the urinary radiocarbon.
In the soil neither GSH conjugation nor conjugation of the hydroxylated
species was observed by Thomas and Holt (41). Identified metabolites in the
soil included hydroxylated molinate and its keto derivative, acetylated
HOOCCH2SCN I
carboxy molinate
F.S
HOH2CCH2SCN J
°/-"\
ii / ^
HSCN I
HMI-thiocarbamate
Or—s.
M ' >
H3CSCN I
£-methyl molinate
M.S,
i-aza-7-oxa-8-oxo
bicyclo [4.2.1] nonane
S-\?t
fNCSC2H8
OH
hydroxy molinate
F.S.M.P.R
ff
C2HSSCN
o
molinate
F,R
HC(CH2)8NCSC2H
£-ethyl s-formyl-
pentyl thiocarbamate
\F.S,R
F.S.M.P
/^\?,
I NCSC2H5
keto molinate
IP
HOC(CH2)8NH2
6-aminohexanoic acid
f 00
UN .
C2H8SCN
C2H5SCN
molinate sulf oxide
}F.R
JGSH
T NCSCH2CHCO
^ f
:OH
HNCCH3
0
molinate mercapturate
H.CCN
N-acetyl-hydroxy HMI
P - photolysis
F-fish
R-rat
S-soil
M- microorganisms
Figure 4. Comparative metabolic pathway of molinate in the rat, fish, soil
and its photolysis products. (Based on: DeBaun et. al., (31);
Lay et. aK, (38); Lay and Menn, (39); and Thomas andlTolt, (41)
26
-------
hydroxy- HMI and HMI. An unusual metabolite involved desethylation of
molinate followed by 3-methylation giving rise to S-methyl-molinate.
Another minor pathway involved formation of carboxymethyl-molinate, also
observed in fish by Lay, et ajk (38).
Golovleva et al_. (17), studying the metabolism of molinate in enriched
microorganism cultures, have also identified 2-hydroxy- and 2-keto-molinate
and the desethylated thiocarbamic acid derivative as terminal degradation
products formed by Mycococcus sp. 22P.
Fish represent the apex of the ecological biomagnification chain in the
hydrosoil environment. Metabolism studies of [^C]-molinate in the living
Japanese carp, Cyprinus carpio var. Yamato Koi, have shown that molinate is
extensively metabolized in the fish giving rise to terminal metabolites (38)
which were also identified in the rat and to a large extent by soil micro-
organisms. These included molinate sulfoxide, ring-hydroxylated molinate,
isomers of keto molinate, keto-HMI, and HMI. These studies confirmed for
the first time the formation of molinate mercapturate resulting from GSH
conjugation in the carp (39).
The molinate degradation and dissipation model outlines the results of
the action and interaction of many of the key biotic and abiotic forces
which govern the rate of persistence of a xenobiotic compound in the soil
and soil/water compartments of the environment. Furthermore the foregoing
studies provide useful data in forecasting the environmental behavior of
molinate and other similar compounds under various edaphic and climatic
conditions.
SYNTHETIC PYRETHROIDS
The photostable synthetic pyrethroid insecticides are a remarkable new
class of potent insecticides (44). These compounds are active at remarkably
low concentrations against a broad spectrum of insects. Kaufman et al_. (45)
have shown that permethrin was rapidly degraded in soil with a major
contribution from soil microorganisms. Primary degradative attack resulted
in ester hydrolysis giving rise to 3-(2,2-dichloroethenyl)-2, 2-dimethyl-
cyclopropanecarboxylic acid (DCYA) and 3-phenoxybenzyl alcohol (PBA).
Hydrolysis of the ester bond was also the major transformation step in soil
degradation of cypermethrin (46); fenpropathrin (47) and fenvalerate (48).
PBA, the alcohol hydrolysis cleavage product of permethrin, undergoes
further microbial oxidation in the soil to 3-phenoxybenzoic acid [Kaufman
et al_., (49)].
Using soil columns for leaching experiments and soil thin-layer
chromatography for determination of mobility of 14C-labeled pyrethroids
(permethrin, decamethrin and cypermethrin) in three diverse soil types,
Kaufman et al_. (45) showed virtual immobility in soil and the parent
pyrethroids could not be leached through the soil, although the alcohol
(PBA) and acid (DCVA) hydrolysis products showed low soil mobility. Based
on these model studies the investigators concluded that these pyrethoids
pose no soil transport hazard to the environment.
27
-------
Soil metabolism studies were conducted in our laboratory with the
synthetic pyrethroid, fluvalinate (50). These studies were conducted in
biometer flasks containing 100 g keaton sandy loam treated at rate of 0.11
kg/ha with [CF3-14C]fluvalinate. The aerobic incubation system was
designed according to Laskowski «rt al_. (23).
Under these conditions the time for 50% degradation of fluvalinate
was approximately six days. Eight weeks after treatment the principal soil
residues were fluvalinate (11% of applied dose), the haloanilino acid
from ester hydrolysis (8%), and 2-chloro-4-trifluoromethyl aniline (9/o).
The latter volatilized rapidly fromosoil (41% of applied Hose). A second
major volatile product was 14C02 (9%) resulting from degradation of the
14CF3 moiety. The. metabolic pathway of fluvalinate in soil is outlined in
Figure 5. Fluvalinate and its principal metabolites were not translocated
into growing crops such as lettuce, radishes and wheat planted in soil 31
days after treatment with [14C]fluvalinate. Since the synthetic pyrethroids
are inactive as soil insecticides, reaching the soil only as unintentional
contaminants where they are readily metabolized and in some instances
volatilized, they are among the least likely pesticides to contaminate the
soil environment.
CONCLUSIONS
While progress has been made in recognizing the soil as the major
compartment for transformation and degradation of pesticides in the
environment, much additional research is needed in the following areas to
obtain a better understanding of the processes involved in the routes and
rates of pesticide dissipation in and from the soil:
1. Significance and identity of soil-bound residues.
2. Mechanisms of adsorption and desorption.
3. Effect of chemical interaction in soil on metabolic fate of each
component chemical.
4. Microbial adaptation, enzyme induction, and the role of plasmids
in soil microorganisms.
5. Standardization of methods to determine biological activity of
test soils.
6. Effects of no- and Ipw- till farming on fate and residues of
pesticides in the soil.
7. Application of mathematical modeling to prediction of fate and
kinetics of persistence of terminal pesticide metabolites in the
soil environment.
It is anticipated that the joint U.S./U.S.S.R. project will place
considerable effort in conducting research in these areas with special
emphasis on item number 7.
28
-------
% Applied Dose
-------
References
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19. Felsot, A., Maddox, J. V., and Bruce, W. (1981) Bull. Environ.
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Pesticide Residues", Kaufman, D. D., Still, G. G., Paulson, G. D., and
Bandal, S. K., Eds., American Chemical Society, Washington, D.C.,
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34. Hammock, B. D. and Quistad, G. B. (1981), In: Prog. Pestic. Biochem.,
1, 2-83.
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35. Verloop, A. and Ferrell, C. D. (1977) In: "Pesticide Chemistry in the
20th Century", Plimmer, J. R., Eds., American Chemical Society,
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36. Mansager, E. R., Still, G. G., and Frear, D. S. (1979) Pestic. Bloc hem.
Physio!., 12, 172-182.
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Agric. Food Chem.. ^6, 1098-1104.
38. Lay, M. M., Niland, A. M., DeBaun, J. R., and Menn, J. J. (1979) In:
"Pesticide and Xenobiotic Metabolism in Aquatic Organisms", Khan, M. A.
Q., Lech, J. J., and Menn, J. J., Eds., American Chemical Society,
Washington, D.C., pp. 95-119.
39. Lay, M. M. and Menn, J. J. (1979) Xenobiotica, 9, 669-673.
40. Soderquist, C. J., Bowers, J. B., and Crosby, D. G. (1977) J. Agric.
Food Chem., 25, 940-945. ~
41. Thomas, V. M. and Holt, C. L. (1980) J. Environ. Sci. Health, B., 15,
475-484. ~ ~ —
42. Crosby, D. G. (1979) In: "Advances in Pesticide Science", Part 3,
Geissbuhler, H., Ed., Pergamon Press, Oxford, England, pp. 568-576.
43. Sethunathan, N. (1973) Residue Rev.. 47, 1243-165.
44. Elliott, M., Ed. (1977) "Synthetic Pyrethroids", American Chemical
Society, Washington, D.C.
45. Kaufman, D. D., Jordan, E. G., Haynes, S. C., and Kayser, A. J. (1977)
In: "Synthetic Pyrethroids", Elliott, M., Ed., American Chemical
Society, Washington, D.C., pp. 147-161.
46. Roberts, T. R. and Standen, M. E. (1977a) Pestic. Sci.. 8, 305-319.
47. Roberts, T. R. and Standen, M. E. (1977b) Pestic. Sci., 8, 600-610.
48. Ohkawa, H., Nambu, K., Inui, H., and Miyamoto, J. (1978) J. Pestic.
Sci.. _3, 129-141. ~
49. Kaufman, D. D., Russell, B. A., Helling, C. S., and Kayser, A. J.
(1981) J_. Agric. Food Chem., 29, 239-245.
50. Quistad, G. B., Staiger, L. E., Milligan, L. E., and Schooley, D. A.
(1980) "Abstracts of Papers", Second Chemical Congress of the North
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Washington, D.C., PEST 117.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency. The contents do not necessarily reflect the views of the
Agency and no official endorsement should be inferred.
32
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ON THE POSSIBILITY OF PREDICTING PESTICIDE
BEHAVIOR IN SOIL
M.S. Sokolov
Institute of Agrochemistry and Soil Science,
USSR Academy of Sciences, Puschino
ABSTRACT
Pesticide degradation in soil depends not only on the
chemical structure and partition coefficient in the system
"soil/ soil solution" but also on the ecological conditions de-
termining the activity of specific soil microorganisms. Some
reference data discussed in the paper, concern the relationship
between the rate of degradation, migration capacity, and other
characteristics. Pesticide degradation in soil is shown to be
controled by main, additional and indifferent factors, that are
all presented. Major mathematical models describing the process
are considered in brief. Particular attention is given to the
conceptual model by Fumidge and Osgerby (196?) describing mi-
gration and degradation in soil of pesticides and other xeno-
biotics, and having certain advantages.
Almost any soil can be viewed as a heterogeneous system
comprising four phases: solid (a soil matrix), liquid (soil
solution), gaseous (soil air), and living matter (soil biota).
Pesticides may be present in soil in three forms: as a solu-
tion, in an immobilized (absorbed, sorbed) state, and in a va-
por form. These forms maintain a dynamic equilibrium which can
be tipped off when certain factors are changed, particularly
temperature and moisture content. As pesticides are, as a
rule, characterized by moderate or low volatility, major in-
terest of a researcher studying their persistence in soil, is
concentrated at that part of the compound which occurs in a
dissolved form or in an immobilized state. The compound parti-
tion coefficient in the system soil solid phase/soil solution
33
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corrected for humus content is likely to characterize adequate-
ly the pesticide sorption by a group of genetically similar
soils.
The task of predicting the dynamics of pesticide content
in soil can be simplified by the introduction of the follow-
ing:
- the removal of pesticide residues with solid and liquid run-
off from the soil arable layer can be ignored, as well as the
chemical loss due to volatilization, phot©degradation and re-
moval with the phytomass;
- we assume that biological and chemical transformation and de-
composition affect only that part of the pesticide which occurs
in soil in a dissolved unsorbed form (14).
This approach permits the researcner to bring multivari-
ant processes of pesticide transformation and degradation in
soil to the analogous processes occurring in water in the sys-
tem bottom sediment/water.
A supposition that, a sorbed pesticide is not available to
microorganisms even in the culture medium or a pure microbial
culture is confirmed by our data on propanil sorbed by montmo-
rillonite (2,3)» and by the data of the US scientists on n-de-
cylamine (. 28).
And finally, while predicting the duration period of pes-
ticide persistence in soil a researcher has to make one more
assumption, namely, that in the dozes recommended for applica-
tion the chemical will not inhibit soil biota or its functions.
Thus, the decomposition in soil of a natural compound or
a xenobiotic is determined by the following groups of factors:
a) chemical structure of the compound;
b) physico-chemical form of the compound in soil, and
c) ecological conditions directly or indirectly affecting
the pesticide biotransformation and biodegradation in soil.
Group of factors (a) includes the data on the compound
physico-chemical and chemical properties. In the opinion of
G.G. Briggs (9) this is sufficient for making a conclusion-on
the persistence of a pesticide in soil. He believes that the
partition coefficient in the system of two immiscible liquids
(P) or in the system soil/water (K ) is the determining charac-
teristic. In the latter case K ispcorrected for the organic
matter content in soil. The vaiue of K characterizes an avail-
able (actual) part of the compound present in the soil liquid
phase (soil solution). According to availability value (A),
Briggs assigns all pesticides to 14 classes. Class 1 comprises
compounds the concentration of which in solution is more than
75$ (high availability) of their total content in soil. Class
14 comprises pesticides with a solution concentration less
than 0.01$ (low availability). Very mobile compounds have
availability 1-2, and practically immobile,( 7-14). The author
also introduces the value of degradability (D). This suggests,
attributively to the molecules of independent organic sub-
stances, the following assumptions.
- Each functional group in a molecule can be transformed in a
specific manner.
34
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- The most labile groups in a poly functional molecule will be
transformed most rapidly.
- Variation of degradability of similar functional groups in a
pesticide molecule is determined by their position as well as
by steric and electron effects.
- And finally, the metabolite degradability is determined by
the properties of new functional groups formed in a pesticide
molecule in the process of metabolism.
Knowing the pathways and rate constants of chemical and
enzymatic transformations of compounds, Briggs groups pesti-
cides, according to their degradability, within a 1-10 scale
depending on the presence in their molecule of certain func-
tional groups. Using the product of the compound "availability
class" (A) and degradability (D), Briggs calculates the half
lives of various groups of pesticides.
The data on organophosphorus compounds and organochlorine
insecticides (12) illustrate the validity of this approach.
Using the data of some other authors, Briggs establishes a
very good reverse relationship between degradability and migra-
tion capacity of 65 pesticides.
Unlike the mentioned approach of Briggs, an attempt made
by the authors (8) to predict pesticide persistence in the en-
vironment on the basis of only the compound chemical structure
seems to us less successful. In their calculations, these au-
thors used the bank of reference data on the rate of degrada-
tion of several chemicals in soil. However, they did not use
K , ignored specific soil conditions and that is probably the
riason for a very wide range of the degradation rate varia-
tions they got.
The factors of group (b) include first of all such repre-
sentative values as the compound partition coefficient in the
system of immiscible liquids (P) and in the system sorbent/so-
lution (K ). Compulsory studies of the process of sorption to
understand the fate of chemical in soil and to define the
share of the immobilized compound are examined in detail else-
where.
The factors of group (c) affecting transformation and deg-
radation of a pesticide in soil are divided on the basis of
reference data and the results of our experiments into main
(temperature and moisture content of soil), additional
(organic fertilizer, aeration, soil pH, soil mineralogical and
mechanical composition, content and fraction composition of
humus, cultivated crop and agricultural technology), and i n-
different (content of NPK, micronutrients, salts, com-
position absorbed cations). As only few of the main and addi-
tional factors can be controled in the open agroecosystems,
the potentialities of artificial control of the degradation
rate of pesticides in soil are very limited.
During the last two decades a large number of various em-
pirical and semi-empirical equations have been suggested. Us-
35
-------
ing these equations and individual parameters of one or sever-
al groups of factors (a)-(c), the authors calculated the rate
of degradation and the residual content of pesticides in soil.
However, we do not know yet of any mathematical models which
describe biodegradation of herbicides or other pesticides in
soil and take into account a combined action of main and addi-
tional factors on their degradation. The only exception is si-
mulative mathematical models of Walker (23-26) simultaneously
considering varying values of soil temperature and moisture
content, ant their various combinations.
The most numerous group of mathematical models can be re-
duced to various equations of linear regression. They are con-
sidered in detail in a review paper by Hamaker (15) . We shall
discuss only some basic principles.
Models describing pesticide degradation in soil are given
for a number of compounds: bromacdl (27), picloram (20,1.3),
2,4-D (4), carbaryl (6) , propyzamide (19,26), and napropamid
(21). However, all these models based on linear regression
equations suffer a common drawback: they are limited, take in-
to account only some randomly picked factors (humus, pH, me-
chanical composition) , and ignore climatic conditions (tempera-
ture, humidity, insolation).
To describe pesticide kinetics in soil a first-order reac-
tion equation is widely used giving an exponential function:
°t • °o e'kt- [i]
where k is the empirical degradation rate constant; C and C.
are the initial and the time-determined (t) concentrations
of a toxicant in mg/kg, respectively.
Thus, according to the conceptual model of Furmidge and
Osgerby (14) pesticide degradation in soil occurs according
to the first-order kinetics, and actually only the portion of
pesticide dissolved in soil solution degrades. The degradation
rate (dc/dt) in soil is calculated by the following equation:
- kc
' [2]
where K is the partition coefficient of chemical in the sys-
tem soil/soil solution;
M and M are the masses of soil solid and liquid phases in
tne systems, respectively.
Formula [Y] can be considered one of the possible empiri-
cal functions, rather than a consequence of a sufficiently
complete mathematical model characterizing specific features
of pesticide disappearance from soil. This expression includes
only one parameter (k) characterizing the pesticide degrada-
tion conditions. It is assumed that these conditions remain
inchanged during the whole period of exposure and that the
36
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process rate does not depend on C
In fact, as it was obseved in the experiment on picloram
(16), the relative degradation rate in several US and Canadian
soils, i.e. Tc0 and Tqq, depended on the initial concentration
of the chemical (C ). ^At lower concentration in soil, degra-
dation rate increased. Analogous facts were observed for pro-
panil (10), linuron (7) and 3,4-DCA (18). In our experiments,
propanil (10, 50 and 250 mg/kg) showed reliable differences in
the T,-n value between the minimum (average) and maximum concen-
trations. 3,4-DCA gave reliable differences in the T^Q value
among all the three concentrations. A change (slowing down) in
the degradation rate is apparently a response of soil microor-
ganisms and testifies to a marginal state of the soil self-
cleansing capacity relative to the introduced toxicant.
So, even if the pesticide degradation in soil is describ-
ed exponentially, the researcher has to calculate value k at
least for the cases where the doses of one and the same chemi-
cal differ sharply.
Several authors (II »I5f I?) stress the point that vari-
ous processes largely affect the content and form of a chemi-
cal in soil in the initial period immediately after its appli-
cation. In case of surface application this initial loss may
constitute up to 30$ (22).
Disking in of a chemical in soil immediately after its
application significantly accelerates the process of its re-
distribution within the soil layer and reduces the time needed
for the system soil/herbicide to reach equilibrium^ 15).In our
field experiment on linuron (5) in the soddy-pale-podzolic
soil the loss of the chemical was approximately 20% quicker if
the herbicide (0.5 kg/ha) was applied on the surface as com-
pared to disking in the 0-5-7 cm layer. This loss rate can be
explained by photodegradation and volatilization of linuron
when it was on the soil surface. Consequently, to increase the
pesticide effect or to reduce its effective dose where it is
agrotechnically permissible, it seems advisable to disk the
chemical in the top soil layer immediately after application.
Hamaker (15) divides all disappearance curves of pestici-
des in soil into three classes (based on the shape of curve
when plotted on aemilog axes):
1) concave upward;
2) concave downward (characteristic also of the case
where degradation occurs after a lag-period);
3) a straight line (i.e., first-order).
The first class is much more common, particularly for
describing the degradation dynamics of a chemical under field
conditions. That indicates a limited application of the first
order kinetics equation.
To assess quantitatively the experimental results on pes-
ticide degradation in soil we used the following expression
(6) for a portion of chemical y(;%) degraded in time tv;
a
37
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y= $ (6{g ^ )-lOO% , [3]
and with the lag period
)•'«>•/, W
where
* .2
Formula [4] unlike the exponent, describes more adequate-
ly the dynamics curves for all the three groups characterizing
the degradation of herbicides and their transformation prod-
ucts in soil.
Based on formulae £3] and [4] , we suggested a method for
predicting the dynamics of organic pesticide content in soil
that is currently being tested under field conditions. The
main point of the method is that parameters T,-Q, TLA& and b
are determined at definite values of main and additional fac-
tors in the course of model experiments under "standard soil
conditions". Testing showed that the estimated and experimen-
tal data for different soils lie within 1-3356 (1,5).
We believe that for predicting the pesticide dynamics
under field conditions it is necessary to determine most accu-
rately its initial concentration after application (introduc-
tion into soil) and to record daily average temperature dur-
ing the exposure period, provided the temperature range stays
within the biological minimum. As in this case only actual
degradation of a chemical is determined, while the disappear-
ance factors associated with removal, evaporation, photolysis
and other losses, are ignored, the residue persistence will
be somewhat overestimated. However, this is not a disadvantage
when similar results are used for regulating application doses
and residues of a chemical in soil, because any recommenda-
tions must have a certain "reserve coefficient".
Hot wishing to set this approach against other methods
for predicting the soil dynamics of herbicides or other pesti-
cides we may list as its advantages the accessible and simple
technical procedure, satisfactory reproducibility and the op-
portunity to create a data bank for different chemicals, soil
types and ecofactors to be repeatedly utilized. It is also im-
portant that experimental data can be obtained in any season
that is of great use for the toxicologists of agrochemical
laboratories in "Soyuzselkhozkhimiya" - they have very limited
resources for arranging any extra studies during the growing
season. This approach will also allow to improve the accuracy
38
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of prediction of soil selfcleansing from pesticides under ex-
treme conditions, e.g. in case of accidental soil pollution
with a considerable amount of chemicals caused by overdosing,
emergency, etc. Any mathematical model is believed to be ade-
quate provided theoretical and experimental data agree well.
Otherwise, the process of pesticide degradation in soil is ob-
viously affected by factors missed in the model. That happens
more often when the models are developed a priori. The objec-
tive of those working in the area is to develop sufficiently
reliable methods capable of extrapolating laboratory data to
field conditions.
LITERATURE CITED
1. Anan'eva, N.D. ; Galiulin, R.V. Effect of 3,4-DCA on the
content of saprophyte bacteria in grey forest soil. Khi-
miya v selskom khozyaystve 1980, No.2, 56-57-(in Rus-
sian) .
2. Anan'eva, N.D.; Sokolov, M.S. Assessment of pesticide
effect on soil microflora and availability of 'bound'com-
pounds for soil microorganisms. Bull. of the A13.-Uni.on
Res. Inst. for Agricultural Microbiology.L.. VASKHNIL
1979, No. 32, 22-24*Un Russian;.
3. Anan'eva, N.D.; Sokolov, M.S.; Tolstova, L.A. Availabil-
ity of propanil 'bound1 by clay minerals to pure culture
Ps aurantica. In "Abstr. of Papers of the Xllth Sci.-
Coordinating Meeting and Symposium of countries-members
of COMECON on theme 1-8.11.3-" (Mulhgausen, March, 1979),
Halle, 1979; 34-35-(in Russian).
4. Buslovich, S.Yu.; Milchina, M.G. Study of the dynamics
of soil degradation of 2,4-D amine salt. Gigiyena i sa-
nitariya 1976, No. 5, 109-110.(in Russian!
5. Galiulin, R.V.; Sokolov, M.S.; Pahcepsky, Ya.A.; Ryzhaya,
M.A. Action of some ecofactors on soil degradation of
propanil, linuron, and product of their transformation
3,4-dichloroaniline. Izv. AN SSSR. Ser. biol., 1978, No.
5, 683-689*(in Russian!
6. Ivanova, L.N.; Molozhanova, E.G. On the transformation
kinetics of some pesticide in soil. Khimiya v selskom
khozyaystve. 1974, No. 5, 43-45.(in Russian;
7. Pavlova, E.A. Inactivation of Linuron in meadow-peat
podzolic soil. In "Problems of cultivation of principal
agricultural crops in the Amur region"; Novosibirsk,
1976; 131-134.(in Russian).
39
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8. Semenov, V.A.; Ijyankova, N.V.; Malushko, T.F. ; Simonov,
V.D. Prediction of pesticide persistence. Khimiya v
selskom khozyayatve, 1975, No. 10, 65-69*(in Russian).
9. Briggs, G.G. Degradation in soils. In "Proc. BCPC Sym-
posium: Persistence of Insecticides and Herbicides". En-
gland. Nottingham, 1976; 41-54.
10. Chisaka, H.; Kearney, P. Metabolism of propanil in soil.
J. Agr. Pood Chem, 1970, 18, 854-858.
11. Decker, C.C.; Bruce, W.N.; Bigger, J.H. The accumula-
tion and dissipation of residues resulting from the use
of aldrin in soils. J. Scon. Entomol. 1965, 58, 266-
271.
12. Edwards, C.A. Persistent pesticides in the environment.
C.R.G. Press Cleveland, 1973.
13. Youngson, C.R.; Goring, C.A.; Meikle, R.W.; Scott, H.H. ;
Griffith, J.D. Factors influencing the decomposition of
Gordon herbicide in soils. Down to Earth, 1967, 23,
2-11.
14. Furmidge, C.G.L.; Osgerby, J.M. Persistence of herbici-
des in soil. J.Sci. Fd. Agric. 1967, 18, 269-273.
15. Hamaker, J.W. The application of mathematical modeling
to the soil persistence and accumulation of pesticides.
In "Persistence of Insecticides and Herbicides". Proc.
BCPC Symposium. England, Nottingham, 1976; 181-199.
16. Hamaker, J.W.; Youngson, C.R.; Goring, C.A.I. Prediction
of the persistence and activity of tordon herbicide in
soils under field conditions. Down to Earth, 1967, 23,
30-36.
17. Hermanson, H.P.; Gunter, F.A.; Anderson, L.D.; Garber,
M.J. Installment application effects upon insecticide
residue content of a California soil. J. Aer. Fobd.
Chem. 1971, 19, 722-726.
18. Kearney, P.S.; Plimmer, J.R. Metabolism of 3,4-dichlo-
roaniline in soils. J. Agr. Food Chem. 1972, 20,
584-585.
19. Leistra, M.; Smelt, J.H.; Verlaat, J.G.; Zandvoort, R.
Measured and computed concentration patterns of propyz-
amide in field soil. Weed Res. 1974, 14, 87-95.
40
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20. Meikle, R.W.; Youngson, C.R. ; Hedlung, R.T.; Goring,
C.A. J. ; Hamaker, J.W.; Addington, W.W. Measurement and
prediction of picloram disappearance rates from soil.
Weed Sci. 1973, 21, 549-555.
21. Wu, C.; Buering, N.; Davidson, J.M.; Santelman, P.M.
Napromid adsorption, desorption and movement in soils.
Weed Sci. 1975, 23, 454-457.
22. Sarat, W.P. Calculation of safe re-entry time into an
orchard treated with a pesticide chemical which produces
a measurable physiological response. Archives of Envi-
ronm. Contam. and Toxicol. 1973, 1, 170-181.
23. Walker, A. A simulative model for prediction of herbi-
cide persistence. J. Environ. Quality, 1974, 3, 396-
401.
24. Walker, A. Simulation of herbicide persistence in soil.
Pesticide Sci. 1976, No. 1, 41-64.
25. Walker, A. Simulation of the persistence of light soil
applied herbicides. Weed Res. 1978, 18, 305-313.
26. Walker, A. Use of a simulative model to predict herbi-
cide persistence in the field. In "Herbicides and the
soil". Proc. Eur. Weed Res. Coun. Symp. Columa, EWRO,
1973; 240-249.
27. Wolf, D.; Martin, J.P. Microbial degradation of bromacil
and terbacil. Proc. Soil Sci. Soc. Amer. 1974, 38,
921-925.
28. Wszolek, P.O.; Alexander, M. Effect of desorption rate
on the biodegradation of n-alkylamines bound to clay.
J. Agr. Food Chem. 1979, 27, 410-414.
41
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FORECASTING PESTICIDE MOBILITY IN SOILS:
DISPERSION AND ADSORPTION CONSIDERATIONS
by
Richard E. Green
Professor of Soil Science
Department of Agronomy and Soil Science
University of Hawaii
Honolulu, Hawaii 96822
ABSTRACT
Mathematical models of pesticide movement in soils can serve two
principal purposes: (a) quantitative evaluation of the dynamics of various
key processes (and their interactions) which control pesticide mobility, and
(b) prediction of the leaching and distribution of pesticides in field soils
for various combinations of pesticides, soils, water-flow regimes and
environmental conditions of practical interest. Considerable progress has
been made in modeling transport processes for well-defined systems such as
laboratory columns of sieved soil, but accurate field-scale predictions are
generally still beyond our technical grasp. Comprehensive models should
include mathematical descriptions of pesticide sorption, dispersion,
transformation, volatilization and plant uptake. This paper focuses
principally on the interaction of sorption and dispersion processes and on
laboratory assessment of sorption for prediction of .pesticide mobility in
field soils. Currently available sorption methodology is probably adequate
for many applications, but improved methods of determining dispersion
coefficients for field soils are needed. Useful field predictions are
hindered by inadequate characterizations of (a) water flow through large
pores or fractures and the consequent lack of equilibrium between adsorbed
pesticides and new additions of water, (b) the apparent increase in
adsorption over prolonged periods of time, especially in surface soils with
relatively high organic matter content, and (c) apparent non-singularity in
the adsorp.tion-desorption process for certain pesticide-soil combinations.
Future research on these processes and their interactions with volatilization
and transformation will depend on the exactness required of predictions.
Simple convection-type models using conventional batch-suspension adsorption
data may suffice for many field applications, while other situations will
require more exact mathematical representations of real processes. Finally,
42
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the successful use of mathematical models to forecast pesticide mobility in
field soils will require reliable prediction of water flux in the soil
profile and also characterization of spatial variability of soil hydraulic
properties and of adsorption and dispersion.
INTRODUCTION — A PERSPECTIVE ON MODELING PESTICIDE MOVEMENT
Though I am somewhat uncomfortable with the word "forecasting" in the
title of this paper, I think "forecasting" probably conveys two important
ideas about our subject. First, we are interested in determining in advance
the outcome of a pesticide practice under a given set of conditions, with
respect to the agricultural efficacy of the practice and also relative to
environmental quality. And second, there is a measure of uncertainty in
predicting pesticide movement on a field scale.Forecasting pesticide move-
ment in soils is somewhat analogous to weather forecasting. Weather fore-
casts are extremely important to agriculture in most parts of the world and
mathematical modeling has contributed substantially to weather forecasting
for over three decades, yet weather forecasting is still far from exact.
Such are the short-term prospects for forecasting the field movement of many
pesticides under a variety of soil-climate-crop management regimes.
Pesticides, as a group of chemicals, include compounds which vary widely
in their water solubility, volatility, persistence in the soil environment
and adsorption on soil colloids. The soils to which pesticides are applied
(whether intentionally or by accident) are also highly variable, in their
chemical, mineralogical, physical and biological properties. Also, soils may
not be homogeneous and isotropic, on the scale of interest to us, as the
assumptions for mathematical analysis frequently require. But despite the
difficulties of adequately representing a host of processes and their
interactions by mathematical equations, the alternative methods of assessing
the environmental risk of using a pesticide are generally less attractive.
Studies of individual processes in physical models are informative, but
interactions between processes (e.g. between volatilization, adsorption and
movement with soil water) are frequently vital to the final outcome; such
interactions are difficult to study in physical laboratory models.
Additionally, physical models in the laboratory often bear little resemblance
to field systems, thus the results of studies with packed soil columns, for
example, may actually, lead researchers to erroneous conclusions about
pesticide mobility in the field. Controlled laboratory experiments with soil
columns are still useful, but not for making field predictions (1).
Laboratory columns allow one to study transport processes under controlled
conditions, to identify cause and effect relationships and to test various
conceptual transport models against experimental data. Extension of
information gained from laboratory columns to field situations requires a
careful assessment of the similarities and differences between the two
systems. Mathematical models which are developed with field systems in mind,
and tested first with laboratory column experiments, can be expected to be
useful for field prediction if the appropriate equation parameters can be
determined for the field system.
The possibility of modeling complex real-world problems involving
pesticides has been enhanced markedly in the past 20 years by the
43
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availability of high-speed computers with capacity to solve large systems of
equations numerically. A large number of simultaneous processes can be
modeled as one integrated system. For example, the Agricultural Runoff
Management (ARM) Model, completed by the U.S. Environmental Protection Agency
in 1976, simulates runoff, snow accumulation and melt, sediment loss,
pesticide-soil interactions, and soil nutrient transformations (2). The
conceptual approach for the ARM Model was described by Bailey, et al. (3).
This model is designed to assess principally the pesticide, nutrient and
sediment losses in runoff from agricultural lands. Adsorption-desorption and
leaching processes have an important role in determining the loss of
pesticides in runoff, but apparently these processes were not modeled
adequately for all of the pesticides with which the model was tested (4).
The success of the model for some compounds and failure for others indicated
the need for a better understanding of the mechanisms which control pesticide
movement into the soil profile. Thus, while large, complex systems can now
be modeled, there is a pressing need to be able to accurately model
individual processes, which can then be incorporated as subprograms in a
larger system. The difficulties of developing and validating models for even
single mechanisms of contaminant transport in soils and groundwater systems
have been emphasized in recent reviews (5,6).
In this paper I will limit the discussion to a consideration of
hydrodynamic dispersion and adsorption as mechanisms which impact directly on
the movement of pesticides into and through the soil profile. These two
mechanisms are closely linked and thus need to be considered together.
Volatilization and degradation, while equally important, are addressed in
other papers in the symposium. The approach will be to consider first the
general types of models which have been developed in the past 15 years, then,
for both dispersion and adsorption mechanisms, examine (a) the nature of the
mechanism, (b) some modeling approaches designed to simulate specific
phenomena, and (c) alternative methodologies for determining key parameters.
Brief attention will be given to other input requirements for useful field
models.
The objectives of this paper are to describe the present status of
modeling pesticide transport in soil water, to provide some historical
perspective, and to indicate what might be considered research areas of
highest priorty in our attempts to forecast pesticide mobility in the field.
The paper is not a review of all relevant published research, but hopefully
it reflects recent progress reported in English-language publications.
MATHEMATICAL MODELS OF CHEMICAL MOVEMENT IN SOIL WATER
There is a wide variety of models which have been developed to describe
and/or predict solute movement in soils. The different basic approaches had
their origin in areas of chemistry (chromatographic approaches), chemical
engineering and hydrology. The series of key papers on miscible displacement
of solutes in soils by Biggar and Nielsen in the early 1960's, summarized
in (7), stimulated research on quantitative means of describing solute
movement in soils, first with respect to salt and fertilizer movement and
later with respect to pesticides and other toxic chemicals. A description of
diffusion and convection-dispersion processes, accompanied by a summary of
44
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miscible displacement techniques and mathematical solutions of the dispersion
equation, are given by Kirkham and Powers (8). Other helpful reviews
describe a variety of solute transport models (9,10,11), and some reviews
specifically address modeling the movement of pesticides (12,13,14).
In this section we will briefly discuss some of the equations that have
been used in solute movement studies and indicate their advantages and
disadvantages for use in prediction of pesticide movement. A rigorous
treatment of this subject is provided by Leistra (14).
Simple Convection Model
If diffusion is neglected and a pesticide is transported in water by
mass flow (convection) in "piston" fashion (without being dispersed in the
direction of flow), the appropriate mathematical expression is
J = vec [1]
where J is one-dimensional solute flux [MIT2!"1], 6 is volumetric water
content [L3L~3], v the average interstitial fluid velocity (Darcy flux
divided by water content) with dimensions of LT"1 and c the solute
concentration in the soil solution. (Use of 6c allows solute concentration
to be expressed in terms of the mass of solute per unit volume of soil rather
than per unit volume of soil solution, and is necessary when solute transport
equations are combined with water flow equations for transient water flow.)
J represents the mass of solute passing through a unit area of soil per unit
time. This simple expression has been used by a number of workers in
empirical compartment-type models in which flowing soil water is imagined to
pass from one compartment into another with a velocity of v along the axis of
flow, and solute entering each compartment equilibrates instantaneously with
solute already in the compartment before the next increment of flow. Some
apparent dispersion is imposed by the way in which solute concentration in
each cell is calculated by averaging incoming and remaining solute
concentration in each compartment. The greater the compartment length
("theoretical plate height" in chromatography terminology), the more is the
imposed apparent dispersion. The model has been used to successfully
describe the distribution of inorganic salts in soil columns (15) and field
soils (16) and effluent concentrations from columns (17). In other studies
the movement of pesticides in soil columns was estimated by incorporating an
independently measured adsorption coefficient in the equilibration
calculation for each compartment (18,19,20). Swanson and Dutt (20) were the
first to investigate the effect of non-singular adsorption-desorption
relationships on pesticide movement through soil columns. Their compartment
model accurately predicted atrazine concentrations in the effluent for two
soils with different adsorption capacities.
While the results obtained with the simple convection-compartment model
are surprisingly good in many cases, the arbitrary adjustment of compartment
length in the model to achieve the calculated dispersion corresponding to
that in the experimental data does not encourage widespread use of such
models. I have discussed this approach for two reasons, in addition to the
historical perspective provided: First, the simple convection equation is
45
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one of the building blocks of the convective-dispersive flow equation to be
presented later, and essentially describes solute transport in the absence of
diffusion and dispersion. A solute transport equation which adequately
describes dispersion is essential for most applications; likewise an equation
coefficient characterizing pesticide dispersion in field soils is a property
of the flow system which we must be able to measure. If we cannot measure a
meaningful dispersion coefficient for use in the more rigorous equation, the
resulting prediction equation may prove to be almost as arbitrary as a
compartment model based on Equation [1]. The second reason for discussing
the simple equation is that models based on piston displacement appear to
have some utility for approximating solute fronts in field soils under some
conditions (21).
Dispersion Equation Neglecting Diffusion
An equation formulated on the basis of Pick's law of molecular
diffusion, but with the coefficient D' [I2!"1] being a dispersion coefficient
rather than a diffusion coefficient, was proposed by Day (22),
03c/3t = eD'(32c/3x2) [2]
in which x is distance [L] and t is time [T]; the other variables or
constants were previously defined. This equation and others which follow are
obtained by combining the appropriate flux equation with the equation of
continuity, 63c/3t = -3J/3x. In the present case the flux is given by
J = -8D'(3c/3x). The derivation of [2] and its solution for a variety of
initial and boundary conditions are presented elsewhere (8). Although
Equation [2] has been found to satisfactorily describe the movement of
inorganic solutes in field soils (23,24), the coefficient D' cannot be
determined independent of the experiment, but must be obtained by fitting the
solution of the equation to at least one set of experimental data (e.g.
solute concentration versus depth for a given water flux over a given period
of time). Also Nielsen and Biggar (25) have emphasized the limitations of a
solute transport equation which does not incorporate fluid velocity effects
on solute mixing in an explicit way. Thus it is evident that an equation
which lumps too many processes into one parameter lacks the generality that
is required for applications involving a variety of porous materials and flow
velocities. This seemingly obvious conclusion is good to keep in mind when
we are evaluating transport equations for their value in predicting pesticide
movement in the field.
Diffusion-Dispersion Equation with Convection
The failure of Equations [1] and [2] to adequately describe solute
movement in porous materials led investigators to develop the equation which
is now most commonly used in modeling solute transport in soil water. The
equation is derived by assuming that the one-dimensional solute flux through
a unit soil area is the sum of fluxes due to three contributing mechanisms:
46
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convection, diffusion and mechanical dispersion, the latter being due to the
range of pore velocities which disperse the solute as it is moved by
convection through the soil. The convection flux (Jx) is given by Equation
[1] and the diffusion and dispersion fluxes can be expressed, respectively,
as J2 = -De (aec/ax) and J3 = -D^ (86c/9x), where De is the effective
diffusion coefficient for solute in porous soil [L2!"1] and D£ is the
longitudinal dispersion coefficient [L2!"1]. Substitution of Jj + J2 + 3$ in
the continuity equation and assuming for the present that both De and D£ are
independent of the distance x,
aec/at = -vaec/9x + (De + D£)92e c/9x2 [3]
While some workers have studied the separate contributions of De and D« to
the dispersion of solute (26), most have lumped them together so that D = De
+ D^ , with D being called the "apparent diffusion coefficient" (27),
"spreading coefficient" (14) or simply "dispersion coefficient" (11). It is
notable that researchers quickly recognized the difficulty of distinguishing
the independent contributions of diffusion and longitudinal dispersion to the
spreading of solute. Thus, even though the two distinct mechanisms were
identified, the nearly impossible task of measuring their magnitudes in a
flowing system required the use of a single coefficient, D, giving the much
used convection-diffusion-dispersion equation
86C/9t = D826C/9X2 - V96C/8X [4]
I have chosen to leave e inside the differential operators in each term to
emphasize that soil water content is a variable in the dispersion equation
when water flow is not steady, which is the more common case in field
situations.
Some important facts about Equation [4] need to be understood lest it be
applied indiscriminately with false expectations as to how well it will
describe pesticide movement in soils. First, both v and D appear as
constants in the equation, while in fact they represent mechanisms which
frequently cannot accurately be represented by constants. The constant v in
the convection term is calculated from the Darcy flux, q/e, and thus
represents an average velocity for the likely wide range of velocities
expected in the broad spectrum of sizes of pores through which solution is
moving. The wider the range of pore velocities, the less representative will
an average velocity be. Wide deviations of velocities for individual pore
sequences from the average velocity will cause solute mixing that is not
described by the convection term, v3c/3x; this mixing must be described by
the diffusion-dispersion term, D 926c/3x2. Most experimental evidence
suggests that D in [4] is a function of average velocity, with 0 increasing
as velocity increases. Biggar and Nielsen (27) have reviewed this subject in
some detail, providing evidence from laboratory column studies (28) and field
studies (29,30) that an appropriate function may be D = De + Xvm , in which X
and m are fitted constants and De is the effective diffusion coefficient
(either assumed or measured) at a given soil water content. Smiles and
Philip (31), on the other hand, have concluded from a laboratory study and
theoretical considerations that D should be velocity-independent in
unsaturated soils, except perhaps in coarse sands.
The diffusion coefficient De can be expected to increase as soil water
47
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content increases (27), but De is generally small relative to the composite
coefficient, D, so that variations in De with water content may not be a
problem. Laboratory studies have shown that D increases as water content
decreases (30), but apparently this relationship has not been defined for
field soils. In most field experiments the effects of pore velocity and
water content on D would probably be confounded.
Despite the uncertainties noted above, the reasonable prediction of the
distribution of non-adsorbed solute in a field soil with Equation [4] by
Warrick et al. (29) is reason for cautious optimism. Variability of v and D
in field soils (30) poses other problems which will be discussed later with
regard to measurement of key parameters and model validation.
Convection-diffusion-dispersion equation with adsorption. The success
of Equation [4] in describing the movement of non-interacting solutes in
laboratory columns and in a few field studies has encouraged researchers to
go one step further, and attempt to model the movement of solutes which
interact with soil colloids; interaction with colloid surfaces can include
cation and anion exchange, anion repulsion, or in the case of non-ionic
organic chemicals (including most pesticides), adsorption-desorption.
Incorporation of solute interaction into the model is accomplished by adding
another term to the left-hand side of [4], giving
96C/9t + p9S/9t = D926C/9X2 - V96C/9X [5]
in which S is the adsorbed concentration, expressed in units of mass of
solute adsorbed per unit mass of dry soil [MM"1], and p is the soil bulk
density [ML"3]. Note that pS then has the same dimensions as ec, i.e. ML"3
or mass of solute per unit volume of soil. The adsorbed quantity is not a
constant but is a function of the concentration in solution. The function
may be quite simple, as in the case of instantaneous equilibrium adsorption
with a singular adsorption-desorption relation, or the S-c function can be
quite complex if a kinetic expression is needed. The use of S in Equation
[5] requires that the appropriate adsorption function be defined. Several
laboratory pesticide movement studies in the 1960's attempted to describe
equilibrium adsorption with a linear function, such as S = kic + k2, but this
relation incorporated into the solution of Equation [5] often failed to
adequately describe the early breakthrough of pesticide in the column
effluent or the asymmetry ("tailing") in the concentration-effluent volume
curve following emergence of the peak concentration in the effluent
(10,11,14). Computed movement was generally poorest for the more highly
adsorbed chemicals and at high flow velocities (14). Subsequently,
researchers have explored a number of different approaches to account for
adsorption effects, including both equilibrium and kinetic expressions.
Summaries of several adsorption equations are presented by Boast (9) and van
Genuchten and Cleary (10). Adsorption effects and their mathematical
description will be discussed in more detail in a later section.
SOLUTE DISPERSION IN FIELD SOILS
At this point I want to focus on some challenging problems with which we
are faced when we attempt to apply Equation [5] to predict pesticide mobility
in field soils. This section will deal principally with (a) the solute
dispersion process, (b) attempts to model the process, and (c) determination
48
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of parameters for solute transport equations. Adsorption enters into the
discussion because the dispersion and adsorption processes are inextricably
coupled, but adsorption aspects will be the subject of a subsequent section.
Structural Characteristics of Field Soils — The By-Pass Problem
During the 1980 Annual Meeting of the American Society of Agronomy, a
symposium with twelve papers addressed the subject "Water and Solute Flow
through Soil with Large Pores." The existence of such a symposium indicates
the increased interest in recent years in applying physical theory and
mathematical modeling techniques to the solution of field problems involving
water and solute transport. Some of the papers stressed the need for new
approaches in applying transport theory to highly heterogenous, anisotropic
soils. Some researchers question whether the Darcy equation and a solute
transport equation such as Equation [5] are appropriate for many natural
soils with extremes in pore sizes.
Also, I was interested to find in a recent issue of the Soil Science
Society of America Journal (May-June, 1981) that both papers which occupy the
Soil Physics section, deal with solute movement in soils, but the research
approach and outlook of the two are quite different. The first (32) states
the following in the Abstract: "The analysis (of hydrodynamic dispersion
with velocity independent dispersion coefficient) is demonstrated using a
chemically inert sandy soil. The results show that during transient,
unsaturated flow, a simple piston-flow model described the process over a
range of water contents. The method may be extended to explore dispersion in
structured and chemically reactive soils." The second paper (33) states in
the discussion: "In soils which exhibit strong structure, water and salt
flow down the larger pores, channels, cracks, root holes, worm holes, and ped
faces can be significant. Soils without these structural features, such as
uniform fine sandy loam and carefully packed laboratory columns, exhibit more
uniform displacement. Thus, laboratory studies carried out with no
appreciable soil structure may not be applicable to soils with distinct
structure." Both works reported research results which likely led to valid
conclusions for the particular soil-water-solute system studied in each case.
But care must be exercised in applying techniques or conclusions developed in
one system to another system which may differ significantly in pore-size
distribution or another critical property.
The fact that equations developed from rational physical principles do
work in some cases and do not work in other cases should lead us to evaluate
the profile characteristics of specific soils with regard to physical and
chemical properties that impact on water and solute movement. This has been
done in some studies with soils evidencing heterogeneity and/or anisotropy in
their hydraulic properties, particularly the hydraulic conductivity-water
content relationship.
In studies of water movement through Houston Black clay, a Udic
Pellustert which undergoes shrinkage and swelling with drying and wetting
cycles, Ritchie et al. (34) demonstrated that most of the water contained
within soil peds (structural units) was not active in the flow process in
comparison to water in larger pores around the peds, especially when the soil
was nearly water saturated. Measurements of nitrate and chloride movement in
the same soil (35) indicated that applied chemicals were transported in
percolating water through large connected pores, by-passing the water inside
49
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the soil peds.
We had measured a similar phenomenon in a non-swelling, highly
structured Torrox soil (36). The field site had been tilled to a depth of
about 0.4 m (common for sugarcane in Hawaii), and was underlain by a
moderately permeable subsoil with coarse, prismatic structure. The potassium
salt of picloram herbicide (a weak organic acid) was applied in a small
volume of water to the previously wetted soil prior to ponded irrigation with
0.24 m water. Picloram concentrations in the soil solution one week after
irrigation were obtained at various depths below the soil surface with porous
ceramic suction probes; the results are shown in the left-hand graph in
Figure 1. Picloram had moved below 0.80 m and was even detected by a deep
probe at 1.43 m. Two subsequent weekly irrigations of 0.12 m water each
resulted in the profile concentration curves in the middle and on the right
of Figure 1. The deep penetration of some picloram with the first irrigation
and retention of the picloram "peak" at about 0.15 m after application of a
total of 0.48 m water led to. the conclusion that rapid, non-uniform water
flow was occurring during irrigation and early drainage. Piston displacement
of the picloram (assuming the laboratory-measured linear adsorption
coefficient of 2 x lO"4 m3/kg) would likely have moved the peak to a depth of
about 1.0 m, based on an average water retention of 0.40 m3m~3 in the
profile. A companion study of nitrate movement (37) indicated a similar
by-passing of solute within soil peds, although nitrate moved more readily
and uniformly than did picloram, probably because nitrate was not adsorbed.
A comparison of nitrate movement when irrigation was applied immediately or
one week after nitrate application to the soil revealed that initial
equilibration rendered the nitrate less mobile, presumably because of
diffusion of added nitrate into micropores within peds.
While our proposed mechanisms for early solute breakthrough and
subsequent slow movement of the picloram peak in the Torrox soil were
somewhat speculative at the time, later soil column studies on the Molokai
soil (38,39) confirmed the importance of bi-modal pore-size distributions to
picloram transport. Comparisons with other soils revealed that secondary
peaks appeared in the effluent breakthrough curves of Molokai soil aggregates
but not in the curves for soils which were less aggregated. These results
were explained by the very high hydraulic conductivity associated with
o
40
80
PICLORAM IN SOIL SOLUTION (/imoles/l)
40
80 0
—i 0
40 80
40
80
7 24cm
Water
40
80 "-o
°f 36cm
Water
40
so i-
48cm
Water
Figure 1.
Picloram distribution in the profile of Molokai soil (Typic
Torrox) after successive ponded applications of water at weekly
intervals; chemical applied at 20 kg acid equivalent per hectare.
From Rao et al. (36).
50
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macropores in the Molokai soils (38) and by the relative size of internal and
external pores observed in scanning electron micrographs of aggregates (39).
In recent years, abundant evidence has been provided for by-pass flow
through large pores or cracks in the soil (e.g. 40,41,42). Extensive flow
channeling in macropores was indicated by rapid deep penetration of chloride
from limited sprinkler irrigation using water containing a chloride
tracer (40). One soil had been planted to bluegrass for 5 years, but a
tilled soil showed similar behavior. Calculated displacement of pore water
initially in the soil near the surface generally ranged between 7 and 30% of
the original water for water applications of about 0.015 to 0.04m (41). The
limited displacement of water initially in soil aggregates indicated the lack
of uniform flow and the likelihood that by-pass flow was a common occurence
on these soils. Thomas and Phillips (42) suggest that many "hydrologists,
soil physicists, and soil scientists in general" do not recognize the
importance and widespread occurrence of macropore water movement; they cite
numerous published observations (from as early as 1882) to emphasize that the
phenomenon has been recognized by numerous workers but is considered by many
to be characteristic of a few unique soils.
Certainly the process of dispersion in soils has been understood for a
long time. Gardner and Brooks (43) in presenting evidence for the dispersion
induced by variable velocities in different sized pores stated: "Large pores
or combinations of large pores give rise to a certain amount of channeling".
It may be significant that these workers studied salt movement in both
laboratory packed columns and field plots, thus observing the generally
higher dispersion in field soils. Biggar and Nielsen (44), in their
carefully designed and conducted laboratory experiments, demonstrated the
striking effects of both aggregate size and fluid velocity on dispersion as
evidenced by the shapes and positions of effluent breakthrough curves.
Emphasizing the effects of velocity distribution on dispersion, they reasoned
as follows: "Because the total number of contacts between aggregates in the
column decreases with increasing aggregate size, the proportion of the total
flux through the pores in the aggregates will decrease with increasing
aggregate size. Thus, as the aggregate size increases, mixing in the column
becomes less complete and the effluent concentration is dominated by flow
through the large pores. In effect this means that tracer will appear in the
effluent earlier and a greater throughput volume will be required to reach
c/c0 = 1.0 the larger the aggregate size." This description of dispersion in
packed columns of soil aggregates appears to explain the major processes
involved in the movement of picloram and nitrate through Molokai soil in the
field, as I discussed previously in reference to Figure 1. Thus, laboratory
studies under controlled conditions allow us to study the nature of the
processes involved; only the magnitude of the effects differ between
laboratory and field systems. But striking differences in poresize
distributions and continuity of pores can result in very different solute
transport in the same soil material, as shown in a comparison of packed
columns of aggreates, soil cores, and field plots (45).
The research of Bouma and Anderson (46,47,48) is especially notable
because of the way in which they related the morphological characteristics of
structured field soils to water and solute movement. In measurements of
chloride movement through columns of structured soil (47,48) they found that
two soils having the same soil texture had very different solute transport
characteristics. Both soils were Hapludalfs with silt loam texture, but one
51
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had coarse prismatic structure and the other had medium subangular blocky
structure. Dispersion coefficients, obtained by fitting the solution for
Equation [5] to effluent concentration data, were D = 4.6 x 10~9 to 1.2 x 10"*
m2s~1(0.4 to 10 cm2 day"1) for saturated soil columns with prismatic
structure and about D = 3.0 x 10~7m2s"1(260 cm2 day"1) for the subangular
blocky structure. Solute breakthrough curves for unsaturated columns
generally evidenced more by-passing of soil pores than did saturated columns
particularly in the soil with blocky structure (48). In some cases, the
theoretical equation could not be fitted to the breakthrough data, so values
of D could not be obtained.
In summary, there is conclusive evidence that in structured field soils,
which are much more common than either single grained or massive soils, water
in micropores within peds is often by-passed by water flowing through large
void spaces. The pattern of water movement in such soils, whether the soil
is saturated or unsaturated, results in extreme dispersion of solutes moving
with the water, ff some of the soil water is actually isolated from the
water in conducting pores so that there is no continuity of water by way of
connecting pores or water films, then Equation [5] is not valid, unless it
can be appropriately modified (49). Some attempts at modifying the
dispersion equation are presented in the next section. There appears to have
been far too little effort to characterize the geometry of pore sequences in
structured soils. Such efforts might be rewarded with a much'better
understanding of water and solute movement in field soils and improved
possibilities of predicting the movement of pesticides and other solutes.
Attempts to Model the Dispersion Process in Structured Soils
In view of the apparent importance of micropore water which is excluded
from active flow in many field soils, I will focus principally on recently
developed models which consider mobile and immobile water in relation to
solute dispersion.
A number of models based on Equation [5] were developed in the 1970's in
attempts to account for adsorption effects on pesticide movement in soils.
Van Genuchten and Cleary (10) give an excellent review of these developments,
to which J.M. Davidson and his associates and M.Th. van Genuchten and his
associates contributed substantially. The models differed primarily in the
way in which S in the term 9S/at in [5] was formulated to handle several
possible types of adsorption behavior:
(a) adsorption equilibrium with linear or non-linear but singular
adsorption isotherms;
(b) equilibrium adsorption but with non-singular, non-linear
isotherms;
(c) kinetic non-equilibrium with linear or non-linear adsorption.
The extent to which each of the adsorption models succeeded depended on the
particular soil and chemical involved and on flow velocity. None of the
resulting flow models accounted for mobile and immoile water, until a
precursor of the model to be discussed here was developed by van Genuchten,
Davidson and Wierenga (50). A comparison of (a) their FREQ model (Fraction
of soil in Equilibrium with mobile water) with (b) Equation [5] incorporating
various adsorption submodels representing S, indicated that physical
non-equilibrium (due to water in micropores being excluded from the mobile
52
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phase) accounted for non- symmetrical effluent concentration curves as well or
better than some of the previous adsorption approaches.
The next step in this development was to modify the model of Coats and
Smith (51) to accommodate adsorption of pesticides in both mobile and
non-mobile phases (10,52). The model envisions five regions in the soil:
1. Air space
2. Mobile water, in inter-aggregate pores, which transports solutes by
convection with dispersion.
3. Immobile water inside aggregates and at the contact points of
aggregates and particles
4. A dynamic soil region in adsorption equilibrium with the mobile
water phase
5. A stagnant soil region in which adsorption-desorption is limited by
diffusion from the mobile water phase through the immobile water
phase.
A parameter f is defined as the fraction of the sorption sites which is in
direct contact with mobile water; thus f determines the relative proportions
of the soil volume which are characterized either dynamic or stagnant. When
f = 0 all adsorption occurs in the stagnant region. The resulting transport
equation, an extension of Equation [5], in its most general form is
9(emcm)/9t + 9(9imcim)/9t
- 8(emvmcm)/3z [6]
in which the subscripts m and im refer to mobile and imobile regions,
respectively and vm = q/8m, q being the Darcy flux.
To describe diffusion between the mobile and immobile regions, van
Genuchten and Wierenga (52) added a sorptive component to the diffusion
equation of Coats and Smith (51), giving
3eimc1m/8t + (l-f)p3S1m/3t - a(cm - C1m) [7]
in which a is a mass transfer coefficient, which has subsequently been shown
to be dependent on other system parameters(53) . Equations [6] and [7]
describe the movement of an adsorbed pesticide through structured soil when
eim» em» f> v , a, p, Sm and Sim are specified. When equilibrium,
Freundlich-type adsorption is assumed, the number of dependent variables can
be decreased from four to two, and with some simplifying assumptions, the
mathematical solutions are expressed in terms of four independent parameters.
Only the retardation factor (R = 1 + pK/e, in which K is the linear
adsorption coefficient) can be independently determined; the others must be
assessed by curve fitting procedures (10,11). Equations [6] and [7], with
appropriate simplifications for constant water flux, successfully predicted
2,4,5-T movement through water saturated packed columns of clay loam*
aggregates less than 0.006 m in diameter. In this system only 50% of the
soil water was calculated to be mobile, this result being obtained by
applying the transport equations to the movement of tritium tracer. The
parameter f for the 2,4,5-T experiment was 0.40, indicating that' 40% of the
adsorption sites were located in the dynamic regions associated with rapidly
moving water in macropores and 60% of adsorption sites were in the
micropores.
53
-------
I have discussed the model of van Genuchten and Wierenga in some detail
because it represents a recent development in deterministic, process oriented
models which specifically addresses the problem of mobile and immobile water
associated with structured soils. It appears to have promise for prediction
of pesticide movement. The principal limitation of the approach presented
above is that most of the key parameters in the equations must be obtained by
least-squares fitting of the appropriate mathematical solutions to effluent
concentration data. The curve fitting techniques are available (11), but
there is considerable uncertainty as to whether a good fit of the conceptual
model to experimental data actually constitutes confirmation of the
designated processes inherent in the model (54,55). The need for methods to
determine key parameters independent of actual measurement of pesticide
movement is apparent. Ultimately we are interested in "forecasting"
pesticide mobility in practical field situations; this will require the
development of methods to measure or estimate relevant parameters. Recently,
Rao et al. (56) demonstrated that independently measured parameters for a
synthetic media with spherical porous "aggregates" gave good predictions of
solute movement in columns with equations similar to [6] and [7]. Whether or
not independent measurement of parameters for field soils is possible remains
to be seen. The more detailed is the specification of physical and chemical
mechanisms controlling pesticide transport, the greater will be the task of
determining the parameters. Extremely simple models, such as that proposed
by Addiscott (57), require much less input than the model defined by
Equations [6] and [7], but such simple models must essentially be calibrated
for a given soil and water flow regime, and thus lack the generality that is
desirable in a predictive model.
Determination of Dispersion Coefficients for Field Soils
Although the measurement of key parameters required in solute transport
equations has been considered briefly above, a few more comments on the
determination of dispersion coefficients in field soils is appropriate.
Dispersion coefficients for packed soil columns are usually determined
for the flow conditions of interest by a least-squares fit of the appropriate
mathematical solution to effluent concentration data for a non-interacting
solute. This same approach was used by Anderson and Bouma (47,48) for
columns of structured soil. In some cases, especially when solute was
transported in water infiltrating under ponded conditions into previously
drained columns, D values could not be calculated; the equation for
convective-dispersive-diffusive transport, Equation [4] in this paper,
apparently did not describe the rapid breakthrough caused by flow by-passing.
When a synthetic porous crust was established at the surface of soil columns
so that unsaturated flow conditions were achieved, the chloride breakthrough
curves were shifted to the right but still did not have shapes that could be
described by the dispersion equation. The early breakthrough indicated
significant by-pass flow even with unsaturation.
The crust technique of Anderson and Bouma (48) was adapted by Khan and
Green (58) to measure the in situ value of D in Equation [4] for a highly
structured Torrox soil located near the site on which the data in Figure 1
were obtained. A steady solution flux of 8.64 x 10"6 ms"1 (10 cm day"1) was
established in an in situ soil column 0.30m in diameter by 0.80m long.
Nitrate concentrations obtained from extracts of soil samples taken at depth
54
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intervals of 0.05 m were used to determine D from an appropriate solution to
the dispersion equation. Equation [4] described the data well, and the
procedure yielded D values of 1.5 x 10'7 and 2.5 x IQ-Vs'1 for two sites a
few meters apart. Measurement of D on unsaturated soil (e = 0.39 m3m"3)
probably minimized rapid flow through macropores so that Equation [4] was
applicable. The dispersion coefficients were required in a simulation study
of nitrogen transport and transformation when nitrogen fertilizer is applied
in irrigation water (59). Although D is likely a function of e and v in [4],
the limited range of e and v under high frequency, low intensity irrigation
seemed consistent with the use of a constant D. The crust technique allows
the determination of dispersion coefficients in field soils for various water
content-flux combinations by varying the permeability of the crust.
Another technique of measuring D in unsaturated field soil was used by
van de Pol et al. (60). Steady water flow was maintained by applying water
at a constant flux of 1.7 x 10'5 ms"1 (2 cm day"1) with a drip irrigation
system having an emitter in every square of a 0.3m x 0.3m grid system on an 8
x 8 m field plot. Chloride and tritium were used as tracers. Seven porous
ceramic cups at various depths down to about 1.2 m allowed sampling of soil
solution, which was analyzed to obtain tracer concentration versus time at
various depths; values of D were then calculated for each depth. The
geometric mean value of D from all probes for this non-uniform profile (clay
over silty clay over silty loam over sand) was 3.2 x 10"6m2s~1.
When one wants to measure the dispersion coefficient appropriate for the
water-saturated condition the experiment appears easier to conduct because
water can merely be ponded at the surface and solution sampled with suction
probes (30). However, in soils with large pores that contribute to
by-passing of water within peds, solution concentrations obtained with a
suction probe under saturated conditions may not be representative of soil in
that horizontal plane (10,36); in addition the solute concentration curves
measured under such conditions would likely give a poor fit to the solution
of the flow equation.
Whether or not suction probes are an appropriate sampling tool in
heterogeneous soil (with respect to hydraulic conductivity) may be
principally a matter of scale. Fried (61) addressed this subject in
reference to dispersion in groundwater aquifers and concluded that "it is
compulsory to make in-situ experiments to collect longitudinal dispersion
coefficients, as laboratory results completely differ from field values." In
general, conventional soil cores and suction probes do not adequately sample
a representative elementary volume (REV) with respect to hydraulic
conductivity and solute dispersion, particularly in soils with large void
spaces and at high water contents. Beven and Germann (62) suggested that in
order to obtain a meaningful spatial average, a suitable REV for a combined
micropore/macropore system might be 1 to 10 m2 in area with a depth
corresponding to the distribution of macropores in the soil profile. Much
more could be said about sampling, but that subject is beyond the scope of
this paper. However, sample or plot size and the number of samples required
are not only matters of concern to the statistician, but may be critical for
the researcher attempting to characterize dispersion in a heterogenous soil.
PESTICIDE ADSORPTION IN RELATION TO MOBILITY
During the 10-year period between about 1965 and 1975 there was a
massive, non-orchestrated research effort in the United States directed
55
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toward gaining a better understanding of pesticide behavior in the
environment. With broad public support the federal government provided
unprecedented financial backing for pesticide research through federal
agencies concerned principally with agriculture, the environment in general,
and water in particular. The availability of funds motivated many soil
chemists, soil microbiologists, and soil physicists to investigate aspects of
pesticide behavior related to their professional skills and interests.
Pesticide adsorption on soils is one area of research that received the
attention of a host of soil chemists and other scientists for several years.
Many continue to contribute new ideas and approaches to pesticide adsorption
research, but funding for this subject has diminished and research activity
is modest compared to former days. This background perspective is relevant
to our present discussion in that (a) a huge body of information on pesticide
adsorption in soils and sediments has been accumulated in" the past 15 years,
(b) there is a need to re-examine the basic information that was developed
during this period of fruitful research and apply the relevant principles to
our understanding of new chemicals, and (c) the likely low level of funding
available for pesticide adsorption research in the future makes it imperative
that researchers indentify the knowledge gaps that most hinder our
understanding of pesticide behavior in soil-water systems and invest their
efforts where they will count the most.
Numerous comprehensive reviews of pesticide adsorption research are
available, although most were published six to ten years ago (63,64,65,66,67,
68). In general the information summarized in these reviews is currently
useful even though some of the compounds studied are no longer in common use.
Two recent reviews focus on specific information requirements: Calvet (69)
identifies aspects of herbicide adsorption that are particularly useful in
understanding herbicide mobility and uptake by plants and suggests areas
which most need additional research effort. Rao and Davidson (70) have
summarized existing information on pesticide adsorption that has particular
relevance to prediction of pesticide movement to non-target areas. They
provided a critical evaluation of various adsorption methods and compiled a
data base of adsorption distribution coefficients for a large number of
pesticides. In view of these contributions, I will limit the present
discussion to only those aspects of pesticide adsorption from solution that
seem particularly relevant to our subject, forecasting pesticide mobility in
soils, and especially in relation to the previous section in which the
interaction of dispersion and adsorption was considered.
Adsorption Process and Its Description
Adsorption of a pesticide from solution onto the soil colloids reduces
the concentration in solution. If the soil solution is moving down through
the profile under the gravitational potential, a reduction in the
concentration of pesticide in solution effects a reduction in the downward
flux of pesticide, as indicated by the flux equation which led to Equation
[4], i.e.
J = vec - Ogee/ax [8]
Thus if adsorption reduces the concentration, c, to c = 0, the flux will be
zero also. And if the downward liquid flow velocity, v, is zero the downward
56
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flux of pesticide is limited to that associated with diffusion, so that
pesticide movement is negligible. This simple analysis may appear trivial,
but it summarizes the impact of adsorption on pesticide movement through the
soil profile. The two principal physical phenomena controlling the movement
of non-volatile pesticides in the soil are adsorption and water flux. Thus,
while the dispersion phenomenon which we discussed in the last section is
especially important to the distribution of non-adsorbed or slightly adsorbed
pesticides, adsorption is probably the dominant mechanism controlling the
movement of most pesticides.
Equilibrium adsorption. Adsorption reactions proceed quite fast in
relation to the time frame normally appropriate to pesticide behavior in
field soils. Rao and Jessup (5) indicate that generally 60 to 80% of the
reaction is completed within a few minutes and equilibrium is achieved within
a few hours in batch suspension adsorption measurements. The rapid approach
toward equilibrium may justify the use of equilibrium adsorption data to
describe the relationship between concentration of pesticide adsorbed on soil
and the concentration in solution for most soil-water regimes (55). This
relationship is conveniently represented graphically by an "adsorption
isotherm", i.e. a plot of concentration adsorbed (mass of pesticide per unit
mass of dry soil) against the corresponding equilibrium concentration in the
solution (mass of pesticide per unit volume of solution); because adsorption
is usually temperature dependent each curve should represent adsorption at a
given temperature, hence the use of the term "isotherm". The concentration
dependence of adsorption varies widely between pesticides on the same
adsorbent and between adsorbents with the same pesticide. The shape of the
isotherm varies widely also, so that no one mathematical expression can
provide a universal adsorption equation for use in pesticide transport
models. A number of the equilibrium adsorption equations which have been
proposed are presented elsewhere (5,9,10). Among these, the two which have
gained the widest acceptance for use in transport models (5) are the linear
S=S0+Kdc [9]
and the Freundlich model, S = Kfc" [10]
S and c have the same definitions and dimensions as in Equation [5] of the
first section, and K
-------
and low concentrations. A graphical presentation of the error versus
solution concentration has also been presented (70). Although N j< 1 is the
most common situation, the exceptions need to be recognized. In a study of
atrazine adsorption on soil from three horizons (71), we found that
adsorption on the subsoil was negligible at low solution concentrations and
difficult to measure reproducibly by the batch equilibrium method (72). With
increased concentration, however, adsorption increased in such a way that the
isotherm had a concave upward shape. The seemingly negligible adsorption at
low concentrations may be more important to pesticide resistance to leaching
than a small value of the distribution coefficient would suggest, especially
if diffusion is the rate controlling process in removal of adsorbed pesticide
from the micropores of aggregates. This mechanism was suggested (36) as the
probable reason for the unexpected retardation of picloram movement shown in
Figure 1.
Since even a fairly complicated transport model, such as that repre-
sented by Equations [6] and [7] combined with appropriate initial and bound-
ary conditions, can accommodate adsorption functions such as Equation [10]
when numerical solutions are used (73), it may not be necessary to represent
a curvilinear adsorption isotherm by a linear approximation. The Freundlich
equation is probably satisfactory for most pesticide-soil combinations.
An additional adsorption complication with regard to pesticide transport
modeling is the frequently measured "non-singularity" in the
adsorption-desorption process. For systems which evidence this behavior the
desorption curve lies above the adsorption curve, such that the value of N in
Equation [10] for desorption (N^) often has a value around two times the
value of N for adsorption (Na), i.e. N^/Ng * 2. This lack of singularity has
been commonly referred to as "hysteresis", in reference to the analogous
behavior of gas adsorption in porous media. However, in contrast to gas
adsorption, there is no thermodynamic rationale for the lack of reversibility
in adsorption of non-ionic organic chemicals from aqueous solution. The
phenomenon has been measured for many soil-pesticide combinations, and has
been confirmed by careful experimentation in some cases (e.g. 74). In some
other cases, however, the apparent non-singularity appears to be an artifact
of the procedure normally used to measure adsorption-desorption curves by
Table 1. Ratio of Adsorbed Quantities Calculated by the Freundlich and
Linear Equations at Different Concentrations of Pesticide in
Solution.
S = KfCN
RATIO:
S = Kdc
Solution concentration, c (yg/ml)
N 0.01 0.1 1 10 100
1.0
0.9
0.8
0.7
0.6
0.5
1
1.6
2.5
4.0
6.3
10.0
1
1.3
1.6
2.0
2.5
3.2
1
1
1
1
1
1
1
0.79
0.63
0.50
0.40
0.32
1
0.63
0.40
0.25
0.16
0.10
Summarized from Hamaker and Thompson (65).
58
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batch equilibration (70). Rao and Davidson (70) describe two alternative
equilibration methods which avoid the usual repeated dispersal-equilibration-
centrifugation cycles. Some pesticide-soil combinations did not evidence the
non-singular adsorption behavior when the alternative procedures were used.
More research is obviously needed on methods of determining
adsorption-desorption of pesticides from solution.
Non-equilibrium adsorption. Despite the fact that adsorption
measurements in a slurry system indicate a rapid approach to equilibrium in
most cases, the assumption of instantaneous adsorption is likely invalid for
field soils when the water flux is high, and especially so with structured
soils in which there is a tendency for flow by-passing. Attempts to use
kinetic adsorption models to describe pesticide transport at high water
fluxes have not been successful (50).
In addition to the short-term kinetic effects, there is also evidence
that in some pesticide-soil systems adsorption continues to increase at
a very low rate after an apparent equilibrium has been reached. This subject
was discussed in some detail by Hamaker and Thompson (65), who concluded:
"There is an urgent need for serious investigation of this aspect of soil
adsorption because of its practical importance". To my knowledge there has
been little if any further definitive research on long-term adsorption
changes since 1972 when Hamaker and Thompson's review was published. Their
conclusions were based partly on the results of Obien (75) who calculated the
ratio of atrazine adsorbed to atrazine in solution (approximating Kj) for a
slurry system over a period of 60 days. Chemical hydrolysis of atrazine to
hydroxyatrazine over the course of the study required that the quantity of
atrazine adsorbed and in solution at a given time be determined by extraction
with methanol and separation of atrazine and hydroxyatrazine. The approach
was simple yet quite unique; the experimental procedure is diagrammed in
Figure 2. During the 60-day period as the amount of atrazine in the system
decreased due to hydrolysis, the adsorbed fraction of the remaining atrazine
in the system actually increased, so that the calculated apparent K^
increased with time. The results for one of the four soils studied are shown
in Figure 3, which is drawn from tabular values (75). Corresponding K
-------
movement more than would be anticipated from conventional adsorption
measurements; this is good from the standpoint of water pollution. Thus
unless "by-pass water flow" is moving pesticides to greater depths than
expected because of non-equilibrium adsorption soon after pesticide
application, actual movement toward groundwater would probably be more
conservative than calculations would indicate.
Another matter should be mentioned with regard to pesticide retention by
adsorption. When the applied pesticide undergoes progressive degradation,
each degradation product will have its own adsorption-desorption
characteristics, and thus will move with water independent of the parent
compound, perhaps faster, perhaps slower. It is quite unusual to find
adsorption-desorption isotherms for degradation products in research reports
which have details on the adsorption and degradation of the parent compound.
Hamaker and Thompson (65) in concluding their thorough analysis of
pesticide adsorption in soils gave the following perspective:
CONCURRENT DETERMINATION OF ATRAZINE
DEGRADATION (HYDROLYSIS) AND ADSORPTION
AQUEOUS PHASE METHANOLIC PHASE
[AFTER 0-60 DAY EQUILIBRATION) (AFTER 2 HOUR EXTRACTION)
CENTRIFUGE
ANALYZE ALIQUOT FOR
TOTAL TRIAZINE(I4C count)
PARTITION ALIQUOT WITH CHLOROFORM
AND MEASURE-HYDROXY ATRAZINE (I4C count)
CALCULATE FOR A GIVEN
ELAPSED TIME=
I. TOTAL ATRAZINE IN SYSTEM
2. ATRAZINE IN AQUEOUS SOLUTION
3. ATRAZINE ADSORBED
4. Kd= ADSORBED/SOLUTION
Figure 2. Diagram of procedure used to measure changes in atrazine adsorption
on soil in a 60-day period. From Obien (75).
60
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"As we attempt to look down the corridors of the future with respect to
research in this area, we see two chief directions of opportunity and need:
(a) achieving a better fundamental understanding of the nature of the soil
adsorptive surface and (b) developing valid kinetics for adsorption by soil.
These two areas, in particular, are needed for the application of sorption to
the practical problems that attend the use of chemicals in soil." They
continued: "Upon these two advances in fundamental understanding, we can
expect an increased ability to handle a number of applications of adsorption
knowledge; and perhaps the most important of these is leaching. At the
present time, the correlation between leaching and sorption is inadequate,
largely because there is no distinction between short-term and long-term
adsorption. Therefore, we can predict only the leaching that would occur on
freshly applied material, and it is not within our knowledge at present to
deal with material that has been in the soil for some time, for which the
leaching process is much less effective." These conclusions, expressed about
10 years ago, are equally valid in 1981, and especially so from the
standpoint of "forecasting pesticide mobility in soils". Perhaps the
prediction of short-term behavior of pesticides in soils is in itself such a
difficult task that we have not been prepared to explore even more complex
problems. The challenge remains to assess the adsorption-desorption
relationship for pesticides and their principal degradation products over
extended periods of time.
100
20 -
60%
KAPAA SOIL
pH 4.9
10
20
30
40
50
60
Figure 3.
TIME (days)
Change in percent atrazine adsorbed on Kapaa soil (Typic Gibb-
siorthox) during a 60-day period in which the total amount of
atrazine decreased by hydrolysis. From Obien (75).
61
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Modeling Adsorption for Use in the Dispersion Equation
The various equations which have been proposed to describe both
equilibrium and kinetic adsorption have been summarized elsewhere (9, 10).
The two most frequently used equations for equilibrium adsorption are the
linear function and the power (Freundlich) function, expressed by Equations
[9] and [10]. The linear equation has the distinct advantage of simplifying
the solution of Equation [5] in that an analytical solution is available
(10). Numerical techniques provide solutions when [10] is used to describe
adsorption. In general, numerical solutions will likely be required for most
practical situations because adsorption of most pesticides on soils is highly
correlated with soil organic matter content, which generally decreases with
depth; thus different adsorption parameters may be required for various
layers in the soil profile. Selim et al. (76) have studied the layered-soil
problem with laboratory columns and predicted effluent concentrations in this
system with Equation [5].
Two kinetic adsorption equations were evaluated by van Genuchten et al.
(50) in a laboratory study by predicting column effluent concentrations with
[5] using the kinetic equations to describe 3S/9t. "The more complicated of
the two equations, derived by Lindstrom et al. (77), gave about the same
results as Equation [11]
as/at = k2(ekicN/pk2 - S) [11]
Equation [11] is a first-order kinetic rate equation, with first-order
forward, k1? and first-order backward, k2, kinetic rate coefficients (T'1 ).
At equilibrium (3S/3t = 0) Equation [11] reduces to Equation [10]. Other
investigators have simply differentiated Equation [10] with respect to time
to get Equation [12],
as/at = KfNcN~1(ac/at) [12]
but this equation does not incorporate different rates for adsorption and
desorption. Although both of the rate equations tested against column data
for picloram displacement gave good results at low flow velocities, neither
equation was satisfactory at high flow velocity (50). These negative results
led van Genuchten and associates to explore other approaches, resulting in
the development of the model represented by Equations [6] and [7]; the latter
approach emphasizes the rate of diffusion of solute into immobile water
rather than the rates of adsorption and desorption. It is likely that the
mass transfer coefficient, a, in [7] actually includes the adsorption kinetic
effects.
Determination of Adsorption Parameters
Equilibrium adsorption at various concentrations of pesticide in
solution can be determined either in a batch-suspension system or in a flow
system. An assessment of a number of the alternative procedures that have
been proposed was presented by Green et al. (78). The relative merit of each
62
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of the methods depends on the objectives of a study. In general, the batch
method can be expected to give slightly higher amounts of adsorption at a
given solution concentration than a flow method which protects soil
aggregates from dispersion. However, in some pesticide-soil combinations the
two methods give essentially the same results (79). The batch method was
found to be inadequate for low-level pesticide adsorption; such as in soils
with low organic matter, because of the indirect determination of adsorption
(72), while direct extraction and measurement of adsorbed pesticide after
establishing equilibrium gave good precision even with very low adsorption
(79).
Determination of kinetic adsorption coefficients that will have meaning
for field soils is a difficult task that has not been studied much, to my
knowledge. The flow procedure developed by Cheung (80) appears to hold some
promise.
A number of indirect methods of estimating equilibrium adsorption have
been proposed and are discussed elsewhere (70). Such methods may be suitable
for applications which require only approximate adsorption coefficients; only
Kd in the linear equation is usually estimated.
INPUT DATA REQUIREMENTS AND OUTPUT EXPECTATIONS
The previous discussion has focused on dispersion and adsorption; some
attention was given to methods that have been used to characterize these
properties. Much remains to be done to develop methods that are field
oriented in their intent, so that measured dispersion coefficients (or
functions) and adsorption parameters will be appropriate for field
applications.
For example, recently we found that soil brought to the laboratory in a
moist condition adsorbed smaller quantities of two different pesticides than
the same soil which was air dried before the adsorption measurement. In most
previous studies we have not been concerned about absolute measured values
because we were not intending to predict field behavior of the pesticide at a
given location or for a given soil condition. But, when field behavior is to
be predicted, laboratory measurement conditions should represent field
conditions as closely as possible. Thus, if we want to predict the movement
of a pesticide in a soil that is normally moist by irrigation, it makes sense
to measure adsorption on the moist soil. The same idea would certainly apply
to measurement of transformation kinetic coefficients or volatilization
parameters.
As mentioned in a previous section, the flux of water in the soil
profile, whether down or up, is responsible for moving the pesticide. Poor
estimates of water flux will surely result in poor prediction of pesticide
movement, except in cases where high adsorption prevents the pesticide from
moving. The vertical flux of water in the soil depends principally on soil
hydraulic properties and on additions and losses of water at the soil
surface, assuming the soil is reasonably well drained. A shallow water table
would serve as a water source at the bottom of the root zone and could have
important effects on the water flux through the profile over time. Growing
plants might have more impact on pesticide mobility in the soil by way of
their effect on the direction and magnitude of water flow than by direct
63
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uptake of pesticide. Thus, characterizations of hydraulic properties of the
soil, including the hydraulic conductivity - water content relationship and
suction - water content relationship, and the water inputs and losses over
time are as necessary as measuring dispersion and adsorption in a reliable
manner. Reliable yet simple methods of measuring hydraulic properties of the
soil profile in situ are available (e.g. 81).
Transient water flow has been described successfully on a variety of
soils (e.g. 82) with the Richard's equation, which combines Darcy's equation
for unsaturated flow with the continuity equation. However, spatial
variability in the hydraulic properties over relatively small distances, such
as a few meters, can result in very different water fluxes in small areas,
let alone large fields (30,60). Variability of field soils is a major
consideration when we propose to forecast pesticide mobility. High
variability in the flux of water through the soil profile may result in a
highly variable depth of pesticide leaching, especially since dispersion and
adsorption processes appear to be coupled with water flux. The best strategy
in forecasting pesticide movement for areas one hectare or larger may be to
obtain measures of central tendency (mean, geometric mean, etc., depending on
the nature of the statistical distribution) and variances for each key
parameter describing the system, and then determine by statistical techniques
the likely variation of the depth of pesticide movement for specified water
applications. It is probably unrealistic to expect to model precisely the
distribution of pesticide in the soil profile at any given point in a field,
but with adequate characterization of an area, it may be possible to predict
the mean or geometric mean depth of the pesticide peak and the maximum peak
depth anticipated within a specified confidence interval. However, even this
level of prediction precision may require more input data than is practical
under most pesticide testing programs. It seems there is a real need for
public agencies and industry to determine the type of information needed on
pesticide mobility in order to protect groundwater quality.
The recent detection of the nematicide DBCP (l,2-dibromo-3-
chloropropane) in groundwater pumped from deep wells on Oahu, Hawaii has
impressed me that superficial assessments of pesticide mobility for
generalized conditions are not adequate. DBCP has been used in pineapple
culture for over 15 years, and only recently was the chemical measured in
groundwater at a depth exceeding 50 m. I am reasonably confident that given
our present knowledge of pesticide-soil-water interactions we could, with the
proper input data, determine the likelihood of a pesticide moving 10 to 50 m
over a period of years. Exactly how detailed the model must be and how
precisely the input parameters must be measured are questions still to be
answered.
Leistra et al. (83) have demonstrated that a transport equation similar
to Equation [5] in this paper can successfully describe pesticide movement in
field soils. They compared predicted and measured propyzamide (herbicide)
distributions for five field sites for periods of 56 to 119 days. Although
the chemical moved only a maximum of about 0.15 m, the predicted
distributions represented measured distributions very well. Subsequently,
Leistra and his associates have used transport equations combined with
transformation kinetic equations to predict the persistence and mobility of
real and model pesticides under a variety of management-soil-environmental
conditions (84,85,86,87). Based on limited, yet reassuring validations of
model computations with field data, these researchers have used the
64
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mathematical models to examine likely effects of various factors on pesticide
movement and thereby evaluate various management alternatives. Yet, Leistra
and Dekkers (87,88) have emphasized the need for better characterization of
(a) water flow, (b) the kinetics of pesticide degradation in the field and
(c) adsorption increases over prolonged periods.
The potential for forecasting pesticide mobility in field soils has only
begun to be realized. Computers and numerical solutions are now available to
solve rather complicated process models that appear to describe the physical
and chemical behavior of pesticides in soils. We must decide just how exact
predictions need to be, and then proceed to develop improved methods of
characterizing key processes and system properties that determine pesticide
mobility.
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Soils. Ph.D. Dissertation, University of Hawaii, 1970; 226 pages.
76. Selim, H.M.; Davidson, J.M.; Rao, P.S.C. Transport of reactive solutes
through multilayered soils. Soil Sci. Soc. Am. J. 1977, 41, 3-10.
77. Lindstrom, F.T.; Boersma, L.; Stockard, D. A theory on the mass
transport of previously distributed chemicals in a water saturated
sorbing porous medium: Isothermal cases. Soil Sci. 1971, 112,
291-300.
70
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78. Green, R.E.; Davidson, J.M.; Biggar, J.W. An assessment of methods for
determining adsorption-desorption of organic chemicals. In
"Agrochemicals in Soils", Banin, A; Kafkafi, U. Eds.; Pergamon Press:
New York, 1980.
79. Green, R.E.; Corey, J.C. Pesticide adsorption measurement by flow
equilibration and subsequent displacement. Soil Sci. Soc. Am. Proc.
1971, 35, 561-565.
80. Cheung, M.W. Equilibrium and kinetic processes of the interactions of
4-amino-3,5,6-trichloropicolinic acid (picloram) and o,o-diethyl-
o-p-nitrophenyl phosphorothioate (parathion) with soils. Ph.D. thesis,
University of California, Davis. University Microfilms, Ann Arbor, MI,
1973.
81. Chong, S.K.; Green, R.E.; Ahuja, L.R. Simple in situ determination of
hydraulic conductivity by power function descriptions of drainage.
Water Resour. Res. 1981, 71, 1109-1114.
82. Beese, F.; van der Ploeg, R.R.; Richter, W. Test of a soil water model
under field conditions. Soil Sci. Soc. Am. J. 1977. 41, 979-984.
83. Leistra, M.; Smelt, J.H.; Verlaat, J.G.; Zandvoort, R. Measured and
computed concentration patterns of propyzamide in field soils. Weed
Res. 1974, 14, 87-95.
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an arable crop. Plant Soil 1978, 49, 569-580.
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application in spring. Soil Sci. 1979, 128, 303-311.
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in winter: 2. Computer simulation. Soil Sci. 1981, 131, 296-302.
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behavior of oxamyl in fallow soils. Pestic. Sci. 1980, 11, 379-388.
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The work described in this paper was not funded by the U.S. Environmental
Protection Agency. The contents do not necessarily reflect the views of the
Agency and no official endorsement should be inferred.
71
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ADSORPTION OF ATRAZINE BY SOIL ADSORBENTS
M.V.Khlebnikova, V.A.Konchitz
Timiryazev Academy of Agriculture,Moscow
Recently much attention has been given to studies on the
adsorption of pesticides applied to soil. It is known that up
to 80# of the herbicides applied is adsorbed by the top humus
horizon, thus almost excluding the movement of herbicides
through the soil profile. Most herbicides almost do not under-
go degradation in the adsorbed state, and their lifetime in
the soil coyer significantly increases (2,3).
Herbicides are most often adsorbed by the soil from the
soil solution. The intensity of adsorption from a solution on
solid adsorbent(soil) at the same temperature,pressure and
specific surface depends not only on the nature of adsorbent,
but also on that of solute and solvent.
Regularities of the process of adsorption on pure adsor-
bents are rather adequately studied(I-5)« Soil is the most com-
plex natural adsorbent, and this fact is responsible for those
difficulties which inevitably occur in studies on the adsorp-
tion of various substances by soils(I6,I7,I8). The properties
of soil as adsorbent depend on the nature and ratio of clay
minerals, quantitative content of the sesquioxides and the form
of their existence in the soil,their bonds with the. mineral
part and organic matter, as well as the amount and nature of
soil organic matter(II).
The main most widespread clay minerals entering into the
composition of various soils have different specific surface
and substantially differing adsorptive properties. Therefore
we began studying the process of herbicide adsorption by soils
with the investigation on the regularities of their adsorption
on pure clay minerals.
Saaquioxides were shown to be characterized by different
adsorptive capacity and selectivity with respect to various
72
-------
herbicides, since the important for adsorptive activity cha-
racterisitcs, such as crystalline structure, pore structure and
chemical nature of the surface depend on conditions of existen
ce of these sesqui oxides.
Several authors studied the behavior of herbicides during
their interaction with soil organic matter. Considerably fewer
works have been devoted to studying the herbicide adsorption
by soils as a whole and the dependence of adsorption isotherm
shapes on the properties of adsorbents(I2,I3,I5).
Up to now, there has been neither exact theory of monomole
cular adsorption nor general equations of sorption isotherms,
though a number of frequently used equations have been derived
on the basis of simple models. Among them, the Freundlich equa
tion
X =K-C
and the Langmuir equation
r=
1
are used most frequently in studies of soil adsorption.
The Langmuir adsorption isotherm equation contains two
constants: K and Tmo^» Constant K is an important characteris-
tic of adsorption system. It is constant for a given adsorption
system at a constant temperature and numerically equal to equi-
librium concentration at which F =Fmax/2 • Thermodynamically,
K is the measure of adsorption energy.
However, some experimental isotherms of adsorption on solid
adsorbents, both porous and nonporous , cannot be interpreted
only on the basis of the Langmuir1 s monomole cular adsorption
theory. Further development of the theoretical concepts result-
ed in the Polanyi's theory, according to which adsorption is
determined by Van der Waals forces, their action radius being
larger than that of residual valencies in the Langmuir1 s theo-
ry. Due to this fact, adsorption leads to the formation of po-
lymolecular layer rather than is localized in the first mole-
cular layer. Characteristic of this type of adsorption are not
the Langmuir isotherms but so-called S-shaped isotherms. In the
latter case, adsorption does not cease on the formation of
a monolayer but continues until a polymole cular layer is form-
ed. The mechanism of polymolecular layer formation is as yet
imperfectly understood, and the theory of polymolecular adsorp-
tion in general is still in the semiempirical stage.
Solution adsorption is complicated by the fact that along
with adsorption of solute there occurs adsorption of solvent.
Therefore various distortions of isotherms of usual type are
widespread. Having studied thoroughly the shapes of a great
number of solution adsorption isotherms, the authors of (19)
suggested a classification of isotherms which had four main
groups(S,Z,H and C) distinguishing by the curvature of isotherms,
73
-------
All the isotherms were considered by the authors on the
basis of different contributions resulted from the interacti-
ons: adsorbate-adsorbent, adsorbate-adsorbate and adsorbent-
solvent, as well as possible orientations of the adsorbed
molecules of adsorbate on the surface of adsorbent.
We made an attempt to establish the dependence of adsorp-
tion isotherm shapes on the properties of adsorbents,using as
an example atrazine adsorption by soil adsorbents. Many authors
studied the adsorption of sym-triazine herbicides by the entire
soil (12,15,21), the mineral part of soil adsorbing complex
(8,22,6) and its organic part (25,9,20,10). But it is difficult
to generalize this large experimental material, since various
authors selected for studying the objects difficult to systema-
tize. Soil organic matter was found to be the main adsorbent
of herbicides applied to the soil (7,11,23). Various soils,
however, contain different amounts of organic matter represent-
ed by different fractions of humus. Therefore various soils
should differ in their capaity to adsorb atrazine. In this work,
we chose as adsorbents of atrazine the soils with equal and
different amounts of humus: top horizons of soddy podzolic soil,
chernozem and red soil. Their physical-chemical characteristic
is given in Table I.
Table I. Brief physical-chemical characteristic of the adsor-
bents studied
Hygrosco-
Clay pHp .._ Humus,
AUCSUJ-UtSUO
Soddy-pod-
zolic soil
Chernozem
Red soil
P
I.
3.
3.
icity
13*0.
60*0.
67*0.
-,*
01
07
01
fraction,
17*2
21*3
34+3
P
6.
6.
4.
HT
73*0
30*0
64*0
euu»
er
.05
.05
.05
2.
4.
5.
»
05*0.06
9*0.05
0*0.1
mVg
62.4*0.
132.0*1.
200*10
6
5
Mineral part of soils represented by various clay mine-
rals, sesquioxides and their hydrates with different degree of
dispersity also has different adsorptive capacity that should
affect the total amount of the adsorbed herbicide.
The main purpose of this work was to perform a qualitative
and quantitative comparative study of atrazine adsorption by
1} various initial soils;
2) mineral humus-free part of the soils that was obtained
through humus oxidation in the initial soil by hydrogen per-
oxide .using the Hedroits's method:
3) mineral part of the soils without sesquioxides that
was obtained through humus oxidation followed by a removal of
74
-------
sesquioxides by the Jackson's method;
4) humic acids extracted from the initial soils by O.I
normal solution of NaOH followed by a reprecipitation by sul-
furic acid and purification by electrodialysis.
Structural and colloidal-chemical properties of the soil
sorbents were studied by various methods of physical-chemical
analysis.
Mineralogical composition of the studied soddy podzolic
soil and chernozem was represented by minerals of the montmo-
rillonite group and hydromicas with an admixture of kaolinite,
whereas in the red soil studied,minerals of the kaolinite gro-
up and mixed-layer minerals prevailed. The initial soils were
composed of clay minerals and organic matter adsorbed extra-
micellarly on the surface of the mineral part.
Oxidation of organic matter by hydrogen peroxide exposed
the surface of clay minerals and sesquioxides. Adsorbents of
the mineral part without sesquioxides were represented mainly
by a combination of minerals which were a part of the soil
adsorbing complex.
Thus, based on the data obtained by various methods of
investigation, it can be concluded that the soil sorbents
studied differ substantially in their mineralogical composi-
tion. This undoubtedly should affect their adsorptive charac-
teristics.
As adsorbate, atrazine has a polar nature, and,therefore,
molecular adsorption through the formation of hydrogen or di-
pole bonds with polar groups and charged surfaces of soil col-
loids is typical of this substance. In addition, atrazine is
a weak base. At pH = pK one half of the molecules of atrazine
should exist as cations and be adsorbed by negatively charged
soil colloids through the cation-exchange mechanism of adsorp-
tion (8).
Atrazine adsorption by the soil adsorbents from aqueous
solutions was studied under static conditions at a temperatu-
re 20°C, soil-solution ratio I:10 and period of interaction
24 hrs, Atrazine adsorption by humic acids was studied at
a temperature 25 C, humic acid-solution ratio 1:50 and period
of interaction 5 days. Concentration of the atrazine solutions
used to study adsorption was determined by spectrophotometry.
The amount of atrazine adsorbed by the unit mass adsorbent
was determined from the concentration difference of initial
and equilibrium solutions.
The results obtained are presented as sorption isotherms
in coordinates T-Ce, where T is the amount of atrazine adsor-
bed and Ce is the equilibrium concentratioa(Fig.I,2,J).
The experimental data obtained are indicative of the
complicated character of the interaction of soil with atrazine.
Atrazine adsorption by various adsorbents is given by linear
and curved isotherms. At low concentrations all isotherms,as
a rule,have linear portions, and as the equilibrium concentra-
75
-------
0.12
0.06
Fig.I. Isotherms of atrazine adsorption by
the adsorbents of the soddy podzolic
soil:
I - initial soil,
2 - mineral part,
3 - mineral part without sesquioxides.
tion increases, there occur nonlinear portions of the type of
the Langmuir curves with saturation or S-shaped curves, accord-
ing to the Giles's classification (19) •
All isotherms of atrazine adsorption by the initial soils
are two-stage curves with linear initial portions (curve I,
76
-------
Oxidation of the soil organic matter by hydrogen peroxide
in the initial soddy podzolic soil and chernozem led to a sub-
stantial decrease in atrazine adsorption. The isotherm of at-
razine adsorption by the mineral part of the soddy podzolic
soil is linear over the studied range of concentrations(curve 2,
Fig.I).
The isotherm of atrazine adsorption by the mineral part
of the chernozem is the two-stage curve with a linear initial
portion, the slope of which changed sharply compared to the
r,
0.08
Fig.2. Isotherms of atrazine adsorption by
the adsorbents of the chernozem:
1,2,3 - same as in Fig.I.
77
-------
a/2
Fig.3 Isotherms of atrazine adsorption by
the adsorbents of the red soil:
1,2,3 - same as in Fig.I
initial soil (curve 2,Fig.2), and with the second stage of
the Langmuir type.
Therefore, the soil organic matter with a high adsorptive
capacity was the main adsorbent of atraziae in the soddy pod-
zolic soil and chernozem.
Atrazine adsorption by the mineral part of the red Soil
sharply increased compared to the initial soil(curve 2,Fig.3).
Organic matter of the red soil was represented by alumino-
and iron-humus compounds with a fairly high content of alumi-
nium and iron. A major part of humic substances of the red
78
-------
soil were bound to aluminium,since its content in the red soil
was 6.1 mg equivalent per 100 g, while in the soddy podzolic
soil and chernozem only traces of aluminum were detected.
As organic matter was oxidized, alumino-humus compounds
were destroyed, and the surface of the mineral part of the
red soil covered itself with amorphous aluminum hydroxide
which had a large surface and adsorptive capacityiresuiting
in a sharp increase of adsorption(Fig.3).
The next stage of soil adsorbent treatment was a removal
of sesquioxides from the mineral part by the Jackson's method.
The removal of sesquioxides caused an increase of adsorption
by the soddy podzolic soil and its decrease by the chernozem
and red soil. However, a contribution of sesquioxides to the
adsorptive capacity of the soddy podzolic soil and chernozem
was insignificant. A different situation was observed for the
red soil. The removal of sesquioxides exposed the surface of
the mineral part of the red soil composed mainly of minerals
of the kaolinite group with a small adsorptive capacity,that
led into a sharp decrease of adsorption.
Thus, the discussed experimental data indicate that there
exists a dependence of the shapes of atrazine adsorption iso-
therms on the physical-chemical properties of adsorbent(soil),
namely, the nature and quantitative content of clay minerals,
sesquioxides and soil organic matter.
LITERATURE CITED
I. Greg S.; Sing,K. Adsorption,specific surface and porosi-
ty. -Moscow,Publishing House "Mir",1970.-407pp./in Russi-
an/.
2. Kovda,V.A.; Sokolov,M.S.; The problems of soil cover
protection against the pollution by biocides. -Proceed-
ings of the 1st All-Union Conference on the Behavior,
Transformation and Analysis of Pesticides and Their
Metabolites in Soil. Scientific Center of Biological
Reserach of the USSR Academy of Sciences,Pushchino,1973,
p.8-13 /in Russian/.
3. Kononova,M.M. Soil organic matter. - Moscow,Publishing
House of the USSR Academy of Sciences,1963. - 314 pp.
/in Russian/.
4. Zonchits,V.A. Physical-chemical characteristic of humic
acids extracted by various methods. Synopsis of the
thesis by a candidate for master's degree ia chemistry.
- Moscow,1975. - 18 pp.
79
-------
5. Nai,P.H.; P.B.Tinker. Movement of the solutions in the
soil-plant system. - Moscow,Publishing House "Kolos",
i960. - 365 pp./translated from English into Russian/.
6. Browm,C.B.j WhitefJ.Z. Reactions of 12 s-triazines with
soil clays. - Soil Sci.Soc.Amer.Proc..1969.vol.33• No.6,
p.863.
7. Farmer|W.Y.; Aochi,J. Picloram sorption by soils. - Soil
Sci.Soc.Amer.Proc.. 1974,vol.38,No.3,p.418-423,
8. Frissel«M.J.; Bolt,G.H. Interaction between certain
ionizable organic compounds(herbicides) and clay mine-
rals. - Soil Sci. , 1962,vol.94,p.284-291.
9. Gilmour,J.; Coleman,N.T. S-Triazines adsorption studies:
Ca-H-Humic acid. - Soil Sci.Soc.Amer.Proc..1971.vol.33.
p.256-259.
IO.Gwo-Chen»I.J.; Felbeck»G.T. A study of the mechanism of
atrazine adsorption by humic acid from muck soil. -Soil
Sci.. ,I972,vol.II3,No.2,p.I40.
II.Hame»R.T. Soil organic matter and the adsorption and de-
composition of the herbicides atrazine and linuron. -
Soil Biol.Biochem..1974.vol.6.No.ItP»39-42.
12. Harris, C.J.5 Warren ,G.P. Adsorption and desorption of
herbicides by soil. - Weeds.1964.vol.12.No.2.p.120-
126.
13. Barter,R.D.jBaker,D.E. Applications and misapplica-
tions 'of the Langmuir equation to soil adsorption
phenomena. - Soil Sci.Soc.Amer.J., I977»vol.4I,No.6,
p.1077-1080.
14. McGlamery,M.D.; Slife,F.W. The adsorption and desorp-
tion of atrazine as affected by pH, temperature and
concentration.-Weeds. 1966,vol.14,p.237-239.
15. Nearpass,D.C, Effect of soil acidity on the adsorption
penetration and persistence of simazine. Weeds.I965.
vol.13,p.341.
16. O'Connor,G.A.;Anderson J.U. Soil factors affecting
the adsorption of 2,4,5-T,. - Soil Sci.Soc.Amer.Proc.,
I974,vol.38,No.3,p.433-436.
17. Ra.stogi,M.GriDhawan B.Z. Adsorption of surface active
agents at soil/water interface. - Indian J.Chem.,I974»
vol.l2,vol.I2,No.2,p.I58-I60.
80
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18. Soldatini, G.F. ;Riffaldi,R. jZevi-Minzi, R. Pb adsorption
by soil. - Water,air and soil pollut.,I976tvol.6,No.I«
p.III-128.
19. Giles,C.H.; Nacewan ,T.H.;Nakhwa,S.N.;Smith ft. Studies
in adsorption. Part XI. A system of classification of
solution adsorption isotherms, and its use in diagno-
sis of adsorption mechanisms and in measurement of
specific surface areas of solids. - J.Chem.Soc..I960.
•vol. 10,No. 10, p. 299-319.
20. Sullivan ,J.D. jFelbeck ,G.T. A study of the interaction
of s-triazine herbicides with humic acids from three
different soils. -Soil Sci..1968.vol.106.No.I.p.42-51.
21. Talbert ,R.E.jFletchall ,O.H. The adsorption of some
s-triazines in soils. - Weeds. 1965,vol.13,p.46-52.
22. Weber»Y.B. Molecular structure and pH effects on
adsorption of s-triazine compounds on montmorillonite
clay. - Amer.Mineral.,I936«vol.3I«P.I637-I670.
23. Weber»Y.B.jWeed S.B.; Ward T.M. Adsorption of s-tria-
zines by soil organic matter.-Weed Sci..1969.vol.I7«
p.417-421.
81
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STUD! ON PESTICIDE SORPTION UNDER IRRIGATION
TO PREDICT AND REGULATE THE PROCESSES OF THEIR
MIGRATION IN THE SOIL-WATER SYSTEM
by
A.I.Yurchenko, V.G.Kovtun,
A.A.Vernichenko
Ail-Union Research Institute of Water
Protection, Kharkov
Scientifically valid prediction of the environmental pol-
lution by pesticides and search for ways to prevent undesirab-
.le aftereffects of their application are impossible without
detailed studies on the processes of toxicant migration in
natural landscapes.
The processes of pesticide migration are determined by
a number of factors, with sorption playing an important part
among them, according to many investigators (3-6). la parti-
cular, toxicant transport with water through the soil profile,
as well as in irrigation and river networks, and,therefore,
the extent of water body pollution by biocides are largely
determined by sorption processes.
Favorable conditions for pesticide migration are establish-
ed under irrigation due to increased water exchange. The main
pesticide sorbents in the soil-water system under irrigation
are soils, plants and microorganisms.
This paper presents the results of laboratory studies on
sorption by soils and microorganisms of some pesticides used
in irrigated lands. The obtained data on pesticide interaction
with these sorbents can be used for predicting toxicant wash-
out from irrigated lands and working out water-protection
measures.
Experiments on soils with a disturbed structure were carri-
ed out under static conditions. Sorption of lindane and diuron
was studied on the soils of cotton plantations of the Amu
Darya-river basin. In studies on the sorption of propanil,its
ma^or metabolite 3,4-dichloroaniline(5,4-DCA) and ordram, sor-
bents were the soils of rice irrigation systems of the Kuban-
river basin(Table I).
82
-------
00
GO
Table I. Physical-chemical characteristic of sorbents
Soil Name
No. (Accord-
I.
2.
3.
4.
5
6
7
8
9
ing to
N.A.Kachin- ,
sky class-
ification')
Loose sand
Loamy sand
Sandy loam
Moderate
loam
Gray-brown
sandy loam
Loose sand
Loamy sand
Light clay
Meadow-cher-
Content of fractions, %
Size of particles, mm
:-o,25 0.25-0.05 0.05-0.01 0.01-0.005 o. 005-0. ooi
-------
Preliminary experiments revealed the optimum ratios of
solid to liquid phases at which the processes of sorption
were studied. Then the kinetics of sorption equilibrium estab-
lishment was studied, i.e. the time variation of sorbate con-
centration in the aqueous phase was regulated. As the equilib-
rium had been attained, sorption isotherms were measured.
The process of organic matter sorption by soils from
a solution is a very complex phenomenon. The behavior of orga-
nic matter in the soil-water system is determined by its che-
mical composition, the nature of functional groups,properties
of a sorbent and a number of external physical-chemical fac-
tors. We did not separate the influence of individual factors:
soil was not divided into fractions; organic and mineral com-
ponents were not isolated from it; and the value of pH of the
water-soil suspension was close to the neutral one.
Kinetic curves show an increase in the amount of the sorbed
substance with time and an asymptotic approximation to the
equilibrium one. A state of the system in which any change of
the concentration in the aqueous phase of the substance stu-
died stopped was taken as the sorption equilibrium. Analysis
of the kinetic sorption curves of lindane, diuron, propanil,
3,4—DCA and ordram shows that within the systems studied, the
sorption equilibrium is reached in a few minutes after the
beginning of phase interaction, irrespective of the nature of
compound, its initial concentration and soil properties. Reach-
ing the sorption eqilibrium in such a short time is indicative
of a prevailing physical interaction between sorbate and sor-
bent.
The study of the relationship between the amount of adsorb-
ed substance and the equilibrium concentration at a constant
temperature enabled us to plot sorption isotherms. As a result
of mathematical processing of the experimental data, it was
found that the isotherms of sorption by soils of the above
pesticides can be adequately given by Preundlich equation;
r= k . c8I/n ,
where F is the specific absorbability of sorbent ,mg/kg$ C_ is
the equilibrium concentration of sorbate in the solution, mg/1,
k and I/n are the experimental constants of sorption isotherm.
The corresponding constants were calculated(Table 2).
One can see from the isotherms of lindane and diuron sorp-
tion by soils that sorption is strongly dependent on pesticide
concentration in the aqueous phase. In the range of the con-
centrations studied, we did not observe complete saturation
of sorbents, with the exception of sand. Relative absorbabi-
lity of soils No.1-5 decreases in the following order: gray-
brown sandy loam - moderate loam - sandy loam - loamy sand -
sand. As seen from Table I, humus content in the soil sorbents
decreases from 1.1% to 0.3$. Larger amounts of pesticides were
sorbed by soils with larger content of organic matter. In addi-
tion, the content of clay fraction in the same row decreases
84
-------
Table 2. Constants of Freundlich equation
Pesticide Soil No.
(Table I)
Lindane I
2
3
4
5
Diuron I
2
3
4
5
Eropanil 6
7
8
9
5,4 DCA 6
7
8
9
Ordram 6
7
8
9
k
0,43
0,76
1,47
1,52
2,57
0,51
0,60
0,96
1,21
1,39
0,17
0,68
9,32
11,27
0,10
0,60
7,61
8,55
0,05
0,07
4,31
5,28
I/n
0,40
0,39
0,88
0,84
0,54
0,50
0,77
0,72
0,59
0,82
0,73
0,41
0,53
0,48
0,8?
0,32
0,74
0,71
0,73
0,32
0,91
0,71
from 26.68J5 to 3.00#,i.e. specific absorbability of soils with
respect to lindane and diuron increases, as the content of clay
fraction in them rises.
Relative absorbability of soils No,6-9 decreases in the
following order: meadow-chernozemic clay loam - light clay -
loamy sand - sand. In this row, humus content (Table I) decre-
85
-------
ases from 2.7^ for meadow chernozemic clay loam to 0.05$
sand. Thus, absorbability of soils No.6-9 with respect to pro-
panil, 3,4—VGA. and ordram obviously correlates with, humus con-
tent in them. At the same time, the content of clay fraction
decreases in a different order: light clay-meadow chernozemic
clay loam - loamy sand - sand. The first two links in this
chain do not provide correlation with absorbability. Therefore
organic colloids apparently play a more important part in
sorption of propanil, 3,4-DCA and ordram than minerals enter-
ing into the composition of soils.
From the results of the studies conducted it can be conclud-
ed that organochlorine pesticides are capable of being sorbed
by the soils of i cot ton fields to a greater extent than herbicides-
derivatives of urea. Amides of propionic acid are sorbed by
the soils of rice checks to a greater extent than thiocarba-
mates.
Consequently, derivatives of urea should have higher migra-
tion capacity under conditions of cotton fields than organo-
chlorine pesticides, whereas soil herbicides of the thiocarba-
mate group possess higher migration capacity under conditions
of rice irrigation systems than contact herbicides of 3*4- -
dichloropropionanilide group.
The processes of pesticide sorption by microorganisms were
studied on propanil - a contact herbicide widely used in rice
growing. As the main part of aquatic microbiocenosis is repre-
sented by bacterial forms, the above processes were studied
on individual species of the main systematic groups of bacteria
(representatives of Pseudomonas, Micrococcus, Bacillus, Achro-
mobacter and other genera). Microorganisms were cultured on
a thick nutrient medium during two days at a temperature 2? C.
Bacterial cells washed off the nutrient medium were suspended
in a small amount of water and transferred to experimental
vessels containing herbicide solutions. At the end of the
experiment the microbial biomass was separated from the liquid
by centrifugation.
It was found that bacterial cells are capable of sorbing
propanil. At the same time, a portion of the toxicant penetrat-
ed into a cell. As it was difficult to make a distinction bet-
ween these processes, we determined mainly the net effect of
propanil concentration decrease in water when in contact with
bacterial cells(Tables 2 and 4).
Increase in propanil concentration in the medium at a con-
stant quantitative content of bacteria promotes increasing
value of toxicant sorption and uptake per I kg of biomass to
a certain range of herbicide concentration, after which the
saturation is attained(Table 3).
Increase in biomass promotes more intensive decrease in
toxicant concentration. However, after reaching some critical
value sorption decreases again (Table 4). This is likely-
accounted for by the aggregation of individual cells at their
too high concentration in the medium, which decreases specific
surface and accessibility of the active centers. Equilibrium
86
-------
Table 3. Influence of propanil concentration in water on its
sorption and uptake by microorganisms Pseudomonas sp.
at a biomass concentration of 2.5 S/l
Concentration of herbicide, ,ug/l p me/kc
initial steady-state
155
210
340
554
680
1300
105
135
248
452
570
1168
12
30
37
41
44
45
Table 4. Influence of bacterial biomass concentration on
propanil sorption and uptake at its initial concent-
ration of 665 >ug/l
Biomass, g/1 Ce, >ig/l T,mg/kg
1,0
2,5
5,0
10,0
20,0
655
570
455
515
565
10
38
42
15
5
state in the experiments to study propanil sorption by bacte-
rial biomass was reached approximately in 2 hours.
We observed certain differences in th» capacity of microorga-
nisms to sorb and accumulate propanil, depending on their
species characteristics. Gram-negative bacteria can sorb
a toxicant better than gram-positive ones. Despite the small
absolute values,the revealed capacity of microorganisms to
sorb pesticides is apparently of great importance for the
redistribution of these substances in the aquatic environment,
87
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considering a substantial uptake of bacterial biomass by
invertebrates.
The studies conducted support the feasibility of using the
sorption processes in the complex of measures aimed at pre-
venting natural water pollution by pesticide residues. In
particular, an increase in the absorbability of soil-forming
rocks with respect to these substances should promote a de-
crease in the level of drainage water pollution. Soil-forming
rocks which contain, as a rule, trace amounts of humus sub-
stances can not hold completely pesticides and their trans-
formation products transported to drainage network at a short
distance of water traveling.
As it was proved experimentally that soil organic matter
plays a leading part in the sorption of herbicides used in
rice growing, we proposed a bed-type screen (2) made of loam
with humic additions. The screen is an interlayer placed
under the root layer over the entire area of check floor or
its part to localize these herbicides within the rice check.
Salts of humic acid form with clay particles a stable clay-hu
mus complex which actively absorbs herbicides. As substances
of humic origin, one can use humus, peat, brown coal treated
properly by alkali agents to extract salts of humic acids.
Increase in absorbability of the interlayer can be attained
by introducing lignin-containing substances into its compo-
sition (I). We found a high absorbability of lignin-contain-
ing substances with respect to herbicides. In addition, lig-
nin is one of those compounds which are most resistant to
enzymatic decomposition and converts to humus substances in
the process of slow degradation. Due to these properties of
lignin-containing substances the interlayer proposed can
function for a long time.
Testing the effectiveness of application of the loam scre-
en with humic additions to reduce washout of herbicide resi-
dues in drainage waters that was conducted under field con-
ditions in the Krasnodar Territory and South Ukraine showed
that the measure proposed promotes almost complete absorption
of propanil and ordram, as well as 3,4-DCA which is formed
during propanil transformation. Microbiological analysis of
the soil layer above the screen and its middle part showed
that introduction of humus substances into the composition
of the interlayer promotes an increase in its biological acti-
vity sufficient for detoxication of the toxicants absorbed
during the growing season.
The screens outlined above are of interest for those areas
of irrigated agriculture with high water permeability where
drainage runoff plays an important part in pesticide washout.
88
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LITERATURE CITED
I. Certificate of Authorship 723595(USSR). Composition for
an interlayer designed to prevent ground water pollution
by herbicides in irrigation systems/' Aleshin,E.P.}
Belonenko, G.M. j Vakulenko, V.I.j et al. Application
date: January 15,1979, publication date: May 15,1980.
In: "Discoveries,Inventions, Industrial Designs and
Trademarks", I960, No.18, p.16 (in Russian).
2. Certificate of Authorship 64697I(USSR). Method for Pre-
venting Drainage Water Pollution by Herbicides in Rice
Irrigation Systems / Belonenko, G.M,| Goncharov, I.Ya.j
Vernichenko, A.A, j et al. Application date : November
23,1976, publication date : February 15,1979. Im" Dis-
coveries, Inventions, Industrial Designs,and Trademarks",
1979. No.6, p.9 (in Russian).
3« Vernichenko, A.A.} Zatula, A.I.j Kovtun, V.G., Migration
of antigrass herbicides in elements of rice irrigation
systems. Proceedings of the USA-USSR Symposium on Environ-
mental Transport and Transformation of Pesticides,October
1976, Tbilisi. Moscow, Hydrometeoizdat,1979,p.70-76
(in Russian).
4. Sokolov, M.S., General laws of the migration of pesticide
residues in the delta landscape under irrigation. Sympo-
sium on Environmental Transport and Transformation of
Pesticides, EPA-600/9-78-003t February 1978. p.38-46.
5. Bailey, G.W., Whithe, J.L., Factors influencing the ab-
sorption, desorption and movement of pesticides in soil.
Residue Rev..1970.32. 29-92.
6. Kenaga, E.E.Partitioning and uptake of pesticides in bio-
logical systems. Symposium on Environmental Transport and
Transformation of Pesticides, Washington D.C.,1978, II?-
125*
89
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CHARACTERISTICS OF SOIL DEGRADATION STUDIES
FOR PREDICTING PESTICIDE BEHAVIOR
by
D. A. Laskowski, R. L. Swann P. J. McCall,
H. D. Bidlack and H. J. Dishburger
The Dow Chemical Company
Midland, Michigan 48640
ABSTRACT
Degradation rates of chemicals in a major environmental compart-
ment like soil are key ingredients of any hazard evaluation process.
Therefore, a primary goal of soil degradation studies is to provide
a general characterization of the degradation rates of chemicals
in soil. Since these rates come from interaction between chemical,
soil, and climate; there must also be assessment of soil type, soil
moisture, and soil temperature on the degradation process. Facto-
rial statistical designs are useful in designing these experiments
because they allow maximization of information for minimal amount
of effort. Data from such studies are appropriate for modeling
environmental fate of chemicals in standard environments representing
distinct land resource regions.
Sooner o.r later pesticides come in contact with soil. They can reach
soil in a variety of ways through direct application during spraying or
soil treatment, and more indirectly through washoff 'of foliage or deposition
from atmospheric fallout. If we are to have knowledge of the environmental
fate of pesticides, then we must know something about their degradation in
soil.
It is very difficult to carry out degradation experiments in the
totally uncontrolled environment of a real field situation.Perhaps the most
successful method of learning about pesticide degradation is to carry out
rigidly controlled experiments in the laboratory. These involve the incuba-
tion of treated soil in suitable containers, which in turn are placed in
controlled temperature chambers during their incubation. In this manner one
learns about the degradation of pesticides in soil under well-defined condi-
tions. This information becomes quite appropriate for evaluation of pesti-
cide behavior.
90
-------
In our laboratory we have chosen the highly controlled laboratory
experiment rather than the field approach to study chemicals in soil. The
studies are designed to accomplish two things. The first is to learn about
the degradation of chemicals in soils in a very general fashion, and to
learn how the degradation might vary from one soil to another. The second
deals with the effect of different soil climates on the breakdown of pesti-
cides in soil. If studies are conducted in the right manner, this will
reveal the fate of a pesticide when added to soil.
Many types of systems have been used to incubate chemicals in soil.
One of the earliest is the perfusion set-up like that in Figure 1. Water
Glass Tube
15 cm dia.
25 cm long
Rubber Stopper
Glass Wool
Glass Wool
Rubber Stopper
125ml
Erlenmeyer
Figure 1. Soil Perfusion Apparatus.
91
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solutions of pesticide generally are pumped through a column of soil. This
particular system uses an air pump to recycle the perfusate, but other types
of pumps have also been used. The fate of the pesticide is followed by
periodic analysis of the water solution.
Perfusion systems are best suited for microbial enrichment studies.
They are not easily adapted to measurements of real soil degradation rates,
or used easily with volatile or water-insoluble chemicals. Other incubation
systems perhaps adapt much more readily to the purposes of predicting pesti-
cide behavior.
One such system is the continuous aeration system described by Kearney
and coworkers (1). A glass container with treated soil is fitted with a two-
hole rubber stopper. Air at low flow enters through one hole and exits from
the other through two traps. The first trap is a polyurethane foam plug that
collects pesticide in the air stream as a result of volatilization. The
second trap is sodium hydroxide for trapping carbon dioxide or other acidic
gases produced during the degradation. These traps allow complete accounting
of all added pesticide an important objective in soil degradation
studies.
One requirement of this system is that corrections must be made for
volatilization if one wishes to characterize degradation of chemicals in
soil.
Another kind of incubation system is a totally enclosed one designed by
Bartha and Pramer (2). It consists of two compartments one for treated
soil and the other for concentrated sodium hydroxide to trap carbon dioxide.
Because of its design, fresh air must be manually introduced periodically to
maintain oxygen inside the system.
Our laboratory re-designed the two-compartment system to that shown in
Figure 2. We removed all rubber stoppers and replaced them with glass so
that pesticide would not be absorbed by the rubber present in the original
design. Then the sodium hydroxide trap was made larger to accommodate the
use of dilute caustic solutions. These do not cause the drying of the soil
observed with the concentrated solutions used originally.
Finally, we provided automatic aeration by connecting each unit to an
oxygen manifold. The units were isolated from each other by delivering the
oxygen through a glass tube extending into the caustic solution. An expan-
sion bulb was provided to protect the manifold from the caustic in the event
of pressure increase in the unit.
The same apparatus is used if we wish to examine anaerobic soil degrada-
tion. Enough water is added to provide a layer over the soil. The flask
atmospheres are changed to pure nitrogen gas; the units are not connected to
the oxygen manifold. Alfalfa ground to a fine size is added to provide
energy for driving the soil redox potential to the anaerobic state.
92
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Spring
Tension
Clip
TEFLON
SWAGELOK
250ml
Erlenmeyer
Soil
02 Inlet
1/4" OD Glass
Expansion
Bulb, 25 ml
Ace Fitting
No. 5029-10
+ 5027
09 Feed Tube
125 ml
Erlenmeyer
C02 Trap
NaOH (0.2N)
Figure 2. Closed System Apparatus For Aerobic
Incubation Of Chemicals In Soil.
A typical experiment is prepared by addition of pesticide to the soil in
water or in no more than 250 \ii of acetone. The soil is then mixed thor-
oughly. Water is added to bring the soil to a desired moisture and the soil
is mixed again. Finally, caustic is added to the carbon dioxide trap, and
units are assembled in constant temperature chambers for subsequent incuba-
tion. There are several chambers and each is set at a different temperature
in order to study temperature effects on the degradation.
Typical sampling periods are 0, 7, 14, 28, 56, 100, 200, and 300 days.
The results from these studies present degradation patterns like those
given in Figure 3 (3). The degradation kinetics of the parent molecule is
displayed; the patterns of breakdown product accumulation and decline are
observed.
Nearly all of our work is with carbon-14 labeled material. Balance
sheets for recovery of total radioactivity with our incubation system show
good accountability of all activity (Table I).
93
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100
,_ 80
"A.
"60
"
< 40
4-
o
* 20
0
2,4,5-T
Anisole
Figure 3. Example Of Information Obtained
From Soil Degradation Studies.
Table 1. Typical Accountability Of Radioactivity,
Average Recovery
Standard Deviation
Number of Samples
98.6%
4.8
209
As indicated earlier, our experiments have two purposes. The first
deals with characterizing the general rate of degradation taking place in
soil. In order to do this in a meaningful fashion, however, we concluded
there must be studies with several soils incubated under standard conditions
of incubation. Table 2 cites the conditions often used by many researchers
and which have been adopted by us.
You may ask why it is necessary to examine the degradation of pesticides
in more than one soil, as specified in Table 2. It is because soils differ
in their ability to degrade chemicals. To illustrate this, consider the
data presented in Table 3. Table 3 was derived from the literature (4) and
lists the variability in soil degradation rate for a variety of pesticides,
each of which was studied in a number of soils under identical incubation
conditions. As you can see the variation in rate of degradation ranges from
two-fold to 80-fold the average equaling more than 10-fold.
94
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Table 2. Standard Incubation Conditions For Soil
Degradation Studies.
Number of Soils
Incubation Temperature
Incubation Moisture
6-10
25°C
75% 1/3 Bar
Incubation in the Dark
Table 3. Variation In Rate Of Degradation Among Surface
Soils For Several Pesticides.
Range of
Observed Differences
Chemical # Soils Among Soils
Crotoxyphos
Linuron
Methomyl
Glyphosate
Aldicarb
Carbofuran
DIAZINON
Thionazin
Methidathion
Nitrilo triacetate
Nitrapyrin
Picloram
Propyzamide
3
4
2
4
2
4
4
4
4
11
10
13
5
36X
2X
2X
19X
2X
25X
2X
7X
3X
SOX
6X
19X
2X
Note: Only experiments that expose soils to chemicals under
identical conditions are cited.
95
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This difference cannot be predicted from any known soil properties.
Therefore, a number of soils must be studied if we are to evaluate the
behavior of chemicals in soil.
Our soils also are collected and stored by a standard procedure. They
are taken from the top 10 cm of an area approximately 9 square meters in
size. They are stored in polyethylene bags at 4°C for no longer than one
year before use. They are never allowed to air dry since this might kill key
microorganisms and alter the soil's degradation characteristics.
It is interesting to contemplate how data from such standardized experi-
ments as these might be used. One such use may be for comparison of soil
degradability among pesticides. Since these studies provide degradation
rates from a variety of soils all incubated under the same standard condi-
tions, it is possible to compare average degradation rates in the manner
shown in Table 4. Pesticides degrading the fastest are at the top of the
list those that are stable are found at the bottom.
Table 4. Ranking Of Pesticides According To Rate Of
Degradation In Soil.
Pesticide Days for 50% Degradation
Malathion 1
2,4-D 4
Alachlor 7
Nitrapyrin 10
Parathion 15
Dicamba 20
DIAZINON 30
Chlorpyrifos 60
Atrazine 130
Monuron 170
Diuron 200
Lindane 600
Dieldrin 1000
Heptachlor 2000
DDT 3800
Endrin 4300
96
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Average degradation rates can be used for other purposes. They can be
categorized so that one can describe the soil stability of a chemical as
being "very low" or being "very high". In essence, they provide descriptive
terms that can be used in discussions of soil behavior (see Table 5).
Table 5. Categorization Of Soil Degradation Rates For
Pesticides.
Category t/2, Days
Very Low <15
Low 15-60
Medium 60-180
High 180-360
Very High >360
So to summarize the first part of our soil degradation studies their
aim is to demonstrate the kinetics of degradation in a variety of soils under
standard conditions. An average time for 50% breakdown is determined, and
this value then becomes a general measure of a pesticide's degradation in
soil.
If you recall, the second main objective of soil studies carried out by
us is to determine the effect of soil climate on rate of degradation. We
present aspects of these studies for your perusal.
The studies essentially relate to how soil climate and initial soil
concentration of pesticide might change the rates of degradation. In addi-
tion to the effects of soil already observed in the first part of the study
the soil temperature, the moisture, and the starting concentration of
pesticide all have influence on degradation. These too must be evaluated
for assessment of pesticide behavior in soil.
It is not easy to examine several parameters in a single experiment
without being overwhelmed by the number of necessary samples. To solve this
problem the experimenter can utilize the efficiency of a statistical facto-
rial design. In our laboratory we chose a modified central composite design
described by Cochran and Cox (5). Values of temperature, moisture, and
initial soil concentration used in the design are shown in Table 6.
The actual design can be viewed as if the combination of incubation
parameters described a cube (see Figure 4). Two soils are used and both
occupy the position shown in the center of the cube. In other words, both
soils are incubated under identical conditions of 25°C, 50% of 1/3 Bar, and
97
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1 ppm initial concentration of pesticide at this point. The corners of the
cube locate the incubation extremes the points in between establish the
surfaces between the extremes.
Table 6. Factorial Design Values For Studying Effects Of
Moisture, Temperature, And Initial Concentration
On Rates Of Degradation.
Temperature, °C
Moisture, % 1/3 Bar
Concentration, ppm
15, 25, 35
Air Dry, 25, 50, 75, 100
0.1, 1.0, 10
50
Moisture
O Soil # 2
• Soil#1
O -Both soils
75
25 Temperature
15
10
Concentration
Figure 4. Schematic Representation Of Central
Composite Factorial Design.
The final outcome of this design is the development of mathematical
expressions describing the relationships of temperature, moisture, and
98
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initial concentration to rate of degradation. Figure 5 shows the temperature
effect from studies with one of our chemicals. The relationship is described
quite well in soil by the well-known Arrhenius Equation.
TEMPERATURE ARRHENIUS EQUATION
Log (t 1/2) =
A H *
2.303 RT
-C
o>
o
1/T
Figure 5. Relationship Between Temperature
And Rate Of Degradation In Soil.
Figure 6 presents the moisture expression we have observed. It appears
quadratically related to rate of degradation but this has not been verified
extensively.
MOISTURE QUADRATIC
11/2 = C + M + M2
t1/2
Moisture
Figure 6. Observed Relationship Between
Soil Moisture And Rate of
Degradation in Soil.
99
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The expression for initial concentration of pesticide and rate of
degradation appears to be somewhat logarithmic in nature (Figure 7). Again,
we do not have a great deal of evidence for this relationship, and future
work may show different patterns.
At any rate, it is possible to assemble the information from the facto-
rial design into a single expression. With this equation one can then
estimate rate of degradation from knowledge of soil temperature, soil mois-
ture, and initial concentration of pesticide.
How might this information be used? One obvious way is for prediction
of behavior of a pesticide at specific locations. If the climatic pattern of
a location is known throughout an event, then the fate of a pesticide at the
site can be modeled during that event. This provides knowledge of environ-
mental fate needed for assessment of hazard.
Another use of perhaps even greater utility relates to the modeling of
pesticide behavior in standard environments. We would like to discuss
briefly the concept of standard environments and consider their future role
in the process of modeling environmental fate of pesticides.
Our laboratory has become interested in building standard descriptions
of environments that would represent regions of our country. We are utiliz-
ing an existing scheme described by Austin (6) that develops regions in the
United States on the basis of soil type, topography, climate, and land use.
We chose several regions considered to be of greatest importance and are now
assembling representative physical descriptions of these regions for use in
assessment of pesticide behavior. The task is not yet complete, but for
those who may be interested the current efforts are described in (7).
We believe the use of standard descriptions of representative regions
will be the way of the future for evaluating the behavior of pesticides.
CONCENTRATION LOGARITHMIC
t1/2
LogC.
Figure 7. Apparent Relationship Between Initial
Concentration And Rate Of Degradation
In Soil.
100
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Their advantage is the ready availability of the vast physical environmental
data required by any model describing environmental fate. It would not be
necessary to go through the expensive process of actually measuring these
physical characteristics whenever a modeling exercise was undertaken.
To attempt modeling of environmental fate of any type, however, we wish
to re-emphasize that there must be knowledge of how climatic factors influ-
ence the degradation of pesticides in soil, and how different soils them-
selves impact on this degradation.
So in summary, soil degradation studies have two basic goals if they are
to be used in the evaluation of pesticide behavior. The first is to provide
a general characterization of degradability from experiments with several
soils at standard incubation conditions. The second is to quantitate the
effects of environment on degradation. This quantitation then can be used
to model pesticide behavior in a wide variety of environments.
REFERENCES
1. Kearney, P. C., and Kontson, A. (1976) A Simple System to Simulta-
neously Measure Volatilization and Metabolism of Pesticides From Soils.
J. Agric. Food Chem. 24, 424-426.
2. Bartha, R. and Pramer, D. (1965) Features of a Flask and Method for
Measuring the Persistence and Biological Effects of Pesticides in Soil.
Soil Sci. 100, 68-70.
3. McCall, P. J., Vrona, S. A. and Kelley, S. S. (1981) Fate of Uniformly
Carbon-14 Ring Labeled 2,4,5-Trichlorophenoxyacetic Acid and 2,4-
Dichlorophenoxyacetic Acid. J. Agric. Food Chem. 21, 100-107.
4. Laskowski, D. A., Goring, C. A. I., McCall, P. J. and Swann, R. L. (In
Press) Environmental Risk Analysis for Chemicals, R. A. Conway (Ed.),
Van Nostrand Rheinhold Co., New York, NY
5. Cockran, W. G. and Cox, G. M. (1966) Experimental Designs 2nd Ed. John
Wiley and Sons, New York, NY.
6. Austin, M. E. (1965) Land Resource Regions and Major Land Resource
Areas of the United States, Agriculture Handbook 296, U.S. Dept. of
Agriculture, Washington, D.C.
7. Laskowski, D. A., Swann, R. L., McCall, P. J., Dishburger, H. J. and
Bidlack, H. D. (1981) Standardized Soil Degradation Studies In: Test
Protocols for Environmental Fate and Movement of Toxicants Proc. Symp.
Assoc. Off. Anal. Chem. 94th Ann. Meeting, Washington, D.C.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency. The contents do not necessarily reflect the views of the
Agency and no official endorsement should be inferred.
101
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TRANSLOCATION AND TRANSFOHMATION OP PESTICIDES
IN SOILS AND PLANTS
Z.V.Novozhilov, T.M.Petrova, Yu.B.Andreev
Ail-Union Research Institute
for Plant Protection,
Leningrad
Ecological approach to chemical plant protection is asso-
ciated largely with a thorough study of the behavior of chemi-
cals applied. An understanding of this problem permits, on the
one hand, to achieve the required effect and, on the other
hand, to reduce as much as possible a detrimental influence of
chemicals on the useful species of organisms,prevent negative
effects of sanitary-hygienic nature, and eliminate environmen-
tal pollution. This problem can be solved with regard to the
toxicological and physiological properties of those pesticide
chemicals that are widely used in agriculture on the global
scale, primarily to their toxicity to warm-blooded animals,
and persistence in soils, aquatic environments and plants.
Accordingly, the assortment(list) of pesticides of domestic
manufacture and ,in particular, that of the last few years,has
been subjected to constant changes. Table I shows the data on
the change in the assortment of insecticides and acaricides of
various chemical groups. It is seen from the table that the
application of arsenic-containing and inorganic chemicals since
I960 has sharply decreased (from 4 items to I), whereas that of
organophosphorus chemicals has considerably increased (from 6
items to 29). The assortment of insectoacaricides has increased
recently due to the included chemicals from other groups, such
as pyrethroids, nitrogen-containing compounds, carbamates, etc.
In the last few years the assortment of chemicals increas-
ed due to new less toxic pesticides. For example, the mean dan-
ger class for the insectoacaricides recommended in I960 was
2.69, whereas in I960 it was 1.69. It should be noted that this
index incorporates all possible manifestations of danger of the
chemicals,i.e. not only an acute toxicity but also potential
chronic, mutagenic, teratogenic and other side effects.Compared
102
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Table I. Changes in the assortment of insecticides and acari-
cides in the Soviet Union in the period from I960 to
I960 (the number of items of the active agents of
insectoacaricides)
Groups of compounds I960 1970 I960
Synthetic pyrethroids - - 5
Arsenic-containing and inorganic
compounds
Organochlorine compounds
Organophosphorus compounds
Carbamates
Nitrogen-containing compounds
Other compounds
Total
4
5
6
-
I
4
20
2
9
17
I
3
4
56
I
10
29
3
3
9
60
to I960, the index of mean toxicity to warm-blooded animals
for the active agents of insectoacaricides increased fivefold
and amounted to 990 mg/kg instead of 220 mg/kg. Specific toxi-
cants with LCcQ over 1000 mg/kg account for about 30$ of the
recommended compounds. Ampng the latter there are highly selec-
tive insectoacaricides, yet the great majority of them are
highly toxic to most species of insects.
Translocation and transformation of pesticides in the
area of their application, that is a combination of migration
processes and quantitative-qualitative changes of the chemi-
cals, ultimately determines tactical approaches to their use
in specific agrocenoses.
The Ail-Union Research Institute for Plant Protection
carried out extensive studies on the translocation of insecto-
acaricides affected by a variety of factors of biological and
nonbiological nature, and established regularities and rela-
tions determining the behavior of chemicals in environmental
objects. The facts revealed enabled the "mild" norms to be
provided for the use of chemicals for a number of crops,which
in turn aide'd in reducing environmental pollution by pestici-
des. Thus, as a result of studying the behavior of insectici-
des in plants and soils under conditions of the Non-Chernoze-
mic zone of the Soviet Union, the amount of dehydroheptachlor
applied as sprays to control potato beetles was reduced
103
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threefold. When using the granulated insecticides bazudin
(diazinon) and dursban (chlorpyrifos) in the Volgo-Vyatskii
region to protect corn and potato plants against wireworms
(Agriotis sputator L.)» it is recommended to apply these che-
micals differently with regard to the type of soils. It is
seen from Table 2 that LCcQ for wireworms in the podzolic soil
can be reached at a lower^concentration of the chemicals(0.3-
0.4 mg/kg),compared to the chernozem(I.2-2.4 mg/kg). This re-
gularity enables one to reduce the rate of insecticide applica-
tion in the podzolic and soddy podzolic soil by over one half,
compared to the chernozemic soil.
Table 2. Content of organophosphorus insecticides in various
soils and their toxicity to wireworms Agriotis spu-
tator L
Insecticide content in ^0™ for
Soil type Applied, the soil three days wireworms,
kg/hec- after its application, mg/kg of
tare mg/kg soil
Bazudin Dursban
(Diazinon) (Chlorpyrifos)
Podzolic soil 0.6 0.52 0.55 0.3-0.4
0.8 O.?l 0.80
Soddy podzolic soil
1.2 1.09 I.15 0.6-1.12
2.5 2.33 2.40
Gray forest soil 1.6 1.44 1.55 0.7-1.60
2.5 2.32 2.48
Chernozem 2.5 2.4 2.48 1.2-2.4
5.0 4.8 4.92
As agricultural crops are protected against pests,pesti-
cides reach the soil either by direct application or indirect-
ly. We pointed out earlier that when spraying plants, one can
find a small amount of toxicants directly in the soil. This
fact was supported by our further experiments(Table 3). While
treating well-developed cotton or potato plants in June-July,
we found on the day of treatment less than 0.2 mg/kg of the
insecticides in the soil and up to 22 mg/kg in the plants,
whereas in case of cabbage plants the content of insecticides
in the soil was about 1.2 mg/kg. The above amounts of the che-
micals permitted for use can completely decompose for 5 to 10
104
-------
Table 3. Distribution of insecticides in plants and soils,
when spraying agricultural crops (on the day of
treatment)
Insecticide
Rate of application, Insecticide
Crop kg/hectare with content,
respect to the mg/kg
active agent in plant in soil
Bazudin
(Diazinon)
Ambush
(Permethrin)
Cabbage
Cotton
Cotton
Zimbush
-cyano-
3-phenoxybenzyl
-2,2-dimethyl-3-
(2,2-dichlorovinyl)-
cyclopropane carboxylate
Actellick Tomatoes
(0,0-Dimethyl-O-
(2-diethylamino-6-
methylpyrimidyl-4)-
thi opho sphate
Thiodan Cotton
(Endosulfan)
1.5
0.2
0.08
3.0
1.0
Valekson
0,0-Diethyl-
thiophoshoryl-0-
( -cyanobenzal-
doxime)
Tomatoes
Potatoes
Cabbage
0.7
0.7
1.2
3.9
5.0
1.8
O.I
not de-
tected
1.2
2.49 I.I
4.0 0.01
0.6 0.002
7.3
22.0
O.I
0.2
days.
Granulated insecticides, both soil-incorporated and surfa-
ce-applied at the moment of planting agricultural crops, cause
much anxiety as soil pollutants. Table 4 presents data on the
persistence of granulated insecticides at a level of 50$ of
the initial amount for a number of agricultural crops. As seen
from the table, the chemicals remain at the above level in the
soils from 25 to 120 days, with the exception of a granulated
phosphamide which can be washed out rather easily by rains or
during heavy watering.
105
-------
Table 4. Persistence of granulated insecticides in the soil
for various agricultural crops
Insecticide
Crop
Rate of
application,
kg/hectare
granulated
Phosphamide
(Dimethoate) ,
1.6% granulated
Peas 0.5
Sugar beets 2.0
Persistence
(500) of
insecticide
in soil,days
Bazudin
100 granulated
Volaton,
50 granulated
BHC,20 granulated
Dursban ,
Alfalfa
Sugar beets
Corn
Cotton
Potatoes
Corn
Sugar beets
Wheat
Corn
Potatoes
5.0
5.0
5.0
5.0
2.5
2. .5
1.0
1.0
1.0
5.0
30-75
50-100
28-75
40-90
25-60
25-60
80-120
70-100
40-80
60-90
15-70
70-100
We have shown that the persistence of granulated chemicals
in soil depends on the ability of plants to remove a toxicant
from soils and metabolize it to simpler compounds. Thus the
lowest level of bazudin in the soil is typical of alfalfa and
corn, compared to cotton or sugar beets. It should be noted
that other chemicals also persist for a longer time in the
fields sowed with sugar beets.
Studies on the dynamics of chemicals depending on various
methods of using granulated toxicants showed that soil-incorpo-
rated insecticides decompose more rapidly than surface-applied
ones(Table 5). It is seen from the table that at a rate of
application 5 kg/hectar the 500-level of the initial amount of
the insecticides was observed only by the end of the I2th week
for the surface-applied chemicals and already by the 4th week
for the soil-incorporated ones. No substantial differences in
the content of soil-incorporated bazudin and dursban were ob-
106
-------
Table 5. Dynamics of bazudin and dursban in three types of soil
depending on the method of insecticide application
(the rate of insecticide application 5 kg/hectare)
Insecticide
Method of
application
Time after
treatment,
weeks
Insecticide content
in soilst% of the
initial amount
Bazudin Surface- I
applied 2
4
8
12
16
Soddy
podzolic
soil
95
87
82
60
42
32
Gray
forest
soil
95
91
89
58
41
39
Cher-
nozem
98
93
88
62
42
36
Soil-
incorporated
Dursban Surface-
applied
Soil-
incorporated
I
2
4
8
12
16
I
2
4
8
12
16
I
2
4
8
12
16
88
78
58
38
25
20
89
88
85
63
54
41
88
80
63
47
48
34
90
61
50
25
18
7
94
86
78
67
56
43
90
78
49
40
27
8
90
63
40
30
17
II
94
84
78
62
52
48
93
65
41
32
21
II
served in the first four weeks after the application,though
later on bazudin decomposed more rapidly than dursban. For
surface-applied granulated bazudin and dursban we observed the
following: at first bazudin decomposed more slowly, then the
reverse process occurred, and by the 8th week the content of
dursban in the soil was higher than that of bazudin. It should
be emphasized that both insecticides decomposed more rapidly
in gray forest and chernozemic soils.
107
-------
The influence of the soil type on the dynamics of insecti-
cides at various rates of their application is shown in Table 6,
The data presented indicate that in the soddy podzolic and
chernozemic soils dursban decomposes much more slowly than
bazudin, which permits to reduce their application rates to
control pests.
Table 6. Dynamics of the in-row applied bazudin and dursban
in soils, depending on their application rates
Soil type
Time after
application,
weeks
Insecticide content in soils
(.% of the initial amount)
Rate of bazudin
application,
kg/hectare
1.25
3.0
Rate of dursban
application,
kg/hectare
0.75 1.25
Soddy
podzolic
soil
Chernozem
4
8
12
16
24
4
8
12
16
24
60.0
32.7
17.3
14.0
10.5
29.7
12. 1
10. 0
5.5
5.1
54.5
36.9
23.1
17.0
10.7
42.5
29.3
16. 1
12.5
7.7
63.4
36.5
34.1
16.1
3.9
37.7
15-5
9.1
3.8
2.7
68.2
49.2
38.5
33.3
30.1
53.7
35.8
26.8
23.8
19.4
Studies on pesticide migration through the soil profile
indicate that there is only slight movement of these pesticides
both in vertical and horizontal directions. The character of
pesticide distribution in horizons is determined primarily by
water solubility of a chemical.
The results of our studies suggest that an investigation
of the problems of pesticide translocation and transformation
in the environment, soil included, should be correlated with
a thorough research into the effects exerted on the processes
of pesticide migration not only by physical-chemical and bio-
108
-------
logical factors but also by those factors which are associated
with the agricultural and economic activity of man. It is
necessary to carry out zonal observations on the behavior of
pesticides introduced to agrocenoses, taking into account
agricultural and climatic features of regions, thus making
possible to create the necessary prerequisites for a reliable
ecological prediction of the behavior of xenobiotics in soils
and other objects.
When monitoring environmental pollution, one should consi-
der that most pesticides are only slightly stable in the envi-
ronment and decompose there during one vegetation period. The-
refore, a more correct approach to predicting a long-term
residence of toxic substances in natural environments is
required. Special emphasis should be given to the products of
pesticide transformation which can be rather resistant to the
influence of environmental factors and have unwanted remote
effects on man, useful fauna and flora. Studies in this area
are currently being carried out by a limited number of scien-
tists, and therefore we consider it extremely important to
direct efforts to more intensive study of this problem. First
of all it is necessary to work out unified methodical princip-
les and provide researchers with instruments of high sensiti-
vity aad adequate selectivity. The studies based on the uni-
fied methodical principles will make it possible to assess
more objectively the results obtained by researchers from
various countries participating in solution of the problem of
protecting man and its environment.
109
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SOIL PROCESSES AND THEIR USE IN PREDICTING
VOLATILIZATION OF PESTICIDES FROM SOIL
by
W. J. Farmer, W. F. Spencer, and W. A. Jury
Department of Soil and Environmental Sciences
University of California, Riverside
Riverside, California 92521
and
United States Department of Agriculture
Agriculture Research Service
Riverside, California 92521
ABSTRACT
Volatilization of pesticides is a major pathway for their dissipation
from soil. In addition, transport in the atmosphere is probably the prin-
cipal mechanism for the wide dispersal of pesticides in the environment
far from the site of application. Large quantities of pesticides even-
tually reach the soil either by direct application, wash-off from treated
plant surfaces, redeposition from volatilized material or by land-disposal
of pesticide waste. Significant progress has been made in our ability to
predict pesticide volatilization from soil. This paper will discuss some
of the soil processes influencing volatilization and their use in predic-
tive models. The volatilization of surface-applied pesticides will depend
on its vapor pressure as influenced by adsorption on soil surfaces and on
its rate of removal from the soil surface by transport into the atmosphere.
The rate of volatilization of soil-incorporated pesticides will depend not
only on its vapor pressure and rate of removal from the soil surface by
transport into the atmosphere but also on its rate of movement to the soil
surface either by diffusion or by a combination of diffusion and convec-
tion in water moving to the soil surface. The quantities of pesticide
available for transfer to the soil surface and therefore available for
volatilization will depend on the distribution of the organic chemical
between the soil, water, and air phases of the soil. A chemical distrib-
uting itself primarily into the vapor phase will move by vapor phase dif-
fusion and its movement will depend in part on those soil factors control-
110
-------
ing vapor phase diffusion. A chemical distributed into the soil solution
will move by convection and its movement will be dependent on water flow
characteristics of the soil. The distribution of a chemical into the var-
ious phases will depend on the physical-chemical properties of the pesti-
cide and of the soil. The primary physical-chemical properties needed to
predict volatilization from soil are reliable vapor pressure and solubili-
ty data for the chemical and its partitioning coefficient between solid
and aqueous phases of the soil. The partitioning coefficient will be de-
termined by such soil factors as soil organic matter and clay contents.
Henrey's constant, which describes the distribution of a chemical between
vapor and solution, has been shown to be valid for soil systems. Several
laboratory-based studies have been used to validate mathematical models
for predicting pesticide volatilization from soil. Predictions from
laboratory-based models would be expected to be useful for many purposes.
INTRODUCTION
Volatilization has been recognized as one of the major pathways by
which pesticides are dissipated into the environment following their
application or disposal. Several excellent reviews are available on
various aspects of the volatilization process (1, 2, 3, 4, 5, 6, 7).
Volatilization may take place from the spray droplets before they arrive
at the target area and be carried away along with some of the spray drop-
lets as part of what is referred to as drift. This paper will not deal
further with the subject of drift. Volatilization may also take place
from the surface of the treated area such as from plant and soil surfaces.
Spencer, Farmer and Cliath (2) have reviewed the various processes and
factors influencing the rate of volatilization from plant and soil sur-
faces. This paper will treat primarily the volatilization of pesticides
from soils. Many of the principles involved will apply to volatilization
of other compounds and to volatilization from other surfaces.
Volatilization from soils can be described as a three-step process.
All steps in the process will not be necessary under all conditions. The
pesticide compound must move to the soil surface. This step applies
particularly to the volatilization of soil-incorporated pesticides and in
many cases will be the process controlling the rate of volatilization.
For pesticides that.are only surface-applied, this step in the volatili-
zation process will not be significant unless the pesticide is subsequent-
ly leached into the soil by rain or irrigation waters. Movement to the
soil surface can take place by diffusion of the pesticide molecules in
either the vapor phase or in the non-vapor phase, movement to the soil
surface by convection when water is evaporating from the soil surface, or,
as is often the case, by a combination of convection and diffusion
processes. Often, when convection is operating to bring pesticides near
the soil surface and the pesticide is evaporating from the soil surface
as rapid as it is transported there, essentially zero pesticide concentra-
tion at the soil surface is maintained. This results in a high pesticide
concentration gradient near the surface. Under these conditions, dif-
fusion will control the movement of pesticide in the last several milli-
111
-------
meters of soil near the surface while convection is bringing up additional
pesticide from deeper in the soil. Hartley (8) has used the term "wick
effect" to describe the influence of evaporating water on moving pesti-
cides to the soil surface and thereby enhancing the volatilization rate
of pesticides. Several reviews are available covering transport of pesti-
cides in soils (9, 10, 11, 12).
A second step in the volatilization process is the transfer of the
pesticide to the vapor phase. This step can take place either within the
soil profile or at the soil surface. When movement to the soil surface is
by vapor phase diffusion, the transfer to the vapor phase will take place
within the soil profile before the movement step can occur. The transfer
of pesticide to the vapor phase is controlled by all those factors of the
soil, the pesticide, and the environment which control the distribution of
a compound between the soil, water, and air phases. This step is a par-
ticularly important process, since it controls the vapor density or the
vapor concentration of the pesticide.
The third step in the volatilization process will be the movement of
the pesticide away from the soil surface either by diffusion into the
atmosphere or by turbulent mixing with the air above the soil.
The extent to which these three steps interact under actual field
conditions to control volatilization can be highly complex depending on
the soil, the particular pesticide, and the environmental conditions. For
example, for a pesticide that is mobile in the aqueous phase alternate
wetting and drying periods in the soil may have the pesticide first moving
down into the soil profile with infiltrating water before moving back
toward the soil surface. For most of these field conditions, the factors
which influence the distribution of pesticides between the soil, water,
and air phases are well-known. The partitioning between the various
phases can be predicted from a few basic properties of the pesticide and
of the soil. The factors involved and how they are used to predict par-
titioning into the soil, water, and air phases will be presented in this
paper. Our ability to predict the movement to the soil surface under
field conditions is limited because of the large variabilities in soil
properties encountered in the field. However, from a consideration of
laboratory-based transport models under well-defined conditions, useful
information can be determined on the relative volatilization rate of dif-
ferent pesticides. In this paper, some of the laboratory-based models for
predicting pesticide volatilization will be examined to determine those
parameters important in controlling volatilization.
PESTICIDE PARTITIONING BETWEEN SOIL, WATER AND AIR
The extent to which a pesticide partitions itself between the solid,
liquid, and gaseous phases of the soil will determine the amount of pes-
ticide available in the soil solution and in the air pores of the soil
for transport to the soil surface as well as the amount of pesticide in
the air phase at the soil surface for loss to the atmosphere as volatilized
-------
pesticide.
The total soil pesticide concentration, Cf Qng cra~3 soil) and the
pesticide concentration in the three soil phases are given by
[1]
where S is the adsorbed concentration (/ng g~l soil), C^ is the liquid
concentration (jig cm~3 solution), Cg is the gas concentration (jig cm~3
air), p^ is soil bulk density (g cm~3), 9 is volumetric water content
(cm3 cm~3) and Pa is air-filled porosity (cm3 cm~3).
A pesticide will distribute itself between the vapor, liquid and
adsorbed phases of Eq. [1] according to the following relationships:
1) Adsorption of a pesticide from the solution phase onto the solid
phase can be described by either the linear adsorption isotherm
S = KC + 3 [2]
or the Freundlich isotherm
S = k C [3]
where K
-------
50
40
30
20
10
O d/do, 3.94% water
a d/do, 10% water
A c/co, l'-5 soil-water
suspension
1
1
0.2 0.4 0.6 0.8
d/do or C/CQ
1.0
Figure 1. Desorption isotherm for lindane in Gila silt loam
at 30C relating adsorbed lindane (x/m) to relative
vapor density (d/do) and to relative solution
concentration (c/co). From Spencer et al. (19).
desorption of lindane from the same silt loam soil. In Figure 1, x/m is
the adsorbed concentration, d is the gas concentration, and c is the
liquid concentration equivalent to S, Cg, and C]_, respectively, in this
paper, and the subscript o refers to the saturation vapor density or
maximum solubility of the pure compound without soil. The data for the
vapor phase desorption and solution phase desorption are both seen to fall
on the same line. This means that the amount of adsorbed pesticide
required to give a saturated vapor density in a soil suspension is the
same as the amount required to give a saturated solution and for any
degree of saturation.
The use of the relationships expressed in Equations [2], [3] and [4]
in conjunction with Equation [1] allows one to express Cf in terms of a
single phase, e.g., Cg, an essential step in any predictive efforts.
This paper will not present the mathematical details of modeling the
volatilization of pesticides as the details have been presented by others
in publications to be discussed in later sections. However, a review of
some of the more important factors that influence the values of the co-
efficients K
-------
are temperature, pesticide concentrations, soil water content, and soil
organic matter content.
As has already been seen, soil pesticide concentration expresses its
effect on volatilization through the coefficients in Equations [2] - [4].
Generally, the higher the total concentration, the higher will be the
concentration in any of the soil phases. Several soil properties will
influence the distribution among the various phases including soil organic
matter, clay content, and soil pH (14, 15, 16). Of these properties, soil
organic matter has been shown to be the most important property in deter-
mining the value of the distribution coefficients for non-ionic and weakly
polar compounds. Pesticides which are non-ionic and weakly polar com-
pounds are also the ones which tend to be volatile. As soil organic
matter increases, the pesticide is more strongly adsorbed in the solid
phase leading to less chemical in both the liquid and solid phases. For
the solution phase, this phenomena has been well documented in the liter-
ature. For the gas phase, dieldrin vapor pressure in five soils was found
to vary inversely with soil organic matter content (17). The effect of
soil organic matter on the distribution coefficient can be expressed
through the relationship
K = (K./percent organic carbon)100 [5]
OC G
Hamaker (1) used this relationship to estimate the relative vapor behavior
of pesticides in soils from vapor pressure, water solubility and K _
uu
values.
The effect of temperature on pesticide volatilization is primarily
through its effect on vapor pressure. The effect of temperature on the
aqueous phase distribution (e.g., K
-------
for adsorption sites leading to a higher vapor pressure and therefore
higher volatilization of pesticides from a moist compared to a dry soil.
MODELS PREDICTING VOLATILIZATION FROM SOIL
Typical volatilization flux curves for the volatilization of soil-
incorporated pesticides exhibit an initial high flux rate followed by a
rapidly decreasing rate as surface pesticide concentrations are depleted
(22, 23i 24). At this point the rate of volatilization will be depend-
ent on the rate of movement of the chemical to the soil surface. Move-
ment to the soil surface will be by diffusion or by a combination of
diffusion and convection with evaporating water. The actual movement
will be determined by all of the factors controlling the partitioning of
the pesticide between the soil, liquid and gas phases. The models dis-
cussed in this paper will generally assume that transport to the soil
surface is the rate-limiting step.
Most mathematical models for predicting volatilization from soil that
have been tested have used data derived from well-controlled laboratory
experiments. Field validation of volatilization models will necessarily
be slow because of difficulties in accounting for all experimental vari-
ables. However, the' current laboratory-based models will be useful for
providing information on relative volatility rates based on properties of
the pesticide and of the soil.
Diffusion Models
A number of models have been developed predicting volatilization of
soil-incorporated pesticides for the case when diffusion to the soil
surface is controlling movement (1, 25, 26, 27, 28). These models are
applicable in the absence of mass flow of the pesticide to the soil sur-
face. Mass flow would not be operating when there is no net-evaporation
of water from the soil surface or in the case of compounds, such as
pesticides with extremely low water solubilities, which distribute in the
solid, liquid and gas phases in such a way as to present little pesticide
in the soil solution for movement with evaporating water.
Mayer, Letey and Farmer (26) and Farmer and Letey (25) have presented
several models to predict the volatilization of soil-incorporated pesti-
cides when diffusion was the principle means of pesticide transport to the
soil surface. The choice of models varies depending on the depth of
treated soil and on air speed moving over the soil surface. One of the
models assumes a non-moving air layer of various depths above the soil
surface so that the pesticide concentration gradient in the air controls
the rate of volatilization. For the models where concentration at the
soil surface is assumed to be zero, and transport to the soil surface is
therefore controlling volatilization, the volatilization flux would be de-
pendent on the initial concentration, the depth of the treated soil layer,
116
-------
and on the apparent diffusion coefficient of the pesticide in soil. Care
must be taken in applying any model that utilizes the diffusion coeffi-
cient of a pesticide through soil. The value of the apparent diffusion
coefficient depends on various parameters including bulk density, water
content, concentration, temperature and adsorption (29). Good agreement
was found by Mayer et al. (26) between predicted and experimental values
for the volatilization of lindane and dieldrin from a silt loam soil.
Figure 2 shows the agreement between Model II of Mayer et al. (26) and
data taken from Spencer and Cliath (23) for the volatilization of dieldrin
from soil in a laboratory study. The predicted values (solid curve) in
Figure 2 were calculated from
J = DCT/(TTD t)
1/2
[6]
where Jp is the pesticide flux, D is the apparent diffusion coefficient
for pesticide diffusion in soil and t is time. The equation assumes that
the depth of the treated layer is sufficiently deep that it does not in-
fluence the volatilization flux in time, t, and that the pesticide vola-
tilizes and is removed rapidly maintaining a zero concentration at the
1,000
10 15 20
TIME (days)
30
35
Figure 2.
Predicted (solid curve) and measured volatilization
flux from a soil surface for a diffusion experiment.
Predicted values derived from Model II of Mayer et al.
(26). The experimental values taken from Spencer and
Cliath (23). The length of the horizontal lines
indicate the length over which the experimental values
were taken. From Mayer et al. (26).
117
-------
soil surface. The diffusion coefficient for dieldrin was taken from Igue
et al. (30). The good agreement between measured and calculated values
indicates that under these conditions zero concentration at the soil sur-
face was a reasonable assumption.
Volatile compounds buried in soil such as pesticide wastes or other
industrial organic wastes placed in landfills present a special circum-
stance for volatilization because of the large concentration gradients
that are created across the soil layer covering the wastes. Farmer et al.
(27, 28, 31) developed and tested a model for predicting vapor loss of
hexachlorobenzene (HCB) from a landfill covered with soil. The model is
based on vapor-phase diffusion through the air-filled soil pores and would
be applicable to predicting steady-state volatilization of other chemicals
which move primarily in the vapor phase. The equation derived to predict
steady state vapor loss is based upon the vapor diffusion coefficient in
air, Do.
Jp. = Do Pa°/3(C2-CS)P?L
where C2 is the vapor concentration at the soil surface, Cg is vapor con-
centration at the bottom of the soil layer, P-p is the total soil porosity
and L is the soil depth. The validity of Equation [7] was experimentally
varified with HCB using a simulated landfill apparatus in the laboratory
(27, 28, 3D. It can be seen from Equation [7] that under these condi-
tions, volatilization flux will be strongly influenced by the soil air-
filled porosity which in turn is determined by soil water content and the
degree of soil compaction (31).
Convection-Diffusion Models
Spencer and Cliath (23) have shown the influence of evaporating water
on moving pesticides to the soil surface and thereby enhancing the
volatilization rate of pesticides. In addition, they reported that the
enhanced volatilization due to water loss was effective only after the
surface soil pesticide concentrations were depleted. In the experimental
studies of Spencer and Cliath (23), water was supplied to the bottom of
soil columns to maintain moist soil while the relative humidity of the air
passing over the soil was varied to regulate the rate of water evaporation
from the soil. In their studies the flux due to convection was estimated
from the relationship
Jp = JwCi [8]
where Jw is the water flux.
118
-------
The concentration of the chemical in the soil solution was estimated
from the distribution coefficient, Kd. With lindane, the flux calculated
due to convection ranged from 356 to 703 MS cm~2 day'1 equivalent to 18-71$
of the total lindane flux. Dieldrin flux due to convection ranged from
9.9 to 23.6 fig cm'2 day1 equivalent to 3-33$ of the total dieldrin
flux. The increased contribution of convection to the volatilization flux
of lindane compared to dieldrin was attributed to the lower Kd for
lindane resulting in a higher solution concentration for movement to the
soil surface.
An interesting finding in the study by Spencer and Cliath (23) was the
accumulation of dieldrin at the dry soil surface when low relative humidity
air was passed over the soil surface. When this soil was rewet by exposure
to air flow at high relative humidity, the dieldrin volatilized at a high
rate. In the field this would result in short term fluctuations in the
volatilization rate as surface drying causes the pesticide to accumulate
at the soil surface thereby reducing volatilization; however, upon
rewetting the pesticide will volatilize so that over longer time periods
volatilization should be directly related to water evaporation if the
pesticide is moving to the soil surface in the evaporating water.
Jury et al. (24) have presented both a diffusion model and a combined
diffusion-mass flow model for predicting volatilization of soil-
incorporated pesticides. Their models, for a uniformly treated soil,
assumes Henry's law applies to the gas and liquid concentrations, assumes
a linear adsorption isotherm and that movement away from the soil surface
was sufficiently rapid to maintain zero pesticide vapor concentration at
the surface. To test the models, vapor losses of the herbicide triallate,
which had been incorporated into two soils with widely different organic
matter contents, were measured in the absence and presence of evaporating
water. The results are shown in Figure 3. The solid line is predicted
flux using the model for diffusion controlled volatilization (100 percent
relative humidity). Good agreement as observed was expected between
predicted and measured fluxes from the diffusion experiments because the
apparent diffusion coefficients for triallate were evaluated from the
experimental cumulative loss measurements. After nine days into the
experiment water evaporation from the soil columns was induced by switching
some of the columns to 50 percent relative humidity. In the case of the
San Joaquin sandy 1'oam, the soil with the lower soil organic matter
content and lower K^ for triallate adsorption, the volatilization flux
is seen to remain steady after nine days. In this soil convection is
operating to move more pesticide to the soil surface to replace that lost
by volatilization. For the Flanagan silt loam, which had the higher
organic matter content and higher Kd value, there was not enough trial-
late in the solution phase to move significant amounts to the soil surface
with evaporating water and the flux continues to decrease after nine days
even when -convection is operating. The predicted values (dashed curve)
for the diffusion-convection experiments provide an upper limit for
volatilization. In the case of the San Joaquin soil, the measured trial-
late flux approaches the predicted flux after 30 days. In the case of the
Flanagan soil, the model tends to overestimate the contribution due to
convection as the measured triallate volatilization flux remained close to
119
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that for pure diffusion. However, the combined diffusion-convection model
did predict a greater influence of convection for the San Joaquin soil than
for the Flanagan soil, as was observed.
1.0
r
Ld
g
cr
^
C/5
o
cr
u.
UJ
o:
o.oi
O.I
0.01
SAN JOAQUIN SANDY LOAM
A 50% RH DATA
• 100% RH DATA
- — 50% RH MODEL
100% RH MODEL
FLANAGAN SILT LOAM
0
10 15 20
TIME (days)
25
30
Figure 3. Predicted and measured volatilization flux from the surface
of two soils for soil-incorporated triallate for the
diffusion experiment (relative humidity = 100 percent) and
the diffusion-convection experiment (relative humidity =
50 percent). The San Joaquin soil had a soil organic
matter content of 1.24 percent and the Flanagan 5.5
percent. From Jury et al. (24).
120
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DISCUSSION
The minimum information required to predict relative volatilization of
a pesticide from soil would be vapor pressure and solubility of the
pesticide at various temperatures and adsorption coefficients for the
pesticide between the soil and solution phases. When this information is
available for pesticides, predictive models such as those of Jury et al.
(2M) can be used to compare the potential for the different pesticides
to volatilize from a soil with a known or assumed bulk density, water
content and porosity. Diffusion coefficients in air and in water can be
estimated if measured values are not available.
Other factors not considered here such as degradation rates, wind
speed, surface roughness, and ground cover will also influence actual
volatilization rates. The degradation rate of a pesticide will be a major
factor in determining if volatilization would be a major pathway for
dissipation of the pesticide. If a compound is highly volatile but is
rapidly decomposed under certain environmental conditions, volatilization
may not be a significant mode of loss under those conditions. On the
other hand, if a compound is resistant to degradation, volatility losses
can be significant even for a compound of low vapor pressure. A greater
effort is needed to include degradation parameters in transport models.
LITERATURE CITED
1. Hamaker, J. W. Diffusion and volatilization. In: "Organic Chemicals
in the Soil Environment", C.A.I. Goring and J. W. Hamaker, Eds.
Marcel Dekker: New York, NY, 1972; 341-397.
2. Spencer, W. F.; W. J. Farmer; M. M. Cliath. Pesticide Volatilization,
Residue Reviews 1973, H9, 1-47.
3. Wheatley, G. A. Pesticides in the atmosphere. In: "Environmental
Pollution by Pesticides", C. A. Edwards, Ed. Plenum Press: London,
England, 1973; 365-108.
4. Guenzi, W. D.; W. E. Beard. Volatilization of pesticides. In:
"Pesticides in Soil and Water", W. D. Guenzi, Ed. Soil Science
Society of America, Inc.: Madison, WI, 197M; 107-122.
5. Plimmer, J. R. Volatility. In: "Herbicides: Chemistry, Degradation
and Mode of Action Vol II", P. C. Kearney and D. D. Kaufmann, Eds.
Marcel Dekker: New York, NY; 1975; 891-931*.
6. Taylor, A. W. Post-application volatilization of pesticides under
field conditions. J. Air Poll. Control Assoc. 1978, 28, 922-927.
7. Spencer, W. F.; W. J. Farmer. Assessment of the vapor behavior of
toxic organic chemicals. In: "Dynamics, Exposure, and Hazard
121
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Assessment of Toxic Chemicals", R. Hague, Ed. Ann Arbor Science: Ann
Arbor, MI, 1980; 143-161.
8. Hartley, G. S. Evaporation of pesticides. Adv. Chem. Series 1969,
66_, 115-134.
9. Biggar, J. W. Pesticide movement in soil water. In: "Pesticides in
the soil: Ecology, Degradation and Movement", Proceedings of the
Symposium at Michigan State University; East Lansing, 1970; 107-119.
10. Hamaker, J. W. The interpretation of soil leaching experiments. In:
"Environmental Dynamics of Pesticides"; Haque, R., Freed, V. H., Eds.,
Plenum Press; New York, 1970; 115-133.
11. "Letey, J; Farmer, W. J. Movement of pesticides in soil. In:
"Pesticides in Soil and Water"; Guenzi, W. D., Ed.; Soil Science
Society of America, Inc.: Madison, WI; 1974; 67-97.
12. Leistra, M. Computation models for the transport of pesticides in
soil. Residue Rev... 1973, 49, 87-130.
13. Spencer, W. P.; M. M. Cliath. Vapor density of dieldrin. Environ.
Sci. Tech. 1969, 3., 670-674.
14. Bailey, G. W.; J. L. White. Factors influencing the adsorption,
desorption, and movement of pesticides in soil. Residue Reviews.
1970, 32_, 29-92,.
15. Hamaker, J. W.; Thompson, J. M. Adsorption. In: "Organic
Chemicals in the Soil Environment", Goring, C.A.I.; Hamaker, J. W.,
Eds.; Marcel Dekker, Inc.: New York, 1972; 49-143.
»
16. Rao, P.S.C.; Davidson, J. M. Estimation of pesticide retention and
transformation parameters required in nonpoint source pollution
models. In: "Environmental Impact of Nonpoint Source Pollution";
Overcash, M. R., Davidson, J. M., Eds., Ann Arbor Sci. Pub. Inc:.
Ann Arbor, MI 1980; 23-67.
17. Spencer, W. F. Distribution of pesticides between soil, water and
air. In: "Pesticides in the Soil: Ecology, Degradation, and
Movement", Michigan State University, E. Lansing, MI; 1970, 120-128.
18. Spencer, W. F.; M. M. Cliath; W. J. Farmer. Vapor density of soil-
applied dieldrin as related to soil-water content, temperature, and
dieldrin concentration. Soil Sci. Soo. Am. Proc. 1969, 33, 509-511.
19. Spencer, W. F.; M. M. Cliath. Desorption of lindane from soil as
related to vapor density. Soil Sci. Soo. Am. Proc. 1970, 34,
574-578.
20. Spencer, W. F.; M. M. Cliath. Volatility of DDT and related com-
pounds. J. Agric. Food Chem. 1972, 20, 645-649.
122
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21. Spencer, W. F.; M. M. Cliath. Factors affecting vapor loss of
Trifluralin from soil. J. Agric. Food Chem. 1974, 22, 987-991.
22. Farmer, W. J.; K. Igue; W. F. Spencer; J. P. Martin. Volatility of
organochlorine insecticides from soil: I. Effect of concentration,
temperature , air flow rate and vapor pressure . Soil Sci. Soc. Amer.
Proc. 1972, 36_, 443-447.
23. Spencer, W. F.; M. M. Cliath. Pesticide volatilization as related to
water loss from soil. J. Environ. Qual. 1973, 2., 284-289.
24. Jury, W. A.; R. Grover; W. F. Spencer; W. J. Farmer. Modeling vapor
losses of soil-incorporated Triallate. Soil Sci. Soc. Am. J. 1980,
25. Farmer, W. J.; J. Letey. "Volatilization losses of pesticides from
soils", U.S. Environmental Protection Agency, Report No. 660/2-74-
054 U.S. Government Printing Office; Washington, D.C., 1974.
26. Mayer, R.; J. Letey; W. J. Farmer. Models for predicting pesticide
volatilization of soil-applied pesticides. Soil Sci. Soc. Am. Proc.
1974, _38_, 563-568.
27. Farmer, W. J.; M. Yang; J. Letey; W. F. Spencer; M. H. Roulier.
Land disposal of hexachlorobenzene wastes: Controlling vapor move-
ment in soils. In: "Land Disposal of Hazardous Waste. Proceedings
of the Fourth Annual Research Symposium11. U.S. Environmental
Protection Agency, Report No. 600/9-78-016. U.S. Government Printing
Office; Washington, D.C., 1978; 182-190.
28. Farmer, W. J.; M. S. Yang; J. Letey; W. F. Spencer. "Land Disposal
of Hexachlorobenzene Wastes: Controlling Vapor Movement in Soils",
U.S. Environmental Protection Agency Report No. 600/2-80-119. U.S.
Government Fringing Office; Washington, D.C., 1980.
29. Ehlers, W. ; W. J. Farmer; W. F. Spencer; J. Letey. Lindane diffusion
in soils. II. Water content, bulk density, and temperature effects.
Soil Sci. Soc., Am. Proo. 1969, 33, 505-508.
30. Igue, K.; W. J. Farmer; W. F. Spencer; J. P. Martin. Volatility of
organochlorine insecticides from soil. II. Effect of relative
humidity and soil water content on dieldrin volatility. Soil Sci.
Soc. Amer. Proc. 1972, 36, 447-450.
31. Farmer, W. J.; M. S. Yang; J. Letey; W. F. Spencer. Hexachloro-
benzene: Its vapor pressure and vapor phase diffusion in soil. Soil
Sci. Soc. Am. J. 1980, 44, 676-680.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency. The contents do not necessarily reflect the views of the
Agency and no official endorsement should be inferred.
123
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MODELING TRANSPORT AND DEGRADATION OF PESTICIDES
IN THE SOIL AND SURFACE WATER ENVIRONMENTS
by
R.C. Johanson,
School of Engineering,
University of the Pacific,
Stockton,
California 95211
A.S. Donigian Jr.,
Anderson-Nichols Inc.,
Palo Alto,
California 94303
T.O. Barnwell
Environmental Research Laboratory,
Athens,
Georgia 30613
ABSTRACT
This paper describes three mathematical models, HSPF,
CREAMS and EXAMS, recently developed in the USA for simulating
the behavior of pesticides and other chemicals in the soil
and/or surface water environments. They are of the
"deterministic conceptual" type. That is, they consist of
sets of linked equations, each of which represents our
understanding of some aspect of the problem. The equations
include parameters which are, as far as possible, physically
based. These models do not include random components.
The HSPF and CREAMS models simulate dynamic behavior. To
achieve this the user supplies several different types of
data, such as:
1. Continuous time series of input data such as
precipitation, air temperature and solar radiation.
2. Information on intermittent activities such as soil
tillage and chemical applications.
124
-------
3. Many parameters, which make the model equations applicable
to the site under study.
The models then operate on this information, evaluating the
equations repeatedly, to simulate processes. The result is a
set of outputs, such as simulated soil moisture, streamflow,
and pesticide and nutrient concentrations.
The EXAMS model is different; it is designed to simulate
the steady state situation in a set of water-bodies -with
steady chemical loading. It does, however, simulate the
unsteady recovery period after chemical loading has
terminated.
All the models start by estimating the movement of water
and sediment (because these are the principal mechanism of
chemical transport). The modeling of chemical reactions and
degradation is then superimposed on this framework.
A central concept in the HSPF model is the separation of
the "land" and "surface water" phases of the hydrological
cycle. To model pesticides in the land phase, HSPF includes
the processes of advection, adsorption/desorption, degradation
and volatilization. CREAMS is similar, in this respect, but
EXAMS does not model the land phase.
The processes which HSPF considers for modeling organic
compounds in the surface water phase are:
1. Adsorption/desorption on six classes of sediment, namely
sand, silt and clay both in suspension and in the bed.
2. Deposition and scour of sediment-attached material.
3. Advection of dissolved and adsorbed material.
4. Degradation, from the dissolved state, through hydrolysis,
photolysis, volatilization, biodegradation and oxidation by
free radicals.
5. The formation of "daughter" compounds through the
degradation of "parent" compounds.
The EXAMS model considers a similar range of processes,
while the CREAMS model does not include the surface water
phase.
This paper also discusses some features of modern model
construction, such as:
1. The use of Structured Programming Technology. This
permits a model to have a clear hierarchical structure, with
125
-------
each subroutine performing a well-defined task. The resulting
ease of program maintenance and alteration or addition is
discussed.
2. The concept of subdividing a study area into a set of
"processing units." This idea, present both in HSPF and
EXAMS, provides the user with great flexibility in applying
the model to complex networks of land and surface water units.
3. The use of sophisticated input scanners which check
user-supplied values for reasonableness, insert default values
where necessary and interpret time series linkage
instructions.
INTRODUCTION
In this paper we discuss three mathematical models which
simulate the behavior of pesticides (and other water quality
constituents) in the land and stream phases of the
hydrological cycle:
1. The IHydrologic Circulation _Program - F_ortran (HSPF),
developed under sponsorship of the United States Environmental
Protection Agency (U.S. EPA).
2. Chemicals, Runoff and Eirosion from Agricultural Management
jjystems, developed by scientists in the United States
'Department of Agriculture (USDA).
3. The Exposure Analysis Modeling £5ystem (EXAMS), developed
by the U.S. EPA.
For the sake of brevity we will refer to them, simply, as
HSPF, CREAMS and EXAMS. These computer programs have all been
developed recently and are representative of the wide variety
of models now being used in the USA for water quality studies.
The three models are all "deterministic conceptual;" that is,
they consist of linked mathematical relationships which
approximate the actual processes occurring in nature, and they
have no built-in random components.
We will first discuss HSPF in some detail, then we will
briefly discuss CREAMS and EXAMS and compare them with HSPF.
There are two reasons for this approach:
1. HSPF simulates processes occurring both in the soil
profile and in streams (Figure 1); the other models consider
only a subset of these processes.
126
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Land Surface
and Subsurface
Processes
Channel and
Lake Processes
Figure 1. Portions of the Hydrological Cycle Covered by the
Models Discussed
2. We have been deeply involved in the development of HSPF
but are not as familiar with the other models.
We will start with a brief overview of the entire HSPF
system and then concentrate on those parts that are especially
relevant to pesticide simulation.
OVERVIEW OF THE HSPF MODEL
HSPF was developed from a set of earlier models, the most
important of which were:
1. The Stanford Watershed Model (SWM) developed at Stanford
University (Crawford and Linsley 1966). It can simulate the
hydrologic behavior of an entire watershed.
2. The Agricultural Runoff Management (ARM) Model, developed
by Hydrocomp Inc. for the U.S.EPA (Donigian et al 1977). It
127
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simulates the hydrology, sediment yield, and nutrient and pesti-
cide behavior of the- land phase of the hydrological cycle. The
same organizations also developed the Non-Point Source (NFS)
Model (Donigian & Crawford 1976) which handles the washoff of
user specified, sediment correlated pollutants from the land.
3. The HSP Quality Model (Hydrocomp 1977). It simulates a
comprehensive set of water quality processes in streams and
lakes, but not pesticides and toxic substances.
4. The SERATRA Model, developed by Batelle Northwest
Laboratories (Onishi and Wise 1979). This model was designed
to simulate the behavior of sediment and associated
constituents in streams. It includes processes such as
hydrolysis and photolysis and is, thus, suitable for modeling
pesticides.
When work started on HSPF in 1976 the objective was to
merge most capabilities of the above models into a single set
of software, with a unified structure and written in the
standard Fortran IV language (A.N.S.I 1966). The approach
taken involved the following:
1. A completely new design. Rather than "patching" parts of
the older models together, their functions were fitted into a
totally new software framework. The concepts of Structured
Programming Technology (IBM 1974) were applied to this design.
The entire set of software was arranged in hierarchical order,
shown on a structure chart. The general idea was that the
entire system should form a tree, branching out from the MAIN
sub-program (Figure 2). "Continuation flags" point to
subordinate structure charts, so that the entire HSPF program
can be viewed by studying the 80 structure charts needed to
completely describe it. Within each sub-program, instructions
were first coded in "pseudo code," similar to Algol (Figure
3). In accordance with the tenets of structured programming,
as developed by Dijkstra and others during the 1960's, the
pseudo code included only the following five basic "structure
figures:" SEQUENCE, IF-THEN-ELSE, DO-UNTIL, WHILE-DO and
CASE. The pseudo code was then translated to standard
Fortran. The benefits of writing in structured code, compared
to Fortran, should be readily apparent from Figure 3.
2. The breakdown of a catchment into "processing units." A
fundamental concept in the models from which HSPF was
developed was that the land surface and subsurface processes
could be separated from processes in stream channels.
Furthermore, the various land surfaces in the catchment could
be subdivided into quasi-homogeneous "land-segments," and the
channel system into a set of "reaches" (Figure 4). This view
of the world was preserved in HSPF. Each processing unit
128
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ro
USRRDR
Preprocess
user's control
input
1,1
-<—
TSSMGR |
Manage time
series store
2.0
i
2.0 >
MAIN
Provide a
system for
operating on
time series
l.O
INTERP
Service \
subprograms
1.2
i_ _ _
t
1.2 >
OSUPER 1
Interpret run
insts. in user's
control input
3.0
3.0 >
Supervise
and perform
operations
4.0
1
4.0 >
Figure 2. Structure Chart of the Upper Levels of HSPF
Software
-------
co
o
PSUEDOCODE:
4.2(14).!
BEGIN DURDMP (Jl,J2,LGTH,FREQ,S.SQ.K,NDUR,
DURAT (*))
INTEGER*2 I,J,Jl,J2,NDUR,DURAT (10)
INTEGER*4 K»LGTH (22)
REAL FREQ(10,K),S(10,K),SQ(10,K)
J= Jl
WHILEDO J <= J2
1=1
WHILEDO DURAT (I)
-------
H Land Segment Number
— Land Segment Boundary
Stream Reach
Stream Reach Number
INPUTS:
Meteorological:
Precipitation,
Temperature,
Etc.
Physical ••
Soil Properties,
Channel Properties,
Land Use,
Etc.
OUTPUTS:
Stream Flows.
Concentration,
Etc.
Figure 4. Breakdown of a Catchment into Land-Segments and
Stream Reaches
(land segment or channel reach) is simulated independently
others, except for contributions from upstream units.
of
3. The use of continuous simulation. In HSPF, the user can
choose a simulation interval ranging from 1 minute to a day.
Once selected, this time step remains constant throughout the
simulation.
The result of -the HSPF development was a set
consisting of:
of software
1. Application and Utility Modules (Figure 5). Application
modules simulate processes. They use input time series such
as precipitation, air temperature and solar radiation, and
produce output time series such as computed flows and
constituent concentrations at various points in the catchment.
The user must also supply values for parameters and initial
conditions. At present, HSPF has a Pervious Land-segment
(PERLND) Module, an Impervious Land-segment (IMPLND) Module
and a (stream) Reach/Reservoir (RCHRES) Module. Utility
modules do not simulate processes; they merely assist the user
131
-------
by performing functions on time series, such as: copying,
generating information for display on an incremental plotter
or in a printed table, or the analysis of time series.
Because HSPF is totally modular, it is relatively easy to
modify existing modules or to add new ones to the system.
2. Time Series Management System (TSMS). This is
that supplies an operating module (application
module) with needed input time series and passes
another operation (e.g. flow and constituent
between river reaches) or to a disc storage medium
Series Store. Figure 6 shows the interaction
operating module and the TSMS, which consists
TSGET, TSPUT, the INPAD and the Time Series Store.
the software
or utility
output to
data passed
- the Time
between an
of modules
The entire system is documented in the HSPF User's
and Programmer's Supplement (Johanson, et al 1980).
Manual
HSPF
PERLND
Snow
Water
Sediment
Quality
Pesticide
Nitrogen
Phosphorus
Tracer
Application
IMPLND
Snow
Water
Solids
Quality
Modules
RCHRES
Hydraulics
Conservative
Temperature
Sediment
Nonconservative
BOD/DO
Nitrogen
Phosphorus
Carbon
Plankton
COPY
Data transfer
DURANL
Duration
Analysis
Utility Modules
PLTGEN
Plot data
GENER
Transform or
combine
DISPLY
Tabulate, summarize
MUTSIN
Input sequential
Time-series data
Figure 5. "Operating Modules" Presently in the HSPF Software
132
-------
THE HSPF PERVIOUS LAND-SEGMENT (PERLND) MODULE
General Comments
The PERLND module simulates a variety of processes occurring
on and under the surface of a pervious land-segment. The
structure chart (Figure 7) shows its various functions and the
names of the twelve sections of the module which handle those
functions. The sections usually involved in simulating
pesticides are SNOW and PWATER (hydrology), SEDMNT (sediment),
MSTLAY (solute transport) and PEST (pesticides). The last 5
sections of the module are of primary importance in simulating
agricultural chemicals.
The user specifies which set of sections will be executed
in a given run. For example, he may initially "switch on"
only SNOW and PWATER, to calibrate the simulated hydrological
behavior of a land-segment to observed data. Then he may turn
on MSTLAY and TRACER so that he can compare the simulated and
observed movement of a conservative substance such as
chloride. Finally, he may turn TRACER off and PEST on, to
simulate up to 3 pesticides.
Operations
Supervisor
Time
Series
Store
—*• Sub-routine Call
—*-Time Series Transfer Path
Figure 6. Activities Involved in Executing an HSPF Operation
133
-------
PERLND
Simulate
a pervious
land
segment
4.2(0
1
ATEMP I SNOW 1 PWATER 1 SEDMNTJ PSTEMP 1 PWTGAS 1 PQUAL 1
Correct air
temperature
4.2(0.1
Simulate
snow and
ice
4.2(0.2
Simulate
water
budget
4.2(0.3
Simulate
sediment
4.2(0.4
Estimate
soil
temperature
(s)
4.2(0.5
Estimate
water
temperature
and gas
concentra-
tions
4.2(0.6
1^ ^y i^y
i ~"
MSTLAY 1 PEST 1 NITR
Estimate
solute
transport
4.2(0.8
Simulate
pesticides
4.2(0.9
| 4.2(11.8 ^> 1 4.2(0.9 ^> •
Simulate
nitrogen
1
1 PHOS 1 TRACER 1
Simulate
phosphorus
4.2(0.11
Simulate
a tracer
(conserva -
tive)
4.2(0.12
> 1 4.2(1). II y | 4.2(0.12^
Stimulate
general
quality
constituents
4.2(0.7
[4.2(0.7 y
--
" " " AGRI- CHEMICAL SECTIONS T
Figure 7. Structure Chart for the HSPF Pervious Land-segment
Module
The HSPF system has been made as "intelligent" as
possible. For example:
1. If a user omits some input, HSPF will supply default
values if they exist, or report an error if they do not.
2. It will ignore unnecessary input. Thus, if a user has
been simulating pesticides and then turns that section off,
possibly to re-calibrate the hydrology, he does not have to
delete the pesticide-related input. HSPF will ignore 'it,
until he once again turns the PEST section on.
3. It can accept input in Metric or English units, (e.g.
pesticide application in kg/ha or Ib/acre, rainfall and runoff
in mm or inches).
4. It can supply printed output in Metric or English units
(or both), regardless of the units used for the input.
134
-------
Furthermore, printed output
specified frequencies: N time
or never.
can be
steps, 1
supplied at
day, 1 month,
several
1 year,
5. Any one (or several) of many time series can be selected
for special display, either on a plotter or in specially
formatted printed tables.
The above points apply
just the PERLND module.
to all the application modules; not
Hydrologic Simulation in the PERLND Module
moisture
Watershed
is, the movement of water into,
Hydrologic simulation is done using the
accounting technique first employed in the Stanford
Model (Figure 8). That
between, and out of, a set of conceptual storages is computed
using a fixed time step. Snow accumulation and melt are
simulated in the SNOW section (if it is turned on) using
energy balance procedures (U.S. Army Corps of Engineers 1956).
Rain and snowmelt are subject to interception. If that
Actual
ET
Potential ET
''Precipitation
Temperature
Radiation
Wind, Dewpoint
Interception I J~
Storage
(Subroutine)
( Input }
Storage )
ET - Evapo-
transpiration
JL Surface ^^
y Runoff pv
Interflow \ \
i v
1 — L//x//cr/
£wer ZM* I * og^
.9>ta«7^ H •"• 5/or^e
1 ^^ t
1 Groundwater
* Storage
Groundwater \
„ Overland
Flow
i
Interflow
1
J
1
/7&
\ Stream
Figure 8. Representation of the Hydrological Processes in a
Pervious Land-segment (HSPF)
135
-------
storage is full infiltration occurs. Infiltration capacity is
a function of the storage in the lower zone and a parameter
INFILT which reflects the permeability of the soil.
Infiltrated moisture passes to the lower zone or to
groundwater storage. Excess moisture either remains on the
surface or enters flow paths leading to the upper zone or to
interflow. Percolation from the upper zone to the lower zone
and groundwater is modeled. The model regards overland flow
as equivalent to that along a plane surface of length, slope
and roughness specified by the user. It uses a kinematic
method to calculate the overland flow rate. Other
contributions to streamflow come from interflow and
groundwater outflow.
Evapotranspiration (ET) can occur from any of the
storages. The model algorithms compute the amount of ET from
each storage, based on potential ET data supplied by the user.
Sediment Simulation in the PERLND Module
The processes modeled in the SEDMNT section are shown in
Figure 9. It also shows the simple equations used, which are
based on one of the first continuous sediment simulation
models (Negev 1967). The rate of detachment by rainfall is a
power function of rainfall intensity, modified to account for
protective cover (C) and any special management practices
(SMPF) (e.g. terracing, contouring). SMPF corresponds to the
factor P in the Universal Soil Loss Equation. Washoff (WS) is
the removal, by overland flow, of detached material. It is
modeled as a power function of overland flow, which is
computed by the hydrology section (PWATER), but washoff is
limited by the supply of detached material. This supply can
be altered by the user at any time, to simulate the effect of
soil tillage. Scour (SCR) is also modeled as a power function
of overland (surface) outflow. This simulates direct erosion
by surface outflow, such as gully formation. For scour, the
model considers the supply of parent material unlimited. The
coefficients and exponents (KRER, JRER, etc) must be
determined by experience and/or calibration.
The sediment section also accounts for soil compaction
(using a first-order process) and deposition or removal of
detached sediment (e.g. by wind).
Pesticide Simulation in the PERLND Module
The procedures used to simulate pesticides were first
developed for the Pesticide Transport and Runoff (PTR) Model
136
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rt$&
w.Af
Atmospheri^
Fallout
Man-made
Influences
/ /Detached
Compaction^ pediment Storage """ •
- -
Detachment: DET = (I-C)* SMPF * KRER* RAINJRER
Washoff: WS = KSER * SUROJSER
Scour:
SCR = KGER * SURO
JGER
Figure 9. Sediment-related Processes in a Pervious
Land-segment, as Modeled in HSPF
(Crawford and Donigian 1973). Testing and refinement led to
the Agricultural Runoff Management (ARM) Model (Donigian and
Crawford 1976, and Donigian et al 1977). After further
modifications these algorithms were included in the PERLND
module of HSPF.
In this model, the soil is viewed as having four layers
(Figure 10), corresponding to the surface, upper, lower and
groundwater storages used in the hydrology section (Figure 8).
The transport and reactions of pesticides are treated
separately.
Transport rates for dissolved material are based on the
internal and external fluxes (flows) computed in the hydrology
section of the module. Soluble chemicals are transported down
through the soil profile and are washed out into steams with
surface runoff, interflow and groundwater flow.
Sediment-associated pesticides (and nutrients) are removed
from the surface layer whenever sediment washoff occurs.
137
-------
The two pesticide reactions simulated by HSPF are:
1. Adsorption and desorption. The user can choose to handle
this using either temperature-corrected first order reaction
kinetics, in which the concentrations are always moving
towards equilibrium but never quite reach it, or he can use a
Freundlich isotherm (Figure 11), in which instantaneous
equilibrium is assumed. With the Freundlich method, he can
elect either to use a single-valued isotherm or a
non-single-valued one. This was included in the model because
there is experimental evidence which suggests that pesticides
do not always follow the same curve on desorption as they do
on adsorption.
2. Degradation. Although the actual mechanisms of
degradation are many and complex, HSPF uses a simple
first-order relationship to approximate this process. A
different rate constant can be supplied for each soil layer.
Adsorption, desorption and degradation are simulated in
each of the four soil layers (Figure 10). Different
parameters can be used in each layer.
Outflow to
Stream with:
Sediment,
Surface
Interflow
Application Degradation
t
Ground
water
Ground
water
A = Adsorption D= Desorption
Figure 10. Pesticide-related Processes in a Pervious
Land-segment, as Modeled in HSPF
138
-------
XMAX
XJCT ~;
XDIF
E
Q.
Q.
X
XFIX
minium
Curve I •
X = K*C
Curve 2'-
I/N
+ XFIX
X= K'*CI/N'+XFIX
where ••
XD\F
C(ppm)
CMAX
Figure 11. Single-valued and Non-single-valued Freundlich
Isotherms, for Modeling Adsorption/Desorption
THE HSPF IMPERVIOUS LAND-SEGMENT (IMPLND) MODULE
This module is designed to simulate processes in areas
where the ground is totally impervious; usually it is used on
parts of urban areas. The computer code has a similar
structure to that of the PERLND module (Figure 7), but has no
agri-chemical sections and the other sections have been
suitably modified. It is not designed to handle pesticides
but one could simulate them as "general quality constituents"
using module section 7, if required.
THE HSPF REACH/RESERVOIR (RCHRES) MODULE
General Comments
As the structure chart for this module shows (Figure 12),
it is designed to simulate the transport and reactions of a
wide variety of constituents in streams and lakes. Like the
PERLND module, each section of the RCHRES module simulates a
different set of processes, and the user can switch on that
combination of sections which i's best suited to simulate the
constituents which he is studying. Most of the the other
general comments made regarding the PERLND module are also
applicable to the RCHRES module.
139
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Section HYDR simulates the movement of water (hydraulic
routing). Section HTRCH evaluates the exchange of heat
between a reach and the atmosphere and, thus, simulates water
temperature. These sections are important because transport
and temperature greatly influence almost all the other
processes simulated by the module. Sections SEDTRN and GQUAL
simulate the movement of sediment and "generalized" quality
constituents (e.g. pesticides). We will discuss them in more
detail later. Section RQUAL simulates the "traditional"
biochemical constituents, such as oxygen, biochemical oxygen
demand, nutrients, phytoplankton, zooplankton, carbon dioxide
and refractory organic products of biological death and
respiration.
One significant limitation of the RCHRES module is that it
assumes total mixing in the water body; thus it does not
simulate stratified impoundments.
Hydraulic Routing in the RCHRES Module
HSPF uses a simple technique for flow routing. • The
catchment stream network is subdivided into "reaches" (Figure
4 ) and calculations start with the upstream ones. Each reach
may have several outflows and each outflow rate may be a
function of storage in the reach (storage routing) or a
RCHRES
Simulate a
reach or
mixed
reservoir
4.2(3)
HYDR 1
Simulate
hydraulic
behavior
4.2(3).l
ADCALC 1
Prepare to
simulate
advection
4.2(3).2
CONS 1
Simulate
conservative
constituents
4.2{3).3
HTRCH 1
Simulate
water
temperature
4.2(3).4
SEDTRN |
Simulate
inorganic
sediment
4.2(3). 5
GQUAL I
Simulate
general
quality
constituents
4.2(3).6
RQUAL I
Simulate
biochemical
constituents
4.2(3).7
Figure 12. Structure Chart for the HSPF RCHRES Module
140
-------
function of time (e.g. to supply demands of irrigators), or a
combination of both.
HSPF can handle a reach network of any complexity; it can
even handle situations where flows are split (diverted) and
later recombined further downstream (e.g. through hydro-power
diversion tunnels). Also, it can handle water bodies of any
shape; for example, streams do not have to be represented with
trapezoidal cross sections.
Sediment Routing in the RCHRES Module
The sediment routing method has been adapted from that
used in the SERATRA model (Onishi and Wise 1979). Each reach
is viewed as containing one "layer" of suspended, or
entrained, sediment and one layer of bed sediment (Figure 13).
Three classes of sediment are handled - sand, silt and clay.
Each is separately routed through the reach and its deposition
or erosion rate is calculated.
Local water
and sediment inflow
Water and
sediment
inflow
from upstream
Reach
Boundary
Deposition
of sediment
Scour of
sediment
Bed layer
3 Classes
of Sediment
Sand
Silt
Clay
Change in
watec level
Water and
sediment
outflow
Reach
Boundary
Figure 13. Sediment Processes Considered in the RQHRES Module
of HSPF
141
-------
For sand, the transport capacity is first calculated using
either the Colby (1964 a and b), or Toffaleti (1968 and -1969)
method, or a user supplied power function of velocity. If the
calculated transport capacity exceeds the load present scour
is simulated and if the opposite is true deposition is
simulated.
For silt and clay, the critical shear stress concept is
used. If the critical shear stress for scour is exceeded,
scour takes place. On the other hand, if the actual shear
stress is less than the critical value for deposition,
deposition occurs. For intermediate values of shear stress,
the bed is stable.
Pesticide and Toxic Substance Simulation in the RCHRES Module
Pesticides and many other toxic substances are subject to
a variety of processes in the aquatic environment (Figure 14).
In the RCHRES module, such compounds are called "generalized
quality constituents" and they are simulated using module
section GQUAL (Figure 12).
The algorithms used by module section GQUAL are, again,
based on those incorporated in the SERATRA model. The
processes included are shown schematically in Figure 15. The
model assumes that the chemical must exist in solution and is,
thus, potentially subject to all the processes shown on the
left side of the figure. These include:
1. movement with the water (advection).
2. hydrolysis. A first order pH-dependent equation is used.
3. oxidation by agents such as singlet oxygen and alkylperoxy
radicals. A second order equation is used.
4. volatilization. This is linked to the oxygen reaeration
rate which can be computed using a variety of equations.
5. biodegradation. A second order equation is used.
6. other methods of decay. A first order equation is used.
7. formation of "daughter" products by decay of "parent"
compounds.
The user decides which of the above processes will be
simulated (active); he need only supply input for those that
are active. In this connection, note that:
142
-------
1. all of the above decay rates can be adjusted for
temperature.
2. much of the supplementary input required for these
processes (e.g. biomass concentrations, free radical
concentration) can be supplied either as time series, or as
monthly cyclic data, or single fixed values.
If the user specifies that the chemical is
sediment-associated, then all the processes shown on the right
of Figure 15 also become active:
1. Adsorption and desorption between the solution phase and
sand, silt and clay in suspension and on the bed. First-order
rection kinetics are used.
2. Transport of adsorbed material with the sediment. This
includes advection, scour and deposition.
ATMOSPHERE
Rainout
Pesticide (Solution)
Dust-Pesticide
\
| Temperature
\0xygen\
9999999999999.9999999999999999
WATER
I Radiation \
99999999999999999
Advection
t
Microbial
Degradation
t
Volatilization
k
|k
Pesticide
Adsorbed
Phase
^
..^
K
Pesticide
Solution
Phase
Degradation =
Microbial
Chemical.
photochemical
K = Equilibrium Constant
k = Rate Constant
Chemical
Degradation
>
Advection
Scour \ \ Sediment\\ Deposition
m' I ^^^CKHBB^fc^
Organism Uptake
Pesticide Absorbed
Phase
Figure 14. Pesticide Transport and Transformation Processes in
Flowing Streams
143
-------
3. Decay of adsorbed chemical, modeled as a first-order
process.
THE CREAMS MODEL
General Comments
This model, developed by scientists in the U.S. Department
of Agriculture (USDA) is documented fully in "CREAMS: A Field
Scale Model for Chemicals, Runoff and Erosion from
Agricultural Management Systems" (Knisel 1980). For the sake
of brevity, we will refer to that publication from now on as
the "CREAMS Report."
The goal of the developers of this model was to
in one year, a program that:
assemble,
II II IV
in sol
mitftffu- ^
in soln.
input from
decay of "parents"
^—
* *
Decay
Fluxes
hydrolysis
oxidation
photolysis
volatilization
biodegradation
\ general (other)
X.
"• N
/
°>
/
i
^
|
-9-
4 i
!
i
:
«1
^ :
i
i
4 :
* |
:
i
1
i
I
1.
Constituent on
susp. sediment
adsorption
desorption
^
— *
k.
— ^
#
J_
On
susp. ——
sand
i
I
f
On
bed
sand
Constituent on
bed sediment
IIHIIIIIII
IIMIHIIUNII IIIMIHHIt
inflo
clay
K
w on
^
w
V ^
^ On
r SUSP.
^ silt
/
+>
\
* °"
silt
-*
/ -
adsorption fc
desorption
/
On
susp.
clay
i
outflow
— ^. on
clay
[
Deposition
ana scour
with sediment
\
On
bed
clay
'
itiiiiiiiiiiiiiiiiiiiiHiiiiiii»iii"iii«ii«iiiii»«iMiiiiiiiiiiiiiiiii«mni»»
Figure 15. Simulation of a "Generalized Quality Constituent"
in HSPF
144
-------
1. would simulate field-size areas. The development of a
model that could handle the groundwater and stream components
of the hydrological cycle and thus, would simulate an entire
catchment, was deferred to a later time.
2. was physically based and reasonably accurate,
enough to be easily understood
3. had as few parameters as possible and required
of calibration.
yet simple
minimum
4. included runoff, percolation, erosion
adsorbed plant nutrients and pesticides.
and dissolved and
5. was directly applicable to the study of alternative
agricultural management practices (e.g. terracing, contouring,
minimum tillage).
To meet their very short time-table the USDA scientists
did not attempt to develop an entirely new system; they merged
and improved models that they already had. The result
(CREAMS) is a model with three components, for hydrology,
sediment yield and chemicals (Figure 16). Like the modules in
HSPF, these components can be run independently. For example,
a user may simulate the hydrological behavior of a field and
store the results. In a subsequent run he might simulate
sediment and/or chemicals using for input either the simulated
runoff data or observed data, if it is available.
Hydrological Simulation in the CREAMS Model
Like most modern deterministic hydrological models, CREAMS
uses a moisture accounting procedure. It has a simple snow sim-
ulation algrorithm. It offers two options for surface runoff.
The SSC Curve number model is used if only daily rainfall data
are available; if hourly data are available, an infiltration-
based model is used. Infiltration of snowmelt and rainfall into
the soil is estimated using a development of the Green and Ampt
model. Soil moisture is accounted for using two storages
(Figure 17):
1. A shallow zone which, through its degree of saturation, con-
trols infiltration.
2. A root zone, which extends down to the maximum rooting depth
of the crop. Moisture that percolates below this zone is called
seepage and is one of the outputs of the model.
Despite the fact that it has only two soil moisture
storages this is indeed a physically based model. The
algorithms used to simulate infiltration, plant growth,
evapotranspiration and overland flow are sophisticated and are
145
-------
based on extensive prior research, and have been tested using
much data from watersheds operated by the USDA.
The methods used in CREAMS differ from HSPF in many
respects. One very significant example is that, whereas HSPF
uses a constant time step (e.g. 15 minutes), CREAMS uses the
storm as the basic time unit. It estimates the total runoff
/
Precipitation
Data
\
r
/
Hydrology
Parameters
Hydrology
Program
Erosion /
Sediment
Yield
Program
Chemicals
Program
Hydrology
Output
Sediment
Parameters
Sediment
Output
Chemicals
Parameters
Chemicals
Output
Figure 16. Components of the CREAMS Model
146
-------
and the peak rate of runoff for the entire
does not attempt to simulate the runoff
constant time intervals during the storm.
storm event, and
for a series of
Simulation of Erosion and Sediment Yield in the CREAMS Model
Sediment simulation is- a very important component of any
model designed to simulate agricultural lands. It is
especially important if the model is to be directly applicable
to the study of alternative management practices, such as
contouring, terracing and ponding. Therefore, the developers
of CREAMS went into great detail to represent the erosion,
transport and deposition processes that take place on farmed
areas. For sediment simulation, they regarded a field as a
combination of three basic element types (Figure 18):
1. Overland flow. This includes both rill, and inter-rill
areas. Erosion is simulated using equations developed from
the Universal Soil Loss Equation (Wischmeier and -Smith 1978)
and the modeling of transport is based on the wjork of Yalin
(1963). The user can specify whether the slope* is uniform,
concave, convex, or some combination, and the model routes
sediment through the various sub-elements, computing
transport and deposition in all of them.
erosion,
l Rain or
\
Evaporation
T Infiltration
Maximum
rooting
depth
1
Seepage
Figure 17. Soil Moisture Zones Used in the CREAMS Hydrology
Program
147
-------
Overland
flow
Impoundment
Concentrated
flow
Underground
outlet
o 1 Overland Flow-*-
J=J Pond Sequence
,•"" Overland f/ow^*
/'J_L /J_U '
I i_L ILL
I Terrace ,
Outjet channel w flow /'
" '- - -flOW-"?'-*-
"Tl Overland Flow — >•
JlJ Channel — +• Channel Sequence
1 h|Over'ancl Flow — *"
1 — (Channel Sequence
Overland flow
Jill A
Channel flow^ (
,/,/,/, M, i.i.i, i.i. i,i\ >S
'-Under-
- ground
i'i'iM'i'i'i'i'i'i'i'i'i')'i'j')'i'fgx outlet
Pond at field'' x*
outlet
\ H 1 Overland Flow -*-
LMJ Channel—*- Pond Sequence
Figure 18. Representation of Typical Field Systems in the
Sediment Component of CREAMS
2. Channel. This represents flow in areas with very
concentrated flow such as terrace channels, but not gullies.
The model simulates spatially varying flow, to account for
backwater effects due to restricted outlets. It breaks the
element into several computational segments and simulates
detachment, transport and deposition of sediment along each
one.
3. Pond. This element simulates
behind small impoundments.
the trapping of sediment
To represent a field, the user "chains" several of the
above elements in a specified sequence. In this way, the
commonly used agricultural practices can be represented quite
realistically (Figure 18).
The CREAMS sediment simulation is very much more detailed
than that in HSPF. Besides the detail discussed above, it
considers five distinct classes of sediment (Figure 19) and
routes them all separately through the field.
148
-------
Pesticide Simulation in the CREAMS Model
Pesticide behavior is modeled by CREAMS as follows (Figure
20):
1. It simulates the interception of pesticides by vegetation
and the subsequent degradation on the foliage before the
chemical is washed off (to the land surface) by rainfall.
HSPF does not include these processes.
2. It assumes that a layer of soil only 1 cm thick is active
when pesticide is leached from the soil by surface runoff.
Processes further underground are not considered. HSPF uses
four layers, extending all the way to the groundwater.
The following processes, in the surface soil layer, are
considered:
1. Leaching of dissolved pesticide by surface runoff.
2. Vertical movement of dissolved pesticide from the surface
layer, with percolating soil moisture.
3. Adsorption and desorption, using a linear isotherm with
constant partition coefficient (Kd). HSPF has three options
for simulating adsorption/desorption.
CREAMS MODEL
SEDIMENT CHARACTERISTICS ASSUMED FOR DETACHED SEDIMENT BEFORE DEPOSITION
PARTICLE TYPE
PRIMARY CLAY
PRIMARY SILT
SMALL AGGREGATE
LARGE AGGREGATE
PRIMARY SAND
DIAMETER
(MM)
,002
,010
,030
,500
,200
SPECIFIC
GRAVITY
(G/CM3)
2,60
2,65
1,80
1,60
2,65
FRACTION OF TOTAL
AMOUNT
(MASS BASIS)
,05
,08
,50
,31
,06
Figure 19. Classes of Sediment Represented in the CREAMS Model
149
-------
Source of
pesticide
for runoff
(soil surface
zone)
interrill
erosion
pesticide
extraction
Washof*
of foliar
pesticide
Surface
runoff
Pesticide
movement
from
surface layer
Depth of soil
•incorporated
pesticide
Figure 20. Pesticide Transport Processes Modeled in CREAMS
4. Washoff of pesticide adsorbed on sediment. This component
of the model uses an "enrichment factor," to account for the
fact that sediment leaving the field is usually of finer
composition than the parent material and, therefore, will
contain a higher concentration of adsorbed pesticide. HSPF
does not use an enrichment factor.
The CREAMS report has a good discussion of the performance
of its pesticide component. The test results were,
apparently, quite good but the following observations were
also made:
1. Because the model only updates the concentration of
runoff-available pesticide once per storm (at the end), it
tends to over-predict pesticide yield for severe storm events.
To us, it seems that this is one disadvantage of simulating
the entire storm in one time step, rather than using a
constant time step.
2. It has been observed that pesticide desorption from soil
is non-linear and becomes more difficult with time. However,
the CREAMS model developers do not believe that sufficient
experimental data have been acquired to warrant changing their
simple adsorption/desorption method.
150
-------
Some Further Comparisons Between CREAMS and HSPF
The documentation of the two models is very different:
1. The CREAMS report is very comprehensive. In the
introduction there is a good review of many different
agricultural chemical models. The report also discusses
thoroughly:
a. the physical processes which the model was designed to
simulate
b. the equations used in the code.
c. results of testing work and sensitivity analysis for the
parameters
In addition, the report contains a user's manual, which
describes the input required to run the model, and methods for
estimating parameter values. It also includes much
supplementary documentation — papers written on specific
aspects of the model.
2. The HSPF report is also a lengthy document, but is very
different. It systematically describes the computer code,.
explaining the algorithms used. It also documents very
logically the input required to run the model. However, it
does not give a great deal of background material in the
algorithm descriptions, nor does it suggest methods for
evaluating parameters. Readers are referred to other sources,
such as the ARM Model Users Manual (Donigian and Davis 1978)
for such information.
In summary, the HSPF code and documentation are extremely
systematic, while the CREAMS documentation is very detailed.
A key difference between HSPF and CREAMS is that the
former model is based on continuous simulation using a
constant time step, whereas the latter model is "event based."
CREAMS calculates values such as runoff volume and peak runoff
rate for each storm and uses a coarse time step between storms
to account for factors such as the gradual drying of the soil
and pesticide degradation.
151
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THE EXAMS MODEL
General Comments
The Exposure Analysis Modeling System (EXAMS) was recently
developed at the Environmental Research Laboratory in Athens,
Georgia and is documented in a very comprehensive report
(Burns, Cline and Lassiter 1981). Our discussion of this
model is brief because it will be discussed in other papers
at this conference.
Basically, EXAMS is a hazard evaluation system. Its
purpose is to permit a scientist to make a rapid assessment of
the likely long-fcerm behavior of a chemical in a waterway
system, assuming it enters the system at an approximately
constant rate. It is intended for use as a "screening tool."
A company wishing to market a new chemical must supply the
U.S. government with data on the properties of the compound.
EXAMS can make use of these data to estimate the likely
long-term effects of the chemical and can, thus, assist in the
licensing process.
EXAMS is limited to the stream and lake phases of the
hydrological cycle. As in HSPF, the stream system is broken
down into a series of elements, called "compartments." EXAMS
includes four "compartment types."
1. Epilimnion. This represents well-mixed water bodies such
as streams and the upper layer of lakes.
2. Hypoliranion. This represents the lower levels of lakes.
3. Benthic. Bottom sediments
4. Littoral. This represents the shallow borders of a water
body, where there is very little transport.
As with HSPF, the user models his stream/lake system by
chaining together a set of compartments. Note that a single
water body, such as a lake, may be represented by several
compartments, usually of different types.
The EXAMS model focusses on the chemical itself; other
aspects of the water system are not modeled in detail. For
example, the model assumes that the flow rates are steady and,
similarly, that environmental factors such as temperature and
pH are constant. With these factors and the chemical loading
rate .specified by the user, EXAMS simulates the behavior of
the chemical under investigation. The results of this work
are estimates of:
152
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1. exposure. The model determines the long-term "expected
environmental concentrations" (EECs) in each compartment.
2. fate. The model shows how much of the chemical has been
lost from a compartment by the various processes of transport
and reaction.
3. persistence. The model indicates how rapidly the
concentrations in each compartment would decrease if loading
suddenly stopped.
EXAMS is an interactive computer program. The user keeps
data concerning various typical aquatic environments on
computer files. To run the program, he need only specify the
"environment" file and then answer a set of questions
concerning the chemical properties of the compound. The
procedure can be repeated very rapidly for a set of different
environments, thus enabling him to assess the effects that the
proposed chemical would have in various different climatic
regimes.
Simulation of Transport Processes in EXAMS
This is very simple. The model assumes that the volumes
of all the compartments remain constant. Then, based on data
concerning the (steady) hydrologic, sediment and chemical
loadings into each compartment, it computes the movement of
water and sediment through the system. The chemical under
study travels with the water, sediment and phytoplankton.
EXAMS also models the processes of diffusion between
compartments and the loss of chemical through valatilization
and seepage through the bed.
Simulation of Chemical Reactions in EXAMS
Because the purpose of EXAMS is to predict the fate of
complex chemicals using basic (laboratory) data concerning
their properties, the typical reactions of organic compounds
are represented in great detail; much more than in the HSPF
model.
lonization and sorption constrain the other
(transformation) processes in the EXAMS model. Up to 15
different products of ionization (species) of a single
compound can be handled. This permits the very different
reactive properties of each species to be represented.
153
-------
Sorption on sediment and two classes of biomass (resident and
transported), is modeled, using instantaneous equilibrium with
a linear isotherm.
The transformation processes modeled by EXAMS include
photolysis, hydrolysis, biolysis, and oxidation. The methods
used are similar to those in HSPF, but more detailed. Most
reactions are represented by second-order equations, in which
the reaction rate depends on both the concentration of the
chemical and the value of some environmental variable, such as
the concentration of biomass responsible for biolysis.
Temperature effects are simulated using Arrhenius
relationships.
CONCLUDING REMARKS
We have described and, to some extent, compared three models,
recently developed in the U.S.A., for simulating the behavior
of pesticides in the soil and stream environments. Our goal
was to provide a concise summary of the capabilities of each
model; not to try to judge their relative merits. Indeed, it
is impossible to say that one model is "better" than the
others; each one is best suited to its own type of
application. In this connection, it should be remembered
that the models are constantly being improved and extended.
Thus, any remark concerning an alleged shortcoming in a model
could quite soon be out of date.
REFERENCES
American National Standards Institute. 1966. USA Standard
Fortran, Standard X3.9-1966. 36 pages.
Burns, L.A., D.M. Cline and R.R. Lassiter. 1981. Exposure
Analysis Modeling System (EXAMS): User Manual and System
Documentation. Environmental Research Laboratory, Athens,
Georgia 30613. 440 pages.
Colby, B.R. 1964a. Practical Computation of Bed-Material
Discharge. Journal of Hyd. Div., ASCE, Vol. 90, No. HY2.
Pages 217-246.
Colby, B.R. 1964b. Discharge of Sands and Mean Velocity
Relationships in Sand-Bed Streams. Professional Paper 462-A,
U.S. Geological Survey, Washington, D. C.
Crawford, N.Bf. and A.S. Donigian, Jr., 1973. Pesticide
Transport and Runoff Model for Agricultural Lands. Office of
154
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Research and Development, U.S. Environmental Protection
Agency, Washington, D. C. Report EPA 660/2-74-013. 211
pages.
Crawford, N.H., and R. K. Linsley. 1966. Digital Simulation
in Hydrology: Stanford Watershed Model IV. Department of
Civil Engineering, Stanford University, Stanford, California.
Technical Report No. 39. 210 pages.
Donigian, A.S., Jr., D.C. Beyerlein, H.H. Davis, Jr., and N.H.
Crawford. 1977. Agricultural Runoff Management (ARM) Model
Version II: Refinement and Testing. Environmental Research
Laboratory, Athens, Georgia EPA 600/3-77-098. 294 pages.
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Nonpoint Pollution from the Land Surface. Environmental
Research Laboratory, Athens, Georgia. EPA 600/3-76-083. 280
pages.
Donigian, A.S., Jr., and N.H. Crawford. 1976b. Modeling
Pesticides and Nutrients on Agricultural Lands. Environmental
Research Laboratory, Athens, Georgia. Report EPA
600/2-7-76-043. 317 pages.
Donigian, A.S., Jr., and H.H. Davis. 1978. User's Manual for
Agricultural Runoff Management (ARM) Model. Environmental
Research Lab., Athens, Georgia. Report EPA 600/3-78-080. 163
pages
Hydrocomp Incorporated. 1977. Hydrocomp Water Quality
Operations Manual. Hydrocomp Inc., 201 San Antonio Circle,
Mountain View, California 94040. 192 pages.
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/
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Manual for Hydrological Simulation Program - FORTRAN (HSPF).
Environmental Research Laboratory, Athens, Georgia. EPA
600/9-80-015. 678 pages.
Knisel, W.G. (Ed.) 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff and Erosion from Agricultural Management
Systems. U.S. Dept. of Agriculture, Conservation Report No.
26. 640 pages.
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Department of Civil Engineering, Stanford University,
Stanford, California. Technical Report No. 76. 109 pages.
Onishi, Y,. and S.E. Wise. 1979. Mathematical Model,
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155
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Application to Pesticide Transport in Four Mile and Wolf
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Toffaleti, F.B. 1969. Definitive Computations of Sand
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This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication.
156
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MODELING THE BEHAVIOR OF PESTICIDES USING
THE ARM MODEL
V.A.Borzilov,Ts.I.Bobovnikova,I.V,Dragolubova
Institute of Experimental Meteorology,Obninsk
A.D.Fokin.V.V.Rachinsky
Timiryazev1 Academy of Agriculture,Moscow
INTRODUCTION
Under the US-USSR joint project "Forms and Mechanisms by
Which Pesticides and Chemicals Are Transported", it has been
recognized as advisable to develop and introduce simulation
models of dynamic type which describe the behavior of pestici-
des in soil( 1,2,4,11-13 ) . It is known that these models are
based on a description of hydrology of an agricultural water*
shed.
In calculations of the total water balance,one uses time se-
ries of hydrometeorological data,such as rainfall,evaporation,
wind velocity, dew point, net radiation, maximum and minimum
air temperature(daily values), as well as watershed topography,
initial moisture storages,the extent of plant coverage and con-
dition of the soil surface.
A hydrologic model has submodels which describe "solid"
runoff formation and the processes of sorption and degradation,
and permit the mass balance of a pesticide in the watershed to
be calculated. After calibration with respect to a number of
hydrometeorological observations and determination of the pa-
rameters of sorption and degradation, the ARM model can be
used as a predictive one.
The purpose of this study was to test the ARM model in
various soil-climatic zones of the Europian part of the Soviet
Union.
The study involved the following stages:
- realization of the model on the Soviet computer system
EC;
- selection of experimental watersheds;
157
-------
- design and instrumentation of small experimental plots
to study pesticide degradation kinetics using the method-of
isotope indicators;
- acquisition of characteristic parameters,for example,
sorption isotherms for various pesticides and soil conditions;
- accumulation in a data base of the data on a continuous
monitoring of hydrometeorological parameters;
- verification of the ARM model for areas of various sca-
les, sensitivity analysis of the selected parameters of the
model and determination of errors.
REALIZATION OP THE MODEL ON THE COMPUTER" SYSTEM EC
While introducing the ARM model,we consulted with the leading
scientists of the U.S. .EPA Environmental Research Laboratory
(Athens,Georgia), and received the technical documentation and
model texts on magnetic tapes. Nevertheless,introducing even
the completely developed model is a very serious scientific
problem that involves primarily its realization on the Soviet
computer system, testing under field conditions and elabora-
tion of recommendations on its use in specific soil-climatic
zones.
The model is a large, relatively complex computer program
comprised of 15 major subroutines and more than 5700 executable
source statements written in the FORTRAN IV language. Much of
the model testing has been performed on an IBM 370/168 at
Stanford University ( 13) . On the IBM 370/168 using the
FORTRAN H compiler, the program requires approximately 360K
bytes of storage. Program execution after its compilation in
a simple structure requires up to 230K bytes of storage depend-
ing on the model options selected. Thus, a computer with a
large storage capability is needed for using the ARM model.
Realization of the model on EC-I050 required certain changes
associated with the specific features of this computer. To
reduce the required storage capacity we organized an overlay
structure of the program by its dividing into segments accord-
ing to functional relations between the program sections. Fif -
teen modules were grouped into four segments.
The overlay program is not loaded to the main storage in
whole. Only one program section, or root segment,remains inva-
riably in the storage,while the rest segments change each other
as the program is executed. In the overlay structure, the prog-
ram execution on EC-I050 requires up to I50K bytes of storage.
The program can be used on other computers of the EC type. In-
coming texts of the ARM model are stored in consequtive magne-
tic tape files at various levels. In addition, there are ope-
rating files on magnetic disks that are used for modification
of individual submodels in the dialog mode of operation.
158
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EVAHJATION OF CHARACTERISTIC PARAMETERS OF THE MODEL
Regarding the way of its realization, the ARM model is
a simulation one. It uses a set of empirical relations and
rather artificially takes into account a spatial inhomogeneity
of a watershed that is given "by an integral function of infil-
tration. Therefore, prior to the evaluation of the characteris-
tic parameters of the model, its hydrological part is calibrat-
ed according to the technique described in (II-IJ) . Following
the hydrological calibration to obtain water balance, we calib-
rate "solid" runoff and evaluate the characteristic parameters
of the model.
Accuracy of pesticide mass balance calculations depends
mainly on an accurate evaluation of coefficients entering in-
to the equations. Because of a very important role of sorption
in pesticide transport, it is necessary first of all to deter-
mine coefficients of the sorption isotherms. To obtain the sorp-
tion isotherms for the watersheds studied, samples were collect-
ed from the wells in the upper, middle and low parts of the slo-
pes. Then a set of experiments on C- labelled BHC was carried
out to measure the sorption isotherm parameters in various
layers. Activity of an equilibrium solution separated from the
soil by centrifugation was determined radiometrically ±ive days
later, using "MARK-II" scintillation radiometer with a dioxan
scintillator. The technique developed specially for these pur-
poses was described in( 9) • Results of the sorption study are
given in Table I. Not only the parameters of the sorption sub-
model were obtained from the above results but also the rela-
tion between the isotherm parameters and physical-chemical
properties of the sandy soddy podzolic soil. Processing the
results presented gave the following relationship between the
degree of sorption and the content .of organic matter
Here Ep is the parameter of Freundlich isotherm
rn rn I/N
[p] s = % [P] w
where (V] s and [P] ^ are the pesticide concentrations in
soil and water, respectively.
Differences in the degree of pesticide sorption correla-
ted with a layer-by-layer distribution of organic matter. Per-
centage of labelled BHC desorption was insignificant and rang-
ed from 5 to T.2% for the samples collected, thus indicating
that the sorption was almost irreversible.
A major part of sorbed BHC (about 8C$) was found in par-
ticles less than 0.001 mm in size.
159
-------
Table I. Average physical-chemical properties of soils studied and average parame-
ters of Preundlich isotherms for I4C-labelled BHC
CT>
O
u
I
Bulk weight, g/cnr
Specific weight , g/cnr
Total porosity in per-
cent of the soil vo-
lume
Mechanical analysis of
fine earth in percent
of the weight
Sand I - 0.25 mm
0.25- 0.05 mm
< 0.05 mm
0.05- 0.01 mm
Dust < 0.01 mm
0.01- 0.005 mm
0.005-0.001 mm
0-20
2
I. 3*0. I
2.5±0.0?
49.8
64.8
18. 1
14.5
5.6
8.9
1.0
0.6
Sampling
20-30
3
1.3*0.1
2,5*0.06
47 »6
56.6
28.1
14.7
5-6
9.1
1.0
1.7
depth, cm
20-40
4
1.4*0.1
2.6*0.04
46.8
56.2
33.5
8.4
3.2
5.2
0.4
0.4
20-50
5
1.4*0.1
2.7*0.04
46.8
62.8
20.1
14.0
6.7
7-3
0.9
0.5
67-75
6
1.5*0.02
2.7*0.06
42.6
16.2
22.8
59.0
34.4
24.6
6.9
8.0
-------
Table I (continued)
I
Silt < 0.001 mm
Treatment loss in per-
cent of the weight
Hygroscopic moisture t%
Humus content, accord-
ing to Tyurin,J6
Total exchange bases,
according to Kappen-
Gikovits, mg-equiv./IOOg
Hydrolytic acidity,
mg.equiv./IOO g
pH water
pH salt
A 1<* accord ing to Sokolov,
. V mg«equiv./IOO g
H J
Adsorption of CI4-labelled BHC
KE
I/N
Desorptioa in percent of the
4 M •» -4- .s «t *i -**+*. *+ ~, **.j* — -. _*T» — j n ^ i« _ T T * .
2
7.3
2.6
0.8
2.lio.2
8.5^2.7
2.3*0.7
6.1±0.7
5.4*0.9
0.1*0.04
0.04*0.01
18.5*2.5
o. 94*0.05
a 5.2*0.7
3
6.4
0.6
0.9
1.9
4.3*1.4
2.0±o.8
5.8*0.6
5.1*0.2
0.02*0.01
0.03*0.01
18*1.7
0.94*0.05
6.6*1.4
4
4.4
1.9
0.3
0.5
1.8*1.3
1.4*0.8
5.7*0.4
4.9*0.6
0.08*0.05
0.04*0.01
3.2*0.4
0.95*0.0
5.1*0.6
5
5.9
3.1
I.I
0.5
2.28*1.2
1.4*0.6
5.7*0.6
5.1*0.5
0.47*0.2
0.05*0.2
3.?*0.3
0.94*0.03
7.2±I.O
6
9.7
2.0
1.7
0.6
5.4*2.7
1.3*0.4
5.6*0.4
4.7*0.4
0.1*0.02
0.04*0.01
2-5*P.2
0.94*0.03
I0.2±0.8
initial mass of sorbed labelled
BHC
-------
Distribution of BHC and humus in various granulometric
fractions of the Valdai soils is presented in Table 2. A high
degree of participation of fine soil fractions in BHC adsorp-
tion can be accounted for not by a high content of these frac-
tions in the soil, but by a high concentration of sorbed BHC
in them. The latter exceeds by an order of magnitude or more
the concentration of sorbed BHC in the soil as a whole. In its
turn, a high sorption capacity of the fine fractions with res-
pect to BHC can be accounted for by their increased humusness
CI4-). The character of humus and BHC distribution in fractions
is almost the same.
Table 2, Distribution of BHC and humus in various granulomet-
ric fractions of the Valdai soil
Particle
mm
o.oi - 0.05
0.005 - o.oi
o.ooi - 0.005
^0.001
Distiribution
Humus
3,4
5.7
2.6
88.6
in fractions, %
BHC
7,4
9.2
1.3
82.,!
T/, In October 1979, we began to carry out experiments on
C-labelled BHC at the Valdai station for water-balance inves-
tigation^) to study the kinetics of BHC degradation. Table 3
shows contributions ©^evaporation and chemical and microbiolo-
gical degradation to C-labelled BHC degradation. The amount
of C-labelled BHC applied to the soil is taken as unity.
RESULTS OP MODEL DEVELOPMENT AND TESTING UNDER FIELD CONDITIONS
To test the ARM model in various soil-climatic zones we
selected two types of water regime of soils - pervious and im-
pervious(J), and four experimental plots where the water balan-
ce was observed for many years. The pervious water regime,i.e.
when evaporation is less than water infiltration to the soil,
and the soil depth undergoes annually a thorough wetting up to
the ground waters for many years, was observed at the Valdai
and Trans-Carpathian stations for water-balance investigation
(5,6). Mean annual rainfall in the Trans-Carpathian region
162
-------
Table 3. Contributions of evaporation and chemical and micro-
biological degradation to the degradation of
•"•^-labelled BHC
Days Evaporation and Chemical Evaporation Chemical
chemical degradation and chemical and micro-
degradation and microbio- biological
logical degra- degrada-
dation tion
2
3
15
45
67
110
O.I?
0.28
0.41
0.71
0.78
0.77
0.13
-
0.15
0.37
0.53
0.50
0.18
0.22
0.41
0.72
0.79
0.84
0.04
-
0.22
0.35
0.54
0.60
(brown forest soils) is 800-1000 mm at an evaporating capacity
600 mm, and in the Valdai (sand podzolic soils) 600-800 mm at
an evaporating capacity 300-400 mm. The impervious water regi-
me,i.e. when almost the whole of moisture accumulated in the
soil reenters the atmosphere through evapotranspiration has
been observed at the Moldavian station for water-balance inves-
tigation since 1954 (7). Mean annual rainfall in this area
(calcareous clay loamy chernozems) is about 500 mm. Based on
the data of continuous hydrometeorological monitoring carried
out at these stations for many years we calibrated the hydro-
logical part of the ARM model, using the calibration
technique described in (11-13). Following the general hydrolo-
gical calibration of the model and calculation of the water
balance we calibrated the submodels of "solid" runoff and pes-
ticide behavior.
As an example, we shall consider the components of pesti-
cide mass balance obtained at the Moldavian station for water-
balance investigation in Baltsat. The intensity of the process-
es of slope erosion depends on three factors: rainfall inten-
sity, soil erodibility, and protective action of plant cover.
Various combinations of these three factors were studied at
four runoff plots in Baltsat from June 6 to September 12,1980.
Characteristics of the experimental plots are given in Table 4.
Commercial BHC (2 kg per 0.8 hectare) was applied on June 6,
I960. In the period under study, there occurred about 70# of
163
-------
Table 4-. Characteristics of the experimental plots
Runoff
plot number
Location
Average Exposure Type of Prevalet type
slope t% land of soil
2
3
Baltsata Brook
Middle part of the
left slope,140 m
from the meteorolo-
gical station
Same as above
Ra-
202
Stantsionnyi
vine
Middle part of the
right slope,70 m
from the meteorologi-
cal station
Same as above
202
III
SW
SW
III
Fallow Calcareous medium-
(plowed deep clay-loamy
on June 5) chernozem on loam
Kidney beans Same as above
Virgin land Calcareous me-
dium—deep clay-
loamy chernozem
Virgin land Same as above
Soil sections are described in (7)
-------
mean annual rainfall and only two events of "solid" runoff out
of six rainfall events. Maximum total rainfall was observed in
June and was as great as 119. 7 mm. The rainfall intensity for
two events of "solid" runoff (on June 7 and 14) varied from
O.I to 0.6 mm/min. It was the first shower on June 7 that de-
termined the subsequent behavior of the pesticide.
Results of BHC behavior modeling and field measurements
for plots No. I (fallow) and No. 2 (kidney beans) are shown in
Fig. I and 2. Curves b,c,d and e in Fig. 2 were obtained using
the values of degradation rate that correspond to the contri-
butions of various processes to degradation given in Table 3«
a)
0-3
A-4
0-
6-
2-
fl-
6-
4-
2-
10'-
Pw,nu#ian
0
7 -I
•
5 -
4 •
3 -
2-
-r
a;
»°-
tfrtr*-*-*~*^» ifr
c)
7, mm/min
t.hti
, 0"^*
t/113
Fig.I. Calculated using the model (I-kidney beans. 2 -fallow)
and measured (3 - kidney beans, 4 - fallow) water dis-
charges and BHC concentrations in the sorbed state on
suspended sediments during the first shower on June 7f
I960; a) water discharges; b)BHC concentration in"so-
lid"runoff; c) rainfall intensity.
165
-------
f J W.5
IT*
• - f
A-2
• -3
•f
June
SepttmOei t,day&
Pig.2. Calculated using the model (a,b,c,d,e) and measured
(1,2- oL- isomer for fallow and kidney beans, res-
pectively; 3»4- - y - isomer for fallow and kidney
beans, respectively) variations of BHC concentra-
tion in the surface zone for two plots(Moldavia):
a - sum of isomers at a constant rate of
degradation;
b and c - oi - isomer for fallow and kidney beans,
respectively;
d and e - V -isomer for fallow and kidney beans,
respectively.
Arrows denote the daily rainfall layers and a cross-
hatched area shows the-background values.
166
-------
Analytical determination of BHC residues was performed by gas-
liquid chromatography in accordance with the technique describ-
ed in (8). BHC concentrations in the surface zone of the plot
with kidney beans were somewhat lower than in the fallow plot
at the same rate of BHC application. This was likely- to be as-
sociated with larger water discharges in the plot with kidney
beans during the shower on June 7, as well as with the entry
of a part of BHC into the plants.
To study more thoroughly the vertical migration of label-
led BHC in soil, field experiments were carried out on small
plots at the Valdai station for water-balance investigation,
beginning on September 22,1979. Monoliths were ,-sampled in the
experimental plots with clover where ? Cl~ and x C-labelled BHC
was applied, and the activity of chlorine-36 and carbon-14 was
measured in the soil layer-by-layer(I cm apart). Results of the
measurements showing the vortical distribution of BHC in the
soil layers, and the distribution of *"C1 and 1^'C between the
mineral part of the soil and plant residues for three seasons
(winter, spring and summer) are given in Fig.3. The content of
BHC in each layer is given in percent of the total content at
the time of measurement. The results are indicative of a very
slow movement of BHC through the soil profile. Three months
after its application almost the whole of BHC (over 902& of its
total amount in the soil) was found in the upper I cm layer
and aerial portions of plants,probably, due to a mechanical pol-
lution of plants, when applying the pesticide. Even at a depth
of 3-4 cm only traces of BHC were detected. During the second
sampling period (May 1980),i.e. after the spring snow melt, BHC
was found at a depth of 5-6 cm,however its greater part was loca-
lized in the upper two centimeters of the soil. A year after
the application,i.e. in September I960, the greater part of BHC
was found in the upper three centimeters of the soil profile.
As to the mechanism of vertical movement of BHC, one can suggest
the following: a strong sorption of BHC by the soil, especially
by its organic part, is almost irreversible(desorption is no
more than IO?S). An insignificant depth of BHC migration enables
one to suggest that the vertical transport of BHC through the
soil profile occurs on fine soil particles which carry BHC in
the sorbed form. Agreement between the characteristic dimensi*-
ons of the vertical transport of BHC and that of the fine par-
ticles (10) is also indicative of this fact. The ARM model does
not describe slight movements of this kind,therefore estimated
characteristic scales of the vertical migration do not exceed
the depth of the surface zone(about I cm).
Based on the presented preliminary results we can con--
elude that:
- the model permits to carry out field experiments in
various soil-climatic zones of the country;
- the model as a whole describes adequately the behavior
of pesticides in a watershed .
167
-------
Dccemtti 7,1971
a m
Matt «, 1980
5.
Stpierrtn 0,1
«5 50 '
Fig.3. Dynamics of the vertical migration of BHC due to the
transport of fine particles carrying it in the sorb-
ed state(Valdai,1979-1980). Solid lines denote the
led BHC,respectively); and dash-and-dot lines -
the data calculated using the model.
The latter conclusion,however,should be confirmed in fur-
ther experiments on various pesticides and larger areas. The
problems arousing in this, case are associated with a spatial
inhomogeneity of watersheds,as well as with inhomogeneity of
rainfall and pesticide application. Attention should be. given
to limitations of the model, such as the assumption on equilib-
rium sorption and instantaneous dissolution,that may affect
substantially the accuracy of prediction. However,primary atten-
tion should be given to degradation rate constant which is pre-
sently thought as a parametric description of the processes of
evaporation and chemical and microbiological transformation.
So far there is no methods for predicting this rate constant.
It is necessary to describe differentially all these processes
and find relations between the parameters of the processes and
environmental parameters.
168
-------
LITERATURE CITED
I. Borzilov,V.A.; Malakhov,S.G. Study on the processes of
pollutant migration in the soil-plant,soil-water system.
In "Proceedings of the International Symposium on Integ-
rated Global Monitoring of Environmental Pollution(Riga,
1978)": Publishing House "Gidrometeoizdat": Leningrad;
I960, 294-297 /in Russian/.
2. Bailey,G.W.5 Nicholson,H.P. Predicting and simulating
pesticide transport from agricultural land: mathemati-
cal model development and testing. In "USA-USSR Symposi-
um on Environmental Transport and Transformation of Pes-
ticides"; EPA-600/9-78-003; 1978, 30-37.
3. Kaurichev,I.S. Soil Science. Publishing House "Kolos":
Moscow; 1969,158-165 /in Russian/.
4. Duttweiler,D.W.; Malakhov,S.G. USA-USSR symposium on
environmental transport and transformation of pestici-
des. J.Agr.Food Chem. 1977, 25,No.5,975-978.
5. Observation data of the Uryvaev1 \Taldai hydrological re-
search laboratory.Issues 22-27:Valdai;I970-I976/in Russian/,
6. Observation data of the Trans-Karpathian station for
water-balance investigation. Issue 16: Kiev; 1979,4-86 p.
/in Russian/.
7. Observation data of the Moldavian station for water ba-
lance investigation. Issue 12: Kiev; 1976,312 p. / in
Russian/.
8. Methodical instructions on soil pollution control; Mala-
khov,S.G.,Ed.; Publishing House "Gidrometeoizdat":Moscow;
1977, 64- p. /in Russian/ .
9. Rachinsky,V.V.; Fokin,A.D.;Dragolubova,I.V..The influen-
ce of vertical inhomogeneity of the soddy podzolic soil
on pesticide sorption. Trudy IEM /Transactions of the
Institute of Experimental Meteorology/,1982, Issue 12(98),
45-53 /in Russian/.
10. Rachinsky,V.V.;Fokin,A.D.Khegai,T.A. Radioactive tracer
study on the behavior of toxicants in soils. Trudy IEM
1981, Issue 12(98), 3-30 / in Russian/.
II. Crawford,N.H.; Donigian,A.S. Pesticide transport and run-
off model for agricultural lands. Technical report EPA-
660/2-74-013,1973, 211 p.
169
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12. Donigian,A.S.; Crawford,N.H. Modeling pesticides and
Nutrients on agricultural lands. Final report EPA-600/2-
76-043,1976,318 p.
13. Donigian,A.S.; Davis,H.H. Userfs manual for agricultural
runoff management model. EPA-600/3-78-080,1978,163 P«
I4-. Khan,S.U. The interaction of organic matter with pesti-
cides. J.Soil Org.Matter: Amsterdam; 1978,135-171.
170
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EMPIRICAL PREDICTION OF SPACE REDISTRIBUTION OP
POLLUTANTS IN SOIL ON THE BASIS OF FIELD TESTS
A.D.Fokin
Timiryazev Academy of Agriculture, Moscow
There are varipus ways to solve the problem of predicting
the behaviour of a substance in soils. Along with the predic-
tion based on physical-mathematical modeling,it is possible in
some cases to provide a long-term prediction of substance
behavior in a soil-plant cover on a purely empirical basis by
way of studying the major processes of migration and transfor-
mation under field conditions.
This paper presents approaches and methods for obtaining
such a prediction.
It has been known a great variety of processes of substan-
ce transformation and migration as to their nature,intensity
and direction, occur in soils. For the purposes of prediction
it is necessary to know a number of parameters that could
characterize to an adequate accuracy these major processes and
their variation depending on soil-climatic,meteorological and
other conditions. Inasmuch as even, the major processes for
any substance in soil are numerous and diversified in their
nature and a great body of data is required for prediction,
it is advisable that the most productive and informative
methods of full-scale observations be used to obtain the data.
A method of isotope indicators that enables one to carry out
direct observations and characterize the processes quantitati-
vely meets this requirement to the greatest extent.
We consider briefly the main parameters required for
prediction, using as an example two substances with different
behavior in soil -BHC and zinc in ionic form. To perform
predictive calculations, the following data should be
available:
I. Quantitative relationship between various forms,groups
and fractions of a substance in soil that differ from each
other in migration capacity,availability for plants,stability
to degradation factors, etc. The groups can be represented by
substances sorbed by moving fine fractions of soil associated
with humus substances that entered plant biomass and reentered
the soil with plant residues, etc. For example, for BHC its divi-
sion into two forms,i.e.the initial substance and the substance
171
-------
changed to a sorbed state is sufficient.
BHC can partly be in the initial state for a long time.
The rate of its change to the sorbed state is limited not by
the kinetics of sorption from solutions as thought sometimes
but by the kinetics of its dissolving in a soil solution. On
surface application in May, up to 30 per cent of BHC left by
late September can remain in the initial nonsorbed state. Mig-
ration and in particular,degradation and evaporation of these
forms of the substance will occur differently, and therefore
predicting the transformation of the initial form of the sub-
stance is an important condition of the migration prediction.
Unlike BHC, zinc requires even more fractional differen-
tiation, at least by 4 forms: I) exchange-sorbed zinc;,
2) zinc that reentered the soil with plant residues; 3; zinc
associated with humus substances; and 4) fixed zinc remaining
ia the soil after separation of the three previous forms.
2. Migration capacity Rj, of various forms of a substance
with respect to migration capacity of water taken as unity.
This parameter is used to estimate the mean downward movement
of a substance in the soil over the seasons. For example,
in sandy soddy-podzolic soils the value R-, amounted to 0.008-
0.013 for BHC and 0.006-0.0? for various forms of zinc.
It should be noted that migration capacity Rp of a dis-
solved substance is determined by its sorption characteristics
in a given soil, and in particular, by sorption isotherm.Thus,
this parameter includes implicitly sorption and other factors
of movement for a moisture nonsaturated soil and directed flow
of soil moisture.
As a substance sorbed on a fine soil material moves,
characterizes the movement of this material itself which is
a carrier of the sorbed substance.
3. Nature of redistribution of individual forms in various
soil horizons in the process of migration. While the parameter
Rp characterizes the mean movement of a substance, in this case
wf consider the form of vertical distribution curve of a sub-
stance in the process of its migration.
The nature of distribution for zinc, BHC and some other
substances in sandy soddy-podzolic soil was determined only
by the value of the mean movement of a substance. The curves
obtained experimentally enable one to perform a layer-by-layer
calculation of substance redistribution at its various mean
movements in soil and any initial distribution. The calcula-
tion is based on the following assumption: the substance move-
ment of the ith layer is independent of that of any other
layer. This is obviously true for the linear portion of sorp-
tion isotherm.
4. Mean movement and distribution of various forms of
a substance over the surface under the influence of surface
runoff in individual seasons. Required experimental data are
obtained and subsequent calculations performed in much the
same way as the calculation of the vertical redistribution of
172
-------
a substance. On surface movement,however, one observes a very
wide-range variation depending on the erosion processes.
5. Plant root uptake by plants of various forms of a sub-
stance localized at different depths(coefficients of uptake
from various soil layers). Based on these data and the infor-
mation on substance distribution between roots and aerial
portions of plants,we predict its movement in the soil as
a part of the living phase,i.e. downward movement of the sub-
stance entered the biomass of plants with growing roots and
its translocation to the surface with areal portions.
For metals that entered the soil surface covered with
grasses this can be a major route of substance transformation
and movement. For example, when entering the soil in early
spring, up to 80 percent of ion Zn2"1" is incorporated in the
biomass by the end of the growing season and migrates to
a considerable depth with growing roots(Fig.I). This route
plays a minor role for BHC,though in some cases it can be
incorporated in the biomass of vegetating plants by 10 per
cent or over.
6. Sunbstance losses from the aerial portions of plants
in the process of transpiration. Experimental estimation of
this route for BHC showed that it can be neglected in pre-
diction calculations.
7. Effective substance losses from various layers of soil
by evaporation, biodegradation and chemical degradation in
various seasons.
It should be remembered that the rate of effective losses
is determined largely by the incorporation of pollutants in
various components of soil. Iia particular,BHC applied to the
soil as a dust was "lost" several times more rapidly than
the same substance sorbed by fine fractions of the soil. Con-
sequently, a necessity arises for a differential determination
of the rates of net effective losses for various forms of
a substance transformed in the soil(Table I).
Table I
Approximated values of BHC half-life (days) in sandy
soddy—podzolic soils covered with permanent grasses
Period of At the surface At the depth of 10 cm
observation "free" sorbed "free" sorbed
Spring
(March I0-May 5) 50-70 120-150 - 200
Summer
(May I5-Sept.20) 20-30 120-150 50-70 200
Autumn
(Sept.20-Bec.I) 60-70 250 70-80 400
173
-------
niLneiatpent of soiE
0 -
2 -
6 -
8 -
12 -
at \
10 5 0
i r
to
1 1 1
Roots
20 30 40
» J n»}»>»}>»»>\> .T/jU^J
so y.
i VfMWWffiffi%%t\
rT
wzzzza
/
2
0
Depth, cm
Fig.I. Vertical downward movement of zino as
a part of herb roots: I - the root biomass
and 2 - the content of zinc. Rectangles at
a level of II cm show the total amount of
zinc in the 7-18 cm layer.
It is advisable to perform prediction calculations for
the soils of taiga region of the Soviet Union in succession
over the three seasons of the year.
Winter-spring period lasts from a stable soil freezing
(or from the time when negative temperatures begin to prevail)
to a complete disappearance of melt waters. In this period,
an intensive abiogenic transport of a substance with moisture
flows is observed, and in the case of complete soil freezing
174
-------
the substance movement over the surface with surface runoff
can prevail.
Summer period lasts throughout an active vegetative sea-
son of plants. In this period there occur an active biological
uptake and redistribution of a substance through the soil
profile, its biotransformation, biodegradation, losses by
evaporation, etc.
Autumn period lasts from the end of the active vegetative
season of plants to the beginning of the winter-spring period.
As a rule, the most intensive downward movement of a substance
through the soil profile is observed in this period.
Prediction calculations are performed by stages from one
period to another, with a substance distribution through the
soil profile over 'any period being an initial one for its
redistribution calculation over the next period.
Fig.2 shows experimental (over the initial period) and
calculated distribution in the soil of BHC surface-applied
in September 1979 over a period of 3 years (to September 1982).
Experimental tests conducted in May and September 1981 showed
that the character of distribution and the depth of penetra-
tion can be predicted fairly accurately. The largest discre-
pancies (about 5Q?&) are related to the estimation of the total
amount of BHC in the soil. The actual losses by evaporation
and degradation occur slower that appears to be associated
with an increasing stability of BHC, as it is present in the
soil for a long time. It is apparent that further refinement
of the degradation parameters is required.
The behavior of zinc can be predicted more accurately
(Fig.5). A biological factor is the major one in the profile
redistribution as soon as zinc entered the soil surface under
study in the form of ZnClp. This is associated with the loca-
lization of the substance in the most active root zone. Four
months after the entry, over 80 percent of zinc was incorpo-
rated in the biomass of plants, with 30 percent being redepo-
sited at the surface as dead residues whereas most of the rest
substance was distributed through the soil profile in accordan-
ce with the distribution of the root system. By the beginning
of the autumn migration period the major portioa of zinc was
transformed to organozinc compounds of plant residues most
capable of migration. This process had a pronounced effect on
the further behavior and distribution of zinc through the
profile.
As a result of abiogenic migration, a considerable part
of zinc was found to be beyond the limits of localization of
the major portion of active roots (the upper 3 cm of soil)
even in the first autumn period. This movement resulted in:
I) sharp decrease in the biological uptake of zinc in the
subsequent periods; 2) sharp decrease in the rates of total
downward migration as a result of decreasing biological pro-
ducing of zinc forms most capable of migration; 3) progres-
sive decrease of zinc washout by spring waters with surface
runoff.
175
-------
0 5 10 15 20
i i i i
§
r
8 J
048
i i
i—•
0 4 X
r-
—j
o 1 y.
Fig.2. Experimental (dashed lines) and predicted
(solid lines) redistribution of BHC in
the soil:
1-6 denote May 1980, September I960,
May 1981, September 1981, May 1982 and
September 1982 Respectively
Approximately 2-4 years after the application, the biolo-
gical factor ceases to be dominant due to the fact that the
major part of zinc is now beyond the area of main root uptake,
and the general migration is determined mainly by abiogenic
factors of the movement.
During 3-4 years after the entry of zinc into the soil
the average effective rate of downward migration decreases
from 3 cm per year to 0.6 cm per year and is practically
176
-------
Fig. 3. Prediction .of zinc redistribution in
the soil(solid lines) .Experimental data
are shown as a dashed line.
1-8 denote September 1978, December 1978,
May 1979, October 1979, December 1979,
May I960, May 1981 and May 1982,respectively.
stabilized. This enables one to estimate to a first approxima-
tion the time of "clearing" the 30-cm soil layer by 50 percent
which is about JO years(for a single application).
Testing the suggested empirical model for zinc for two
years showed a satisfactory agreement between the experimental
and calculated data.
177
-------
PREDICTING THE BEHAVIOR OF PESTICIDES IN SOIL
by
E.I.Spymi, E.G.Molozhanova, P.E.Sova
All-Union Research Institute of Hygiene and
Toxicology of Pesticides,Polymers and Plastics,
USSR Ministry of Health,Kiev
V.S.Kikot1
Institute of Cybernetics .Ukrainian Academy of
Sciences,Kiev
Predicting the consequences of pesticide use makes it
possible to regulate most efficiently the selection of pesti-
cides to be used and their application conditions (application
rate, frequency of treatments, alternation of chemicals, etc),
as well as the choice of crops to be treated, with regard to
climatic and geographical factors, etc. This approach permits
to protect the environment effectively even before a new pes-
ticide is brought into use or the area of application of the
pesticides already used is broadened(7)* This relates primari-
ly to soil which is the site of maximum accumulation of persis-
tent pesticides and the most important link of substance mig-
ration in ecological chains of the biosphere.
One way of solving the problem is mathematical modeling
of complex systems, among which is a pesticide-soil subsystem.
The work in this area is being carried out in three directions.
I. The first direction is synthesis of a mathematical mo-
del of pesticide destruction in soil from field data. The most
universally employed is modeling the process of pesticide dest-
ruction in soil using the expression
C0e~*. [f]
Experience suggests, however .that such an expression per-
mits the values of concentration C(t) to be predicted fairly-
accurate ly for short periods of time only.
Therefore it would be possible to use* a more complex
expression
178
-------
[2]
where constants C satisfy the following condition:
L-1 u
However, evaluating the corresponding parameters is a very com-
plex problem.
It is knownO) that the expression
' n (±Jl-<
is more appropriate for long-term prediction that [2] .
To assess effectively parameters C_, T and n we worked
out an algorithm of self-organization. Tfie main point of this
method (I; is as follows: two sequences-learning and checking
are chosen from the set of initial data. The learning sequen-
ce is used to optimize the parameters of the polynomial which
models the process, and the checking sequence to choose a deg-
ree of the polynomial. Optimization of parameters in the learn
ing sequence is performed as in a conventional analysis of
regression, whereas in the checking sequence a regularity cri-
terion is used for this purpose.
We shall determine an optimum structure of [4J for the
process of dipterex(chlorofos) destruction in soil. Experi-
mental data are presented in Table I. We choose any three
experimental values as learning sequence and the entire series
as checking sequence. This procedure enables us to perform
optimization over four points only. Assume that a value
=1,2
-— —
z[c(t)-c]
is the regularity criterion ( (^ ) if summation is over all
points, and the optimization criterion ( di ) in the learning
sequence if summation is over three points. In formula [5] ,
C_(t) is the calculated value of pesticide concentration at
a corresponding value n, C(t) is the experimental values, and
6 - -i- £ C?*>
It rs necessary to specify an interval of permissible
values T. In this example T was assumed to range from 2 to 40
days. Step of change in T was 24 hours.
Table 2 presents calculation results when the first
three points were chosen as learning sequence. It is seen from
Table 2 that both
-------
Table I. The process of chlorofos destruction in chernozem
(field studies)
t, days
C(t),mg/kg
3
2.13
12
1.59
27
0.84
46
0.03
should be chosen by criterion 6^ , as it has the properties
of external addition, whereas 8~2 is the internal criterion
and reaches its extreme only due to a specific character of
expression [4] . In addition to this, ^ characterizes di-
rectly the accuracy of approximation over all points. To es-
tablish the dependence of model selection on sampling divisi-
on we determined optimal values of n for all four possible
versions. The results indicate that irrespective of the divi-
sion, one and the same model structure is selected by crite-
rion rf; ( n ss 3). Position of the fr2 extreme ranges from
n = 2 to 5« Note also that criterion d-J reached its deepest
minimum when the point of the initial data that was latest
in time had been included into the checking sequence.
Table 2. The process of model structure optimization
n
T
h
^2
Cn
I
27
6,8
0.38
2.4
2
13
2,7
0.29
2.15
3
8
1.72
2.17
2.14
4
6
2.4
4.3
2.04
5
5
3.1
5-8
1.98
6
4
4.2
8.3
1.98
Thus,to model the process of dipterex destruction, it is
advisable to use the following expression:
9 -t/8
Cj s 2.14 (I + t/8 + tV!28)e
Calculations with formula [&] are much more accurate
than those with [l] that is seen from Table 2, where the
case n = I corresponds to a simple exponential function jjfj ,
Similar methodology can be used to work out a new app-
roach for describing biocide destruction in the pesticide-
180
-------
soil-plant system. In this case, unlike the previous example,
w« set ourselves a task of determining the parameters of exp-
ression [4] in terms of physical-chemical and biological pro-
perties of the modeled system. Synthesis of the model is per-
formed using the algorithm of the group method of data handl-
ing (GMDH) (2). For this purpose we can use the initial data
obtained under field conditions(Table 3).
Vector of input variables X consists of four groups of
factors:
I) properties of chemical: XT - molecular mass; Xo - so-
lubility in fats; X* - melting temperature; X,, - persistence
at pH s 5 to 8 ; Xq - persistence at pH < 5; Xfi - persistence
at pH > 8 ; X? - solubility in water; XQ - volatility;
2) properties of plant (percentage composition): XQ - wa-
ter; XTf) — nitrogen—free substances; XTT - fats; XI2 — cellulo—
~ - ash; Xm - sugar; XIC- - albumin; XI6 - protein;
properties" of soil: Xt^ - pH of salt "water) extract;
XIg - hydrolytic acidity; XIQ - humus content, % ; X20 T macha-
nlcal composition; X2T - total absorbed bases; X22 - content
of PpOc, mg/kg ; XPX - content of KP0, mg/kg; XP,. - volume
weigfit? * * *
4) application conditions of chemical: Xpt- - application
rate.kg/hectare; X26 - frequency of treatments; X27 - average
temperature, C ; X2g - average moisture,^ ; X2g - -cotal rain-
fall, mm; X,Q - degree of plant coverage of sSfl (on the thret-
point system;.
Of 16 realizations, 12 art used for self-organization of
th» model by th« algorithm (2), and the rest (the last four in
Tablt 3) constitute the checking sequence intended for a final
choice of an optimum model among several models of equal value.
An accuracy in the checking sequence serves as optimality cri-
terion.
Modeling algorithm has two levels:
- self-organization for each experimental curve of deg-
radation;
- synthesis of th« polynomial dependences of chain para-
meters ( a , T, C0) on th« vector of input variables X =
r Y x 1
^Modeling results indicate that all curves of dipterex deg-
radation can b» given with an adequate accuracy by polynomial
(4) at A = 2.
Thus, w« s«t ourselves a task of self-organizing the mathe-
matical models for T and CQ. This stage is the most important,
sine* the models obtained are expected to be used as a basis
for synthesis of a comprehensive model of the process of pesti-
cid« destruction.
The process of self-organization of the model of the dipte-
r«x-soil-plant system consists of two stages: I) self-organiza-
tion of equations; and 2) test of their workability. Instead of
[5] , we us* the following formula:
181
-------
Table 3. Initial data for modeling the chlorofos-soil system
Input
9
77
77
76.3
76.3
59
78
78
78
78
59
93.5
93.5
78
76.3
81
90
10
43
43
84
84
46
44
44
44
44
46
0.
0.
44
43
81
53
II
.8
.8
.2
.2
.3
.8
.8
.8
.8
.3
95
95
.8
• 9
.7
• 9
2.
2.
0.
0.
3
3.
3.
3.
3.
3
0.
0.
3.
3
I.
2.
6
6
45
45
2
2
2
2
2
2
2
7
8
12
26.
26.
3.
3.
29.
20.
20.
20.
20.
29.
0.
0.
20.
26.
12.
0.
6
6
5
5
I
5
5
5
5
I
9
9
5
3
3
8
variables
13
7.4
7.4
4.9
4.9
7.3
13.6
13.6
13.6
13.6
7.3
0.5
0.5
13.6
9.9
22.3
0.7
14
7.2
7.2
I
I
6.9
7.7
7.7
7.7
7.7
6.9
2.7
2.7
7.7
4.7
12.7
4
15
13
13
2
2
10
9
9
9
9
10
0.6
0.6
9
15.3
9.5
1.7
16
16
18
7
7
13.4
II. 3
II. 3
H.3
II.3
13.4
O.I
O.I
II. 3
20.3
II.9
0.3
17
6.5
6.8
6.05
6.3
6.3
5.4
5.4
5.3
5.3
5-3
5.7
6.2
5.4
6.7
5.2
5.5
182
-------
Table 3. (continued)
18
I.
I.
I.
I.
I.
4.
4.
4
4
4
I.
2.
4.
I.
4
7
53
82
82
2
2
98
46
2
8
2.46
2.
46
19
3.9
4.6
3.48
3.9
3.9
3.2
3.2
3.1
3.1
3.1
4.9
4.9
3.2
5.52
4.9
4.9
20
30
40
30
20
20
25
25
15
15
15
20
20
25
40
20
20
Input
21
27.8
26.8
21.2
27
27
71
71
70
70
70
32
30.2
71
27.8
29
48
variables
22
115
115
86
189
189
100
100
50
50
50
139
140
100
91
125
44
23
120
120
41
80
80
80
80
80
80
80
220
230
80
118. 7
134
72
24
I
I.
I.
I.
I.
2.
2.
2.
2.
2.
I.
I.
2.
I.
I.
I.
06
19
09
09
63
63
62
62
62
2
2
63
I
2
2
25
2
2
3
3
3
I.
I.
0.
I.
I.
I.
I.
0.
I.
I.
I.
2
2
9
2
5
75
75
6
75
75
75
26
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
183
-------
Table 3 (continued)
Input variables
r*
27
17
12.3
I5.I
17. 1
17. 1
19
16.5
I7.I
17.2
17
17.3
17.3
18
21.7
17.3
17.2
28
74.6
74
79.7
74.6
74.6
77
80
70
72
67
71
70
70
52
71
69.6
29
628
I94.I
347.1
264
264
36.1
39
33.1
22.1
53
246
239
118. 5
115. 3
246
237.1
30
2
2
I
I
2
I
I
I
2
I
I
I
I
2
2
I
0
0.12
0.3
0.63
0.14
0.13
0.15
0.22
0.19
0.18
0.4
0.3
O.I
0.98
0.2
0.19
O.II
°o
O.II4
0.306
0.6
0.15
0.17
0.13
0.18
0.19
O.I8I
0.4
0.254
0.14
0.7
0.28
0.23
0.14
T
12
13
3
19
17
4
3
I
5
3
3
5
I
II
4
5
T
11.02
12.92
2.27
18.93
17.44
3.88
3.64
0.976
5. II
3.05
3.52
5.03
0.74
II. 4
3.8
7.8
184
-------
H
where N is the aumber of observatioas; y± is the experimeatal
values of respoase (T or CQ) ; y* is the calculated values
of respoase; y is the meaa respoase.
We obtaia the followiag expressioas for T
T(x)=-9.63 1-370.5 1/X19X2ft+(28-3l59
0.4 X13/X30-0.8 X21 /X17 ) X25//X2A ,
aad for CQ :
(the values a.^ aad Zj^ are givea ia Table 4 ).
Table 4-. Values of terms of the series ia expressioa |_9j
Term of the series
No. (i)
0
I
2
3
4
5
6
7
8
9
Argument (Zj)
I
I/XI7X29
Z][/xI9X30
2 X27/X28
Z2 Xoc/Xjn
2 2
Z4 X25
z2 z
z|xI9/X50
ZgXgc/XjQ
z8/X24X2c
CoefficieatCa.^)
-0.775
57.43
3135
0.3831
2. 141
-5-977
35510
-587.4
O.I65I
1.654
185
-------
Corresponding calculated values of T and C are given
in Table 3. The models obtained are resistant to variations
of the initial data, as evidenced by a small value of error
in the checking sequence.
Substitution of equations [8J and [9JillL H gives
a complete model of the dipterex-soil-plant system. The model
permits predicting the degradation curve of a chemical at
known values of input variables (Xj*%^-^nt%--£at%2It*'2*\-t^'2.59^'27t
2ft '29'30' *
Synciiesis of a comprehensive model of the dynamic pesti-
cide-soil system is possible by self-organization of the third-
level model based on the second-level dependences. In this
case, an adequate quantity of experimental data should be
available to assess coefficients of models [8] and [9] for
a large number of pesticides. The third-level model is the
dependences of the coefficients of expressions [8] and [9l
on the physical-chemical properties of chemicals(X,.,...,Xg).
Such a model will enable one to choose a chemical with consi-
deration for its properties and detoxicative potentialities
of the medium, as well as to predict the behavior of new che-
micals under various conditions.
2. The second direction is physical modeling of the pro-
cess of pesticide destruction in soil under laboratory condi-
tions. In this case,various methods of mathematical design
of the experiment are used (5|6,8). A multi-factorial experi-
ment enables one to bring the modeled conditions closer to
natural ones.
The suggested approach was tested in experiments aimed
at studying the process of destruction in soil of BDT.BHC,
lindane, dilor (dihydroheptachlor), 2,4—D, phosphamide(dime-
thoate), etc. Methods of mathematical design of the experi-
ment were chosen with regard to the set hygienic task and
specific character of the object studied. In this work, we
used the methods of complete factorial experiment of the type
2m, dispersion diagrams, the method of fractional replicas
and so called mixed plans. These latter seem to be most appro-
priate for the set task, since they permit carrying out expe-
riments with both quantitative and qualitative factors present.
We shall consider the approach suggested,using as an exam-
ple the study on the process of ll'ndane degradation in soil.
To carry out a 5-f actor experiment we used a mixed plan on the
basis of the complete factorial experiment of the type 2 made
coincident with Latin square of the side 4 (4).
As factors, we tested soil temperature, its moisture,
presence of microorganisms in the soil, sample hermetization,
and t^rpe of: soil. The first four factors were varied at two
levels. The fifth factor(soil type) included four varieties:
grey forest, soddy-podzolic, meadow-gley and chernozemic soils.
In all, we carried out 16 experiments in three replicas
with various combinations of the factors studied. Lindane re-
sidues were analyzed by gas-liquid chromatography using chro-
ma tograph flTsvet-5". The experiment was carried out for 60 days,
186
-------
Dynamics of lindane disappearance from the soil was approxima-
ted by an exponential function of the form [l] .
Based on the exponential, we calculated half-life of the
chemical TCQ which was considered a characteristic of the ac-
tual stability of lindane in the soil in each of 16 experi-
ments(Table 5). Relative error in Table 5 was calculated with
Table 5, Comparison of actual and calculated data on lindane
disappearance from soil
Experiment
No.
I
2
3
4
5
6
7
8
9
10
II
12
13
14
15
16
the following
Half-life ,
T50 act.
28.0
26.9
53.8
31.8
41.2
26.9
53.8
46.7
58.3
36.8
63.6
26.9
63.6
58.3
41.2
35.0
formula:
> _ 1 Tact ~T exile 1
days
Hie.
26.1
32.3
57.5
28.5
44.1
23.5
48.7
48.1
66.9
41.7
66.9
20.3
70.7
49.3
36.1
40.3
o/
Relative
error
6.8
20.0
6.9
10.4
7.0
12.6
9.5
3.0
14.8
13.3
5-2
24.5
II. 2
15.4
12.4
I5.I-
187
-------
Experimental results were processed by the method of reg
ression analysis. The following equation was obtained:
X-i-4.7 X4-1.9 X,X2 -3.0X1 X3 -
where u is the half-life in days; XL is the values of fac-
tors in the standardized form (-1) or (+1).
Table 5 shows half-lives calculated by equation JlO]
(T50 calc > compared to actual half-lives^™ aat ). T_ct
vaf leS f$om 26.9 to 63.6 days and T , frdffi 1873 to 70:7
days. Relative error exceeded 15$ in tw6 cases only. Root-
mean-square error was 11.695.
Taking into account independent assessments of regression
coefficient and rather high accuracy of prediction, equation
[JO) can be considered as a model of the process of lindane
disappearance from soil.
Analysis of this model enables us to draw the following
conclusion. Among the factors studied, temperature had the
strongest influence which was inversely proportional to per-
sistence of the chemical. As temperature increased from 4°C
to 40 C, the half-life decreased by 14.2 days (the effect of
this factor is equal to a doubled coefficient of term XT).
Next is sample hermetization. The half -life for a hermetized
sample exceeded that for an open vessel by 9«4 days, all other
factors being equal. The influence of microorganisms showed
itself as a decrease in the half-life by 5 days, with soil ste-
rilization increasing this index by 5 days. The influence of
soil moisture proved to be invalid. It should be noted, however,
that th#re was a noticeable interaction ~of this factor prima-
rily wiih sample hermetization factor (term X^X*), as weil as
with temperature and the presence or absence of microorganisms
(term XjXpXz).
Tie influence of the fifth factor (type of soil) was
determined by the method of one-factor dispersion analysis.
A period during which lindane persisted in soddy-podzolic soil
was the longest (in the average, Tc0 = 55»2 days); in cherno-
zem it was somewhat shorter (TCQ = ¥6.5 days); and in meadow-
gley and grey forest soils mucfi the same.
Results of the dispersion analysis indicate that the ex-
tent to which the type of soil influences the persistence of
the chemical is, on average, JGfr of the total influence.
Thus, by means of a designed experiment we can study and
assess, quantitatively the extent to which not only one or
another factor exerts its influence, but their various combina-
tions as well. The data obtained are used to predict the beha-
vior of pesticides under field conditions.
5. The third direction is hygienic .regulation of pesti-
cides in soil, since the extent to which it is polluted by
188
-------
pesticides that are foreign to soil can be assessed only by
comparison with their maximum permissible concentrations(MPC).
Up to the present, MFC for about 30 pesticides in soil have
been experimentally substantiated and approved by the USSR
Ministry of Health, In all, the assortment/list/ of pesticides
used in this country contains about 200 items. The data presen-
ted indicate that experimental hygienic regulation alone of
pesticides in soil, as weel as in other environmental objects
fails to keeprpace with chemicalization of the national agri-
culture. Therefore the task has been set to work out a method
for accelerated substantiation of permissible content of pes-
ticides in soil.
To substantiate MFC in soil it is necessary to take into
account the extent to which chemicals migrate from soil to
adjacent media (air, water, foodstuffs) and criteria for the
hygienic norms on the permissible amounts of the compounds
under study in the same media. Taking into account this pecu-
liarity, we performed multiple correlation analysis of pesti-
cide MFC in soil and of three factors: permissible residual
concentrations (PEC) in foodstuffs, MFC in water bodies and
MFC in the air of working zone. The sampling included 18 che-
micals for which MFC in soil had been experimentally substan-
tiated. Multiple correlation coefficient E = 0.73 is indica-
tive of a high degree of interrelation. The analysis showed,
however,that the interrelation revealed is based upon a close
paired correlation for PEC (r = 0.72), whereas for the other
two norms the correlation coefficient was 0.01 and 0.28,
respectively. This result indicates that critical concentra-
tions in soil, of almost all pesticides were determined from
their transldcation to plants.
Based on the above assumptions, the following equation
was obtained:
y= 1.23 +0.48lgrX ,
where y is MFC in mg/kg of soil; x is PEC in mg/kg of vege-
tative food product.
Comparison of the experimentally substantiated norms with
the values calculated by equation [II] showed that of 18 cal-
culated values 16 did not exceed double MFC, and only for dala-
pon and phosphamide/dimethoate/ the ratio of calculated values
to MFC was 2.46 and 4.01, respectively (Table 6). This permits
equation [ll] to be recommended for calculated hygienic regu-
lation of pesticides. The corresponding norm was called appro-
ximate permissible concentration (AFC) of pesticides in soil.
It is recommended to establish AFC in those cases where
MFC of pesticides in soil have not been substantiated yet or
when their experimental substantiation is not reasonable becau-
se of low persistence of a chemical in soil, its limited usage,
etc.
Thus, at the stage of preventive control it is necessary
to calculate the norm for a new pesticide and its lifetime in
189
-------
Table 6.. MFC and APC of pesticides in soil (mg/kg)
Pesticide
BHC
- BHC
DDT
Carbofos/Malathlon/
Prometryne
Polychlorcamphene/Toxaphene/
Polychlorpinene
Sevin
Chlorofos/Dipterex/
Gardona
Heptachlor
Dilor/Dihydroheptachlor/
Eel thane
Zineb
Dalapon
Metaphos / Methyl parathion/
Fozalon
Phosphamide/ Dimethoate/
MPC
1. 00
1. 00
1. 00
2.00
0.50
0.50
0.50
0.05
0.50
1.40
0.05
0.50
1. 00
1.80
0.50
0.10
0.50
0.30
APC
0.88
0.88
1.08
I.2J
0.74
0.74
0.10
0.10
0.74
1. 18
0.10
0.74
1.23
1. 12
1.23
0.10
0.88
1.23
APC/MPC
0.88
0.88
1.08
0.61
1.48
1.48
0.20
2.00
1.48
0.84
2.00
1.48
1.23
0.62
2.46
1. 00
1.76
4.01
soil, and then, by comparing the calculated values, to assess
the regulations proposed (application rate, etc.). Regulating
the use of pesticides in agriculture will make it possible to
prevent soil and foodstuff contamination by pesticide residues.
190
-------
LITERATURE CITED
I. Ivakhnenko,A.G.; Zaichenko,Yu.P.; Dimitrov,7.D. Decision-
making on the basis of self-organization. Publishing
House " Sovetskoe Radio": Moscow* 1976$ 280 p./in Russi-
an/.
2. Kulchy.V.N.; Patereu,S.G.; Sheludko.O.I. Modified and
simplified algorithm worked out by the group method of
data handling. Republican fund of algorithms and prog-
rams. Institute of Cybernetics, Ukrainian Academy of Sci-
ences: Kiev* 19755 No.185, 14 p. /in Russian/.
5. Kurilenko,O.D.; Shiroky.D.K. Modeling the long-term pre-
diction. Avtomatika / Automation/ 1976,No.3,76-78
/ in Russian/.
4. Markova,E.V.; Lisenkov,A.N. Design of experiments under
conditions of inhomogeneities. Publishing House "Nauka":
Moscow; 1973; 219 P- /in Russian/.
5. Melnikov,O.V.; Aleshin.V.R.; Roschin,P.M. Design of ex-
periments in studies of agricultural processes. Publish-
ing House "Kolos": Moscow; 1972; 200 p./in Russian/.
6. Nalimov,V.V. The theory of experiment. Publishing House
"Nauka": Moscow; I971; 207 P. /in Russian/.
7. Spynu,E.I.;Ivanova,L.N. Mathematical prediction of and
preventive measures against environmental pollution by
pesticides. Publishing House "Meditsina:M Moscow; 1977;
186 p. /in Russian/.
8. Pinni.D. Introduction to the theory of design of experi-
ments. Publishing House "Nauka". MOSCOWJ I97o; 287 P*
/in Russian/.
191
-------
BIOTIC RESPONSES TO PESTICIDE POLLUTION OP
NATURAL ECOSISTEMS (PREDICTION ASPECTS)
L.D.Voronova, A.V.Denisova
Laboratory of Natural Environment and Climate
Monitoring
USSR State Committee for Hydrometeorology and
Control of Natural Environment and USSR Academy
of Sciences
Moscow
The background level of environmental pollution and the
state of the natural environment are observed through the use
of a special observation system called monitoring (6)* Monito-
ring implies not only the detection of the level of environ-
mental pollution by persistent chemical compounds but the
assessment of ecological effects of the detected levels as well.
The USSR State Committee for Hydrometeorology and Control of
Natural Environment is the institution responsible for coordi-
nation of activities in this sphere. Prediction of biotic re-
sponses to pesticide pollution is an essential part of predict-
ing the behavior of toxicants in the biosphere,which id deter-
mined by three main factors: (I) alien nature of most pesti-
cides for natural chemical compounds; (2) wide spectrum of
their biocidal effects and purposeful introduction directly
into the natural environment; (3) interrelations of natural
processes with the result that the effect of a pesticide on
a certain natural component involves a chain of alterations
in other ecological components.
Prediction of the fate and migration of pesticides is in-
complete unless their interaction with biological objects is
taken into account. It is necessary first of all to consider
the possibility of toxicant accumulation in plants and animals,
bearing in mind that pesticide deposition,on the one hand,and
its degradation and excretion from the organism,on the other.
are two sides of the same problem. Even small residual amounts
of pesticides may seriously affect the organism and the popu-
lation as a whole. Thus, negative effects of pesticides on the
propagation of wild birds and other classes of animals were
192
-------
identified through observations on persistent organochlorine
insecticides in the environment. This is noteworthy,as back
in the sixties it was widely believed that the portion of DDT
in biota is insignificant compared to its total mass circulat-
ing in the biosphere and amounts to about one-thirtieth of its
annual production,i.e. no more than I«IO tons (II).
The interaction of a pesticide with abiotic components in
the environment ends with its degradation, while any contact
of living components with pesticides often proves to be only
the beginning of a long chain of transformations and leads to
further effects which sometimes show themselves long after
pesticide degradation. For example,rapidly degrading but high-
ly toxic pesticides may exert a prolong effect on pedobionts.
Thus,it takes about two years to restore the population of
soil-inhabiting collembola,exposed to DD (dichloropropane-
dichloropropene) with a degradation period in soil of 30 days
(9).
Indications of the character of pesticide effects and the
sphere of their application enable one to identify in advance
those biological objects which are potentially most susceptib-
le to these effects.
As a rule, insecticides are toxic not only to vermins but
to various invertebrates as well (5,9fIO). For example,carbo-
phos (Malathion) in terrestrial ecosystems is especially hazar-
dous to pollinator insects (which should not be exposed to
this insecticide) and predaceous invertebrates - natural ene-
mies of plant-eating insects. Malathion used against blood-
sucking insects in their breeding places is highly toxic to
mollusks inhabiting these places. Metathion (sumithion) re-
duces the total numbers and biomass of herb-layer and soil-
inhabiting invertebrates, ground beetles and other ground Cole-
optera. It is hazardous to chironomid larvae, oligochetes and
other aquatic invertebrates. Chlorophos (dipterex) causes
death of the most important soil-formers —earthworms, etc.
Therefore, when predicting the behaviour of an insecticide
intended to reduce the numbers of destructive invertebrates,
one should reveal possible side effects exerted by this insec-
ticide on other animal species of the same taxonomic group,
taking into account their role in the ecosystem and specific
conditions of its application. The same procedure should be
used to assess pesticides intended for other purposes* Roden-
ticides used for reducing the numbers of sousliks, field mice
and other destructive rodents can be hazardous to hare-like.
Herbicides can injure non-target plant species, and so forth*
Insecticides, herbicides and rodenticides can constitute
a direct threat not only to invertebrates, plants and rodents,
respectively, which is also true for other pesticides. The ran-
fe of pesticide activity, the degree and duration of its mani-
estation determine largely the danger of side effects. For
example,some carbamate insecticides are very hazardous to
mammals (7). There are herbicides (triazines, DNOO, etc) which
cause significant long-term changes in the composition ana com-
193
-------
ponent interrelation of pedocenosis. Many organophosphorus in-
secticides are highly toxic to birds and hydrobionts. Lethal
doses LDcQ of actellic, baytex, iodophenphos and carbophos to
birds are within rather low range (50-60 mg/kg) in comparison
to that for mammals (225-1400 mg/kg) (8). In the Soviet Union
carbophos, chlorophos and actellic residues in analitically
detectable amounts are inadmissible in fishery water bodies,
and maximum permissible concentration of methathion is
0.0004 mg/1. Some insecticides possess phytotoxic properties:
carbamate sevin causes ovaries of trees to fall off; dipterex
is harmful to some tree and shrub species, and so forth.
Consequently.predicting the behaviour of pesticides and
after effects of their application should be based upon the
data on their toxicity to various classes of living organisms
irrespective of the direct purpose of these compounds.
Responses of natural populations to toxicants are determined
largely by the mechanism of their action which is responsible
for their neurotropic, gonadotropic, embryotropic or pther
effects. Therefore, it is important first of all to consider
possible side effects of ;neurotoxic compounds on the behavior
of animals under natural conditions; gonadotoxic compounds on
reproduction processes; embryotoxic compounds on the posterity
embryonic development for several generations, and so forth.
Neurotropic pesticides affecting the behavior of animals
are most hazardous during the important periods of their life,
such as breeding. A set of relevant data is presented in the
Review Information on the world literature (4). Changes in the
response to danger, mother's calls, feed and disturbances in
motor activity and calling play a significant role in the prey-
predator relations. Insecticides reduce the alertness of small
mammals with the result that they no longer hide from preda-
tors and sometimes become more active. Such changes in their
behavior result in increased prey devouring,for example, by.
birds of prey,which most often attack moving animals, Neuroto-
xicity of dieldrin causes death of birds since they cease
feeding and starve rather than die of direct poisoning by this
pesticide. Similar effects were also observed in the experi-
ments on mammals. Herbicide 2,4-D is known to reduce the motor
activity of some species of ground beetles, while organophospho-
rus insecticides increase it. A female brown shrike(Lanius
cristatus)ceased feeding its fledglings whose behavior had
changed under the effect of iodophenphos, and remained in the
nest in daytime (the active feeding period). In this case the
fledglings died from both the direct toxic effect and hunger.
Pesticides characterized by gonado- and embryotoxic effects
are of particular importance for the fates of natural popula-
tions. The experiments on sevine, a compound with moderate
toxicity and weak accumulative capacity,carried out in diffe-
rent landscape-geographical zones under natural conditions
revealed these effects on various species of small mammals.
No mass death of the animals such as ban* vole (Clethrionomys
glareolus), Pallas's pica (Ochotona price!),and great gerbil
194
-------
(Hhpmbomys opimus) was observed immediately after the trial
use of this insecticide in their habitats. Later on,however,
the number of males and females participating in population
reproduction decreased as did their general fertility,and
the number of cubs in the populations reduced as a result of
destructive changes in reproductive organs and embryo resorp-
tion. Even after a single treatment of the habitats this effect
became more and more acute from brood to brood (2,6).
It is evident from the foregoing that when predicting the
behavior of pesticides,one should take into accoutre not only the
possibility of acute effects, but the results of chronic expo-
sure as well. The latter manifests itself both in case of per-
sistent compounds and repeatedly used less persistent ones,
since along with a material cumulation of pesticides in tissu-
es, there exists a physiological cumulation which determines
the accumulation of the effects.
It is also important to foresee possible indirect effects
of pesticide application on animal populations which have no
direct contact with the pesticide. These effects may be of
great importance for biogeocenoses (ecosystems) depending on
environmental conditions (2).
The danger of a pesticide in the environment is determined
by the nature of ecological relations between various popula-
tions and the reproductive abilities of individual species.
By way of example, we consider savin. Laboratory experiments
revealed its strong insecticidal efrect on Collembola, small
insects actively participating in plant litter decomposition.
However, quite an unexpected effect was observed under natu-
ral conditions: the numbers of Collembola was maintained at
a low level only for about a month and then suddenly increased.
This was due to their short life cycle and great dispersal
ability as well as by the mass destruction by sevin of their
enemies: spiders, predaceous mites and other invertebrates
whose populations did not reproduce themselves for a long
time (I). Similar processes are responsible for propagation
outbreaks of the pests subjected to chemical destruction and
sometimes, of those species that earlier have not caused se-
rious damage.
A steady drop in the population numbers of certain species
can adversely affect the adjacent populations. A drop in the
numbers of molluscs led to a decrease in the numbers of water
fowl feeding on them. Pacts are known that the mass death of
earthworms in the forests treated with insecticides resulted
in a sharp decrease in the population of moles and other ani-
mals feeding on the earthworms (I).
The extent of indirect changes in the biogeocenosis is de-
termined by the ecological significance of species exposed to
a pesticide. This is best seen in isolated ecosystems,such as
island, upland,etc. where possibilities of population migra-
tion are limited. Under these conditions, the impact of gonado-
and embryotoxic pesticides excludes the possibility of self-
fregulation of the population numbers. Similar experimental
pesticidal impact on Pallas's pica, the most important trophic
195
-------
and topical link in the mountain-steppe plateau,caused not only
a steady decrease in the numbers of these animals, but also
the destruction of their empty burrows used by other warm-blood-
ed animals (some species of birds and predatory mammals) and
invertebrates. In this experiment, a disturbance in the chain
of biogeocenotic processes gradually led to certain changes in
phytocenosis and,therefore, in the animal habitat conditions
(I,3)» The .experiments on this insecticide enabled one to deve-
lop the methods for ecological assessment of pesticide effects
on the fauna, and prepare for consideration the prediction in-
formation which permits foreseeing its negative effects on ter-
restrial ecosystems. In the Soviet Union.sevin is not used in
tundra,boreal coniferous and mixed forests, and steppe. What
is more, the presence of its residues in foodstuffs is inadmis-
sible.
It is necessary to take into account that side effects of
pesticides show themselves against the background of the exist-
ing levels of pollution by combinations of chemical compounds
of anthropogenic origin. Therefore, biota is exposed to the
sombined action of various substances which can mutually enhan-
ce or weaken the toxic effect.
Thus, predicting the behavior of pesticides in the biosphere
should give an idea of the integrated effect of these substan-
ces, including biotic responses to environmental pollution,
against the background of the interaction between various na-
tural and anthropogenic factors. Revealing in detail the role
played by individual factors requires relevant experiments in
ecostats and under natural conditions, in whic£ case the pre-
diction assessment of pesticide side effects acquires a closed-
cyclic character.
Since the integrated prediction of pesticide behavior in
the biogeocenosis cannot be based on the examination of all its
components, of great importance is the choice of the proper key
species to serve as useful ecological indicators.
Human interference in the processes occurring in natural po-
pulations is not -necessarily harmful to them. However, predict-
ing should take into account the scale 'of possible disturbances
of the dynamic equilibrium and their ecological significance.
LITERATURE CITED
I. Pesticide effects on wild animals of terrestrial and
aquatic ecosystems. Collection of scientific papers of
the Central Laboratory of Nature Protection of the
USSR Ministry of Agriculture, Moscow,1977, 160 pp.
/in Russian/.
2. Voronova,L.D., Denisova, A.V. Methodological aspects
of assessing pesticide side effects in nature. Ecolo-
gical Cooperation. CMEA Information Bulletin,Bratislava,
1979, I, 4-3-53 (in Russian).
196
-------
3. Voronova, L.D., Denisova, A.V. Pesticides and nature
protection. In "Intensification of agricultural produc-
tion and the problems of environmental protection".
Moscow, Publishing House "Nauka", I960, I06-II4
/ in Russian/.
4. Voronova, L.D., Denisova,A.V., Pushkar,I.G. Pesticide
effects on the fauna of terrestrial ecosystems. Review
Information, Moscow,Ail-Union Research Institute of
Information and Technical-Economical Studies on Agri-
culture, 1981, ?8 pp./in Russian/.
5. Gal'vialis, A.G. Some data on the effect of pesticides
on earthworms and their regeneration."Problems of soil
zoology,"Proceedings of the Fifth All-Union Conference,
Vilnius,1975» I09-III / in Russian, .
6. Izrael, Tu.A. General principles of natural environment
and climate monitoring. Proceedings of the International
Symposium on Integrated Global Monitoring of Environmen-
tal Pollution,USSR,Riga,12-15 December I978.Leningrad,
Publishing House "Gidrometeoizdat",I980, p.5-14-
/ in Russian/.
7. Krylova, T.V., Shilova, S.A., Krylov, D.G., Denisova,
A.V., Smirnov A.A. On the aftereffects of the use in
nature of pesticides which affect the reproductive fun-
ctions of mammals. Zoological Journal,1973«Issue 12,
54, 1874-1879 /in Russian/.
8. Reference Book on Pesticides. Kiev,Publishing House
"Urozhai",l974, 448 p. /in Russian/.
9. Edwards, C.A. Faunal responses to pesticides in soil
and the ecological implications of pesticide effects.
Implic.pesticide use trop.,freshwater and terrestr.
ecosyst.inform.Workshop Meet.Cent.Overseas Pest.Res.,
1975, London, 1976, 6.
10. Desi, I., Dura, J., lonczi, L., Strohmayer, A., Szabo,Z.
Tpxicity of malathion to mammals, aquatic organisms and
tissue culture cells. Archives of Environmental Contami-
nation and Toxicology, New York,Springer-Verlag,I976, 3,
410-425.
II. Woodwell, G.M., DDT in biosphere,where does it go?
Science. I97I,m, p.1102.
197
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PROGRESS IN PREDICTING THE PROCESSES THAT DETERMINE
PESTICIDE CONCENTRATIONS IN AQUATIC SYSTEM
by
G. L. Baughman, S. W. Karickhoff,
F. Paris, N. L. Wolfe, and W. C. Steen
Environmental Protection Agency
Athens, Georgia 30613
The impact of a chemical on an aquatic system is clearly
related to its concentration in the environmental compartments
of interest (e.g. water, sediment, biota). Consequently,
considerable research has been devoted to developing methods
for "forecasting" expected environmental concentrations (EEC)
of organic pollutants.
One predictive approach requires the use of computerized
models of systems envisioned as having input and output (e.g.
flow and transformation). These models incorporate
mathematical descriptions of transformation and transport
pathways as subunits. In such models, the pathways (i.e.
processes) are usually described by either kinetic or
equilibrium equations depending on our understanding of the
processes, their temporal scales and the environments of
i nterest.
The process descriptions, in turn, incorporate
characteristics of both the environment and the chemical. To
the extent that these characteristics can be generalized, they
potentially provide a basis for extrapolating environmental
behavior from one chemical to another or from one environment
to another. The benefit of being able to make such
extrapolations is obvious and accounts for much of the interest
by government agencies or by industries that must assess the
behavior of large numbers of chemicals in many different
envi ronments.
The processes shown below are those that seem most often to
be important and that have been,most intensively studied for
uncharged organic molecules such as pesticides.
198
-------
Sediment Sorption
Volatilization
Microbial Transformation
Chemical Transformation
Photolysi s
Hydrolysis
Sediment Sorption
Photolysis and volatilization are covered by others at this
symposium and will not be discussed in detail here.
Both sorption and volatilization are partitioning phenomena
driven by the fugacity difference in two phases. The
equilibrium distribution is given by the ratios of fugacity
capacities in the respective phases. The ratio of any two
partition coefficients (air/water, sediment/water, biota/water)
also is given by the ratio of fugacity capacities which
ultimately can be related to the activity coefficients in the
respective phases (1).
For uncharged molecules, sediment/water partition
coefficients may be described by Equation 1:
K _ organic _ aorganic Ywater _ Ywater /.»
oc E ~ a ... Y . Y .
water water 'organic organic
where a and Y are the appropriate activity and activity
coefficient (Raoult's law reference state in both phases),- and
C is the chemical's concentration in the respective phases
(KOC = Kg/organic content of sediment). Mackay and
Karickhoff (2-4) have suggested that differences in Koc for
different chemicals are largely due to changes in Ywater with
Yorganic being relatively constant. This presumably accounts
for good correlations observed between such parameters as the
octanol/water partition coefficient, Kow and the
sediment/water partition coefficient, Koc, and between Kow
and the bioconcentration factor, Kg. Mackay (2) has recently
demonstrated the constancy of Yorganic f°r a wide variety of
compounds and has also shown that Yorganic varies for highly
polar compounds such as organic acids or for high molecular
weight chlorinated compounds. The reasons for these deviation
are unknown but presumably the acids associate in the organic
phase and the chlorinated compounds form less and less ideal
solutions in organic solvents as the molecular weight increases.
Attempts to relate water solubility, Sw, to the
octanol/water partition coefficient (5) or soil sorption
coefficient Koc are well known, and reasonable correlations
have been obtained with data from both solids and liquids. If
the crystal lattice energy is taken into account (usually
199
-------
through a melting point correction), however, significant
improvements can be made. These approaches are discussed in
the work of Mackay (4), Karickhoff (3), Yalkowsky (6), and
Hansch (5). At present, as Karickhoff has pointed out, Koc
can be computed through three independent approaches: from
Kow, from Sw and melting point, and from a fragment
constant approach. For most pesticides of interest, these
methods should give parameter estimates that are within a
factor of two of experimental values. They also provide an
approach for examining the reliability of measured values.
The difficulty of measuring Kow and Koc, especially when
these numbers are large, has prompted considerable interest in
the use of high pressure liquid chromatography (HPLC) (7,8).
This approach is usually based on a calibration between Kow
and retention volume. It is perhaps worth noting that the HPLC
approach is based on partitioning between solvent and an
organic sorbent. Consequently, the changes in Y0rganic
mentioned earlier can be expected to result in large errors if
broad extrapolations are made.
As noted earlier, sorption is usually assumed to be an
equilibrium process. Although this assumption is probably
valid for many environmental problems, it is important to
determine the conditions under which one may expect kinetic
limitations. There is abundant evidence in the literature that
isotherms may exhibit hysteresis, and Karickhoff (9) has
suggested that the time required for equilibration in sediments
is directly related to the magnitude of Kp. Other data, such
as those illustrated by Figure 1, show that "irreversible"
sorption may strongly influence the transformation of chemicals
in some cases. These results clearly indicate the necessity of
an extensive reappraisal of sediment sorption kinetics because
every fast transformation pathway can potentially be rate
limited by sorption/desorption.
Hydrolysi s
Transformation by abiotic hydrolytic mechanisms is
relatively well understood. Even the important problem of
abiotic catalysis, however, has only recently been examined for
aquatic systems. Solution of the Br^nsted equations indicates
that general acid-base catalysis by dissolved inorganics or
humic materials is unlikely to be important, at least in
natural waters (10). Indeed, little is known about other
catalysis mechanisms such as those involving metals or free
enzymes or even about the kinetics of other chemical
transformation pathways especially in sediment-water systems.
It is often tacitly assumed that sediments will catalyze
chemical reactions or retard them due to sorption. For
200
-------
reactions in suspended sediments, however, there is little or
no convincing evidence to support these contentions. There are
several reasons for the lack of such data. First, the
experiments are difficult to perform in a manner that
guarantees sterility without compromising the chemical
composition of the system. Second, precise quantisation is
difficult in sediment systems, and it is not straightforward to
account for the effects of sorption. Finally, observation of
catalytic effects requires pH control, long reaction times, and
competitive pathways must be slower. These conditions are
difficult to attain while maintaining precision at low
concentrations.
Some results from our laboratory illustrate the need for
better understanding of the role of sediments. Table 1 shows
the influence of sediment sorption on hydrolysis of
hexachlorocyclopentadiene (Hex) (11). This compound is
believed to react by an Sr\2' mechanism that is pH independent
in water (Equation 2 where [Hex]j is the total amount of Hex
per unit volume of water).
In suspension, equilibrated
60 days
In suspension, equilibrated
4 days
-0.2 -
-0.4 -
o|o
tafl
O
D
-0.6 -
-0.8 -
-1.0
8
10
Relative Dose of Light
Figure 1. Comparison of kinetic data for photolysis of DDE
under various conditions in suspensions of Mississippi River
sediments.
201
-------
rate = - T = k[Hex]T (2)
If sorption is a rapid, passive equilibrium condition, the rate
equation should be
rate . „ T = T = k'[Hex]T (3)
where p is the amount of sediment per unit volume of suspension
and Kp is the sediment/water partition coefficient for Hex.
It is clear from the data that this reaction is not retarded as
expected and is almost independent of the extent of sorption.
The explanation for this behavior is unclear, but certainly
there is no evidence of catalysis.
Table 1. Decay Rate Constants for Hex in Sediment-Water Systems
Sediment Decay Rate Fraction in
Concentration Constant Solution
Sample
EPA-13b
g/i
0
0
0
0
0
0
1
2
00 ml
.0
.05
.10
.15
.20
.40
.0
.0
1
3
4
4
3
2
3
2
k
•
•
•
•
•
•
*
•
>
5
5
4
5
7
7
4
1
sec
+
T
T
T
T
T
T
+"
"
0
0
0
0
0
0
0
0
1
•
•
•
•
•
*
•
, x 106
6d
4
3
5
4
3
3
4
1
0
0
0
0
0
0
0
x/Ta
.79
.65
.55
.48
.32
.16
.08
Oconee River(GA) 1.0C 1.78 + 0.09
1.0 1.5 +_ 0.2
USDA (pond, GA) 1.0C 1.8 + 0.3
1.0 2.0 +_ 0.2
Hickory Hills
(pond, GA) 1.0C 3.4 + 0:7
1.0 5.4 + 0.7
aThe fraction of hex in solution (x/T) was calculated using a
value of Kp obtained by the relationship between Kp and
Kow as described by Hassett e_t al . (14). bOrganic content
of the sediment is 3.0%. GSterTTe systems. ^Average value
from Table 1.
202
-------
The 2,4-D n-octyl ester behavior shown in Figure 2
illustrates rate limitation by sorption when "pH jump"
experiments are carried out in hydrolyzing this compound (12)
Figure 2 can be explained by rapid hydrolysis of the ester in
the aqueous phase and rate limiting desorption from the
sediment. This again emphasizes the necessity for thoroughly
understanding sorption kinetics. We have also observed a
similar kinetic limitation that was caused by wall adsorption
when studying this compound in water alone (13).
2. On
0.6
2000 4000
Time (min)
6000
Figure 2. Hydrolysis of 2,4-D octyl ester in sediment/water at
pH 10 from Wolfe (12).
Microbial Transformation
Transformations by microorganisms has received perhaps more
extensive study than any other degradation process. It is only
in the recent past, however, that microbial kinetic approaches
have been pursued. Table 2 shows results obtained in our
laboratory that have led us to suggest that the second-order
203
-------
Table 2. Second Order Microbial Rate Constants (Average and
Relative Standard Deviation for n Sites).
Compound
(liter organism~lhour~l}
2.4-D butoxyethyl ester
malathi on
chl orpropham
phenanthrene
£ - chl orotol uene
phenol
£-cresol
5.
4.
2.
1.
2.
3.
2.
4
4
4
6
7
5
7
X
X
X
X
X
X
X
10-
10-
10-
10-
10-
10-
10-
10
11
14
10
11
10
10
+ 50%
+_ 60%
+_ 42%
+ 75%
+_ 36%
+ 60%
_+ 70%
31
14
11
5
5
5
5
microbial rate constants, kg, are independent of site and
population. The rate constants for the first three compounds
(14) along with rate constants for several other (15) compounds
that undergo hydrolytic transformation permitted establishment
of the linear free energy relationship (LFER) shown in Figure 3
(16). These results have important implications for predictive
efforts because alkaline hydrolysis constants are more readily
available than microbial constants or can often be estimated
from existing data. Thus, site dependence could be reduced to
variation in population size.
To determine whether it was possible to develop similar
relationships for other microbial transformation pathways, we have
recently examined the kinetics and products of phenol oxidation by a
pure culture of Pseudompnas putida (17). These conditions were chosen
to optimize the possibility of developing a LFER rather than for
environmental relevance. As can be seen from Table 3 and Figure 4, the
rate constants do vary systematically with structure. Other data (17)
not presented here also demonstrated that the rate is proportional to
both population size and phenol concentration (i.e. second order) and
that, for natural populations, the rate constants are reasonably
independent of site.
In conclusion, scientific understanding of transport and
transformation processes at the molecular level has advanced
greatly over the past five years. Good tools are available for
predicting those parameters that are essentially
204
-------
thermodynamic. Even the kinetics of simple transformation
processes such as direct photolysis, hydrolysis, and microbial
transformation are reasonably well understood.
There, however, remains the challenging problems of
determining how these processes are influenced by association
of the chemical with other environmental components as in
sediment sorption, humic interactions and catalysis. Of these,
the roles of catalysis and sorption are potentially important
for a very wide range of chemicals. Interest in these
phenomena is also stimulated by their role in transforming
chemicals that pollute soil and ground water.
Much work also remains in understanding and applying the
complexities of microbial kinetics to predictions of pollutant
behavior. Many researchers still question even the application
of kinetic equations to environmental problems. Certainly, the
simple second order kinetic equations are not expected to
describe the net rate where attached microbial populations
-10 -
01
i_
o
-12 -
CD
O -13 -
-14 -
m = 0.50 ± 0.04
c = -11.4 ± 0.1
r2 = 0.973
O
O
-5
-4
-3
-2
-1
Log kOH ,M sec
-1
Figure 3. Correlation of second-order alkaline hydrolysis rate con-
stants (27°) determined in distilled water with second-order biolysis
rate constants (25°) determined in natural water samples (16). The
compounds are: 1, jv-butoxy-ethyl ester of 2,4-D; 2, malathion; 3,
methyl benzoate; 4, methyl anisate; 5, methoxychlor; 6, chlorpropham.
205
-------
Table 3. Rate Constants (Kb) for Microbial Degradation of Eight
Phenols.
Compound
phenol (1)**
£-cresol (2)
£-chl orophenol
£-bromophenol (
£-acetyl phenol
£-methoxyphenol
p-ni trophenol (
£-cyanophenol (
kb (liter organism"1
(3)
4)
(5)
(6)
7)
8)
(7.
(4.
(1.
(1.
(3.
(2.
(1.
(1.
0
7
7
6
1
0
0
5
+_ 1
± 2
± °
± °
± °
+_ 1
± •
± •
.3)
.4)
.9)
.8)
.64
.2)
54)
99)
X
X
X
X
) X
X
X
X
hou
10-
10-
10-
10-
10
10-
10-
10-
r-1)*
12
12
12
12
-13
13
13
14
*Mean and standard error of estimate for 10 runs.
**Compound number in Figure 4.
account for most of the transformation (18). This area of
research continues to be plagued by lack of rigorous,
unambiguous definition and careful testing of concepts. Thus,
microbial process prediction will probably be the least
convincing aspect of environmental models for a long time.
Finally, the development of LFER, or structure-activity
relationships as they are often called in biology, will
undoubtedly continue to receive considerable research attention
for both biotic and abiotic processes. Demonstration of LFERs
for microbial transformation will undoubtedly increase interest
in this work and enhance the credibility of the kinetic
relationship themselves.
LITERATURE CITED
(1) MacKay, D., Finding fugacity feasible. Envi ron. Sci.
Technol . 1979, _13, 1218-1223.
(2) MacKay, D.; Shiu, W. Y.; Bobra, A.; Billington, J.; Chau,
E., Yeun, A.; Ng, C.; Szeto, F., Volatilization of organic
pollutants from water. U.S. EPA, Athens, GA, (Research
project report in preparation).
206
-------
(3) Karickhoff, S., Semi-empirical estimation of sorption of
hydrophobic pollutants on natural sediments and soils.
Chemosphere, 1981, 10, 833-846.
(4) MacKay, D.; Bobra, A.; Shiu, W.; Yalkowsky, S.
Relationships between aqueous solubility and octanol-water
partition coefficients. Chemosphere 1980, £, 701-712 .
(5) Hansch, C.; Quinlan, J.; Lawrence, G. The linear free
energy relationship between partition coefficients and the
aqueous solubility of organic liquids. J. Org. Chem. 1968,
JJ3_, 347-350.
(6) Yalkowsky, S. ; Valvan, S. Solubility and partitioning I:
solubility of nonelectrolytes in water. J. Pharm. Sci.
1980, £9, 912-922.
(7) Mirrless, M.; Moulton, S.; Murphy, C.; Taylor, P. Direct
measurement of octanol-water partition coefficients by
high-pressure liquid chromatography. J. Med. Chem. 1976,
19, 615-619.
-ii-i
log k,, = -9.30(±0.27)-1.36(±0.19)7W
r2 = 0.956
-12-
ti
cC
OC
(-,
O
fn
Q)
-13-
Figure 4. Relationship between the logarithm of the
second-order microbial rate constant and van der Waals' radius
of substituent group in Angstrom. Compounds in Table 3.
207
-------
(8) Veith, G.; Morris, R. T. A rapid method for estimating log
P for organic chemicals. 1978, E.PA-600/3-78-049, U.S. EPA,
Duluth, MM,.
(9) Karickhoff, S. Sorption kinetics of hydrophobic pollutants
in natural sediments. R. A. Baker (ed.) Contaminants and
Sediments, 1979, Ann Arbor Sci. Publ., Ann Arbor, MI, 2,
193-205. ~
(10) Perdue, E. M.; Wolfe, N. L.; Karickhoff, S. W.
Prediction of buffer catalysis in field and laboratory
studies of pollutant hydrolysis reactions. Submitted for
publication (1981).
(11) Wolfe, N. L.; Zepp, R. G.; Schlotzhauer, P.; Sink, M.
Transformation Pathways of Hexachlorocyclopentadiene in
the Aquatic Environment, submitted for publication in
Chemosphere.
(12) Wolfe, N. L., unpublished data.
(13) Perdue, E. M.; Wolfe, N. L., Modification of
pollutant hydrolysis kinetics in the presence of humic
substances, submitted for publication in Environ. Sci.
and Techno!.
(14) Paris, D. F.; Steen, W. C.; Baughman, G. L.; Barnett,
J. T, Jr. Second-order model to predict microbial
degradation of organic compounds in natural waters.
Appl. and Environ. Micro, 1981, 41, 603-609.
(15) Steen, W. C.; Paris, D. F.; Latimer, B. E. Microbial
degradation kinetics in suspended sediment systems,
submitted to Appl. and Environ. Micro.
(16) Wolfe, N. L., Paris, D. F., Steen, W. C., and Baughman, G.
L., Correlation of microbial degradation rates with
chemical structure. Environ. Sci, and Technol. 1980, 14,
1143-1144. ~~ —
(17) Paris, D. F.; Wolfe, N. L.; Steen, W. C.,
Structure-activity relationships in microbial
transformation of phenols, submitted to Appl. and
Environ. Micro..
(18) Lewis, D. L.; Holm, H. W. Rates of transformation of
methyl parathion and diethyl phthalate by aufwuchs
microorganisms. Appl. and Environ. Micro., in press 42. 4
1981. ~ —
208
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(19) Hassett, J. J.; Means, J. C.; Banwart, W. L.; Wood, S. G.
Sorption properties of sediments and energy related
pollutants, 1980, EPA-600/3-80-041, U.S. EPA, Athens, 6A.
This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication.
209
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PREDICTION OF PESTICIDE BEHAVIOR IN WATER
by
V.T.Kaplin, T.P.Likhovidova
USSR State Committee for Hydrometeorology and
Control of Natural Environment,
Hydrochemical Institute,
RostoY-on-Don
A great body of data has been obtained on the behavior
of various pesticides in natural waters. The purpose of this
paper is to summarize the information available in the Soviet
literature on the studies of the behavior in aquatic objects
of those pesticides which are, or can be used under conditions
of the Soviet Union. Special attention is concentrated on the
processes of physical-chemical transformation of pesticides in
water, particularly on the quantitative characteristic of their
kinetics and mechanism, the influence of pesticides on the com-
position and properties of natural waters,prediction of pesti-
cide content in water, and control and quality monitoring of
natural waters polluted by pesticides.
Much attention has been given in the Soviet Union to che-
micalization of its agriculture. For the period 1970-1975* &s
a result of plant protection measures, an extra output has been
obtained to a total value, on average, of 5»5-6.0 billion roub-
les per year (44). Undoubtedly, this demands appropriate nature
protection measures (9).
Pesticides can reach water objects either as direct spray
or dust applications using ground equipment or aircraft, or from
the atmosphere, soil and plants. The behavior of pesticides in
water bodies and streams depends on many factors, such as che-
mical structure of a substance, its water solubility, chemical
interaction with water, stability to UV radiation, adsorption
by suspended substances and bottom sediments, volatility from
the water surface (55), amount and activity of microflora and
hydrobionts, ability to accumulate in living aquatic organisms,
water temperature, pH, the content of dissolved oxygen,biogenic
elements, some specific pollutants, etc.
Surface runoff from a watershed is one of the main sour-
ces of pesticide entry into water bodies and streams (34). We
210
-------
considered the results of studying the entry of DDT and tf-BHC
in surface runoff during spring flood and periods of rain.
Surface runoff coefficients for a watershed with regulated
flow were 0.02% for DDT and 0.06% for BHC, and for a watershed
with natural flow 0.39& for DDT and 0.1% for BHC. The results
obtained revealed that the runoff coefficients for organochlo-
rine pesticides at natural watersheds and experimental runoff
plots were less than one percent (53)•
Using DDT as an example, the local, regional and global
scales of atmospheric transport were considered, as well as
pesticide fallout to the surface of soil. The regional trans-
port was discussed from the measurement data on the vertical
profiles of DDT concentrations in Fergana Valley and Khorezm
Oasis (2).
The results of studies carried out for many years in the
basin of the Middle Dnieper and in the area of the West Pole-
sie (woodlands in the Ukraine) showed that the seasonal entry
of organochlorine pesticides into water objects was nonuniform.
In summer, pesticides were detected in 22$ of the samples, in
spring in 6I.29&, in summer in I?.60S, and in autumn in &%>, which
coincided with periods of agricultural usage of pesticides and
supported the fact of their predominant entry in surface run-
off (10).
Atmospheric transport is also one of the sources of surfa-
ce water pollution by pesticides, particularly in the areas
remote from sites of application (6).
Studying the removal of BHC, metafos/methyl parathion/
and chlorofos (together with DDVPh /0,0-dimethyl-0-(2,2-dichlo-
rovinyl)-phosphate/) in surface runoff waters from experimen-
tal runoff plots 100 m in size showed that the amount removed
was directly proportional to the amount applied and the volume
of water runoff. The removed amount of BHC did not exceed
O.OJfc of the amount applied, that of metafos 0.04$, and of
chlorofos (together with DDVPh) 0.2J52&. According to the data
of 1973, half-life for BHC in the chestnut mid-loamy soil was
2 to 50 days, for metafos 1.3 to 1.8 days, for chlorofos 0.6
to 0.8 days, and for DDVPh 1.4 to 1.5 days (58).
Field studies in the Krasnodar Territory and South Uk-
raine show that the migration of anti-cereal herbicides(propa-
nil, its metabolite 3f4—DCA, saturn /S-(4~chlorobenzil)-N,N-
diethylthiocarbamate/ and ordram /molinate/) in the elements
of irrigation systems is determined mainly by the physical-
chemical properties of pesticides,edaphic-climatic conditions
and peculiarities of the water regime of rice fields. As rice
checks are flooded, part of herbicides from the surface of
soil and plants enters the water. A certain amount of pestici-
des travels with filtration flow to the depth of soil. Herbici-
de residues from the fields reach the collector-drainage net-
work in irrigation waste and ground waters.
As irrigation water is discharged, herbicides are remov-
ed from the field to the collector-drainage network with hyd-
robionts and microorganisms. The content of herbicides in the
211
-------
biomass of algae growing in the rice checks exceeds the con-
centration of toxicants in the water 1000 to 10,000 times,and
in the cells of bacteria approximately 100 times (8).
Under laboratory conditions, the rate of disappearance
of rogor /0,0-dimethyl-S-(N-methyl-carbamoylmethyl)-dithiophos-
phate/ from natural waters was studied depending on various
factors, such as temperature, pH, the presence of bottom sedi-
ments, and the type of water. It was found that a rise in the
temperature of solution by 15 C accelerated the process of ro-
gor transformation almost ten times(5I). The rate of rogor
hydrolysis was shown to be directly proportional to pH of the
solution: in an acid medium rogor was stable,whereas in an al-
kaline one it underwent a rapid hydrolysis. At pH 3.5 rogor
hydrolysis was negligible and during a month its concentration
remained almost constant. At pH 9-0 and temperature 16-18 C
there occurred saponification of 2^5 of the initial amount of
rogor during the first thirty minutes and up to §&% on the
third or fourth day. In the presence of bottom sediments, the
process of rogor transformation in the water occurred in half
the time. This process proceeded differently in various water
objects. In the water of the Ghirchik River, its intensity was
twice that in the collector water,which was, according to the
authors, due to the presence of a large number of vegetable
and animal organisms in the river water.
A standard procedure of modeling the transformation of
organic substances, including pesticides, in the artificially
prepared "natural" water is described in (67). The method en-
ables one to assess rapidly the persistence of pesticides in
the "natural" water under reproducible conditions.
When studying on models the kinetics of disappearance of
thiocarbamate herbicides from natural water, it was found that
under dynamic conditions the degradation of yalan /molinate/
and saturn was half as rapid again as, or twice that under sta-
tic conditions. This resulted from an increase in the enzyma-
tic activity of water mass due to the entry of enzymes from
the cells destroyed in a turbulent flow (20).
Over a temperature range 5-30 C, the rate of disappearan-
ce of thiocarbamate herbicides (K^jfrom natural water sharply
increased: at 5 C it was 0.04 day L for yalan and 0.03-,-day""1
for saturn, whereas at 30 C 5.63 days"1 and 3.18 days , res-
pectively.
Sorption by bottom sediments varied from 51% to
average. 65^) of the initial content for yalan and from
to 7995 (on average,4-7%) for saturn (62).
Laboratory studies showed that the persistence of the
derivatives of phenoxyalkylcarboxylic acids and thiocarbamates
increased in the order of 2,4—D chlorocrotyl ester, 2,4—D octyl
ester —"~2-methyl-4— chlorophenoxypropionic acid (2M-4-ChP),
2-methyl-4—chlorophenoxybutyric acid (2M-4ChB) —— eptam/S-
ethyl-N,N-dipropyl thiocarbamate/ —— tillam/S-propyl-N-ethyl-
N-butyl thiocarbamate/. Relatively increased stability of thi-
ocarbamates can be accounted for by the presence of a methyl
group at ortho-position in the phenolic ring(55).
212
-------
Unlike the derivatives of phenoxyalkylcarboxylic acids,
thiocarbamates^are volatile compounds, volatility of eptam
being 1.57 g/nr and that of tillam O.II g/m-5. Therefore their
disappearance from water was due to biochemical oxidation and
also their ability to volatilize to the atmosphere (55).
In model experiments, the degradation rate was studied in
the water of irrigation waste water collectors in the Krasno-
dar Territory for the following herbicides: propanil, its me-
tabolite 3,4-dichloroaniline (3,4-DCA), ordram and benthiocarb
/saturn/. Their rate of transformation was in the order of
propanil >• 3,4-DCA > benthiocarb >• ordram. The rate cons-
tants of transformation at a temperature 18-27 C were 0.45,
0.058, 0.042 and 0.013 day ,respectively.
It was shown that as mineralization of the water increas-
ed from 514 mg/1 to 43 g/1, the rate of disappearance of pro-
panil decreased by over three times (K = 0.13 day""1). In the
presence of bottom sediments (2 g/1), the rate of disappearan-
ce of ordram was half as much again as in absence of bottom
sediments (60).
Under laboratory conditions, the kinetics of metafos dis-
appearance from the water of the Severny Donets River and Dni-
eprodzerzhinsky Reservoir was studied. Microorganisms of water
and bottom sediments were shown to have a dominant role in
metafos transformation, while its chemical destruction was in-
significant. Depending on specific conditions of natural wa -
ters,the rate constant of metafos hydrolysis varied from
5.93 x IO"5 days'1 to 1.92 daygr* (15,30,39,72,75).
Complex physical-chemical and biochemical processes of
transformation of organic and inorganic substances occur in
water objects. The mechanism of pesticide transformation in
natural waters is determined mainly by the structure of
a substance and the prevailing processes. There are two main
routes of this transformation: I) via a number of successively
oxidized compounds which are simpler in structure; and 2) via
the formation of humic acids or other more complex and stable
substances.
The basic types of biochemical pesticide transformations
in natural waters are oxidation,reduction, hydrolysis, dehalo-
genation, isomerization, and formation of polymers and con-
jugates with the substances of hydrobionts.
The most widespread are reactions of oxidation in water
of individual functional groups entering into the composition
of pesticides. Compounds of aliphatic series, such as organo-
phosphorus substances, 2,2-dichloropropionic acid (dalapon),
trichloroacetic acid, etc., as well as hydroxyl-containing
compounds, such as carbolic acid,cresols, etc. are, as a rule,
easily oxidized ,unlike the derivatives of benzene and naph-
thalene which do not contain in the ring sulfur or nitrogen
as a substituent of oxygen.
Reductive reactions were observed under anaerobic conditi-
ons for 2,4-D (acid), DDT, lindane, thiofos /parathion/ and
other pesticides (5,36).
213
-------
The major transformation route for BHC in natural waters
is dehalogenation. The first product of BHC transformation is
pentachlorocyclohexane released as several isomers. In the sub-
sequent metabolism,observed the formation of all possible iso-
meric di-, tri-, and tetrachlorophenols is observed (36,59).
Metabolism of the herbicide propanil in water begins with the
process of dehalogenation (36,52).
Organophosphorus pesticides and their metabolites (36,46,
37), as well as the derivatives of phosphoric, thio-,. dithio-
phosphoric and phosphpnic acids (for example, chlorofos) (36,
18,42,3,70) form conjugates with the products of the vital
activity of plants.
Under the action of UV radiation, oxidation of thionic sul-
fur of the derivatives of thiophosphoric acid, such as parathi-
on (thiofos), metafos (26,34,45), methyl nitrofos /fenitrothi-
on/ (11,28,47) and trichlorometafos-3 /0-methyl-0-ethyl-0(2,4,
5-trichlorophenyl)-phosphorothioate/ (26,43) can occur, as well
as isomerization to S-ethyl- and S-(4—nitrophenyl)-derivatives.
In the Soviet Union, a ban has been imposed on the use of
DDT. However, its extensive use in the ^9508 and 1960s, as well
as high stability in the environment are responsible for the
fact that DDT is still being detected in certain regions of the
country.
The results of some studies (34,71) indicate that in the
south area, there occurs a relatively rapid destruction of DDT
under anaerobic conditions, as the soil is flooded with water.
Under aerobic conditions, DDT destruction proceeds slower due
to a different mechanism of transformation. In the first stage
under anaerobic conditions, there occurs DDT reduction to DDD
and a rapid destruction of the latter,whereas under aerobic
conditions, the first stage of DDT destruction is the formati-
on of a highly stable I,I-dichloro-2,2-bis(4-chlorophenyl)ethy-
lene.
DDT mobility in soil is extremely small (less than 10 cm
per year at a temperature 25 C and annual rainfall 1500 mm),
since it is readily sorbed (up to 98^) by clays from aqueous
solutions, that inhibits its leaching to lower layers and pre-
vents evaporation to the atmosphere (no more than O.I kg of
DDT evaporates annually from an area of I hectare) (36,55).
DDT is capable of accumulating in aquatic living organisms,
such as plankton, invertebrates, various species of freshwater
and marine fish (40). The presence of polychlorinated biphenyls
(PCB's) in water enhances toxic effects of DDT on phytoplankton
(36). DDT from water can penetrate into plants and accumulate
in small amounts in fruits (49) and in relatively high concent-
rations in leaves.
Under laboratory conditions, DDT uptake and accumulation
by Ceratophyllum (hornwort) and Potamogeton perfoliatus were
studied (74). As DDT content in water increased to 2 mg/1,
concentrations in the plants per I g of raw substance gradual-
ly reached 104 ,ug for DDT, 41.4 >ug for DDD and 3.3 ^g for DDE.
A high content of DDT in tissues of the plants had a strong
214
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toxic effect on the vital activity of Potamogeton: the plants
had chlorosis and a gradual necrosis of leaf tissues. The oc-
currence of DDT metabolites in the plants and water was like-
ly due to a partial dechlorination of DDT yielding less toxic
DDD and DDE. With increasing concentration of DDT in the wa-
ter of experimental aquariums, on the eighth day after its
application there occurred a three-times decrease in the con-
tent of water-dissolved oxygen (to 2.9 mg 02/1) and a gradual
decrease of pH of the water(from 7.4 to 5.7 in the aquariums
with Potamogeton and from 8.0 to 6.3 in those with hornwort).
Pesticide stability depends on their ability for hydroly-
sis, i.e. chemical interaction with water. In most cases, hyd-
rolysis leads to pesticide decomposition to yield less toxic
compounds. Hydrolysis of organophosphorus and other pesticides,
particularly herbicides, such as propanil, carbofos/malathion/
Q27,75), phthalofos /0,0-dimethyl-S-phthalimidomethylphospho-
rodithioate/ and dieldrin proceeds most easily in moist soils
(36,68). The rate of pesticide hydrolysis increases with in-
creasing water temperature and pH (35t36).
Oxidative hydrolysis of the derivatives of dithiophospho-
ric acid proceeds in water very actively. The rate of trans-
formation of dithiophosphates, such as menazon /0,0-dimethyl-
S-(4.6-diamino-I,3,5-triazin-2-ylmethyl) phosphorodithioate/,
fozalon /benzophosphate/, phthalofos, fencapton /0,0-diethyl-
S-(2,5-dichlorophenylthiomethyl) phosphorodithioate/ and ci-
dial /phenthoate/, increases most appreciably under ultravio-
let light (23). Metabolism in water of phosphamide /dimethoa-
te/ and formothion /0,0-dimethyl-S-(N-methyl-N-formylcarbamoyl-
methyl) phosphorodithioate/ (36,29) extensively used in the
Soviet Union to control pests of cotton and some other plants
begins with oxidatiye hydrolysis. For phthalofos and benzophos-
phate this process is slower than for carbofos, phosphamide
and formothion (36,50,28,44). Menazon (36,46) and bipyridylium
herbicides, such as diquat and bipyridylphosphate /I, I -dime-
thyl-4,4'-bipyridylium-dimethylphosphate/(36,69), are charac-
terized by even larger stability.
Dealkylation and hydrolysis are the most important degra-
dation pathways of such thiocarbamates as diallate, triallate,
molinate, eptam, vernolate, tillam, cycloate, benthiocarb(sa-
turn), etc. Benthiocarb concentration in the water of a river
flowing near the treated rice checks did not exceed 3 mg/1
and rapidly decreased during the first two weeks after treat-
ment (36,33)«
The main products of thiocarbamate oxidation by microor-
ganisms are sulfoxides (36,33)» which are hydrolytically un-
stable in aquatic systems. At pH over 7.0 they are unstable
even at room temperature.
Diallate, triallate and eptam were found to be capable of
stimulating the reproduction of aerobic saprophytic bacteria
and inhibiting the processes of nitrification for some time
(32,16). Migration of eptam and tillam through the soil profi-
le was significant (36,56).
215
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When molinate was applied as emulsion, it was detected in
the water of rice checks during a week after application at
a concentration of 0.2 mg/1, while as a granular formulation,
during 15 days at a concentration of 0.03-0.08 mg/1 (36,64).
At pH close to 7«0 the derivatives of aryIhydroxyaIkane-
carboxylic acids are rather stable in aqueous solutions. As
temperature in an acidic or alkaline medium increases, hydroly-
sis proceeds through the ether linkage to form phenol and
a corresponding acid (36).
A certain role in water body self-purification of pesti-
cides is assigned to the processes of their volatilization
from the water surface (I?). However, the losses of pesticides
from water due to their transformation under UV light are con-
siderably more intensive. Research into the kinetics of pesti-
cide photolysis and identification of their possible metaboli-
tes has received attention of many investigators (for,example,
(22)). When exposed to ultraviolet, polychloropinene and poly-
chlorocamphene which are stable in water are degraded to form
several nonidentified products. Under the influence of UV light,
polychlorocamphene may decompose releasing hydrogen chloride.
In natural conditions,the amount of polychlorocamphene in the
arable soil layer decreased by 50!% in twelve months, whereas
upon UV irradiation in 45 min. Polychloropinene is a persistent
pesticide: one year after the soil treatment its amount decre-
ased only by 17% of the initial content. Half-life of polychlo-
ropinene under UV light is 1.9 hrs. (21).
The rate and mechanism of photochemical transformation of
ten organophosphorus pesticides were studied under laboratory
conditions upon irradiation,using a lamp HPE-4 (23). Half-
lifes of the pesticides varied from 4 min. (bytex/fenthion/)
to 630 min. (saifos/menazon/).
Some herbicides, such as propanil, are degraded under the
action of UV light (I). As solution is'stirred and saturated
with oxygen, no significant increase occurs in the degradation
rate of propanil, probably, due to the specific character of
the mechanism of photochemical degradation of the herbicide with
chlorine atoms in the benzene ring: the reaction of hydrolytic
dechlorination yielding oxycompounds takes place.
As propanil undergoes photochemical decomposition,this
does not give rise to appreciable amounts of its metabolite
3,4-dichloroaniline which is the main intermediate product un-
der natural conditions. It is likely that in this case 3-chlo-
ro-4-hydroxyaniline, 3-hydroxy-4-chloroaniline and 3,4-dihydro-
xyaniline are formed,which are easily oxidized by oxygen of
the air (I).
Given the photolytic stability of a herbicide and illumi-
nation conditions of the climatic zone under consideration,it
is possible to estimate the amount of the substance which can
remain in the water phase, taking into account the irrigation
regime of rice checks (13,14).
Clay minerals and organic substances of bottom sediments
participate in pesticide sorption which is also affected by
216
-------
water temperature and pH. The sorption may proceed by the fol-
lowing major mechanisms:
I. Physical sorption and formation of hydrogen bonds bet-
ween neutral molecules of pesticides and active centers on the
surface of colloid particles of bottom sediments, occurring,
as a rule, at pH close to 7»0-
2. Ionic sorption of cationic forms of pesticides that
proceeds due to ion exchange with cations located on the sur-
face of clay minerals and colloids of organic polyelectrolytes,
and increases with decreasing pH.
3. Formation of pesticide complexes with hydrogen ions on
the surface of micelles of bottom sediment colloids.
The value of pesticide sorption is directly proportional
to organic matter content in bottom sediments, cation-exchange
capacity and specific surface of natural sorbents (63).
The sorption processes are affected by the chemical struc-
ture of organic matter. Thus for the derivatives of sym-tria-
zine replaced at position 2, sorption decreases in the order
of SCpOc > SCH, > OCH, > OH > 01. Sorption of dialkylamino-
compounas proceeds more actively than that of monoalkylated
derivatives and decreases in the order of tertiary C Jkq :> se-
condary C,,Hq > C2?5 ^ iso-C,Hr, > CH, > C(CH,)2CN. ^ ^
A methdd has t>een proposed for* determining the sorption
characteristics of organic matter including pesticides such
as propanil; 2.4-D; 3.4-DCA and 2.4-dimethyltetrachlorotereph-
thalate- (2,4-DChPhj, on samples of soils and clay minerals. It
is necessary that extreme conditions be revealed, in which
the sorption processes occur depending on the following main
factors: liquid-to-solid phase ratio, time during which the
liquid and solid phases are in contact, pH and temperature of
aqueous solution, sorbent moisture and sorbate concentration.
It has been noted that the results of laboratory modeling en-
able one to establish a number of regular patterns in the be-
havior of pesticides in soil and aquatic environments(24-,25)»
As equilibrium between solution and sorbent is reached
instantaneously, Freundlich sorption isotherm can be used as
a model of equilibrium. If equilibrium is not reached instan-
taneously, two kinetic models of the corresponding reaction
can be proposed. In the first of them, it is assumed that the
pore space through which solution flows is homogeneous,where-
as sorbent is inhomogeneous. In the second model, the presence
of open (through) and closed (blind) pores is taken into acco-
unt, and sorption is assumed to proceed equally throughout the
pore space. Application of the proposed mathematical models
describing the dynamic sorption of pesticides in soil enables
one to determine most accurately the constants of isotherm
and the rates of exchange(4I).
Under laboratory conditions, the sorption of propanil,
3,4-DCA and yalan was studied on four samples of soils of the
Krasnodar Territory; sand, loamy sand, light clay and clay
loam. Sorption equilibrium in the "soil- pesticide solution"
system was reached in a few minutes after the beginning of pha-
217
-------
se interaction, which appeared to be caused by pesticide sorp-
tion mainly on the surface of sorbents due to Van der Waals
forces. Pesticide sorption by soils was adequately described
by the Freundlich equation. A relative sorptive capacity of
soils decreased in the order clay loam > light clay j> loamy
sand > sand. The change in sorptive capacity of soils was
directly proportional to humus content in the sorbents (from
0.05^ in sand to 2.7% in clay loam). The extent to which pes-
ticides were sorbed by the sorbents studied was in the order
of propanil > 3,4-DCA > yalan (76).
In aquatic objects, pesticides affect a wide variety of
biological processes thus disturbing ecological equilibrium
in the aquatic environment. This is evidenced indirectly by
variations in oxygen balance, the content of water-dissolved
oxygen, pH and other indices of water composition and proper-
ties.
Exposure to pesticides can produce changes in plankton
dominants, as well as in the order of ecological successions
and the structure of plankton communities.
Low concentrations of pesticides may intensify decomposi-
tion of individual alga species or complexes, acting as eutro-
phication agents. As a result of destruction of dead "organic
matter", there occurs an increase in the concentration of car-
bon and nitrogen-containing compounds in water, which ultimate-
ly accumulate as nitrates readily assimilated by algae (7).
The process of nitrification is strongly inhibited by tillam
(10.0 mg/1) and 2,4-D chlorocrotyl ester (20 mg/1): the con-
centration of nitrites and nitrates does not increase during
15 days (55).
After a rice check was treated with saturn at a concent-
ration in water of 2.15 ni6/l» a sharp decrease was observed
in numbers of microorganisms (3.6-times), biomass (7.1-times)
and species composition of zooplankton (31)•
Studies on the models of microchecks revealed that at
concentrations up to 0.2 mg/1, yalan did not exert toxic ef-
fects - quite the reverse, it stimulated the development of
bacterioplankton. Contact with yalan changed the species ratio
of bacteria, fungi and actinomycetes. Heterotrophic ammonifi-
ers intensively assimilated yalan and increased their numbers
3 to 15 times compared to natural populations. A high degree
of correlation was found between yalan concentration in water,
potential catalase activity of the aquatic environment and
saprophyte numbers (61).
Little data have been published that characterize quanti-
tatively the influence of pesticides on the composition and
properties of natural waters. Predicting the content of pesti-
cides in water is possible if regularities of the processes
affecting pesticide concentration are studied quantitatively.
A number of prediction models describing pesticide migration
in water bodies and streams have been proposed. Since informa-
tion is Tacking on the quantitative characteristics of complex
physical-chemical and biochemical processes of transformation
218
-------
which depend on numerous parameters, investigators, as a rule,
have to adopt a number of assumptions in their models. This
inevitably affects reliability of the obtained prediction data.
The principles of mathematical modeling of pesticide migra-
tion in some components of the environment based on statistical
analysis are discussed in (66), where the authors concentrate
their attention on the models for calculating the amount of
pesticide remaining in a plant under given conditions and in
various periods of time after treatment. In all, they conside-
red schematically 4-5 factors which determine the content of
pesticides in various media and the dynamics of migration bet-
ween them. The methods for calculating single and mean daily
maximum concentrations of pesticides in sanitary-domestic wa-
ters and streams appear to be promising.
A mass-transfer equation has been proposed to predict the
chemical composition of underground (drainage) waters and mig-
ration of the carbamate insecticide sevin /carbaryl/ in them
(4-) . The investigators took into account the processes of se-
vin entry from the surface and its movement with filtration
flow, as well as the physical-chemical processes of its adsorp-
tion by, and leaching from soil. Under laboratory conditions,
they determined by a statistical scheme the type of sorption,
the rate of sorptive exchange,maximum sorptive capacity of
the solid phase, and partition coefficients for the studied
sandy-clayey sediments. Unfortunately, the authors have not
compared the results of prediction calculations of sevin con-
tent in control sections with the data of field observations
carried out in irrigated agricultural plots in Chardzhouskii
Oasis, Turkmenistan. Therefore it seems impossible to assess
reliability of the calculated prediction data obtained by the
model proposed.
Chromatographic methods have been suggested to be used not
only for a separate determination of pesticide residues( the
derivatives of phenoxyalkane-carboxylic acids), but also as
a source of useful information that can be applied to predict
the behavior of pesticides in aquatic objects. Water body pol-
lution will be insignificant in the case of pesticides with
small Rj=. and large volumes of retention due to their rapid
sorption by suspended substances and bottom sediments. At the
same time, pesticide inactivation proceeds more rapidly at
small Rp, which results in decreasing probability of seconda-
ry water pollution (75).
According to the authors of (65) concerned with determina-
tion of the role of experimental hygienic studies in prediction
of pesticide behavior in the environment, the key factors cha-
racterizing the level of accumulation of substances in environ-
mental objects and their possible migration in various ecoce-
noses are persistence and mobility. The authors suggested
determination in model installations of the quantitative cha-
racteristics of pesticide persistence and migration mobility
in water, soil and plants due to hydrolysis, oxidation, photo-
decomposition, microbial breakdown, etc. The assumption was
219
-------
supported of running water in rice checks and discharged irri-
gation water, rather than filtration,being the major routes of
propanil and yalan entry into irrigation waste water canals.
In model experiments, yalan persisted in water during 60 days,
whereas in water with meadow-bog soil up to 30 days.
A first-order equation was used for prediction calculations
of pesticide content in water. The discrepancy between the
actual and calculated data did not exceed 19% (57)•
Prevention of pollution of aquatic objects by ,pesticides
can be effective if the complex of measures is taken at all
stages of pesticide production, transportation, storage, app-
lication, etc.
The use of granular pesticides with inert fillers is of
great water-protective significance. In this case, granules
gradually release the pesticide into the soil, that prevents
its removal by water flow over the surface during periods of
rain and promotes the process of its transformation or sorp-
tion by the soil.
Improvements in spraying techniques and equipment appear
to be promising. The use of ultrasmall-volume spraying(USVS)
by reducing the diameter of droplets which are formed from
spraying solution, permits the amount of pesticides applied
to be reduced substantially (hundreds of times) and at the
same time, increases their effectiveness.
First experiments on the use of foam-spraying also give
promising results from the view point of decreasing amount of
pesticides applied (?0).
In the last few years, research and experimental field
works have been successfully carried on with the aim of develop-
ing recommendations to prevent, or at least decrease substanti-
ally the removal of pesticides and other pollutants in surface
runoff from agricultural land to aquatic objects.
The general principles of constructing riverside water-pro-
tective zones through the use of the system of defensive strips,
that are being developed, and the complex of measures of water-
protective reclamation, as well as erosion-preventive agricultu-
ral and hydraulic engineering will allow in future,according to
some investigators, a purposeful control of natural water quali-
ty by a simultaneous protection of aquatic objects from pollu-
tion by pesticides and biogenic substances, silting as a result
of washing away of arable layer subject to water and wind erosi-
on, etc.
Employment of the ability of some pesticides to be active-
ly sorbed by bottom sediments, suspended particulate matter,
sand and soil appears to be promising. Pesticides sorbed by
soil and sand undergo natural transformation processes during
the inter-vegetation period. As a result, the adsorptive capac-
ity of natural sorbents is restored. Technical realization of
this principle of protecting aquatic objects from adverse ef-
fects of collected discharges of irrigation return waters under
conditions of Central Asia has already given positive results
(19, 48, 12, 37, 77).
220
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Almost complete destruction of herbicides of the 3,4-D
group and their metabolites can be attained by holding irriga-
tion return waters, prior to their discharge to aquatic ob-
jects, for 2-3 weeks in reservoirs adapted specially for this
purpose and formed by cutting off portions of firths using
dams, or by broadening the terminal portions of discharge col-
lectors (38).
Substantial decrease in the content of herbicides in water
occurs due to their natural destruction in the flooding layer
of fallow field, to which it is recommended to pump the water
from the collector-drainage network (38).
Thus, to predict the behavior of pesticides in water it is
essential that information be available on the quantitative
characteristics of the main processes of their transformation
and migration in natural waters. Experience in the work on
aquatic objects supports the conclusion that it is advisable to
study the processes of pesticide transformation by the scheme:
"aquatic object - modeling - aquatic object".
The kinetics of pesticide transformation in v/ater depends
on numerous factors and interaction of various processes. There-
fore it is essential that the methods of cybernetic modeling be
used to develop prediction techniques and ultimately, to perform
an effective control of the "pesticide - environment" system
with the aim of preserving the purity of human habitat.
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Influence of Natural Substances on the Photoreactivity
of Pesticides in the Aquatic Environment
by
R.G. Zepp, P.P. Schlotzhauer and G.C. Miller
U.S. Environmental Protection Agency
Athens, Georgia 30613
Environmental problems associated with the use of certain pesticides in
the United States have prompted research efforts oriented towards forecasting
the behavior of such chemicals in aquatic environments. This paper emphasizes
investigations of sunlight-induced photoreactions of pesticides and other syn-
thetic chemicals in natural waters. The overall objective of this research
has been to define kinetic expressions and parameters that can be employed to
predict photolysis "rates," or conversions per unit time, under the wide vari-
ety of conditions that exist in aquatic ecosystems. Results of these studies
are being used in computer models that integrate rate and equilibrium data to
provide estimates of exposure concentrations of pesticides in various environ-
mental compartments (1).
Past studies have indicated that photochemical transformations occur
upon direct absorption of light by a reactive chemical or, alternatively,
through indirect processes that involve light absorption by another substance
that is in the system with the reactive chemical. The photolysis rate of a
pesticide in a water body equals the sum of the rates of its various competing
direct and indirect photoreactions. Procedures have been developed for pre-
dicting direct photolysis rates of chemicals at low concentrations in air-
saturated distilled water (2,3). Recently our attention has turned to the
more complex problem of quantitating the effects of substances indigenous to
aquatic environments on photolysis rates and products. Such natural substances
influence photolysis rates of pesticides in aquatic environments through at-
tenuation of sunlight in water bodies, through initiation of indirect photo-
processes, and through physical or chemical interactions that alter the speci-
ation or microenvironment of pesticides. Recent research on these various ef-
fects is discussed here, with emphasis on studies of the influence of humic
substances and suspended sediments on photoreactions of trace organic chemi-
cals.
Influence of Humic Substances on Photoreaction
Indirect Photolysis
Several studies have demonstrated that photolysis rates of certain or-
ganic chemicals are enhanced in the presence of humic substances (4-10). In
some cases, chemicals that are unreactive in distilled water were found to
230
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photoreact rapidly in solutions of humic substances or in natural water sam-
ples (7). Usually, when photolysis rates were enhanced by humic substances,
different photoproducts also were formed (4,5,7). The studies have indicated
that humic substances are capable of photosensitizing several types of organic
photoreactions. A scheme that summarizes the various reaction pathways is
shown in Figure 1, where S represents the part(s) of humic substances that
acts as a sensitizer and S* represents the sensitizer in its electronically
excited state. The excited sensitizer can transfer energy to molecular oxygen,
02, to form singlet oxygen, 102*, a species capable of oxidizing certain types
of pesticides represented by A (6). Reactions involving singlet oxygen are
's* + o.,
J22L* s + lc
Products
PH
I
S + JPH*
SH- + P-
^ S- + PH-
+ A
AO,
Figure 1. Skeletonized mechanism for pesticide
reactions photosensitized by humic substances.
231
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called "oxygenations." Direct interactions between S* and pesticide include
energy transfer, hydrogen transfer, or electron transfer to form various
reactive intermediates I (7). At pesticide concentrations that are very low,
the photosensitized reactions can be described by rate expressions that are
first order with respect to pesticide concentration [PjU that is, the rate
equals ks [P] where ks is the rate constant, which is expressed in units of
reciprocal time.
Oxygenations photosensitized by humic substances have been studies in
detail (6-9). Zepp and coworkers (6,7) have reported oxygenations of furans
and sulfides in sunlight (Figure 2). The oxygenation of 2,5-dimethylfuran
(DMF) was studied to obtain detailed information about photosensitization by
humic substances. The DMF studies were conducted at sufficiently low concen-
trations to insure that the reaction was not complicated by the competing free
radical reactions that can be initiated by the peroxide products (9). Several
mechanistic tests established that the oxidation of DMF in natural water sam-
ples was mediated by singlet oxygen (6). For example, the oxygenation of DMF
in a swamp containing dissolved humic substances was affected by DABCO (1,4-
diazabicloE 2,2,2 ] octane) and deuterium oxide in the same way as the photo-
oxygenation of DMF sensitized rose bengal, a known singlet oxygen sensitizer
(Figure 3). Other studies involving DMF as a substrate indicated that rates
of sunlight-induced oxygenations sensitized by the humic substances in river
water were independent of hydrogen-ion activity from pH 5 to pH 10 (18)
(Figure 4).
Because humic substances are known to be mixtures of refractory chemicals
derived mainly from plant decay, it seemed likely to us, as we started studies
of sensitized reactions, that humic substances from different locations would
exhibit very different photochemical behavior. Results of several studies,
Disulfoton
S 0
, " ]l
(CzHsO)tPSCHtCHgSCaHe
Disulfoton Sulfoxide
CH3 ^ // CHa
+ other products
2,5-Dimethylfuron
Figure 2. Photooxygenations sensitized by humic substances in water (7)
232
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• Rose Bengal
D Okefenokee Swamp
0.2 0.4 0.6 0.8
FRACTION OF HALFLIFE
Figure 3. Effect of DABCO and deutrium oxide on photooxygenation
of 2,5-dimethylfuran (DMF) in Okefenokee Swamp water and in dis-
tilled water containing rose bengal; initial DMF concentration
[DMF]0,1.0 x 10~^M. Duration of light exposure expressed as
fraction of half-life compared with systems with no added DABCO
or deuterium oxide, B, 1:1 mixture of swamp water or 10 M aqueous
rose bengal with 0.019 M aqueous DABCO. Light source was a 450-
watt medium pressure mercury lamp; light was filtered through
Corning 7-83 glass filters to isolate 366-nm line (Ref. 6).
however, have indicated that just the opposite is the case. Detailed studies
with sunlight or monochromatic light indicate that humic substances in a wide
variety of water bodies or humic and fulvic acids extracted from soils have
very similar photosensitizing capacities (7,8). These similarities are illus-
trated by comparisons of kinetic data obtained for photosensitized oxygenations
of DMF and the insecticide disulfoton in sunlight (Table 1) (7). Similar
findings have been reported for other types of photoreactions that are sensi-
tized by humic substances (4,7) including a cis-trans isomerization that in-
volves electronic energy transfer from sensitizer to 1,3-pentadiene and reac-
tions involving hydrogen transfer from aniline to sensitizer (Figure 5). The
reaction shown for aniline also occurs with the herbicide metabolite 3,4-dichlo-
roaniline in competition with its direct photoreaction (4).
233
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1.60-
1.25-
I
e
N*A
ft; 1-00-\
0.7S-
A Aucilla River Adjusted
Various pH Values
O 2x1O^M Rose Bengal
Aucilla River
No pH Adjustment
~r
5
T~
7
T~
8
~r
9
T~
10
11
pH
Figure 4. pH dependence of photosensitized oxygenation of 2,5-dimethyl'
furan (8).
To more quantitatively determine the effects of varying reaction condi-
tions on the photosensitizing capacities of various humic substances, response
functions, Xs x, have been determined for the photosensitized oxygenation of
DMF employing monochromatic light.
Eq.l:
Response functions were computed using
s,x
's,x
(1)
where k? A is the observed first order rate constant for the oxygenation and
E§VU) is the average irradiance in the reaction cell at wavelength x. As
predicted by theory (8), experimental results indicated that Xs for the DMF
reaction is directly proportional to the concentration of humic substance.
Kinetic studies at various wavelengths indicated that response functions for
photooxygenation of DMF sensitized by humic substances in the Aucilla River
and by soil-derived humic acids were the same within a factor of 2 in the near
234
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Table 1. KINETIC DATA FOR OXYGENATION REACTIONS
PHOTOSENSITIZED BY HUMIC SUBSTANCES IN WATER EXPOSED
TO SUNLIGHT (Ref. 7)
Humic Source
Relative Rate Constant3>b>c
•2,5-Dimethylfuran
Di sulfoton
Disti1 led Water
Aucilla River, FL
Okefenokee Swamp, GA
Quincy Bog, NH
Williamson River, OR
Fulvic Acid, CAf
f>9
Con tech Fulvic Acid
9
Aldrich Humic Acid'
Karl Roth Humic Acid
Fluka Humic Acidf>g
Lot A
Lot B
Lot C
Lot D
f>9
<0.01
1.0
1.13 +_ 0.32 (4)
1.52 jf 0.08 (4)
1.22 i 0.40 (4)
1.52 + 0.40 (3)
0.98 (1)
1.21 + 0.17 (6)
0.50 + 0.25 (4)
0.81 (1)
0.68 (1)
0.80 +_ 0.14 (9)
0.80 + 0.15 (4)
1.0
1.2 +_ 0.2 (3)
1.1 + 0.1 (2)
1.1 + 0.4 (3)
1.2 + 0.4 (2)
1.15 +_ 0.4 (2)
1.30 + 0.53 (5)
1.2 (D
aRatio of the sunlight photolysis rate constant to that
observed for the photooxidation in Aucilla River water. "All
solutions optically matched by adjusting absorbance to 0.20
(1.00 cm cell) at 366 nm. cNumber of experiments in
parentheses; each experiment involved triplicate kinetic runs
for each solution. dHalf-life in Aucilla River water ranged
from about 20 minutes in winter to 5 to 10 minutes during
summer at midday. eHalf-life ranged from about 5 hours of
midday sunlight during summer to 12 hours during winter.
^Isolated from soils. QObtained commercially.
ultraviolet (uv) and visible spectral regions (Figure 6). The humic substances
in the river water water were nuch more effective in the middle uv, however.
The wavelength dependence for the reaction (300-500 nm) in Aucilla River water
can be described by Eq. 2 where XSjA is the response funcition at wavelength
A and x is in nanometers. ' °
o
's,x
s,x.
0.023U -x)
e o
(2)
Employing computer generated values for near-surface solar spectral ir-
radiance E0(x,0) (2) and Eq. 3, rate constants were computed for the photo-
sensitized oxygenation of DMF in sunlight using Eq. 3 (Figure 7) (8):
235
-------
ks,x • E0<».<» Xs,x (3)
The computations indicate that solar uv light is most effective at promoting
reaction sensitized by humic substances in the river water whereas blue light
is most effective in the case of the humic acids. The computer was also used
to integrate ks,x over all wavelengths to obtain sunlight rate constants kx.
The computed values indicate that near-surface half-lives for the DMF reaction
should be 5 to 10 minutes at midday during summer at latitute 40 deg. N. These
findings are in close agreement with experimental values (Table 1).
In addition to the studies described above, other studies by Mill and
coworkers (10) have indicated that alkoxy, alkylperoxy, and hydroxyl radicals
are formed upon exposure of natural waters to sunlight. Such radicals may be
derived from photolysis of peroxides that form as products of reactions be-
tween singlet oxygen and substances in the water. Mill and coworkers (10)
used cumene (CUH) and pyricine (CsHsN) as probes to determine steady state
concentrations of the radicals [R0']and [HO'] in sunlight-exposed natural waters,
Zepp and coworkers (6) computed steady^-state concentrations of singlet oxygen
in natural waters[10?*] assuming [10?] = ks/k/\ where ks and k/y are sunlight
rate constants (1-cm path length) for t)MF oxygenation in the water and k/\ is
the bimolecular rate constant for reaction between DMF and singlet oxygen.
Results of the studies by Mill and coworkers and Zepp and .coworkers are sum-
marized in Figure 8. Bimolecular rate constants for reactions between these
H
—c - - c=c
C
H
Cis— 1 , 3 —Pentadiene Trans -1,3 —Pentadiene
N=N-(\ /} + other products
Aniline Azobenzene
Figure 5. Reactions photosensitized by humic substances in water (7),
236
-------
60-1
50-
40-
30-
20-
10-
A Aucilla River
O Aldrich Humic Acid
Fluka Humic Acid
300
350
400
4SO
500
550
Wavelength, nm
Figure 6. Action spectra for photosensitized oxygenation of 2,5-dimethyl
furan. Reaction conditions: absorbance of each solution at 366 nm was
adjusted to 0.20 in a 1.00-cm cell, pH was adjusted to 6.0 (8).
oxidizing species and various organic substrates are available in the chemical
literature. These can be used in conjunction with the data in Figure 8 to
estimate near-surface rate constants for the sunlight-induced oxidation of
pesticides in natural waters (6,10).
Light Attenuation
To calculate photolysis rates of pesticides or other photochemical of
photobiological processes in aquatic ecosystems, it is necessary to quantitate
the penetration of the photochemical ly active uv and visible wavelengths of
sunlight. The solar spectral irradiance just beneath the air-water interface
of a water body E0(x,0) is attenuated by dissolved and suspended matter as it
penetrates downward. The irradiance is attenuated approximately in exponential
fashion; that is, irradiance at depth I, E0(x,Z), is approximately E (A,0)e^T
The constant Kj(x), the diffuse attenuation coefficient) is the focal point
of efforts to predictively model light attenuation in natural waters Cll>12).
Smith, Baker, and coworkers have measured the penetration of sunlight
into a variety of natural waters. They have used the experimental measurements
to develop a model for sea water that partitions "the diffuse attenuation co-
237
-------
efficient into various components responsible for total attenuation (13, 14).
This model assumes that the primary species responsible for attenuation in
sea water are water itself, chlorophyll-like pigments derived from algae, and
dissolved organic matter; the latter is particularly important for attenuation
of uv light in coastal waters. The dissolved organic matter is referred to as
humic substances in this paper, although .oceanographers refer to it as "yellow
substance" (12). Studies of Miller and Zepp (15) indicate that suspended
sediments also play a key role in the attenuation of solar radiation in fresh-
waters. These studies are discussed in a subsequent section. A general
equation for KJ(A) in natural waters is:
KT(x)
KW(A)
KC(A)
Kh(x)
Ks(x)
(4)
where KW(A), KC(A), Kh(A) and KS(A) are components of the attenuation coeffici-
ent attributable to water, chlorophyll, humic substances and sediment, respec-
tively. Equations and coefficients required for the calculation of KW(A) and
KC(A) are discussed fully elsewhere by Smith and Baker (13,14).
16-\
Aucilla River
Aldrich Humic Acid
' \ \ Ftuka Humic Acid
300
350
400 450
Wavelength, nm
500
550
Figure 7. Wavelength dependence of rate constants computed for photo-
sensitized reaction of 2,5-dimethylfuran in sunlight (midday, summer,
latitude 40 deg. N). Rate constants, ks , integrated over the entire
spectral region are: Aucilla River, 5.4 hr~'; Aldrich humic acid,
6.5 hr"1; Fluka humic acid, 4.3 hr~' (8).
238
-------
WATER PROBE
AUCILLA RIVER, DMF
FL CUH
GULF OF DMF
MEXICO, FL
MISSISSIPPI DMF
RIVER, LA
COYOTE CREEK, CUH
CA CsIfeN
OXIDIZING SPECIES CONCENTRATION (M)
*] X 1012 [ RO-] X 109 [ HO-] X 10 17
2 2
1.8
0.3
0.5
2.8
9.1
5.0
1.8
1.6
Figure 8. Near-surface steady-state concentrations of oxidizing species
in natural water samples exposed to sunlight (6,10).
As interest in the effects of solar uv radiation in aquatic ecosystems
has intensified in recent years, so have efforts to quantitate the spectro-
scopic properties of humic substances. Recent papers by Bricaud and coworkers
(17), Htfjerslev (18), Baker and Smith (14), and Zepp and Schlotzhauer (19)
have presented evidence that the humic substances in sea waters and freshwaters
have closely similar spectral properties. Zepp and Schlotzhauer computed
specific absorption coefficients khU) for humic substances present in natural
waters and extracted from soils (12):
k(x)
2.303
(5)
where A^ = absorbance at wavelength
Ch = concentration of dissolved organic matter
(humic substance) in mg organic carbon/liter
1 = cell pathlength in meters
ah,x= absorption coefficient of solution
Similarities in specific absorption coefficients were found for humic
substances in soils and natural waters form the US and the USSR (Table 2,
Figure 9). As originally reported by Jerlov (12), the decrease in k (x) with
increasing wavelength is exponential (Eq. 6):
kh(x)
(6)
239
-------
Table 2. SPECIFIC ABSORPTION COEFFICIENTS, Iq,, FOR
DISSOLVED HUMIC ACIDS AT pH 11 AND 465 nm (Ref. 19)
Source of
Organic Carbon k., l(mg Org. C) m
Fluka Humic Acid 1.8
Aldrich Humic Acid 1.9
Karl Roth Humic Acid 2.4
Fulvic Acid - CA Soil 1.1
Aucilla River, Lament, FL 0.70
Humic Acid - Sod Podzolic3 0.92
Humic Acid - Gray Forest3 2.46
Humic Acid - Ordinary Chernozem3 2.51
Humic Acid - Meadow Solonetz3 1.70
Humic Acid - Serozem3 1.84
Humic Acid - Brown Mountain Forest3 1.50
Humic Acid - Mountain Meadow3 2.21
Humic Acid - Shallow Red Soil3 1.91
Humic Acid - Cinnam-on Brown Soil3 1.40
aData of Orlov concerning humic acids from Soviet soils (20)
Zepp and Schlotzhauer (19) determined values of A and B for a variety
of humic substances (Table 3) and found that, for humic substances in natural
waters and for soil-derived fulvic acids, mean values of A and B were 0.6
and 0.014, respectively, with wavelength x in nanometers. Remarkably, both
H0jerslev (18) and Bricaud and coworkers (17) also found mean values of 0.014
for B when Eq. 6 was applied to their spectral data for humic substances in
various parts of the sea. Assuming that additional studies confirm the gener-
ality of Eq. 6, the component of the diffuse attenuation coefficient attribu-
table to humic substances K^x) can be computed using Eq. 7 where the distri-
bution function D (11) equals about 1.2 in the uv region (2,18).
Kh(x) = D kh(x) Ch (7)
The data in Table 2 indicate that the mean value for the absorption
coefficients an,450of natural waters at 450 nm equals 0.6 meter-! when C is
1 mg organic carbon/liter. Thus Eq. 7 also can be expressed as:
240
-------
Kh
-------
Table 3. PARAMETERS THAT APPLY TO COMPUTATION OF SPECTRAL VALUES OF
SPECIFIC ABSORPTION COEFFICIENTS FOR VARIOUS HUMIC SUBSTANCES, kh,
WHERE kh=AeB(450-A) liter (mg Org. C)-lnH( Ais the wavelength in
nanometers) (Ref. 19)
Source of
Humic Material
PH
B
A
r2
(No. of Values)
Fluka humic acid 6.0
Aldrich humic acid 6.0
Roth humic acid 6.0
Humic acid - mean values
Fulvic Acid*- NC Soil 6.0
Fulvic Acid - CA Soil 6.0
Contech Fulvic Acid 6.0
Fulvic acid - mean values
Aucilla River, Lament, FL 6.0
Okefenokee Swamp, Waycross, GA 4.1
Suwanee River, FL
Fenholloway River, Foley, FL 7.7
St. Marks River, St. Marks, FL 8.7
Quincy Bog, Tilton, NH 5.2
Williamson River, Klatnath Agency, OR 6.6
Ogeechee River, Savannah, GA
Ec^onfina River, Perry, FL
Freshwater aquatic humus -
mean values
6.5
4.2
0.0100
0.0104
0.0104
0.0102+0.0002
0.0146
0.0139
0.0128
0.0138+0.009
0.0147
0.0145
0.0140
0.0134
0.0116
0.0150
0.0152
0.0175
0.0152
0.0145+0.0017
1.86
1.90
1.52
1.76+0.21
0.861
0.909
0.623
0.80+0.15
0.680
0.634
a
1.09
0.780
a
0.382
a
0.702
0.71+0.26
0.993 (31)
0.999 (31)
0.999 (31)
0.997 (31)
0.999 (31)
0.996 (28)
0.999 (31)
0.999 (31)
0.996 (31)
0.996 (31)
0.998 (16)
0.994 (31)
0.999 (31)
0.996 (31)
0.998 (31)
Gulf of Mexico, St. Marks, FL 8.1
North Seab 8.1
Gulf of Mexico, near Tampa, FLC
Marine aquatic humus - mean values
All aquatic humus - mean values
0.0151
0.0140
0.0149
0.0147+0.0006
0.0145+0.0014
0.249
0.42
a
0.33+.09
0.60+0.28
0.998 (16)
0.952 (13)
aNot determined.
bComputed from data of Hjfjerslev (18) assuming yellow substance contains 50% by weight of
organic carbon.
cComputed from diffuse attenuation coefficients (320 to 380 nm) for Gulf of Mexico near Tampa
(14).
Additional studies indicated that Ti02 photocatalyzed the decomposition of
£-dichlorobenzene in water through an indirect photoprocess but that various
clays and natural sediments were ineffective. Finally, it was shown that
photolysis of methoxychlor (300 nm) was slower in the presence of suspended
sediments than in distilled water. The slowdown was attributed to light at-
tenuation by the sediments.
Miller and Zepp (22) compared the photolysis rates and products of four
organic chemicals at trace concentrations in suspensions of natural sediments
obtained from water bodies of the United States. The kinetic data were analyz-
ed to disentangle the effects of light attenuation and scattering from the
influence of sorption upon photoreactivity of the chemicals. The photoreacti-
vities of the DDT metabolite DDE and the phenyl ketone, m-trifluoromethylpen-
242
-------
tadecanophenone, were found to be different in the sorbed state than in water,
increasing with DDE bur decreasing with the ketone. Product studies indicated
that the sorbed chemicals were in a microenvironment that is a considerably
better hydrogen donor than water. Major products derived from photolysis of
DDE sorbed on sediments are shown below:
<-•; CHCl
V II
Cl (9)
+ other products
Cl
The studies discussed above were conducted after equilibrating the
suspensions for short time periods, usually less than one day, prior to irradi-
ation. Pesticides that are resistant to biodegradation or hydrolysis, however,
may remain sorbed in soil or bottom sediments for a long time prior to intro-
duction into the water column of water bodies by runoff or resuspension. Zepp
and Schlotzhauer (23) have reported that photolysis rates of DDE sorbed in
aqueous sediment suspensions are affected by the length of time that the DDE is
sorbed on the sediment (Figure 10). The terms Co and C in Figure 10 refer to
the initial concentration of DDE in the suspension and the concentration after
exposure to light, respectively.
The kinetic data for photolysis of sorbed DDE could be satisfactorily
described by the model show in Eq. 10.
XrKp
P^
R ^
^ U
(10)
-u
Products
Products
This model, which is consistent with Karickhoff's interpretation of sorption
kinetics (24), provides that the sorbed DDE, although predominantly photoreac-
tive immediately after sorption, gradually diffuses into a microenvironment
where the chemical is unreactive. The symbols p, R, and U represent concentra-
tions of DDE dissolved in the aqueous phase, sorbed on the reactive part, and
sorbed on the unreactive part, respectively. When the system is irradiated P
and R rapidly photoreact, then photolysis becomes rate-limited by diffusion of
DDE form the unreactive to the reactive environment. Kinetic parameters de-
rived from analysis of the data (Table 4) indicate that: (1) after 60 days
about half of the sorbed DDE was unreactive, (2) the DDE sorbed in the reactive
part reacts at a higher rate than when it is dissolved in water, (3) at least
200 hours are required for one-half of the DDE sorbed in the unreactive part
243
-------
to diffuse into the reactive part. Karickhoff's work indicates that the mag-
nitude of k-u for various pesticides is likely to be inversely proportional
to the sediment-water partition coefficient Kp (24).
Light Attenuation and Scattering by Suspended Sediments
The studies described in the preceding section all had one common finding:
the predominant influence of suspended sediments was a reduction in photolysis
rates caused by light attenuation. Basic principals involved in predictive
modeling of light attenuation are discussed in an earlier section. Miller and
Zepp (15) studied the photoreactions of several organic chemicals in sediment
suspensions to obtain quantitative data concerning the effects of light atten-
uation and scattering by sediments on photolysis rates. Photolysis rates of
the uv-sensitive (330 nm) actinometer, malachite green leucocyanide, were
determined as a function of depth in aqueous sediment suspensions under sun-
light. Diffuse attenuation coefficients, Ks, were computed from slopes of
plots of the natural lorarithms of the photolysis rates of the actinometer
versus depth in the suspensions. Specific attenuation coefficients, ks, were
computed by ratioing Ks to the sediment concentration (mg/liter) (Table 5).
H In suspension, equilibrated
60 days
* In suspension, equilibrated
4 days
O In aqueous phase
2468
Relative Dose of Light
10
Figure 10. Photoreaction of DDE in aqueous suspension of Ohio River
sediments (80 nig/liter) and in aqueous phase of the sediment suspension
(23).
244
-------
Table 4. KINETIC PARAMETERS DESCRIBING PHOTOREACTION OF
SORBED DDE IN SEDIMENT SUSPENSIONS (Ref. 23)
Parameter
(Xr)eb
k.u.hr-l
(ku + k_u), hr-1
Light attenuation factor
60-day ks/kw
EPA-12
0.56
5.8 x 10-3
1.4 x ID"2
0.33
3.2
Sediment3
EPA-26
0.43
3.7 x ID"3
6.3 x ID"3
0.30
3.1
EPA-13
0.62
4.7 x 10"3
c
c
c
asediments obtained from an Ohio field (EPA-12) , the Mississippi
.River (EPA-26) , and the Ohio River (EPA-13) .
Fraction of sorbed DDE that was photoreactive after being
sorbed for 60 days.
CNot determined.
Results indicated that k$ values fell in a surprisingly narrow range consider-
ing the diversity in the origin of the sediments.
Other experiments were conducted by Miller and Zepp (15) to ascertain the
effects of light scattering on photolysis rates. The photoreaction of
Y-methoxy-rn-trifluoromethylbutyrophenone, MTB), (Figure 11), dissolved in
various clay or sediment suspensions, was studied with Georgia sunlight as the
light source. Under the reaction conditions the MTB was predominantly dissolved
in the aqueous phase of the suspensions. Typical kinetic results with the
clays are shown in Figure 12. The photolysis rate constant for MTB was actual-
ly enhanced in the presence of the uv-transparent clays. The enhancement was
Table 5. ATTENUATION COEFFICIENTS FOR SUSPENDED SEDIMENTS
(Ref. 15)
Sediment
Concentrati on
mg/1 Ks ,cm"^a
Oconee River (GA)
USDA Pond (
Hickory Hil
Broad River
Missouri Ri
Mi ssi ssippi
GA)
1 Pond
(GA)
ver
Ri ver
82 +
17 +;
(GA) 44 +
41 i
100 i
106 +_
6
5
12
6
20
3
0.
0.
0.
0.
0.
0.
35
14
34
16
32
50
k
4
8
7
4
3
4
s >
.8
.2
.7
.0
.2
.7
1 mg'1 crrrlb
± °-
± 3-
± 2-
± °-
± °-
± °-
5
0
5
9
6
5
X
X
X
X
X
X
ID'3
10-3
10-3
ID'3
10-3
10-3
aDiffuse attenuation coefficient at greater than two optical depths.
^Specific attenuation coefficient due to sediment; average value
5.4 + 2.0 X 10-3 i mg-l cm-l.
245
-------
OCH?
Sunlight
m—CF3C6H5
-OH
MTB
Figure 11. Photoreaction of y-methoxy-m-trifluoromethylbutyrophenone
(MTB) in sediment suspensions (15, 22, 23).
attributed to the increased pathlength of light caused by scattering by the
clay particles. Light attenuation by the uv-absorbing kaolinite offset this
increase at higher clay concentrations.
Smith and Fahy are presently conducting a more detailed study of the
effects of sediments on penetration of uv and visible solar radiation into
natural waters (25). These studies include experimental measurements of the
transmission of solar spectral irradiance into sediment suspensions and
development of equations that can be used to predict the light attenuation in
unmeasured aquatic environments.
Effects of Microorganisms on Photolysis
Several investigations indicate that microorganisms, especially algae,
may influence the photolysis rates of certain pesticides in the aquatic envi-
ronment (26-30). That algae can mediate indirect photodegradation of certain
chemicals is well established. For example, O'Kelley and Deason (27) presented
data that established that the algal transformation of malathion proceeds much
more rapidly in the presence of visible light than in the dark or in irradiated
culture medium (Table 6). Cerniglia and Gibson (29) have found that blue-green
algae can catalyze the hydrolylation of aromatic hydrocarbons.
Table 6. MALATHION DEGRADATION RATE CONSTANTS AND PESTI-
CIDE HALF-LIVES FOR ILLUMINATED CULTURES (calculated as a
pseudo-first-order process) (Ref. 27)
Organi sm
Replicate
Half-life, hr
aChlorella
(Isolate #1)
aAnacy sti s
ni dul ans
Control
(no al gae)
1
2
1
2
1
2
0.454
0.241
0.044
0.064
0.006
0.004
1.54
2.89
15.9
10.8
116.4
240.2
aCell concentration 4 g wet weight/1 of culture medium
246
-------
Research is presently underway to examine the kinetics of light-induced
microbial transformations. Several para-substituted derivatives of nitroben-
zene, including methyl parathion, photoreacted more rapidly in distilled water
containing green algae than in distilled water or in growth medium (Table 17).
Nitrobenzene itself, however, was not susceptible to this effect. In all of
these studies, controls established that no biodegradation occurred in the
dark during the photolysis period. Because the studies were conducted at
algae concentrations that are much higher than natural levels, additional ex-
periments are required to assess the significance of these reactions.
Conclusions
The results described herein lead to the following major conclusions.
(1) Humic substances accelerate photolysis of pesticides in natural
waters by photosensitizing several types of reactions, including oxygen-
ations, isomerications of unsaturated compounds, and hydrogen transfer
reactions. Rates of these reactions vary from one pesticide to another,
but generally the reactions are most rapid in colored natural waters
such as swamp water.
2.0r
1.6
§ 1.2
0.8
0.4
0.0
w
Halloysite
'Hectorite
Kaolinite
10
100 1000
CLAY CONCENTRATION, mg/l
Figure 12. Effects of suspended clays on the photolysis of MTB; kclay
and kwaterare first-order rate constants for photolysis in clay suspen-
sions and in distilled water, respectively, at a depth of 2.9 cm under
sunlight (15).
247
-------
Table 7. EFFECTS OF THE GREEN ALGAE Chlorella pyrenoi-
dosa ON LIGHT-INDUCED TRANSFORMATION OF VARIOUS CHEMI-
CALS IN MAY SUNLIGHT (Ref. 30)
Chemical
Methyl parathion
£-Nitroanisole
p-Nitroacetophenone
Ni trobenzene
Am' 1 i ne
Medi urn
Distilled H20
Distilled H20
Distilled H20
Benson-Ful ler
Benson-Ful 1 er
Benson-Ful 1 er
Distilled H20
Distilled H20
Distilled H20
Distilled H20
Distilled H20
Distilled H20
Distilled H20
Distilled H20
Algae Cone.
(g/l)
0
0.79
0.15
0
0.57
0.057
0
0.15
0
0.18
0
0.15
0
0.18
kp, hr-1
0.01
0.30
0.09
0.05
0.19
0.09
0.094
0.12
<0.01
0.10
<0.02
<0.02
<0.005
0.03
Malathion Benson-Fuller 0.57 0.05
(2) Photosensitizing and spectral properties of humic substances derived
from diverse natural waters and soils fall in a remarkably narrow range.
Humic substances in water bodies attenuate solar radiation most strongly
and photosensitize oxygenations most rapidly in the uv and blue spectral
region.
(3) The major effect of suspended sediments on photoreactions appears
to be retardation attributable to light attenuation, and in the case of
hydrophobic chemicals, to diffusion of the chemical into unreactive
sorption sites. Light attenuation by suspended sediments in the main
determinant of the penetration of photochemically active solar uv radi-
ation into many inland surface waters. As with the humic substances,
specific attenuation coefficients for sediments from diverse water
bodies were found to be very similar.
These conclusions are based on very limited data, and additional research
is required to determine their generality. Nonetheless, results such as the
finding that absorption coefficients of complex humic substances in freshwater
and the sea can be computed by a simple relation (Eq. 6) provide encouragement
that prediction of the influence of natural substances on photolysis rates may
not be as complicated as it originally seemed.
References
1. Baughman, G.L. and L.A. Burns. Transport and Transformation of Chemicals:
A Perspective. In: The Handbook of Environmental Chemistry, Vol.2. Part A.
Hutzinger, 0. (Ed.). Springer-Verlag, New York. 1980. P. 1-17.
248
-------
2. Zepp, R.G. and D.M. Cline. Rates of Direct Photolysis in Aquatic Environ-
ments. Environ. Sci. Technol. JJ_:359, 1977.
3. Zepp, R.G. Quantum Yields for Reaction of Pollutants in Dilute Aqueous
Solution. Environ. Sci. Technol. 12:327, 1978.
4. Miller, G.C., R. Zisook, and R. Zepp. Photolysis of 3,4-Dichloroaniline
in Natural Waters. J_. Agr. Food Chem. 18:1053-1056, 1980.
5. Khan, S.U. and M. Schnitzer. UV Irradiation of Atrazine in Aqueous Fulvic
Acid solution. J_. Environ. Sci. Health. B13: 299-310, 1978.
6. Zepp, R.G., N.L. Wolfe, G.L. Baughman, and R.C. Hollis. Singlet Oxygen in
Natural Waters. Nature. 267: 421-423, 1977.
7. Zepp, R.G., G.L. Baughman, and P.P. Schlotzhauer. Comparison of Photo-
chemical Behavior of Various Humic Substances in Water: I. Sunlight Indu-
ced Reactions of Aquatic Pollutants. Chemosphere. 10: 109-117, 1981.
8. Zepp, R.G., G.L. Baughman, and P.P. Schlotzhauer. Comparison of Photo-
chemical Behavior of Various Humic Substances in Water: II. Photosensitized
Oxygenations. Chemosphere. IJh 119-126, 1981.
9. Wolff, C.J.M., M.T.H. Halmons, and H.B. van der Heijde. The Formation of
Singlet Oxygen in Surface Waters. Chemosphere. 10: 59-62, 1981.
10. Mill, T., D.G. Hendry, and H. Richardson. Free-radical Oxidants in Natural
Waters. Science. 197: 886, 1980.
11. Smith, R.C. and J.E. Tyler. Transmission of Solar Radistion into Natural
Waters. Photochem. Photobio. Rev. 1_: 117-155, 1976.
12. Jerlov, N. Marine Optics. Elsevier: Amsterdam. 1976.
13. Smith, R.C. and K.S. Baker. Optical Classification of Natural Waters.
Limnol. Oceanog. 23: 260-267, 1978.
14. Baker, K.S. and R.C. Smith. Bio-optical Classification and Model of Natu-
ral Waters II. Limnol. Oceanog. 1981. In press.
15. Miller, G.C. and R.G. Zepp. Effects of Suspended Sediments on Photolysis
Rates of Dissolved Pollutants. Water Res. 1_3: 453-459, 1979.
16. Smith, R.C. and K.S. Baker. Optical Properties of the Clearest Natural
Waters (200-800 ran). Appl. Optics. K): 177-184, 1981.
17. Bricaud, A., A. Morel, and L. Prieur. Absorption by Dissolved Organic
Matter of the Sea (Yellow Substance) in the UV and Visible Domain. Lironol.
Oceanog. 26_: 43-53, 1981.
18. H0jerslev, N.K. On the Origin of Yellow Substance in the Marine Environ-
ment. Univ. Copenhagen Inst. Phys. Oceanogr. Report 42. 1981. In press.
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19. Zepp, R.G. and P.P. Schlotzhauer. Comparison of Photochemical Behavior
of Various Humic Substances in Water: III. Spectroscopic Properties of
Humic Substances. Chemosphere. 10: 479-486, 1981.
20. Orlov, D.S. Soviet Soil Sci. 1278, 1967.
21. Oliver, G.G., E.G. Cosgrove, and J.H. Carey. Effect of Suspended Sedi-
ments on the Photolysis of Organics in Water. Environ. Sci. Techno!.
13: 1075-1077, 1979.
22. Miller, G.C. and R.G. Zepp. Photoreactivity of Aquatic Pollutants
Sorbed on Suspended Sediments. Environ. Sci. Technol. ]_3: 860-863, 1979.
23. Zepp, R.G. and P.P. Schlotzhauer. Effects of Equilibration Time on
Photoreactivity of the Pollutant DDE Sorbed on Natural Sediments.
Chemosphere. JO; 453-460, 1981.
24. Karickhoff, S.W. Sorption Kinetics of Hydrophobic Pollutants in Natural
Sediments. In: Contaminants and Sediments. Vol. 2. Baker, R.A. (Ed.).
Ann Arbor Science: Ann Arbor. 1980. p. 193-205.
25. Smith R.C. and B. Fahy. The Effects of Suspended Sediments on the
Penetration of Solar Radiation into Natural Waters. Scripps Institution
of Oceanography, Visibility Laboratory. Personal Communication. 1981.
26. Wright, S.L.J. Interactions of Pesticides with Micro-algae. In: Pesti-
cide Microbiology. Hill, I.R. (Ed.). Academic Press: New York. 1978.
p. 535-590.
27. O'Kelley, J.C. and T.R. Deason. Degradation of Pesticides by Algae.
U.S. Environmental Protection Agency: Athens GA. Report No. EPA-600/3-
76-022. 1976.
28. Matsumura, P. and E. G. Esaac. Degradation of Pesticides by Algae and
Aquatic Microorganisms. In: Pesticide and Xenobiotic Metabolism in
Aquatic Organisms. Khan, M.A.Q., J.J. Lech, and J.J. Menn (Eds.).
American Chemical Society: Washington DC. Symposium Series 99. 1979.
29. Cerniglia, C.E. and D.T. Gibson. Algal Oxidation of Aromatic Hydro-
carbons: Formation of 1-Naphthol form Naphthalene. Biochem. Biophys.
Res. Comm. 88:50-57, 1979.
30. Zepp, R.G. and P.P. Schlotzhauer. Light-induced Transformations of
Trace Contaminants Involving Algae. 1981. In preparation.
This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication.
250
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APPROACHES TO THE STUDY ON THE KINETICS OF
LIQUID-PHASE PESTICIDE TRANSFORMATION
Yu.I.Skurlatov
Institute of Chemical Physics, Moscow
L.S.Ernestova, T.V.Shpotova
Institute of Experimental Meteorology, Obninsk
In the general case, the rate of chemical transformation
of a pollutant in environmental objects is the sum of its
transformation rates in various reaction channels which to
a first approximation can be considered independent. In each
of the channels the rate is a function of environmental para-
meters. Despite a great variety of factors determining the sta-
te of the environment, only some of them affect the rate of
chemical detoxication of a pollutant in one or another reaction
channel.A ma^or purpose of laboratory research is to reveal
possible transformation routes of a chemical and significant
parameters as well as to establish a functional relationship
between the effective rate constant and these parameters. In
this case, two approaches are possible: either carry out stu-
dies on natural water samples and find statistical regularities;
or study the mechanism of chemical transformation of a pollu-
tant on the principle "from simple to complex", the latter
approach being considered by us as a more productive one.
As an example, we have studied the mechanism of liquid-
phase transformation of 3,4-dichloroaniline(DCA), an interme-
diate transformation product of amido-containing compounds.
Data are presently available on the destruction of chlorinated
anilines, mainly due to photochemical and microbiological proces-
ses, as well as on the overall characteristics of the DCA de-
composition dynamics in some natural waters ( I ) . On the other
hand, information is almost lacking in literature on the kine-
tics and mechanism of DCA transformation in the dark, and on
the photoinduced oxidation of DCA, with free radicals partici-
pating. And the processes mentioned can play an important part
in the pollutant transformation under environmental conditions.
Studies on the kinetics of the process in the dark were
carried out in isothermal regime using an installation with
251
-------
flowing gas and light-isolated reaction vessels. In these expe-
riments, a contribution of evaporation to the observed rate of
the process did not exceed 2%. Analytical control of DCA con-
centration was performed by spectrophotometry and gas chroma-
tography. Kinetic analysis was carried out by the initial
rates method.
Under natural conditions chemical processes occur, as
a rule, at insignificant rates. Consequently, it is necessary
to intensify chemical reactions in laboratory studies due to
increased temperature and introduced additions of natural water
components at concentrations exceeding their natural content.
In the general case, multicomponent natural water can be
subdivided into a number of subsystems. The simplest of them
is distilled water with regulated additions of various compo-
nents of natural water. We considered a great number of such
subsystems under both aerobic and anaerobic conditions in
the dark and with photoinitiation of radicals.
Additions of phosphate buffer to distilled water were
found not to affect significantly the rate of DCA transforma-
tion. The rate of the reaction depends only slightly on pH of
the medium over the pH range 5.3 to 8.8 and can be given by
the first-order equation with respect to DCA concentration:
d [ACA] .
In the neutral medium at a temperature 2^>2c, the effective
rate constant Keff is (0.7 I O.I) '10 s,.1 and the effective
activation energy is 6.5 * I.Q Kcal»mol •! .
Low activation energy indicate that the process of DCA
destruction under anaerobic conditions is associated with
the presence of indigenous ions of transition metals. Ions of
Fe(II,III) are known to be very abundant impurities. Their con-
centration can be as great as 100 mg»l in natural waters
and I0"~6 mol»l"~i in distilled water. The assumption of a possib-
le role of ferric ions was tested by sets of experiments where,
on the one hand, we varied the concentration of Fe(III), and
on the other hand, introduced sodium ethylene diamine tetraace-
tate(EDTA) converting ferric ions to a nonreactive form. As
seen from Fig. I, the rate of the reaction is proportional to
£p , Extrapolation to zero rate corresponds to a content of
inaigenous ferric ions 2 to 3 •10""" mol'l . The same amount
was found colorimetrically in the initial solution using vtfL' -
bipyridyl after the reduction of ferric ions by ascorbic acid
additions.
Anaerobic conditions were establihed by purging argon
through, the solution. This technique,however,did not enable us
to eliminate completely the traces of oxygen.Residual concent-
ration of oxygen was about 10
252
-------
1-1
K}4[EDTA],TT,ol-r
0,5 1.0 1,5
2.0
8 -
CO
Fig. I. Dependence of the DCA oxidation rate on additions
of ferric ions(I) and EDTA (2) ; 3 -
tion in the presence of ICT? mol'l
-jDCA transforma-
presence o T mo' r of Fe(III) and
mol-l"1 of EDTA. t = 70°C, [DCA]0o = 5'IOj?
mol-1-, pH = 7 , [phosphate! = 6.7- 10"^ mol-1 .
Experiments were carried out at the atmosphere of
argon with a small addition of oxygen at a content
of indigenous ferric ions in the solution of
2*IO~"b mol'l""1. Arrows show the corresponding
abscissas.
Introduction of LDTA into the system completely inhibits
the process (Fig. I). Thus, the main route of chemical transfor-
mation of DCA in distilled water is not hydrolysis but its oxi-
dation by indigenous ferric ions likely to be present in water
in the form of microcolloid hydroxide. Apparently, one should
take into account a possible role of ferric ions, when analyz-
ing the influence of other factors, such as the content in water
of hydrogen peroxide and ligands of organic and inorganic ori-
gin, exposure to sunlight, etc.
Considering the obtained data on the oxidative character
of DCA destruction by ferric ions, it is interesting to study
the influence of ions of copper and other heavy metals. As seen
from Fig. 2 (curve I), the rate of DCA transformation depends
253
-------
Fig.2. Dependence of the DCA oxidation rate on additions of
Cu(II) ions in the absence of Wn r,, (I) and in the
presence of 0.5 mol'l"1 Nad fW0ctc, (2); 3 - linear,-
anomogphosis of curve (I): (W0 Cu' - W0 )/[Cu2t! = 2.5 •!(>""?
5'10 [Cuz+J ,where Wo is the background reaction raT
te without Cu(II) additions. I - [DCAL = 5»IO~;?mol-l7-L
2 ~ &C£10 = 3±p~:?mol.l~r, pH=7, t = 70°C, [phosphate] =
6.7*10 mol»l .Conditions are close to anaerobic
ones (argon with a small admixture of CU). Arrows show
the corresponding ordinates.
quadratically on the content of Cu(II) ions. Along with the
one-electron channel, the contribution of which is characteriz-
ed by a section cut off by a linear anamorphosis(Fig.2,curve 3)
on the ordinate, the two-electron channel is realized due to
the cooperation of two Cu(II) ions in an elementary act of DCA
oxidation.
Experiments with additions of cations of manganese(II),va-
nadium(V) and mercury(II) at concentrations to lO-^mol'l""1 did
not reveal the influence of these ions on the rate of DCA oxi-
dation.
In addition to the ions of polyvalent metals,the rate of
substrate transformation can be influenced by various anions
and organic compounds present in natural waters. This influ-
ence can show itself as the change in concentration and com-
position of the coordination sphere of soluble forms of tran-
254
-------
sition metal ions; formation of donor-acceptor complexes with
the substrate; and interaction with intermediate free radicals.
0_ We varied the concentrations of anions Cl~", NO*, SO^^, and
COz from 0 to 4.0 mol«l for Cl~ and to I0~^ mol-l for the
otners. The chosen interval of concentrations overlaps the ran-
ge of possible levels of these nucleophiles in natural water
bodies. Additions of chloride-ions were found to result in
increased rate of the process. _j
It is interesting that in the presence of 0.5 mol'l
chloride-ions the order of the reaction with respect to Cu(II)
concentration becomes first(Fig.2,curve 2). This fact appears
to be associated with increased redox potential of Cu(II) du-
ring the formation of chlor-aquacomplexes, resulting in incre-
ased contribution of the one-electron channel. _2 _j
Varying the concentration of sodium sulfate to I0~ mol-1
and sodium iodide to I0~^ mol»l did not affect significantly
the rate of DCA transformation,whereas additions of nitrate-
ions had a pronounced inhibiting effect. Dependence of the ini-
tial rate of DCA oxidation on the concentration of nitrate-
ions is given by hyperbolic function. Similar character of the
dependence was also observed in the presence of ferric ions at
a concentration of lO""-3 mol'l .
Organic compounds in natural waters are represented by
a great variety of substances, among which are humic and ful-
vic acids tending to form donor-acceptor complexes and associ-
ates.
As humic acids were added to distilled water, the rate of
the process was found to decrease, amounting to one half the
initial rate even in the presence of 5 mg/1 of humic acids.
These results agree with the literature data,according to which
iron converts from the microcolloidal state to a watersoluble
complex with dissolved organic matter (DOM) in the presence of
DOM of natural origin. It is likely that the polydentate charac-
ter of this ligand sharply reduces the redox potential of fer-
ric ions, thus acting similarly to EDTA.
Oxygen and the product of its two-electron reduction-
hydrogen peroxide, are the most important environmental factors.
Typical content of HpOo in natural water bodies amounts to
10 - I0~5 mol "I"1 \ 2 ) . In some cases,however, it can be
even higher. Hydrogen peroxide is known to be an effective
source of free radicals in the presence of metallic ions.
Under anaerobic conditions in the presence of hydrogen
peroxide the rate of the reaction increases, as does the effec-
tive activation energy which amounts to (° ° n 0>l Tr~""1 rl'1
in the neutral medium at a concentration
In the presence of J^Oo, the
to substrate concentration remains, as well as the linear cha-
racter of the process rate dependence on the content of Fe(III)
ions. In this case, as in the absence of H20o additions, the back-
ground reaction rate corresponds to that in the presence of indi-
genous ferric ions in the initial solution at a concentration of
255
-------
(2.0 - 0.5)«IO mol'l , irrespective of pH of the medium.
This suggests a catalytic character of H^Op interaction with
DCA where ferric ions act as catalysts.
The character of C H2o2 influence on the rate of DCA
transformation is determined by pH of the medium and the con-
tent of metallic ions. At pH = 7.0 the rate of DCA oxidation
is proportional to CC°'H that is indicative of the chain
mechanism of the proc2ess with a quadratic chain interruption.
The chain mechanism can be realized due to the initiation of
radicals, as ferric ions interact with both DCA and Ho02. The
resulting ions of bivalent iron interact with HpOp that leads
to the occurrence of highly reactive radicals OH In the system.
At high concentrations of hydrogen peroxide , radical OH
interacts with ^Op to form superoxide radicals (OZ) in the
system. As a result, a catalytic process of HpOo decomposition
to water and oxygen can be realized.
In the absence of competitive additions and at relatively
low concentrations of HpOo » radicals OH and Op interact with
DCA, and those of DCA formed in the system interact with fer-
ric ions. Occurrence of the chain mechanism of DCA oxidation
in the dark is evidenced by a decrease in the reaction rate,
as ethanol which is a typical inhibitor of chain-radical reac-
tions is introduced into the system (Fig. 3). ^he rate decreases
to the same value, irrespective of the initial concentration
of Hp02» this value coinciding with the rate of DCA oxidation
in the absence of ^Op additions. In this case, the initiation
of radicals is accomplished mainly through the interaction of
ferric ions with DCA
[2]
where P is the product of the two-electron channel of DCA oxi-
dation.
When using copper ions as catalysts, one observes a linear
increase in the rate of DCA oxidation, as the concentration of
HpOp and Cu(II) increases, suggesting the chain process.
In the presence of ^Opjthe inhibiting character of the
influence of nitrate-ion and humic acid additions remains.lt
is seen that the effect of these additions shows itself mainly
at the stage of radical initiation. It is this stage that de-
termines the rate of DCA transformation both in the absence
and in the presence of Hp02 additions.
We have studied the dependence of the process rate on the
concentration of oxygen in the absence of ^02 additions. The
experiments were carried out in an atmosphere of pure oxygen
and its mixtures with argon. The influence of oxygen on the
256
-------
[CjH5OH],mot-r
Fig. 3. Influence of ethanol additions on the rate of
DCA oxidation in the absence (l) and in the pre-
sence(2) of I0~3 mol-1"1 HpOp. Conditions are
close to anaerobic ones (barnotage of the system
with argon and a small admixture of 02) • IpCALo
6 • ICT-'mol • I"-1 , pH = 7 , fphosphatel = 5.7* W
-1"1, t = 70°C. L J
process rate turned out to be much the same in its character
as that of hydrogen peroxide: the rate of DCA oxidation in the
presence of oxygen increases, and depending on pH of the medi-
um, the order of reaction with respect to Co2 changes from zero
(pH = 9.0) to first (pH=4.0). The effective activation ener-
gy of DCA oxidation at pH = 7.0 was found to be about 6.0 Kcal*
mol-1 irrespective of Q£ concentration.
At high concentrations of oxygen, radical of dichloroani-
line formed in the stage of initiation interacts with Oo to
yield peroxide radical. Then the latter can either undergo
recombination, or break down to form superoxide radical, or
interact as an oxidant with other components of the system,
such as DCA and ferric ions.
In a mixed oxygen-peroxide system, the rate of DCA trans-
formation does not depend on pH of the medium over the pH
range 5«3 to 8.8 and is proportional to the concentration of
ferric and copper ions. Here, too, the inhibiting influence of
nitrate-ions and humic acids remains. The order of the reacti-
on with respect to C n2o2 is fractional, suggesting the chain
mechanism of the process.
Thus, the data presented indicate that the kinetics of
DCA oxidation in aqueous solutions is determined by the inter-
257
-------
action of this substrate with microimpurities of metallic ions.
The influence of other' components of water shows itself through
a change in the content of active metallic form in the solution.
Since the influence of these factors has an additive character,
the expression for the' overall rate of DCA transformation can
be presented,depending on the significant parameters of natural
waters for the process in the dark,in the form:
where WQ is the background rate of reaction with indigenous
ions of metals; [^"Hadd is the addition of ions of a transi-
tion metal M™" ; ana[MRj is the background concentration of
the same metal in water. °
In this expression, the coefficients and order of the re-
action with respect to the concentration of oxidants in the
general case depend on pH of the medium. At pH 7.0 the order
of the reaction with respect to 02 and EUOo is close to 0.5.
Under these conditions, the initial rate or DCA oxidation measu-
red experimentally in the simultaneous presence of varied addi-
tions is in satisfactory agreement with the rate calculated by
equation [4]
The next stage of the work was studying the photochemical
transformation of DCA and its oxidation by free radicals OH
and HCpCO^). Photolysis was studied under anaerobic conditions
in a tnermostated quartz cell under the action of filtered
light produced by a mercury-vapor lamp AHU -1000. Additions of
2? 2 were use(i "to generate free radicals. In this case, the
rate of 02 absorption or liberation was measured.
In agreement with the data presented in ( 3) , we found
that under irradiation of DCA solutions with light at a wave-
length X = 313 nm there occurs photoinduced hydrolysis yiel-
ding 2-chloro--5-aminophenol. In this case, no radicals of the
substrate are formed,since oxygen and additions of ascorbic
acid acting as acceptors of free radicals do not affect the
rate of the process. UV-irradiation of the DCA -^02 system
leads to the effects of two types: photoinduced hydrolysis and
oxidation of DCA at a rate equal to that of radical initiation
during the photolytic decomposition of H202» It should be noted
that under irradiation of DCA solutions with light at a wave-
length of 313 nm a major contribution to the measured rate of
the process is made by direct photolysis, whereas at a wave-
length of 365 nm there occurs radical oxidation of DCA. In the
absence of DCA, the rate of photochemical decomposition of
^2^2 ^y ^k6 chain-radical nfechanism is proportional to the squ-
are root of the light intensity,suggesting a quadratic chain
interruption. In the presence of DCA, the rate of ^02 decom-
position is proportional to the value of light intensity and
concentration of hydrogen peroxide to the first power,i.e.
the chain interruption becomes linear. Consequently, DCA par-
ticipates in the chain interruption.
258
-------
Data on the inhibitory influence of DCA on the rate of
H~0P decomposition are given in Fig.4. Analysis of these data
l§aas to a conclusion that at the stage of chain interruption
DCA interacts with superoxide radical^. The rate constant of
this reaction is about 3*10 mol" ~"
half the order of magnitude.
1's to an accuracy of one
co
8
-6
r
2 3
Kr[DXAj,moM~1
Fig.4. Dependence of the rate of 02 release during the
photolytic decomposition of H?0P under the action
of light ( X = 365 nm) on additions of DCA(I).
\ = 365 nm. 2 - linear anamorphosis of curve
(I). Arrows show the corresponding ordinates.
We have also studied the influence of various components
of natural waters on the rate of photochemical transformation
of DCA. In this paper we shall not consider these data. Just
note that as a result of these studies it was concluded that
there occurs no photosensitized reaction due to humic acids
introduced to the solution at concentrations up to 10 mg/1.
Based on the data obtained, it is possible to give some
recommendations on the technique for extrapolating the results
of laboratory measurements to natural conditions. The studies
conducted showed that DCA has two major distinctive features:
259
-------
relatively high quantum yield of direct photolysis and parti-
cipation in oxidation-reduction transformations. Therefore,
without considering microbiological processes, it is possible
to select two main environmental parameters which determine
the mechanism and rate of DCA transformation in natural waters,
^hese are the intensity of insolation and the presence of oxi-
dants and catalysts in a given natural water.
Effectiveness of direct photolysis depends on the absorb-
ing capacity of natural water and the values of light scatter-
ing. Taking into account these factors is amenable to numeri-
cal modeling.
Oxidative properties of natural water,namely,the content
of transition metallic ions(in particular,iron in the active
form),hydrogen peroxide, and active intermediate particles,
such as hydroxyl and superoxide radicals, depend on pH of the
medium and the presence of iron and copper ions, as well as
ligands and inhibitors in the solution. At present it does
not seem possible to relate the oxidative capacity of water
to any of the accepted parameters of natural waters. The pur-
pose of further studies is to develop methods for determining
concentrations of active intermediate particles under natural
conditions.
LITERATURE CITED
I. Strekozov,B.P.;Gryzlova,G.K.; Chimishkian,A.L. et al. On
herbicide transformations in water, air and soil. Khimija
v selskom khozjaistve /Chemistry in Agriculture/ 1979>
zja,
-31
17, No.12, 28-51 /in Russian/.
2. Sinelnikov,V.E. The mechanism of v/ater body self-purifi-
cation. Publishing House "Stroyizdat":Moscow} 1980,
III p. /in Russian/.
3. Miller,L.S.; Mill, M.J.; Crosby,D.L.; Scutum,S.; Zepp,
R.L. Photosolvolysis of 3,4—DCA in water: evidence for
an aryl cation intermediate. Tetrahedron I979f 35,No.15,
1797-1800.
260
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EFFECT OF SOME ECOF ACTORS ON 3 , 4-DICHLORO ANILINE
DEGRADATION IN NATURAL WATER
O.K. Vasilyeva, N.D. Anan'eva, M.S. Sokolov
Institute of Agrochemistry and Soil Science,
USSR Academy of Sciences, Puschino
ABSTRACT
The studies are concentrated at 3,4-dichloroaniline (DCA)
behavior in the modeled system "natural water". Disappearance
of DCA (5 and 50 mg/1) from water is affected by physico-chemi-
cal and microbial processes. In the water based on a meadow-
chernozemlike soil, transformation and degradation of DCA occur
mostly under the action of facultatively-aerobic bacteria, all
processes being accompanied by the DCA dechlorination. In the
water based on a grey forest soil, the dominating factors in
the pollutant disappearance are its absorption by organo-miner-
al colloids and chemical oxidation. Degradation of DCA is opti-
mized by aeration and neutral or mildly alkali medium reaction
(pH 7-8). When assessing the rate of natural water sources
self-cleansing capacity in regard to DCA, the researcher has
to take into consideration acidity of the medium, the content
of dissolved oxygen and the ability of the population of sap-
rophyte microorganisms to dechlorinate DCA in the process of
its transformation.
Wide use of phenylamide herbicides in agriculture at-
tracts special attention to 3,4-dichloroaniline (3,4-DCA), one
of the most persistent and widespread products of their de-
gradation. The paper deals with the studies of DCA microbial
transformation in "natural water" and with the assessment of
the effect of some ecofactors on the degradation rate. Two
soils were selected as the study target: grey forest (GF)
slightly loamy soil and meadow-chernozemlike (MC) moderately
261
-------
loamy soil with, humus 2.1 and 3«9%» and pH_ t 5*40 and 6.25,
respectively. "Natural water" was prepared rroffi the mixture of
preliminary incubated soil (composted at 29 C) and distilled
water (1:10). A top layer of suspension formed after a 24-hour
settling was used in the experiment. The layer comprised col-
loid and subcolloid fractions of soil (4). The ratio of solid/
liquid phases in GP/ and MC/natural water was 7:1000 and
16:1000, respectively. To maintain a required value of pH
(5.5; 7.0; 8.0) phosphates of sodium and potassium were intro-
duced into the medium. Glucose (0.01 and 0.1$) was used as an
additional source of carbon supply. Erlenmeyer flasks (100 ml)
were filled with 15 ml of the suspension. Then water solutions
of DCA were added to form DCA concentration in the medium of
the order of 5 and 50 mg/1. Incubation was carried out under
steady-state conditions at 29°C in the dark. Oxygen supply was
also varied (aeration by shaking).
Simultaneously DCA content was registered in the native
and sterile versions of natural water. The samples were ster-
ilized in an autoclave at 120°C and 1 atm overpressure. Con-
tent of DCA and its anilides in the suspension components was
determined after centrifugal separation by gas-liquid chro-
matography and photometry (3). Concentration of chlorions was
measured by potentiometric titration (2). DCA metabolites were
recorded with the help of thin-layer chromatography. The quan-
tity of saprophyte bacteria in natural water was determined by
suspension inoculation in agarized medium No.1 composed accord-
ing to our suggestion (1).
Following DCA introduction (5 and 50 mg/1) into MC-natural
water, it is rapidly redistributed between liquid and solid
phases. Dissolved and reversibly sorbed DCA extracted by an
acetone-hexane mixture (9:1) gradually disappears from the
suspension. DCA partition coefficient in the solid and liquid
phases remains within 14.6-2.0. However, when the dissolved
and reversibly sorbed DCA disappears the colloid fraction of
soil suspension still retains up to 5-14# of the original
chemical. It may be released only as a result of alkali hydrol-
ysis under drastic conditions. This immobilized pollutant seems
to be unavailable for microorganisms and unlike dissolved or
reversibly sorbed DCA, is not degraded.
In the MC-natural water DCA disappearance consists of at
least, two stages (Pig. 1). The first stage is slow disappear-
ance of DCA comparable in rate to this process in sterile sam-
ples. The second stage is rapid disappearance of the pollutant
accompanied by the release of chlor ions. Slow disappearance
of DCA in the first stage (a lag period) is indicative of the
adaptation of microorganisms to the substrate. Simultaneously
there occurs a gradual accumulation of inducible enzymes par-
ticipating in DCA destruction. In addition, at a DCA concen-
tration of 50 mg/1 the substrate itself and its metabolites,
such as 4,5-dichloropyrocatecol, may show a bactericide effect.
During this period one observes a fragmentary formation of
262
-------
tO 20 30
time of incudation, day*
40
Fig.I. Dynamics of DCA disappearance (5 mg/1) from native
(solid line) and sterile (dotted line) samples of
MC-natural water(I,2), and GF-natural water(3,4).
various anilides (formanilide, acetanilide, propioanilide,
butyranilide, etc), aminophenols and phenols. Depending on the
DCA concentration (5 or 50 mg/1), the first stage may continue
from two to twenty days.
When introducing DCA into the CF-natural water, the major
part of the toxicant remains in the solution (Z = 9»8i0.2;,
however, the aquatic systems under study differ-^from each other
both in the toxicant behavior in them and in the content of
saprophyte microorganisms (13-22 and 22-38 mln cells in I ml
of GF- or MC-natural water, respectively). In the native GF-
natural water and its sterile version, DCA disappears slowly,
apparently due to DCA binding to the organomineral colloids
and further transformation under the effect of physico-chemi-
cal factors. After a four-month incubation of GF-natural water
and DCA (5mg/l) the liquid phase produced IJfc of the initial
DCA. Fifty five percent of that amount was an immobilized form
and could be extracted by a mixture of solutes or alkali hyd-
rolysis. No release of inorganic chlor was recorded.
Bacteria inventory showed that DCA (5 mg/1) does not noti-
ceably inhibit saprophyte growth in both natural waters. An
increased dose of DCA (50 mg/1) in the MC-natural water leads
to a slight decrease in the bacteria number. However, some
time later this index reverts to the initial level. In time
this coincides with the beginning of the second stage of DCA
degradation. After a complete degradation of DCA a considerab-
263
-------
le increase in the saprophyte bacteria quantity is observed,
particularly in case of an increased concentration of the
chemical.
In the MC-natural water, DCA degradation was studied un-
der the action of available carbohydrates,oxygen content and
pH of the medium. At pH 7*0, glucose (0.01 and O.I?6) was found
to increase the number of saprophytes by a factor of six and
ten to forty,respectively. Slight acceleration of DCA degrada-
tion was observed only at increased concentration of glucose.
Microorganisms using glucose as energy material do not appa-
rently participate in the true DCA degradation. Buffering of
the medium to pH 8.0 (in the presence of 0.01% of glucose) le-
ads to the double increase of saprophyte number on average,but
does not show any significant acceleration of the DCA degrada-
tion rate as compared to pH 7 (Fig.2). In case of acidification
•1500
Sine of tncutotion, days
v>
<
HWOg
-j
*
c:
Fig.2. Dynamics of DCA (50 mg/1) disappearance from MC-
natural water (with 0.01% glucose) depending on
acidity of the medium; I) pH 5.5 } 2) pH 7.0;
3) pH 8.0. Influence of pH of the medium on sapro-
phyte microflora: the number of bacteria reflects
the difference between the version with pH 5«5 (4),
pH 8.0 (5), and the version with pH 7.0; the initi-
al content of saprophyte bacteria is 424.89 mln/ml.
264
-------
of the medium (pH 5.5), the number of saprophyte bacteria
drops 1.5-2 times, and a significant slowing down of the DCA
degradation rate is observed(Fig.2).
Aeration plays an important part in DCA degradation.With-
out the available air ( in nitrogen atmosphere) DCA disappea-
rance from water was slowed down and approached a sterile ver-
sion (Fig.3). Additional aeration (shaking), though led to
an increase in the number of microorganisms, did not influence
the rate of pollutant disappearance.
fimc of incufation, days
Fig.3. Dynamics of DCA (50 mg/1) diappearance from MC-
natural water (with pH 7.0 and 0.01% glucose) depend-
ing on oxygen supply of the medium: I) anaerobic con-
ditions; 2) steady-state regime; 3) aeration by-
shaking. Influence of aeration on saprophyte micro-
flora: number of bacteria shows the difference
between the versions with anaerobic conditions (4);
aeration (5), and a steady-state regime where the
initial content of saprophyte bacteria is 424.89 mln/
ml.
Thus, the degradation of the major part of DCA in the MC-
natural water occurs under the effect of microorganisms, main-
265
-------
ly facultatively-aerobic bacteria. This is confirmed by the
drop of toxicant degradation rate in case of acidification of
the medium and the absence of degradation under anaerobic con-
ditions.
In the MC-natural water at pH 7-8 and in the presence of
oxygen when dissolved and reversibly sorbed DCA disappears com-
pletely, over 60$ of chlor ions of the maximum possible quanti-
ty are released independent of the glucose content. No accumu-
lation of monochlorinated or unsubstituted anilines is observed
in this case.This indicates that under the influence of certain
species of bacteria there occurs cleavage of the aromatic ring
and complete mineralization of the parent compound. Monochlori-
nated and unsubstituted compounds are known to be easily utili-
zed by soil microflora, as a rule. One can suppose that DCA
transformation in the MC-natural water follows the pathway of
oxidizing deamination and cleavage of the ring of newly-formed
chlorinated pyrocatechol, as it was found in the experiments
on pure cultures. This also explains a significant increase in
the number of saprophytes after the complete disappearance of
DCA. The products of DCA intermediate metabolism apparently
serve as a source of carbon supply for the microorganisms.
Slow disappearance of DCA from the GF-natural water unac-
companied by the release of chlor ions, may be explained by the
absence in the soil of bacteria capable of transforming this
compound. As the grey forest soil unlike meadow-chernozemlike
one, has never been treated with phenylamide herbicides, the
lag period during which a system of corresponding inducible
enzymes is to be formed, is probably distinguished by a longer
duration. The absence of direct correlation between the total
amount of saprophyte bacteria in all the versions of the exper-
iment on the natural water and the rate of DCA degradation in-
dicates that only some definite group of saprophyte bacteria is
responsible for the degradation and utilization of DCA.
Thus,
- disappearance of DCA in the natural water is determined by
physico-chemical and microbial processes;
- in the water formed on meadow-chernozemlike soil, the main
role in the DCA transformation and degradation is played by
microbial processes which is confirmed by a release of chlor
ions;
- in the natural water based on grey forest soil dominating
factors in the pollutant disappearance are its absorption by
the organo-raineral colloids and chemical transformations; the
process of DCA disappearance is not accompanied by a release of
chlor ions;
- pH 7-8 and aeration are the optimum factors for DCA degrada-
tion in the natural water, therefore pH and dissolved oxygen
content should be determined to predict the rate of water
source self-cleansing from DCA. Preliminary assessment of the
capacity of natural microorganism population to dechlorinate
substituted anilines in the process of their transformation is
also important.
266
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LITERATURE CITED
1. Anan'eva, N.D. ; Galiulin, R.V. Effect of 3,4-DCA on the
content of saprophyte bacteria in a grey forest soil.
Khimiya v selskom khozyaystve, 1980, No. 2, 56-57. (in
Russian).
2. Lyalikov, Yu.S. Physicocheraical methods of analysis.
M.: Khimiya, 1974; 357. (in Russian).
3. Knyr, L.L.; Sokolov, M.S.; Perfilova, U.S.; Sukhoparova,
V.P. Methods of determining propanil, linuron, and 3»4-
dichloroaniline in water, soil, and bottom sediments.
Khimiya v selskom khozyaystve, 1976, No. 9, 65-68. (in
Russian).
4. Strekozov, B.P. Study of xenobiotic transformation in
the aquatic medium. In "Problems and methods of ecotox-
icological modeling and prediction". Dep. VINITI, No.
532-78. M.I 1978.
267
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TRANSPORT OF PESTICIDES AND
RELATED CHEMICALS ACROSS AIR-WATER
INTERFACES
by
Louis J. Thibodeaux
Professor of Chemical Engineering
College of Engineering
University of Arkansas
Fayetteville, Arkansas 72701 U.S.A.
ABSTRACT
Anthropogenic chemicals including pesticides and related organic
chemicals are transported in both directions across the interfaces of
surface waters. Attempts at realistic prediction depend upon iden-
tifying the correct transport mechanisms and then using the appropriate
thermodynamic and transport coefficients. From the standpoint of fate
it is important to be able to treat vaporization and absorption from
water surfaces; but it is equally important to consider all the sources
and sinks associated with air-water interfaces. Those interfaces are:
industrial wastewaters from manufacturing, wastewater treatment, the
receiving streams, rivers, ponds, lakes and the oceans. Some of these
interfaces can be sources and others can be sinks.
Pan evaporation treatment of woodtreating wastewater containing
pentachlorophenol and naphthalene results in the volatilization of these
chemicals into air. The rate equation is of the form:
N = NTT(x -x *)
A W a a
Where N is the flux rate of chemical A and water (W), xais the mole
fraction of A in water and xa* is the mole fraction in equilibrium with
A in the air. This equation has been verified for volatization of
pesticides in the presence of high water evaporation rates. As the water
evaporation rate decreases the more conventional rate equation, with Ny
replaced with K^2» the overall mass-transfer coefficient, may be used.
The latter relationship has been verified for the case of methanol
vaporizing from 7 to 110 acre ponds (1 acre = .4047ha).
268
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The above rate equations are examples that reflect extremes in
volatilization mechanisms. The ramifications of the natural environment
also presents interface extremes and corresponding variability in air
and water phase transport coefficients. This paper will review the
existing body of transport science associated with the entire range of
natural air and water interface processes, the availability of models,
verification studies, important missing information and areas for pos-
sible research.
INTRODUCTION
Much is known about the physicochemical processes which control
the movement of volatile substances across air-water interfaces. The
volatile substances of concern include pesticides and a host of other
man-made chemicals. Although the latter are not specifically applied
to the natural environment they do find their wav to this place. There is
a continuing need to update and re-think existing models, validate
existing models with field data and to fabricate new, more general models
that will encompass all the variations in transport that exist in nature.
As the desire for more realistic prediction methods develop highly
sophisticated models will be needed. The new models will need to have
time, position, and circumstance as independent variables.
It was sufficient in years past that air-water interface transport
models had the capability of predicting order of magnitude flux rates,
residence times, and compartmental concentrations in idealized eco-
systems. (See Figure 1) This level of sophistication was considered
adequate for general fate studies, screening of classes of chemicals
for distribution, general aspects of hazard assesment and similar
studies. In recent years there has arisen a need for a set of specific
purpose transport models capable of making more precise predictions of
rates, times, and concentrations.
Figure 1. Chemical Volatilization
from an Idealized Ecosystem.
269
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Where the focus has been mainly on waterbodies, such as oceans and
large lakes, it has now changed to include smaller portions of such
bodiesandalso small lakes, rivers, streams and surface impoundments.
The occurances of interest are accidental chemical spills on water,
treatment, and storage of hazardous waste in impoundments, etc. With
increasing frequency these occurances involve population centers where
there is the need to assess the hazard in exposing humans and related
biota to volatile chemicals. (See Figure 2) Somewhat apart from the
emphasis and tack of the pesticide chemist there have been efforts by
industrial chemists to build volatile chemical transport models that
involve a broad spectrum of organic and inorganic chemicals. (See
Table 1)
This paper will emphasize the general problem of volatilization of
chemicals from water and to a degree the deposition onto water. The
perspective will be that of an industrial chemist whose primary goal is
to develop a set of models that apply to the near-field region of the
/•v
Figure 2. Volatilization and Exposure of Biota.
270
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source. The sources in this case are operations associated with
manufacture, transportation and disposal of chemicals. These sources
are driven by natural processes such as wind and natural temperatures
but are also driven by chemical process-type mechanical and thermal
devices.
TABLE 1 BRIEF SCENARIOS OF CASES INVOLVING VOLATILIZING CHEMICALS
FROM WATER SURFACES
PCB from the Hudson River the source of which are contam-
imated sediments
Chlorinated hydrocarbons from a harzardous waste storage
impoundment
Odorous and nuisance substances from wastex^ater treatment
basins.
Ammonia from a stream receiving ammonium nitrate
Miscible chemicals spilled in bulk quantities on water
which have appreciable vapor pressures
Chemical slicks created from immiscible floaters which
are pure or mixtures _ __
BASIC PRINCIPLES AND SPECIAL CIRCUMSTANCE MODELS
Interphase transport principles find their basis in Fick's first and
second laws of molecular diffusion. Failure of the molecular derived
laws in the presence of turbulence has resulted in the use of a simple
rate equation
NA = KACA (1)
which contains the parameter K which has been termed a mass transfer
coefficient, turbulent transfer velocity, and chemical transport coeffi-
cient. In Equation 1, N. is the flux of substance A in mol/cm .S, C^
is molar concentration of A in dilute solution in mol/cm and K is the
coefficient in cm/s. The complexity of arriving at realistic coefficients
belies the simplicity of the equation. Employing a Fickian version of
the flux equation with a turbulent diffusion coefficient equated to
Equation 1
« ' B (2)
A
Ay
suggest that K may have inherited yectorial properties upon its creation
since AC./Ay is a vector and K E D^'/Ay. There seems to be ample theo-
retical basis to assume that K is dependent upon the direction of mass
271
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general principles and relations by which chemical vaporization (or
absorption) rates can be obtained. Attention need be focused on the
individual transfer coefficients for it is the proper selection of these
values that will determine the realism of the calculated volatilization
rate from the particular water surface circumstance.
Resistances in Parallel and Series
At the moment, there is little alternative but to use such models
in attempts at determining sources and sinks of some substances on a
global scale. In 1974, Liss and Slater used this method to calculate
the direction and flux of methane, carbontetrachloride, freon, methyl
iodide, dimethyl sulfide, S0~, N_0 and CO across the air-sea interface.
In the calculation the average coefficients k (H?0) = 3000 cm/hr and
k (C02) = 20 cm/hr were used in the resistance's in series relation.
THat same year Thibodeaux and Parker used the same general formulation
to calculate emission rates of alcohols, aldehydes, acids and related
organic residuals present in wastewater treatment. The wastewater
basins varied in size from a few hectares to several hundred hectares.
Mackay and Leinonen also used the same method for estimating volatil-
ization half-life from water of sixteen organic chemicals and metallic
mercury. The organics included the pesticides DDT, lindane, dieldrin
and aldrin.
In the case of the wastewater volatiles, the simple resistance in
series idea was inadequate. Due to the placement of mechanical surface-
water stirrers in the basins there were localized regions in which
coefficients were enhanced and driven by altogether different mechanisms
(see Figure 3). It was necessary to account for the parallel volatili-
zation paths by
f / — \ / j_ \ 1 A
(5)
where KV. and K are overall coefficients that are characteristic of
the natural and turbulent zones respectively, and where the total water-
air interfacial area is A = A + A which is made up of the natural area
A and the turbulent area -A_
n t.
The overall coefficients for each zone were obtained from Equation
3. However four individual coefficients were needed to characterize the
localized transport mechanisms. The natural gas-phase coefficient was
obtained from water evaporization studies performed on lakes since trans-
port in this zone of the surface impoundment was not unlike that of
lakes. The coefficient for water vapor is:
K(n) = 11.8 v /A1/2° (6)
g on
where vft is the wind speed at eight meters above the water surface in
miles per hour (1 mi/hr = 0.447 m/s), A is in acres (1 acre = .4047ha)
272
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Figure 3. Mechanical Aerated Zones and Natural Zones
of a Wastewater Treatment Surface Impoundment.
transfer. This aspect has particular application to employing measured
values of chemical absorption coefficients in estimating desorption rates.
The implied assumption is that the driving force, AC., accounts for the
directional aspect.
The essence of the two-resistance theory for the interphase movement
of chemicals was first proposed by Lewis and Whitman^ in 1924. The film-
theory for mass transfer was also in its prime at the time and to date
the two-resistance theory is erroneously referred to as the two-film theory.
The idea of resistances in series developed is designing an absorber for
hydrogen chloride in water. For the case of volatilization (or absorption)
of pesticides and other chemicals across an air-water interface, the famil-
iar form of the two-resistance theory is
_
K
_ -i
kT
H k C
* g g
(3)
where K]_ is the overall coefficient to be used with the flux equation
N. = K
containing a liquid phase concentration difference in mol/cm,
k^ is the liquid side coefficient in cm/s, kg is the gas side coefficient
in cm/s, C± and Cg are the molar density of water and air at the existing
temperature and pressure in mol/cm-^ , and Hx is Henry's law constant in
mole fraction form (i.e., Hx = YA/x.).
The thermodynamics of phase equilibrium of the chemical species vola-
tilization is manifest through the Henry's law constant. Reduced to more
fundamental parameters the 'constant
Hx =
PA/PT
(4)
is a term which contains the activity coefficient of molecule
A in water, y t the pure component vapor pressure of the chemical,
P ° and total pressure P_ • The activity coefficient is a physical
A ' f
chemistry parameter which reflects the forces of attraction or repulsion
the molecule has with the surrounding layers of water molecules. Molecules
273
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which do not interact adversely with water have yA values of unity and
those with low solubility have large positive values of y^. Billing
developed simple formulation containing chemical vapor pressure and
solubility that can be used to estimate Henry's law constant; however, the
equation is limited to chemicals with finite solubility. Warner, Cohen
and Ireland^ measured Henry's law for 41 potentially toxic compounds and
found that the equation obtained values corresponded closely to their
experimental values. For those substances that are infinitely soluble in
water, it is necessary to calculate activity coefficients directly and
group contribution methods (see Sherwood, Reid and -Prausnitz^) are most
convenient to use.
Presuming concentrations in either the air or water are available, then
only the appropriate gas and liquid side coefficients need be obtained to
make flux calculations. In summary, Equations 1, 3 and 4 represent the
and k^11' is in ft/hr (1 ft/hr = .0085 cm/s). Equation 6 results from the
work of Harbeck. The natural liquid-side coefficient was synthesized from
stream re-aeration studies based on oxygen transfer to flowing water. The
mechanical mixers impart a horizontal velocity to the surface water not
unlike the velocity in natural streams. Based on detailed studies of the
flow patterns in such impoundments equivalent water velocities and water
depths can be chosen to use the work of Owens, Edwards and Gibbs :
= 1.67 v;-67/h°'85 (7)
where v is the average velocity of the surface water in cm/s, h is the
depth of this moving surface layer in cm and k j~ is the oxygen absorption
coefficient in ft/hr. In fairly large impoundments well away from the
influence of the mechanical stirrers, it seems reasonable to use
0.035 v,. = v and h of one-half the impoundment water depth.
Details of transport coefficients for the turbulent zones and areas
of influence will not be given here. Obviously, these parameters will be
a strong function of the energy imparted to the water surface by the
mechanical stirrers and the number of stirrers employed. Freeman and
Freeman and Klieve ^ extended the parallel resistance concept, further
developed the methodology for characterizing the turbulent zone and essen-
tially verified the model by measuring the volatilization of acrylonitrile
from a laboratory simulated wastewater treatment reactor. Thibodeaux,
Parker and Heck recently completed extensive field measurements on
methanol emissions which verify significant aspects of the model. Typical
transport coefficients for this partially natural and partially machine-
influenced volatilization process are shown in Table 2.
It is fairly obvious that in the above case of volatilization from
wastewater treatment it is necessary to account for variations of transport
mechanisms on the surface in order to obtain realistic emissions. Even
this degree of adapting to circumstance will not satisfy some critics who
make the case for diurnal variation in meterology, particularly the wind,
as being unaccounted for, and indeed Equations 6 and 7 are only valid if
wind is present. Both these equations reflect the wind speed as a parameter.
274
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TABLE 2 NATURAL VS TURBULENT TRANSFER COEFFICIENTS FOR METHANOL IN
SURFACE IMPOUNDMENTS
12
Zone
gas-side, natural zone
liquid-side, natural zone
gas-side, turbulent zone
liquid-side, turbulent zone
Symbol Average
k^ 2,680
k(n) 9.30
k(t) 13,900
g
k^ 8,700
Range (cm/hr)
630 to 3920
2.87 to 18.0
6900 to 18,800
2560 to 13,700
Emissions will continue under low or no-wind, conditions and chemical concen-
tractions in air will likely be much higher in the near-field of such surface
impoundments. Alternative mechanisms are operative and alternative para-
meterized expressions reflecting the transport circumstance need be devel-
oped and verified. Despite these criticisms, the general model is being
proposed by Hwang1-^ to calculate source strength when volatilization from
surface impoundments is occuring in an attempt to predict downwind concen-
trations in air.
Vectorial Nature of Transport Coefficients
The creation of transport coefficients by definitions such as Equation
1 simplifies the flux expression but requires that the coefficient inherit
all the dependence of the particular environment and circumstances of the
transport. As was mentioned previously, a certain amount of vectorial flavor
has also been imparted to the coefficient. This aspect is particularly
important when a calibrated expressions (i.e., Equation 6 and 7) obtained
under species absorption conditions is used for volatilization predictions.
Volatilization of pesticides from water surfaces involves dilute
solutions both in the air and aqueous phases. The chemical flux rates are
also low when compared to the rate of water evaporation from the same sur-
face. The direction and rate of water vapor movement across the air-water
interface can affect the magnitude of the K-type transfer coefficient.
Bird, Steward and Lightfoot-^ present a theoretical argument of how this
can occur and show the magnitude of the effect based on the film, penetra-
tion and boundary layer theories. In the case of pesticide evaporation,
the movement of water vapor through the interface distorts the concentra-
tion profiles. As an example of the results of the theory, if the liquid
phase controls the rate of mass transfer, the film theory rate equation is:
" '
275
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where N is^water flux rate, x is the mole fraction of chemical A in the
water and x is the mole fraction in equilibrium with that in air, Y .
If there is a negligible amount of A in the surrounding air (i.e.,
x = 0) and N » N Equation 8 transforms to:
A B A
exp (Vl) ] (9)
exp (NB/kl) -ij
For the case of pesticides with vaporization rate controlled by the liquid
phase resistance, the direction and magnitude of water vapor flux can
dramatically affect the rate.
Phenomena which create interfacial turbulence can also be direction
dependent. Interfacial turbulence occurs when a liquid surface moves due
to interfacial tension differences created by uneven concentrations of
sorbed substances on the surface. Localized density gradients near the
surface due to the flux of one or more substances through the surface, can
also cause interfacial turbulence. Interfacial turbulence can enhance or
suppress the flux rate of chemical species moving through the interface.
See Bergl5 for a review of observed interfacial turbulence effects on
mass-transfer rates. The presence and/or effect of interfacial turbulence
with respect to volatilization or absorption or" chemicals at the natural
air-water has not been reported to date to the knowledge of this author.
It seems plausible that cool rain falling upon salty water, warm water,
etc. would create a situation of intense density gradients at the surface
and affect chemical transport rates to some degree.
The "acid rain" problem which is acute in the Northeastern United
States, Canada, Sweden and other regions of the world has a dry deposition
component which appears to be as important as the wet removal process.
Williams^^ quantifies the net dry deposition flux with an extension of
Equation 1:
N. = k AC* + k.AC* + V C* - V C* (10)
A c Al d A2 g A2 r AS
where k is the turbulent transfer velocity (= D /Ay), k, is the molecular
transfer velocity (3)/Ay, D is the molecular diffusivity) V is gravita-
tional settling velocity, C?_ is concentration at the water surface and V
is the resuspension velocity. This simple formulation neglects explicit
consideration of several more complex phenomena, including diffusiophoresis,
thermophoresis, humidity effect on particle size and, at the interface, the
roles of surface film, bubbles and sprays.
For a neutrally stratified turbulent layer over a deposition layer next
to water, which will completely absorb (i.e., V,. = 0) the impacting mole-
cule or particle, Equation 10 can be simplified to:
NA - Vl.10
*
where CA is measured in the constant flux ...(or mechanical turbulence) layer
at a height of 10 meters and the deposition velocity, V is estimated by:
276
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D V V + kV^S
o
The first term on the right-hand side is the resistance in the turbulent
layer and the second is that in the deposition layer. In this equation,
V-. is the wind speed at 10 meters, V^ is the friction velocity, Sc is the
Schmidt number (ScHV/D), and k = 0.4, von Karman's constant.
The vectorial nature of the deposition velocity is easily seen if one
considers large particles, for which Sc is very large, being deposited
under conditions where the turbulent resistance term is small. In this
case, V - V , the deposition velocity is approximated by the gravitational
settling vel§city of the particle. In the case of gases:
VD * kV^Sc~2/3 (13)
For extensions to the deposition velocity concept see Slinn and Slinn.
Effect of Meteorological Conditions and Geometry on Volatilization
Transport Coefficients
In order to obtain realistic volatilization rates of chemicals from
specific water bodies, it will be necessary to have available the specific
meteorology, micrometeorology, hydrology, water chemistry, geometry,
location of man-made structures and other circumstances of the site.
Coupled with this information, realistic mixing models, reaction(chemical,
biochemical, photochemical) kinetics, thermodynamic, sorption, and inter-
phase transport coefficients will be needed. This section will consist
of a review of what is available with respect to transport coefficient
estimation, a listing of gaps in this area which must be addressed and
some numerical results that point out the variability of coefficients.
Wind and Natural Convection Driven Transport
Oxygen transport coefficients intended for purposes of water quality
studies and conventionally termed "reaeration coefficients" provide a
source of k, expressions. A report by Zison, Mills, Deimer and Chen18
gives numerous expressions and tabulates k^ for rivers, lakes and estuaries.
The section on river reaeration is very complete. There is also a section
on reaeration coefficients for stratified systems, such as stratified
lakes and estuaries. Generally, the expressions contain terms for wind
velocity in addition to, or in place of hydraulic parameters, since wind
can be the major driving force inducing turbulence into the flow field.
This is especially true in lake systems where the net advective velocity
may approach zero. For stratified systems the surface transfer coefficient
is only valid for a depth over which the dissolved chemical concentration
can be considered constant. This depth is always less than the depth of
the system. Six emperical expressions for the surface transfer coefficient
277
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of water bodies influenced by the wind are presented.
dependence appears most often in the following form
k, = a + bVn
The wind speed
(14)
where V is wind speed, a, b and n are constants. Values of n reported are
h, 1, 1% and 2 depending upon the investigator. Another functional form
presented is
= c/(d - eV2)
(15)
Wind also affects the reaeration rate of flowing water. The ultimate
expression predicts the reaeration in streams with wind blowing across the
surface. Apparently, wind begins to play a significant role in reaeration
at speeds above 1.6 miles per hour (0.72m/s). It is possibly not sufficient
to consider wind speed alone, but direction as well, in dealing with inland
waterbodies protected by complex surroundings and topographic features.
A brief review of k coefficients covering experimental measurements
in laboratory wind tunnels and insitu measurements at the sea surface
has been given by Thibodeaux.19 Numerical values observed in wind tunnels
ranged from approximately 1 cm/hr at near zero wind speed to 60 cm/hr at
30 m/s wind speed for C0_ and oxygen. Insitu sea surface measurements
ranged from 4 to 36 cm/hr for CO . A reported common range for 0 at lake
surfaces is given as 1.3 to 6.4 cm/hr. Of specific note in this review is
the work of Cohen, Cocchio and Mackay?^ Benzene and toluene mass-transfer
coefficients of - 0.5 to 2.0 cm/hr at zero wind velocity to near 40 cm/hr
at 40 m/s wind velocity. A predictive expression based upon water surface
roughness Reynolds number is presented.
21
Brtko and Kabel develop models that they claim are a first attempt
to actually predict liquid phase mass transfer coefficients from first
principles. In particular, two models characterizing wind induced turbu-
lence in a water body are invoked to estimate the coefficient. The "eddy
cell" model result is:
1/4
(16)
and the "large eddy" model result is:
(17)
where V^ is the friction velocity at the surface, Z' is some depth below
the surface, and H is the total depth of the water body. The other terms
in the equations are k=0.4, V, the kinematic viscosity of the water, Sc,
the Schmidt number, pa, air density, pw, water density, D, molecular
diffusivity and k , the liquid phase coefficient. Predicted values of both
the models underestimated field measured values. The agreement for eddy
cell model was best, and at the frequently observed low wind speeds, the
E78
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agreement is good. The field measurements used for comparison was data
gathered by R. R. Weiler from several sources and ranged from 2 cm/hr at
1 m/s wind speed to 200 cm/hr at 20 m/s. It should be noted that both
these models contain a depth parameter. This work is apparently the first
to include such a geometric parameter in a non-flowing, liquid phase
coefficient formulation.
22
O'Connor, in an unpublished report by DiToro and O'Connor, presents
the following equation for estimating the liquid phase coefficient when
the wind velocity controls the transport at the water surface:
Sc v
where C is the drag coefficient and X is "related to" the viscous sublayer
fetch and height of the waves. For low wind speeds, C = .0016, X = 6 is
suggested. With Sc = 1842, pa/pw = .0012, and V = 1 m/3, a value of
k1 = 0.46 m/d (1.9 cm/hr) is reported for the volatilization of PCB from
trie Hudson River.
Direct evidence on chemical emissions from surface impoundments is
scant. Horton, Corey and Wallace present some field data on the loss
of HTO from a basin 13,000 m^ surface area and 4.5 meters in depth. Based
on the tritium desorption data for a three year period, it appears that
k, varies from 0.020 cm/hr to 0.085 cm/hr and the transport is liquid
phase controlling. It also appears that the basin was likely stratified,
in that the authors mention slow water mixing caused by the slight specific
gravity difference between the waste containing a small amount of salt and
the rainwater.
The above considerations for k.. are limited in that they do not
generally apply to those atmospheric conditions characterized by very low
wind speeds or no wind. It is these conditions which are referred to as
extreme meteorological conditions and that require modification of the
mass transfer method. Estimation of the chemical volatilization rates in
the presence of surface winds may tend to grossly overestimate the rate
if an average daily rate or an average yearly rate is desired. On some
days and on many nights wind ceases altogether. During this period natural
convection processes would appear to control the emission rate. Sill and
Gaertner^4 and Ryan and Harleman^S consider the natural convection trans-
port aspects of water in the gas phase above cooling ponds.
Figure 4 shows hypothetical profiles of a common natural convection
volatilization situation for surface impoundments when the water (water is
chemical B) is warmer than the overlying air (air is chemical A). This
scenario can also occur when lakes are cooling in the fall of the year.
The center profile demonstrates a decrease in water temperature as the
surface is approached, equilibrium at the interface and then a further
decrease in temperature with increase in distance from the interface.
Both microregions (i.e., air and water) immediately adjacent to the
interface are unstable thermally. The left profile demonstrates the
decrease in water vapor content of the air with increase in height above
279
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the interface. The evaporating water creates an additional instability
in the air microregion above the interface. The right profile demonstrates
the change in concentration of the volatile chemical of interest from the
water side, through the interface and then above the interface. The discon-
tinuity at the interface is due to phase equilibrium. Due to the low con-
centrations on either side of the interface, it is unlikely that sizable
density gradients exist due to the chemical concentration gradients there.
Formally, the density gradient on the air side can be expressed by
dpa =
— §.
dy
— R + _
a[dy P dy
(19)
, o - 1 3pa
where 3 = — -fi- Y l
B ^" "*B T,P are the coefficients of thermal
expansion of the air and the coefficient of mass expansion respectively.
Correlations exist, based upon numerous experimental measurements,
for heat transfer from a heated plate facing upward in a fluid. This
situation simulates, to a degree, the heat transport of either side of the
interface of a surface impoundment. The correlation for turbulent heat
transfer is:
Nu = 0.14 (GrPr)
1/3
(20)
3 2
where Nu (Nusselt No.)=nL/K, Gr (Grashoff No.)EL g£AT/V , Pr(Prandtl No.)E
C pV/K, and h is the surface heat transfer coefficient, L is length of the
heated surface, K is the thermal conductivity of the fluid, g is the gravi-
i nterface
'WATER
H2o vapor
Direction of heat
transfer, water evapora-
tion and chemical emiss-
i on.
Temp.
Chemical Cone.
Figure 4. Natural Convection Profiles for T T
w
280
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tational constant, AT is the temperature difference between the interface
and well beyond, V is the kinematic viscosity of the fluid, C is the heat
capacity of the fluid and p is the density. Based upon a thermal difference
a heat transfer coefficient, h, can be obtained and transformed to an
equivalent mass-transfer coefficient, k , by use of the Chilton-Colburn
analogy: 'T
l/3
An equation analogous to Equation 20 can be used to estimate kg , gen-
erated by the water vapor difference: '
ShB = 0.14 (GrgSc)- (22)
where Sh,, = k L/D, Gr E L3g?AY_/V2.
B g,y B
For the purposes of demonstration, a calculation was made based on a
water temperature of 31°C and air temperature of 24 C. These are typical
summer temperatures for industrial lagoons. Methanol was chosen as the
volatile chemical for calculation purposes. Equation 20, for each phase,
yielded hg = 3.27J/M2'S'K and h = 149.36J/M2' S«K and Ti (interface) =
30.8°C. Converting to the "heat transfer" generated mass-transfer coeffi-
cients yield kg T= 811 cm/hr and^ T = 0.540 cm/hr. Using Equation 22 for
the gas-phase yielded a water vapor 'mass-transfer coefficient of 913 cm/hr.
This value corrected for methanol is k = 668 cm/hr. If it is assumed
that the thermal and water vapor instability derived mechanisms of trans-
port are additive so that the sum of the coefficients can be made; then
the entire gas-phase coefficient for methanol is:
k =k + k (23)
g g,T g,y
22
which yields k = 1479 cm/hr. Ryan and Harleman combine the thermal and
water vapor gradients by use of a virtual temperature but admit that their
proposed equation is somewhat conservative at very high virtual temperatures.
Deviations of 40 percent to 110 percent were displayed when compared to
pond data. The two resistance equations, Equation 3, with Henry's law
constant H(=y/x) = 0.27 yields an overall liquid phase coefficient of
KI = 0.188 cm/hr. Both phases contribute to the coefficient.
The above considers the case of unstable microregions near the air-
water interface. For the case of neutral and stable conditions without
wind, there appears to be no published material with respect to volatili-
zation from water. In both these cases, the volatilization is controlled
by molecular diffusion processes. By invoking some reasonable scenarios
and simple transport models, it is possible to obtain some order-of-
magnitude estimates of kg and k^ for the neutral and stable conditions.
The occurrence of radiation fog over low lying land or water on clear
nights confirms the calmness of the winds on those nights. As the land or
water radiates heat and becomes cooler, it cools the air immediately above
281
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the surface. This causes a temperature inversion to form, the temperature
for some distance upward increasing with height. When the air is cooled to
its dew point, fog forms. Radiation fog is often quite shallow and is
usually most dense at the surface. Even on nights without fog formation
extremely calm conditions do exist.
On such nights molecules of volatile chemicals desorb from the sur-
face and diffuse upward. The molecular diffusion process becomes dominant
in the evening after the convective effects of wind and thermals have sub-
sided. It is unlikely that absolutely still air exists throughout the
entire evening, but it is not unrealistic to assume that such conditions
exist for a few hours. The penetration theory, which allows for unsteady
state diffusion after periods of interruption, will be used as the model
for chemical vaporization from the water surface and diffusing into the air
layer. This process is interrupted periodically by stray air currents, the
remnant of day-time currents. The penetration theory interpretation of the
gas-phase coefficient is:
k = 2 /D/TTt (24)
O
where t is the lapse time between stray air current interruptions. Using
carbon dioxide with a diffusivity in air of 0.164 cm 2/s at 25°C, yields
k values of 27.4, 19.4, 13.7 and 11.2 cm/hr for interruption times of 1,2,
4 and 6 hours.
Another order-of-magnitude estimate can be obtained by assuming laminar
air flow across the water surface and using the laminar boundary layer
theory:
For a wind of 5400 cm/hr across a water surface of L = 100m in length, the
gas-phase coefficient obtained for C02 is k = 12.2cm/hr. The computed
values for both models are approximately 308 times less than the unstable
day-time values.
Calculations similar to the above, but for the liquid phase coeffi-
cient, can be made using oxygen as a basis. The penetration theory model
suggests that k-^ = .287, .203, .144 and .117 cm/hr for Q£ with interruption
times of 1,2,4 and 6 hours. Interruption times are the same as for air
currents since the air is assumed to be the same source of disturbances
for the water. Assuming the water velocity is 3.5 percent of the air
velocity, a diffusivity oxygen in water of 1.80E-5"cm /s yield a
ki = 0.0086 cm/hr for the laminar boundary layer equation. It appears
that the model predicted liquid phase coefficients can be approximately
100 times less for neutral and stable water than for unstable or day-time
values.
In summary, it is instructive to display the transport coefficients
in graphical form with time-of-day as the independent variable. This
display is shown in Figures 5 and 6 for log kg and log k^ of the values
282
-------
computed above. The diurnal variation is assumed to be dominated by calm
conditions at 5:00 a.m. and windy conditions at 2:00 p.m. These times
are where the minimum and maximum environmental extremes are suspected to
be on a "typical" day. The variation in both coefficients is dramatic.
Emission of volatiles likely decreases at night. This is also the time
of day in which most odor complaints associated with surface impoundments
are made. Although the rate of emission is lower, the concentration of
volatile material in air is higher, due to the lack of mixing by winds or
thermals.
Co-transport of Water Vapor, Geometry and Other Factors
There are several other parameters that affect chemical volatilization
transport coefficients from the air-water interface to a degree that they
cannot be overlooked when considering special circumstances. The co-trans-
port of water, particularly evaporation, has been mentioned in association
with Equation 9 as being one such factor. Water, due to its extremely
high concentration, moves through the air-water interface at rates several
thousand times faster than any other volatile component. Table 3 shows
the effect that water evaporation rates can have on the liquid phase
transport of pentachlorophenol. These calculations are for the film co-
transport model. Similar enhancements are also predicted by the penetra-
5
^E 4
o
c
o 3
CVJ
1C
° 2
D)
J3> 1
cn
o
0
m i dn
ze
ro water fl
ux rate
^2^_£^^^
8 f ]_ "^x unstable
/ stableS
vx / ssx
o
• measured in field
© calculated values
-0-accepted ocean average
46 10 1
ight noo
2 13 20
n m
24
i dn i ght
0
o
1 1
4
Figure 5. Diurnal Variation of Gas-Phase
Coefficient.
283
-------
tion and boundary layer theories. Recent work by Thibodeaux, Merrill
and Walbacb.26 demonstrates that the flux rate of pentachlorophenol and
naphthalene is tied to the evaporation rate of water during pan-evaporation
"treatment" of the wastewater from the wood preserving industry. The
flux equation predicted by the film co-transport model, Equation 9, and
observed in the laboratory is:
NA - NB XA
(26)
Observed KI for pentachlorophenol and naphthalene in laboratory pan-
evaporation experiments varied from 0.18 to 1.8 cm/hr. The water evapo-
ration rate was 0.18 to 1.35 cm/hr.
o
c
CM
O
O
0
^
Dl
O
•1
-2
zero water flux rate
• measured in field
o calculated values
.0. accepted ocean average
mi d
0 4
n i ght
10 12 13 20 24 4
noon m i dn ight
Figure 6. Diurnal Variation of Liquid Phase
Coefficient.
234
-------
TABLE 3 PENTACHLOROPHENOL VOLATILIZATION RATE COEFFICIENTS
ENHANCED BY WATER EVAPORATION
Condition
(cm/hr)
NB (cm/hr)
(cm/hr)
Enhancement
Unstable
8.12
8.12
8.12
,000
,144
,648
8.12
8.19
8.45
.00
.86
4.1
Stable
.187
.187
.187
.000
.144
.648
.187
.268
.669
.00
43.
260.
k is with no water flux, k* is with water flux.
Geometric factors of the surface impoundment and lake environments
such as depth and fetch could have a sizable effect of k^ values. One
aspect of this is contained in the "eddy cell" and "large eddy" models of
Brtko and Kabel.^l Both models contain a water depth parameter associated
with mixing and penetration of turbulence. On another scale, the resis-
tance to air induced water movement for those basins with shallow depth
need be addressed. Mixing and water currents in shallow lakes, due to
wind, will likely be significantly less than those in deep lakes. The
same can be questioned about the position of the thermocline on k. values.
Lake and ocean surface transport is affected by interactions of wind
and water, which can cause high degrees of localized turbulence and
transport. Measurements involving the Radon anomly and similar techniques
in the near-surface waters of the ocean, yield time integrated transport
coefficients that is effective for periods of weeks to a month. Phenomena
such as Langmuir curculations and breaking wind-waves (i.e., whitecaps)
can conceivably result in high localized volatilization. The magnitude
of the effect of these and other intense events upon k^ need be investi-
gated before average sea-surface and lake-surface coefficients can be
used with confidence.
MEASUREMENTS OF IN-SITU CHEMICAL VOLATILIZATION RATES
There are very few measurements of chemical volatilization rates
from natural water surfaces. Most information and correlations have been
obtained from laboratory measurements involving wind-water tanks or simi-
lar devices operated to simulate natural water surfaces. A few "field"
measurements have been made in the water phase and the chemical volatili-
zation rate inferred from disappearance rates. Current efforts associated
with quantifying emissions from surface impoundments holding hazardous
material may partially alleviate the lack of data. Current efforts
reported by Thibodeaux, Parker, Heck and Dickersorr^ are aimed at. developing
235
-------
air monitoring techniques which have the capability of measuring the flux
of volatile chemicals from water surface. This method is an adaptation
of the turbulent profile method used by pesticide chemists to measure
flux of pesticides from soil surfaces. A method based upon the Gaussian
plume model concept is also under development. Hopefully, the use of
these two techniques will yield chemical volatilization rate data which
can then be used in transport model verification studies.
SUMMARY
Although much is known concerning the volatilization of pesticides
and related chemicals from water surfaces, our capability of predicting
emissions must be refined to address questions of biota exposure. Empha-
sis is changing from the general ecosystem box-model concept to special
circumstance considerations, and the exactness of the transport coefficient
should change accordingly. There is a need to develop more specific
transport models which necessitates study of mechanisms. The use of
average transport coefficients is unrealistic. Diurnal variations in
emission rates can be significant and therefore, some time dependency need
be incorporated into the final algorithm. Variations due to the seasons
of the year will need be incorporated also. There is a general lack of
"field" data on emissions of volatile chemicals from water surfaces. This
gap need be closed and laboratory derived coefficients verified with
measurements of emission rates derived from air sampling methodologies or
other such cross-checking means.
286
-------
REFERENCES CITED
(1) Lewis, W. K. and W. G. Whitman, "Principles of Gas Absorption",
Industrial and Engineering Chemistry, 16, 12, 1215, (1924).
(2) Billing, W. L., "Interphase Transfer Processes II, Evaporation
Rates of Chloro Methanes, Ethanes, Ethylenes, Propane and Propylenes
from Dilute Aqueous Solutions: Comparison with Theoretical Predic-
tions", Environmental Science and Technology, 11 .(4), 405-409 (1977).
(3) Warner, H. P., J. M. Cohen and J. C. Ireland, "Determination of
Henry's Law Constants of Selected Priority Pollutants", Municipal
Environmental Research Laboratory, Office of Research and Develop-
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1980.
(4) Reid, R. C., J. M. Prausnitz and T. K. Sherwood, The Properties of
Gases and Liquids. 3rd Ed., McGraw-Hill, N.Y. (1977), Ch. 8.
(5) Liss, P. S. and P. G. Slater, "Flux of Gases Across the Air-Sea
Interface", Nature, 247, 181-184 (1974).
(6) Thibodeaux, L. J. and D. G. Parker, "Desorption Limits of Selected
Industrial Gases and Liquids from Aerated Basins", presented at 76th
National American Institute Chemical Engineers Meeting, paper No. 30,
Tulsa, Oklahoma, March, 1974.
(7) Mackay, D. and P. J. Leionen, "Rate of Evaporation of Low-Solubility
Contaminants from Water Bodies to Atmosphere", Environmental Science
Technology, 9 (19), 1178-1180 (1975).
(8) Harbeck Jr., G. E., "A Practical Field Technique for Measuring
Reservoir Evaporization Utilizing Mass-Transfer Theory", Geological
Survey Professional Paper, 272-E, U.S. Government Printing Office,
Washington, D.C., (1962).
(9) Owens, M., R. W. Edwards and J. W. Gibbs, International Journal of
Air and Water Pollution, 8, 469 (1964).
(10) Freeman, R. A., "Stripping Hazardous Chemicals From Surface Aerated
Waste Treatment Basins", Presented at Air Pollution Control Association
Water Pollution Control Association, Specialty Conference on Control
of Specific (Toxic) Pollutants, Gainesville, Florida, Feb. 13-16,
1979.
(11) Freeman, R. A., and J. R. Klieve, "Experimental Studies on the Rate
of Air Stripping of Hazardous Chemicals from Wastewater Treatment
Systems" Air Pollution Control Association Meeting, Montreal, Canada,
June, 1980.
287
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(12) Thibodeaux, L. J., D. G. Parker and H, Heck, "Measurement of
Volatile Chemical Emissions from Wastewater Basins" Final report
U.S. Environmental Protection Agency, Industrial Environmental Re-
search Laboratory, Cincinnati, Ohio (1982), Also Paper No. 137E,
Annual American Institute Chemical Engineers Meeting, New Orleans,
LA, Nov. 1981.
(13) Hwang, S., "Land Disposal Toxic Air Emissions Evaluation Guide-
line" Guidance Document for Subpart F, Air Emission Monitoring, 46
Federal Register 11158-11159, Office of Solid Waste, U»S. Environ-
mental Protection Agency, Washington, D«C,, December 1980..
(14) Bird, R. E., W. E. Stewart and E. N. Lightfood, Transport
Phenomena , Wiley, N.Y. (1960), p. 656 to 676.
(15) Berg, J. C., "Interfacial Phenomena in Fluid Phase Separation
Processes" in Recent Developments jLn Separation Science, Vol II,
N. Li, Ed., CRC Press, Cleveland, 1972, p. 1 to 31.
(16) Williams, R. M., "Exchange of Particles and Gases Across Water
Surfaces", in Atmospheric Pollutants in Natural Waters, S, J.
Eisenreich, Ed., Ann Arbor Science, Michigan (1981), p. 67 to 78.
(17) Slinn, S. A. and W. G. N. Slinn, "Modeling of Atmospheric Parti-
culate Deposition to Natural Waters", in Atmospheric Pollutants in
Natural Waters, S. J. Eisenreich, Ed., Ann Arbor Science, Michigan^
(1981), p. 23 to 54.
(18) Zison, S. W., W. B. Mills, D. Deimer and C. W. Chen, "Rates,
Constants, and Kinetics Forumlations in Surface Water Quality Modeling",
U.S. Environmental Protection Agency, Environmental Research Laboratory
Athens, Georgia, Report No. EPA-600/3-78-105, December, 1978, p. 123
to 153.
(19) Thibodeaux, L. J., Chemodynamics - Environmental Movement of
Chemicals in Air, Water, and Soil, John Wiley, New York (1979), p. 179
to 193.
(20) Cohen, Y., W. Cocchio, and D. Mackay, "Laboratory Study of Liquid-
Phase Controlled Volatilization Rates in the Presence of Wind Waves",
Environmental Science and Technology, 12, 15, May 1978, p. 553 to 558.
(21) Brtko, W. J. and R. L. Kabel, "Pollutant Transfer into Water Bodies",
Water, Air and Soil Pollution, 6 (1976) 71-95.
(22) DiToro, D. M. and D. J. O'Conner, "Estimate of Maximum Probable
PCB Flux to the Atmosphere from the Hudson River Sediment Disposal
Basin", unpublished report, Hydroqual, Inc., Mahwah, New Jersey,
April 14, 1981.
288
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(23) Horton, J. H., J. C. Corey and R. M. Wallace, "Tritium Loss from
Water Exposed to the Atmosphere", Environmental Science and Technology,
5, 4, April 1971, p. 338 to 343.
(24) Sill, B. L. and J. P. Gaertner, "Calculation of Evaporation for
Extreme Meteorological Conditions", in Advances in Heat and Mass
Transfer at: Air-Water Interfaces, American Society of Mechanical
Engineers, New York, (1978), p. 61 to 69.
(25) Ryan, P. J. and Harleman, D. R. F., An Analytical and Experi-
mental Study of Transient Cooling Pond Behavior, Ralph M. Parsons
Laboratory, Department of Civil Engineering, Report No. 161,
Massachusetts Institute of Technology, Cambridge, Mass., Jan. 1973.
(26) Thibodeaux, L. J., R. Merrill and D. Wolbach, "Pentachlorophenol
and Naphthalene Emissions to Air During Thermal Evaporation of
Wastewater", Paper No. 119B, Annual Meeting American Institute of
Chemical Engineers, New Orleans, Louisiana, November, 1981.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency. The contents do not necessarily reflect the views-of
the Agency and no official endorsement should be inferred.
289
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MODELING HLRBICIDE RESIDUE BEHAVIOR IN AQUATIC
ECOSYSTEMS, USING 3,4-DICHLOROANILINE AS AN EXAM PIE.'*
V.A.Borzilov, L.S.Ernestova, N.I.Troyanova
Institute of Experimental Meteorology, Obninsk
M.S.Sokolov
Institute of Agrochemistry and Soil Science,
USSR Academy of Sciences,
Puschino
G.L.Baughman,D.L.Brockway,D.S. Brown, R.R.Lassiter,
W.C.Steen
Environmental Research Laboratory, U.S. Environmental
Protection Agency,
Athens, Georgia 30613
This paper presents the results of joint efforts of Ameri-
can and Soviet scientists under the USA-USSR Project 02.03-31
directed towards developing models of pesticide behavior in aqu-
atic ecosystems. The proposed mathematical model incorporates
the processes of pesticide volatilization from the water surfa-
ce, its sorption to suspended particulate matter and bottom
sediments, and chemical. photochemical and microbial degradation
for a well-mixed running-water system. Using 3»4— DCA as an exam-
ple, we have found dependences of the model parameters on the
environmental characteristics: for the processes of volatiliza-
tion - on the intensity of mass exchange in water and air, and
on the Henry constant; for sorption - on the content of humic
substances in suspended particulate matter .and bottom sediments;
for chemical degradation - on the concentration of dissolved
oxygen and dissolved organic matter; for photochemical degrada-
tion - on the intensity of illumination; for microbial degrada-
tion - on the total number of microorganisms. Verification of
the model performed in model ecosystems(microcosms) with waters
of various composition has shown practical significance of the
approach based on the use of physical-mathematical models.
x
Both sides agreed that in the Symposium proceedings this pa-
per will be presented as an abstract only. The complete text
will be published in the Soviet journal "Meteorology and
Hydrology" .
290
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RATIONALE AND RESULTS OF TESTING A CHEMICAL FATE MODEL
IN AN EXPERIMENTAL ECOSYSTEM
by
Ray. R. Lassiter
Environmental Research Laboratory
U.S. Environmental Protection Agency
Athens, Georgia 30613
Abstract
Experimental ecosystems were used to test the validity of the Exposure
Analysis Modeling System (EXAMS). The experimental ecosystems were
operated in the open, steady state mode and colonized with natural
assemblages of organisms collected locally. Tests were carried out
specifically to identify adverse effects on EXAMS' predictive capability
caused by ecological complexity not incorporated in the model and by
environment-specific tnicrobfal decomposition rate coefficients. No
evidence was found for either adverse effect. It was concluded that
EXAMS and similar models should be used with caution and that additional
work is needed toward developing methodology for operating laboratory
ecosystems.
PROGNOSTIC ASSESSMENT
Mathematical models are widely applied for assessment of the
probable fate of toxic chemicals in the environment . One of
the major specific applications in this general realm of use is
prognostic analysis of new chemicals to forecast their fate
prior to introduction into aquatic environments. In this
application usefulness of results depends heavily upon the
theoretical content of the model.
In the prognostic mode, assessment models are used in a way that
requires confidence in the models' predictive fidelity over a
wide range of conditions. It is not necessarily well known, a
priori, what specific environments chemicals will enter.
Therefore, prognostic analyses will naturally consider the fate
of a new chemical in response to a wide range of environmental
factors. In these analyses, combinations of factors, in general,
will be found for which the chemical reaches relatively high
steady state concentrations. These combinations of factors
indicate potential environmental conditions under which damage
291
-------
could result from introduction of the chemical. For such a
broad analysis the limitations of time render it infeasible to
consider highly detailed representations of specific sites.
Instead, low resolution descriptions of environments can be used
to achieve the scope of analysis needed within a reasonable
time. The level of environmental factors represented should be
selected within the domain of corresponding factors in
environments that are likely to receive the chemical, but it is
not necessary that any real environment correspond to any of the
particular hypothetical ones selected for prognostic analysis.
In analyses of this sort, the kind of information sought is an
eKpected behavior pattern for the fate of the chemical. In form
this kind of information is much like a summary that one could
make if he were knowledgeable of the behavior of a real chemical
that had been widely used for many years. Of course prognostic
analysis summaries are necessarily of lower confidence than are
summaries of observed behavior. They provide a potential,
however, for regulation of harmful chemicals without the
necessity of first experiencing the environmental damage This
information will probably prove most useful in planning for
specific data requirements prior to complete formulation of
regulatory rules for the chemical. For such information to be
useful, a high level of confidence must exist that the results
are generally correct. Many factors conspire to make it
unlikely for this confidence to be derived from cumulative
experience. Among these is the use of prognostic analyses alone
for new chemicals, the long lag time between use of a chemical
and the acquisition of useful data, and the probable
predominance of hypothetical environments, or at least low
resolution representations of real environments, in prognostic
ana 1 yses.
Confidence in the results arises primarily from two bases. The
first basis derives from the consideration that models used in
prognostic analyses (e.g. the Exposure Analysis Modeling System,
EXAMS) are comprised of expressions based upon the current state
of understanding of the chemistry and biology of xenobiotic
organic chemicals. Aspects of chemical behavior that are
influenced by these model components, therefore,are based upon
the current state of science. Representations of transport
processes and temporally varying environmental factors are
purposefully simplified to permit rapid analyses that are broad
in scope with respect to environment. If the selected level of
resolution for prognostic analyses is accepted, EXAMS represents
the best capability possible at the current state of science.
Still, one would prefer to have some empirical confirmation, as
well as some quantification of a model's predictive
capabilities. The second basis provides some of this empirical
confirmation. It is useful to consider the nature of the
confidence that is needed for results with a new chemical and a
hypothetical environment. Assurance is needed that, for such
292
-------
environments that exist, results of the model and observations
on those environments would be in acceptable agreement This
statement of need essentially defines the characteristics of
tests that, on the one hand, could increase credibility of
prognostic results, or on the other hand, uncover model
deficiencies. Results of tests in this vein provide the
empirical basis for the appropriate level of confidence in
prognostic assessment models.
BASIS FOR TESTS OF ASSESSMENT MODELS
Laboratory ecosystems are simple systems that can be described,
for our purposes, at the level of resolution of the hypothetical
environments of prognostic assessments As such they constitute
an instance of environments with which we expect EXAMS'
predictions to be in acceptable agreement. Laboratory
ecosystems are not subject to the vagaries of weather. This
removes the possibility that variation due to uncontrolled
weather influences will either render tests uninterpretab 1 e or
lead to a spurious interpretation. Because of these properties,
laboratory ecosystems were selected as test systems.
The laboratory ecosystems used for these tests were located at
the EPA's Environmental Research Laboratory, Athens, Georgia
Eight systems were maintained with different levels of
environmental factors (Figure 1). These systems were well
stirred, the environmental factors were maintained at constant
levels (except for light levels to simulate day and night), and
the mixed biological communities were allowed several weeks to
stabilize before the experiments began. In this mode of
operation the laboratory ecosystems were considered to be well
suited to representation in the same manner as the hypothetical
environments of the prognostic analyses. We used the EXAMS to
make predictions of expected concentrations in these ecosystems
and compared these predictions to measured results to test
whether, in these instances, model results and observations were
in acceptable agreement.
As noted above, in such a test, if model results are in
acceptable agreement with observations, then credibility is
increased. As Polya (1954) expressed it, the model has become
"more plausible." A test of a model is most profitable,
however, if it identifies existing deficiencies. If the model
results are found not to be acceptably close, the problem exists
of identifying the cause of the discrepancy. In anticipation of
this problem we designed our experiments specifically to test
for two potential problems. EXAMS and other models for the fate
of chemicals in the environment have been criticized as being
subject to these two problems. First is the general criticism
that the model, in calculating the disappearance of a compound
293
-------
METHYL PARATHION
ALIQUOTS OF MIXED BIOTA FROM LOCAL AQUATIC ENVIRONMENTS
UV
LIGHT
NaOH
INORGANIC
NUTRIENTS
GLYCERIN,
10mg/l
v\
\
PH
1
= 7.0
H 1
pH = 10.0
H <
m
NOg-Nl
NHl-Nl.
P 0.
1
f.
g/1
25
25
15
« <
1
*<
1
1 \
INORGANIC
SEDIMENT:
8AHD - 74X
OIBBSm - MX
RITIB SIP - IX
V 1
RIVER
1
SEDIMENT
* 1
2
3
4 5
CSTR
Conditions
8
H20 Flow
CSTR Retention Time
Mixing Rate
H20 Temp
Light
500 I/Day
12 Hr
2 RPM
20°C
2500 or 3000 Foot Candles
(12 hr light, 12 hr dark)
Figure 1 Schematic of the conditions imposed on the series
CSTRs to maintain different environments
by joint operation of several processes whose rates can be
determined separately in the laboratory, ignores complex
ecosystem processes such as symbioses. It is considered that
these complexities might render chemical behavior unpredictable
by such models. Second is the criticism that the process of
biological decomposition cannot be adequately described by a
second order or other description in which the rate is
proportional to measured microbial density. It is often
asserted that biological decomposition is a more complex and as
yet unknown function of environmental factors.
If ecological complexities cause chemical fat
that predicted by EXAMS, then the more comp
higher the probability that such deviation
provide the greatest opportunity for such pot
to be expressed, we developed duplicates of
aquatic systems (two of the total of eight
essentially abiotic). These ecosystems
separate, serially connected, 240-liter compa
controlled environment chamber (Holm et al.,
residence time in each compartment was
environments differed primarily in their
their position in the serially connected, fl
Corresponding to these differences, di
communities developed in each environment.
e to deviate fr om
lex the system, the
will occur. To
ential complexities
six different open
environments were
were developed in
rtments housed in a
1981). The average
12 hour s . The
chemical inputs and
ow-through series.
fferent biological
To pr omo t e high
294
-------
diversity we obtained samples of organisms from several local
environments and introduced them weekly into each of the
environments. During this time the test chemical, methyl
parathion (MP), was introduced into the upstream environment in
both of the duplicate series. After approximately 6 weeks,
measured system values were acceptably stable. We then measured
MP concentration and the parameters used to specify hypothetical
environments on each of the environments. These measurements
were taken four times at intervals of two weeks, with the
exception of the last sample which was separated from the third
sample by one month.. With this mode of operation of the
laboratory ecosystems, a high degree of complexity developed in
the microbial, algal, and small metazoan groups of organisms
-------
assessments practically impossible by requiring
environment-specific first order rate coefficients for each
chemical and each environment.
An appropriate descriptor for microbial decomposition must be a
function of microbial population density and concentration of
the chemical. The simplest meaningful function that includes
both variables is the second order expression:
kCB][Sl
where k is the second order rate coefficient, CB3 is the
concentration of biomass, and CS3 is the concentration of the
chemical. One other commonly used expression, the Monod or
Michae1is-Menten expression, reduces to the second order
expression when substrate concentration is very low relative to
the half saturation constant. The expression for second order
kinetics is a good initial choice, therefore, for describing
microbial decomposition (Paris et al., 1981). Use of this
expression is based upon the underlying assumption that the
second order rate coefficient is a property of the chemical;
that is, it is independent of the environment. The degree to
which this assumption holds true is an open question.
The question is critically important for prognostic assessment.
At issue is whether analyses for various environmental
conditions are of benefit without specific microbial first order
rate coefficients for those conditions. If second order
microbial decomposition rate coefficients are not a function of
environment, then assessments can proceed based upon one set of
information on the chemical, a set of descriptive information
for each environment, and no environment-specific information
about the chemical.
We tested this question by making predictions in two ways. One
set of predictions employed second order, biological
decomposition, rate coefficients specific to the environment and
time of prediction. These coefficients were estimated in
independent experiments on the specific environments within two
days of the time for which the prediction was made. For the
other set of predictions", a single, overall, average biological
decomposition rate coefficient was used for all predictions
Microbial densities used in both kinds of predictions were
measured separately in each environment. If biological
decomposition is a function of specific populations that vary
from environment to environment, we expect improved predictions.
That is, predictions made using rate coefficients from specific
environments should be a significant improvement over those made
using a single, average rate coefficient for all environments.
296
-------
RESULTS
If predictions and observations are identical, a regression
analysis of observed versus predicted concentrations will result
in a linear relationship with unit slope and zero intercept
Accordingly, we carried out regression analyses for both sets of
predictions. Our null hypotheses were
H : intercept = 0
H : si ope = 1
and alternate hypotheses were
H ; intercept = 0
H : slope = 1
We were unable to formulate an objective criterion to establish
the probability level for rejection of the null hypothesis
Instead, we decided to use the tests as guides for further
consideration of the model. That is, whenever test results gave
rise to reasonable doubt about the model's validity, we sought
the cause in both model and experiment
The hypotheses were the same using both specific biological
decomposition rate coefficients and averaged ones. All data
gathered during the experiment are graphed as a function of
specific environment in Figure 2. Table 1 contains summarized
results of the tests for specific rate coefficients, and Table 2
for averaged ones. The test statistic is Student's t, and "P>t"
is the probabilty of a greater value of t by chance alone. A
graph of predicted concentrations of MP using specific rate
coefficients versus observed concentrations is presented in
Figure 3.
During the interval (approximately one month) between the third
and fourth sampling, marked visible changes occurred throughout
the systems. These changes were accompanied by variations in
the system parameters such as greater amplitudes of fluctuations
in pH and dissolved oxygen (Holm et al., Appendices 21-28).
Because of these phenomena, we chose to carry out an additional
set of analyses in which date 4 was analyzed separately from the
other dates. Data for specific biological decomposition rate
coefficients were available only for dates 3 and 4.
With dates 3 and 4 combined there is no evidence that predicted
concentration differs from the observed concentration when
environment-specific rates are used. With averaged rate
coefficients and with date 4 combined with all other dates,
however, both intercept and slope differ significantly from the
hypothetical values, providing evidence that predicted
297
-------
50-i
P!
o
•i— i
•*->
ctf
o
d
o
o
30-
20-
10-
m
m
m
T
I
m
m
1
m
m
B
i
m
m
0-
3
4
CSTR
5
6
7
8
Figure 2. Graph of methyl parathion concentration plotted
function of specific experimental environment (CSTR)
concentrations differ markedly from the observed concentrations.
When date 4 is considered separately, however,
rather different. There is evidence, although
differences between predicted and observed
environment-specific rate coefficients, but
differences at date 4 (Table 3). With
the results are
perhaps weak, for
for date 3 using
no ev i dence for
the averaged rate
coefficient, no differences are evident for dates 1-3, whereas
Table 1. Tests of intercept • 0 and slope s 1 from regression
of observed on predicted concentrations for environment
specific biological decomposition rate coefficients
dates 3 and 4
n=20
r = . 87
int ercept
s 1 ope
parameter value
2 . 75
. 91
1.18
1 . 10
P > t
. 25
. 29
298
-------
Table 2. Tests of intercept = 0 and slope = 1 from regression
of observed on predicted concentrations for averaged
biological decomposition rate coefficients
dates 1-4 parameter value
n571
r = . 71
intercept 7.10
slope .83
t P > t
3.40 < . 01
2.67 .01
with date 4 differences
observed concentrations.
clearly exist between predicted and
The changes in the systems between sampling dates 3 and 4
occurred only in the environments with biota. One additional
regression analysis was carried out incorporating only
50-|
&0
40-
ti
O
•|H
-l->
cd
O
ti
O
30-
20-
eu
a
-d
0)
rH 10-
0)
OT
20
30
50
Predicted MP Concentration (/Ltg/1)
Figure 3. Graph of observed versus predicted methyl psrathion
concentrations Predictions were made using EXAMS
299
-------
Table 3. Tests of intercept = 0 and slope = 1 from regression
of observed on predicted concentrations for environment
specific biological decomposition rate coefficients;
sampling date 4 analyzed separately.
date 3
n=12
5
r = . 93
parameter value t P > t
intercept
slope
4 . 54
. 82
2.41
•2 . 63
04
03
date 4
n!8
r « . 82
intercept
slope
•2 . 93
1.13
- . 45
. 60
. 67
. 57
environments with biota
rate coefficients on date
mean square error was
determination was only
intercept estimate, 8.02,
from zero and the slope
there is no evidence for
observed values.
for averaged biological decomposition
4 (Table 5). For this analysis the
high (about 45) and the coefficient of
0.46. Against this variation the
was not judged significantly different
is very close to one Hence, again
differences between predicted and
DISCUSSION
The experiments were carried out to test the existence of
complex ecosystem effects leading to low predictability, and to
test for environmental specificity of the biological
decomposition rate coefficient. In regard to the latter, there
is little evidence for environmental specificity of the rate
coefficient. When all dates were analyzed together there
appeared to be such evidence. When decomposed to allow for
known disturbances, however, the evidence was mixed. On date 3
(Table 3) there appears to be evidence directly against
environment-specific rate coefficients at least as we measured
them. And on date 4 (Table 4) there is evidence against the use
of a single value for the rate coefficient. When the abiotic
environments were discounted, however, even this bit of evidence
disappeared. It is known that factors of the environment, other
than those that can be considered reactants (such as the
hydrogen ion or bacteria) affect the second order rate. For
300
-------
example, temperature certainly affects the rate signficantly if
a broad enough temperature range is considered. Temperature,
however, is included in EXAMS, whereas most other properties of
the environment are not. Our tests must be considered to be
tests of whether these complex properties of the environment,
which we excluded from the model, affect its predictive
capability by affecting the second order rate coefficient. We
concluded that, over the range of factors that existed in our
systems, any such effects must be small.
Generally, the tests suffer from small sample sizes and high
variability. It might be argued that this is evidence of
complex ecosystem phenomena reducing predictability. It is more
probable, however, that the variability resulted from imperfect
operation of laboratory ecosystems. Few studies have been made
toward developing a technology of experimental ecosystem
operation, even though a great deal of experience exists for
various types of microcosms. Laboratory ecosystems, however,
should be clearly distinguished from microcosms, or at least
viewed as a special case. Microcosms typically have been
operated as closed systems and have seldom been operated with
highly diverse naturally derived biota (Brockway et al., 1978;
Lassiter, 1978). A property usually associated with natural
ecosystems is that their characteristic biotic components are
derived from highly diverse biota from outside the system
through competition for resources. The competition within a
given ecosystem is unique to that system as a result of unique
abiotic factors. When these charaacteristics are preserved,
Table 4. Tests of intercept = 0 and slope = 1 from regression
of observed on predicted concentrations for averaged
biological decomposition rate coefficients, dates
1-3 analyzed separately
dates 1 - 3
n548
r « . 87
intercept
slope
paramet er value
- .31
. 99
. 16
. 23
P > t
. 87
. 82
date 4
n = 23
r =. 66
intercept
slope
17 . 64
. 64
5 . 64
-3 . 57
< . 01
< . 01
301
-------
Table 5. Tests of intercept = 0 and slope = 1 from regression
of observed on predicted concentrations for averaged
biological decomposition rate coefficient; sampling
date 4 omitting environments 1 and 2.
d&te 4
n«17
r = . 46
parameter value t P > t
intercept 8.02 1.40 .18
slope 1.12 .43 .67
laboratory systems can be referred to legitimately as
ecosystems. In this view microcosms that are operated as
described (closed systems, artificially chosen biota, etc.) are
not ecosystems. Our approach, however, was to preserve the
biotic diversity and derivation of climax communities through
competition under characteristic environmental conditions.
Therefore, we refer to our systems as laboratory or experimental
ecosys tems.
In most scientific disciplines there are characteristic
laboratory apparatus, such as the particle accelerator of
particle physicists. Use of the apparatus is beneficial in a
given experiment because it provides appropriate conditions, and
because its operating characteristics are well known relative to
the scientific question being studied. The laboratory ecosystem
can be considered an apparatus for ecological or environmental
science studies. The major drawback deterring use to its full
potential, however, is the low level of experience with its
routine operation. Because we are developing this experience
base concurrently with the model tests, we expect some degree of
unexplained variation in the data from early experiments, but
improvements thereafter. Laboratory ecosystems are of high
potential for ecosystem and environmental science studies, but
much is yet to be learned in regard to their consistent
oper at i on.
Overall, no evidence was produced in these studies to indicate
that the biological decomposition rate coefficient is
environment-specific. Likewise, no evidence arose implicating
synergisms or other complex ecosystem phenomena, other than high
variability. The lack of evidence for these specific, potential
problems favors continued, cautious application of models for
prognostic assessments. It was concluded that more work was
needed toward developing the experience base for operating
laboratory ecosystems.
302
-------
REFERENCES
Brockway, D. L., J. Hill IV, J. R. Maudsley, and R. R. Lassiter
Development, replicabiIity, and modeling of naturally
derived microcosms. Intern. J. Env . Studies 13:149 - 158
( 1979) .
Burns, L. A., 0. M. Cline, and R. R. Lassiter. Exposure
Analysis Modeling System (EXAMS): User Manual and System
Documentation. U. S. Environmental Protection Agency,
Athens, Ga . , EPA 600-3-82-023 (in press).
Holm, H. W., H. P. Kollig, L. P. Proctor, and V. R. Payne.
Laboratory ecosystems for studying chemical fate: an
evaluation using methyl parathion. U. S. Environmental
Protection Agency, Athens, Ga., EPA 600-3-82-020
(in press).
Lassiter, R. R. Microcosms as ecosystems for testing ecological
models. In: State-of-the-Art in Ecological Modeling. Vol
7, Int. Soc. Ecol. Modeling, Copenhagen, Denmark, p. 127 -
161 ( 1978) .
Paris, D. F., W. C. Steen, G. L. Baughman, and J. T. Barnett,
Jr. Second order model to predict microbial degradation
of organic compounds in natural waters. Appl. Env. Microb
4 : 603 - 609 (1981 ) .
Polya, G. Patterns of Plausible Inference, vol II of
Mathematics and Plausible Reasoning. Princeton Univ.
Press, Princeton, N. J., 190 p (1954).
This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication.
303
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VERIFICATION OF A TOXICS FATE AND TRANSPORT MODEL
by
Jerald L. Schnoor
University of Iowa
Iowa City, Iowa 52242
ABSTRACT
A mathematical model of the fate and transport of pesticides (or other
toxics) has been developed and applied to two reservoirs in Iowa. The
insecticide dieldrin and the herbicide alachlor were simulated. Soluble
and biodegradable herbicides were shown to have considerably different fate
than chlorinated hydrocarbon insecticides. Utility of the model was demon-
strated under steady flow and unsteady state conditions. Bioconcentration
was included to assess biological effects of dissolved toxic chemicals.
INTRODUCTION
Agricultural usage of pesticides in 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, aldrin. Aldrin is microbially
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 in water (0.25 ppm) and low vapor pressure (2.7 x 10~6 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].
Aldrin application in Iowa during the mid-1960's amounted to some 2.94
x 106 kg/yr on 5.0 million acres (2 x 1010 m2). 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 problem is
to determine the fate and transport of the pesticide dieldrin and to assess
when the residual concentrations will be acceptable for commercial fishing.
One of the most widely used herbicides in the U.S. and Iowa is the
soluble, acetanilide alachlor (LassoR). It is applied at 1.1 kg/ha as a
304
-------
preemergent for grass control in corn during the period from mid-April to
mid-May (see Table 1 for structure). Concentration in Iowa waters range
1-100 yg/£ in headwater streams with dilution and biodegradation accounting
for lower concentrations ('x-l yg/£) in large rivers directly after application.
The fate of alachlor (Lasso) is simulated for a large reservoir in south-
central Iowa, Lake Rathbun.
Coralvilie Reservoir is a mainstream impoundment of the Iowa River in
Eastern Iowa. It drains approximately 7978 km2 of prime Iowa farmland and
receives extensive agricultural runoff with 90% of its drainage basin in
Cl
DIELDRIN
Cl
Cl Cl
HEPTACHLOR
Cl — C—Cl
I
Cl
DDT
Cl
Cl
CHLORDANE
CH3—CH2
Cl — C—Cl
DDE
CH2—CH3
FONFOS
CH3 — CH2 0
w
\!
P —S CH2 S CH2 CH3
CH3 — CH2 0
PHORATE
Table 1. Chemical Structures of Selected Iowa Pesticides
305
-------
CH2CH3
•N
JL- jf i \ t
CH2 \0
i_c_NH_CH3 'CH2CH3 CH2 6
II ' I
I
CARBOFURAN CH3
ALACHLOR
CH3\ /Nx,. ci.
5^>CHNH—C^ ^Cl
CH,
CH,' f "q' N' ^
N^ M C2H5HN-
xCi^—NHC2H5 ^N^ A
CH,
ATRAZINE CYNAZINE
CH3 CH2 CH2 —N —CH2 CH2 CH;
N°2-||^rN02
'2
CF3
TRIFLURALIN
Table 1. (Cont.)
intensive agriculture. It is a variable-level, flood control and recreation-
al 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? m3, a surface area of 1.98 x 107 m2, 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? m3.
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-
tute 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
306
-------
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 are
kinetic formulations which describe the chemical, physical and biological
reactions which a pesticide can undergo once it reaches the water body. Chem-
ical hydrolysis, volatilization, photolysis, and biological degradation
comprise the reactions considered in these models. Instantaneous adsorption
and desorption 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 paper, a pesticide transport and bio-
concentration model is developed and applied to Coralville Reservoir to
assess the fate and effects of dieldrin in the ecosystem. Results have
aided the Iowa Conservation Commission in their decision to lift the commer-
cial fishing ban on November 7, 1979.
MODEL DEVELOPMENT
A schematic of pesticide fate and transport within a reservoir is
presented in Figure 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 exists little in-reservoir data for which to calibrate a multi-compart-
ment model. Figure 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 reflects 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. Coralvilie 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 in the model. The bioconcentration
part of the model formulation is depicted separately in Figure 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.
307
-------
SRI MODEL
MODIFICATIONS
POSSIBLE
wetfall
dryfall
absorption
A microlaytr
{sensitized formation?
j photolysis
hydrolysi
biolysis
(anaerobic)
Figure 1. Pesticide Fate and Transport
Mass Balance for Pesticides in Reservoirs
The distribution of pesticides in reservoirs is established by applica-
tion of the principle of continuity or mass balance. Each phase, the dis-
solved and particulate, is analyzed separately, taking into account the
interaction with the other. Thus for the dissolved component, the mass bal-
ance 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:
308
-------
in which
dC _ Wc
dt " V
. C -
M C
k C
rj pj
V = reservoir volume L3
W = rate of mass input of the dissolved component, M/T
t = mean hydraulic detention time
C = dissolved chemical concentration
k.
sum of the first order decay rate constants including the
following:
-2O km-
Cv
J
r
E
K)
d
A
2-COMPARTMENT POND
8-COMPARTMENT LAKE
-H 5000m |«- H—7800m—•« M—7800m—H
f
f
3
/T A
6
/T i
iiiiiiii&iiiiinn
/
\
/
h
/
f
OUTFLOW
10
d
I I WATER COMPARTMENT
SEDIMENT COMPARTMENT
Figure 2. Physical Configurations of the Reservoir Model
309
-------
k-, = k? [Bacteria] = pseudo-first order biological degradation
-1
rate constant, T
k? = kp [OH"] = base catalyzed, pseudo-first order hydrolysis
-1
rate constant, T
k- = k_ (quantum yield) = first order direct photolysis rate
0 O -I
constant, T"
k. = first order volatilization rate constant, T~
n
M f. = sum of the adsorption rate constants for the j size
fraction, n total fractions, L3/M-T
.th
M. = suspended solids concentration in the j size fraction,
J '
= sum of the desorption rate constants for the jth size fraction,
j n total fractions, T-1
C = particulate chemical concentration due to the jth 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 chem-
ical of interest, then Equation 1 reduces to:
(2)
N
>
dC
dt
>ut Inj
Rapid
Sorptive
, Equilibrium N
>-
Particulate Dissc
s
\
ks
t \
- ~ -4 - x^- - zkC - k. MC + k CD
: V t f f P
>ut
i
>ivea — ~r r isn . *
Biouptake Excretion
2k
t Hydrolysis
Sedimentation Biolysis
Photolysis
Volatilization
Figure 3. Bioaccumulation Kinetics of the Pesticide Transport Model
310
-------
in which
= overall decay coefficient of the dissolved chemical, T
For the participate chemical concentration in the j size fraction:
. i _ U p _|/ p + k MP f "3^
t s D r D f i
in which
W = rate of mass input of the particulate adsorbed chemical of
*j size fraction j, M/T
th -1
k = sedimentation coefficient of the j size fraction, T
J
Summing the total over j size fractions or if only one size fraction is
considered, equation (11) reduces to:
dCp W C
6T- -T - l£ - ks Cp - kr CP + kf MC (4)
in which
-i
_-i
k = overall sedimentation coefficient, T
k = overall desorption rate constant, T
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 Cy in terms of the
dissolved and parti cul ate:
CP
in which
Cy = 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
311
-------
participate phases. Thus instantaneous local equilibrium is achieved between
the two phases - i.e. the rates of transfer and decay are so low that, com-
paratively, liquid-solid phase equilibrium is achieved very rapidly. The
concentrations, C and Cp may be replaced by their equivalents in terms of
CT providing that the adsorption isotherm is known and that equilibrium is
achieved. Linear adsorption isotherms have been reported elsewhere [6].
in which
r = K C
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 Cp may be expressed in terms of Cj Under conditions
of local equilibrium:
C = (1 + kp M) (7)
CT Kn M
C_ = J p
+ Kp M> (8)
Substituting for Cp and C from the above relationships into Equation 5 the
mass balance differential equation is:
dCT W(t) C k K M
L - "VW
t0 1 + Kp M ^T - 1 + Kp M ^T (9)
which, under steady-state conditions may be expressed as:
r = W/V
rV" + KpMks] (10)
Equation 9 is written for only one water compartment with sedimentation of
suspended solids into the sediment compartment. It is straightforward to
extend the analysis to a number of compartments (such as the lake con-
312
-------
figuration of Figure 2) with interflow and bulk dispersive transport between
compartments. The equations are linear and may be solved analytically or
numerically.
Bioaccumulation Model
The bioconcentration model follows the simple kinetics of Figure 3.
The total pesticide concentration is the sum of the parti cul ate 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:
- • ^r1 • - (£k)fi CT - ks f2 CT
in which
r i
f1 = —• = /, + K—m = fraction of dissolved pesticide
T P
C KM
f~=7f-= /, .".,—rr\ = fraction of particulate pesticide
2 CT U + Kp M;
The mass balance for the concentration of pesticides tied up in fish
biomass per unit volume of water, Cp, is:
dCF
L = k f r - k r
dt Kl Tl LT Kd LF
in which
. L- 4. . + +- +• liters filtered kg fish T-l
kj = bnouptake rate constant, kg fish-day liter ' T
k . = depuration rate constant, T~
k^dissolved fraction) (k^day) (f^
kd (Biomass) (Fish Partition) (kg fishy (Vg/kg)
If one divides Equation 12 by the fish biomass, a final bioaccumulation or
fish residue equation results:
313
-------
defining dF /, ,. r /DX . r
dt= (klWB) - kdF
F = whole body fish residue level, M/M wet weight
q
B = fish biomass concentration, M/L 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, k,/kdB. If
pesticide is not metabolized in the fish, Figure 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 inte-
grated numerically. In the case of a pesticide ban, the W(t) might typical-
ly decline in an exponential manner due to degradation by soil organisms.
For an expoentially declining loading function at rate u, the analytical
solutions to Equations 11 and 13 are:
cr
CT = CT e'6t + -^2. (e-ut - e-6t) (14)
(15)
in which
_3
CT = initial total pesticide concentration in lake, ML
o
_3
CT- = initial total pesticide inflow concentration, ML
o
ID = rate of exponentially declining inflow concentration, T~
a = (Ek)fj + kjfj + ksf2, T'1
Y = kd - a (l/to), T'1
6 = a + (l/t0), T"1
314
-------
e = at + 1 - wt , dimensionless
e = k - co, T
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.
Multi compartment Solutions
Multi compartment solutions of Equations 11 and 13 must include inter-
flows and bulk dispersion as well as an assumption regarding suspended
solids and fish biomass distribution. For each constant volume compartment:
dCT
(16)
where
V = compartment volume (m )
CT = total pesticide concentration of the compartment
t = time (d)
Qa = inflow of water from adjacent compartments (m3/d)
Qb = outflow of water to adjacent compartments (m3/d
Ca = J°^ Pesticide concentration in the adjacent compartment
fj = fraction of the total pesticide in the dissolved phase
f2 = fraction of the total pesticide in the particulate phase
Kda = reactl'on rate constant for the dissolved phase (d"1)
Kpa = react1on rate constant for the'particulate phase (d"1)
315
-------
k = settling rate constant of the compartment (d~ )
k = settling rate constant from the above compartment (d )
sa
2
E = bulk dispersion coefficient for adjacent compartments (m /d)
A = surface area between two adjacent compartments (m)
£ = mixing length between midpoints of adjacent compartments (m)
V = volume of above compartment (m )
The general mass balance equation for the compartments can be reduced
to a general matrix equation, Equation 17.
d {Cj}
VJ
{cj>+ If2,iks,i TT""
J
{c.
Ei.J AJ
(17)
where i =
J =
C, =
subscript denoting adjacent compartments
subscript denoting the jth compartment
total pesticide concentration in a compartment (yg/£)
total pesticide concentration in an adjacent compartment
o
Q. . = flow into compartment K (m /d)
• »J
q
Q. ^ = flow out of compartment K (m /d)
f-| . = dissolved fraction of a pesticide in compartment K
^2 j = Partlculate fraction of a pesticide in cpmparmtnet K
Kda = sum of dlssolved ^action rate constant (d'1)
K = sum of particulate reaction rate constant (d"1)
316
-------
K . = settling rate constant for compartment K (d~ )
s, i
K . = settling rate constant from an above compartment (d~ )
s»J
E. . = bulk dispersion coefficient between adjacent compartments
1sJ (m2/d)
2
A. = surface area of compartment K (m )
J
A. . = length between the midpoints of adjacent compartments (m)
i >J
V. = compartment volume (m )
j
3
V. = volume of above 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 in fate and transport modeling is to
determine the predominant reaction and transport pathways. Coralville is a
short dentention time, flood control reservoir with a mean annual hydraulic
detention time of only 14 days. This corresponds to a washout 0.0714 per
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 Table 2. Dieldrin should strongly adsorb to sediments and
bioconcentrate, but degradation reactions are very slow. FuradanR, a
carbamate insecticide, is quite reactive, but biological degradation should
predominate (Table 2). Selected herbicides and insecticides of usage in
Iowa are listed in Table 2 with their laboratory protocol rate constant,
half-lives, and partition coefficients.
The dieldrin time series of Figure 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-H to 1.0 x 10-9 kg/m2 - day,
depending on the streamflow and hydrograph.
317
-------
TABLE 2 PHOTOLYSIS, HYDROLYSIS, BIOLYSIS, AMD SORPTION COEFFICIENTS FOR SELECTED IOWA PESTICIDES
Co
00
Near Surface Alkaline Rini«i« P^T^r^ff
Direct Photolysis Chemical Hydrolysis Blo1ysis Partition Coeff.
Dieldrin
COT
DDE
Carbofuran
Fonofos, Thiraet,
Curbufos
Atrazine
Cyanazine
Alachlor
Trifluralin
k , day"' t, , days kg.M'W"1
t t
t t
[91
0.7 1 -10 3
0.003 -200" 6*10~5
t t 10"4
9MO"6 81.000 10"16
[I'+J
0.03 22
Vdays ^(fflfte)'
t
t
[10]
1000 t
>io.«Jli:i -lo-11
>365 4x10"'°
[13] .„
742 -10 "
>365 -10'11
>365 -3x10"' '
t
>1 0.000
t
t
~3Cli:J
^g[l2]
35
35
>2
t
pg/kg dry
lig/1
10,000
100,000
100,000
500
200-500
5-30
3-30
50
500
Fish/H20
Bioconcentration
wg/kg wet
ug/T
10-100,000
130,000
70.000
-10
80
30
[15.
1800-5800
Denotes probable reaction of unknown rate
-------
CORALVILLE OUTFLOW
o» = 0.164/yr
ks = 0.18/d
r = 14d
CTino=0.05Mg/J?
KPM = 0.50
y\/ 'JPARTICULATE
I LI i i i
68 ' 69 ' 70 ' 71 ' 72 ' 73 > 74 ' 75 ' 76 ' 77
0.0
Figure 4. Model Results for Dieldrin Concentration in the Coralville
Reservoir Outflow
Two Compartment Model The goal of this simulation was to analyze
annual average concentrations, so transport properties (i.e., flow, volume,
and sedimentation rate) were averaged over the period of simulation. A
two compartment model as depicted in Figure 2 was utilized. Preliminary
model results are presented for the Coralville Reservoir outflow in Figure
4. The dashed line in Figure 4 represents monthly grab sample field
measurements, and the solid line is the annual average model simulation.
Note the smooth decline in total and dissolved dieldrin concentrations
due to an exponentially decreasing input loading function (Figure 5). The
peak concentrations during runoff events recorded in the field data are
not matched by the model results, since average inflow and a smooth loading
rate function was assumed.
A sedimentation coefficient (ks) of 0.18 per day was calculated from
suspended solids removal rates in the Reservoir while a partition coeffi-
cient of 6250 yg/kg per yg/1 (Kp) was estimated from field data [6]. The
average suspended solids (M) in the reservoir from 1968-78 was 80 mg/1, so
KpM was 0.50, indicating the ratio between the particulate and dissolved
pesticide. Initially the total pesticide inflow concentration was 0.05
yg/1, but it was assumed to decline exponentially thereafter. The sum of
the first order and pseudo-first order rate constants for dieldrin are be-
lieved 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
319
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Figure 6. Calculated Mass Flux of Dieldrin to the Sediment
CORALVILLE OUTFLOW
w = 0.164/yr
ks = 0.18/d
r= 14d
68 ' 69 ' 70 I 71 I 72 ' 73 I 74 ' 75 1 76 ' 77 I
-KPM=0.10
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KPM = 0.50
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0.0
Figure 7. Sensitivity Analysis of the Effect of the Partition
Coefficient, K
321
-------
The sedimentation coefficient (ks) also affects the dieldrin removal by
sedimentation. Figure 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 to a 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 3pm, 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.
Results presented in Figures 4-8 did not include biological uptake by
fish. Figures 9 is identical to Figure 4 except for including the effects
of biological uptake and metabolism. Fish biomass and productivity in
Coralville Reservoir is extremely large, estimated at 1,000 Ib/acre (0.11
kg/m2) (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 Coralvilie Reservoir.
Figure 9 presents the model results and field data for dieldrin residues
in sediment and the edible tissue of bottom feeding fish in Coralville
0.06 -
^s = 0.08/d
,s = 0.13/d
CORALVILLE OUTFLOW
w = 0.164/yr
KPM = 0.50
r = 14d
68 ' 69 ' 70 I 71 I 72 I 73 I 74 ' 75 I 76
0.02 -
0.01 -
0.0
77
Figure 8. Sensitivity Analysis of the Effect of the Sedimentation
Coefficient, K
322
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B = 4.67 x 10"5 kg/1 biomass concentration
FQ = 1150 yg/kg initial whole body residue
k2 = 0.005/day sediment biological degradation
A total of 1.4 percent per day of the dissolved pesticide is filtered
by bottom feeding 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
Iq. The partition coefficient between fish and water was estimated from
field data to be 70,000 yg/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 Figure 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/£.
The decline in sediment concentration follows the declining mass input
rate to the sediment (Figure 6), but it also biodegrades at a rather slow
rate of ^0.005 per day. Biodegradation in the sediment compartment is
assumed to occur for both dissolved and particulate dieldrin.
Bioconcentration of hydrophobic pesticides in Coralville Reservoir
fishes is directly proportional to the oil or lipid content of the catch.
By normalizing all of the fish residue data on an oil basis, it is possible
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 extracta-
ble with petroleum ether. Figure 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 Figures 9 and 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 Figures 9 and 10 indicate that the average catch no
longer exceeds the FDA 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 in the water, and less than 12%
adsorbed to suspended solids. Sediment and fish (and fish oil) are essen-
tially in equilibrium with mean dissolved dieldrin concentrations. If
biouptake is ignored in the model or if depurated dieldrin is not metaboliz-
ed but rather returned to the dissolved phase, then the transport in the
outflow is 54% and the net sedimentation is 46% of the total dieldrin load.
324
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-------
estimated dispersion pattern. Assuming a longitudinal dispersion coeffi-
cient of ^4 mi'2/day, it is possible to estimate the proper number of longi-
tudinal compartments necessary to reflect this degree of dispersion. For
Coralville 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
(Figures 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 dieldrin in Coralville Reservoir during 1976. Inflow and outflows
are shown in Figure 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 (Figure 14). Mass flux to the
sediment (Figure 15) is approximately 20% 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 diel-
drin, 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 Figures 4-12 is warranted.
VOLUME, INFLOW AND OUTFLOW
IN CORALVILLE LAKE, 1976
VOLUME
INFLOW
U15,000 I- -60 "' —OUTFLOW
. u
cc
£50
ho
O 10,000 (- §40
LJ 30
^
z>
5,000 I- o 20
>
10
f I t I I t I t t t I
JANl FEB1 MAR1 APR1 MAY1 JUN1 JUL1 AUG1 SEP1 OCT1 NOV1 DEC1
Figure 13. Unsteady Flow Simulation
327
-------
240
X
CD
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0-
".30
O
$
20
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O
z
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O
10
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220
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15
10
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O
z
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O
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DIELDRIN IN CORALVILLE LAKE,
1976
INPUT CONCENTRATION (MODEL)
LAKE CONCENTRATION (MODEL)
V LAKE CONCENTRATION
(OBSERVED)
SK = 6X10-5 DAY'1
Ks = 0.18 DAY'1
Kp = 6250
M = 80mg/j?
! t t t ! ! I t I t I
JANl FEB1 MART APR! MAY! JUN1 JUL1 AUG1 SEP1 OCT1 NOV1 DEC1
Figure 14. Unsteady Flow Results
0.20
Q
O 0.15
X
3)
0.10
(f)
V)
0.05
DIELDRIN IN CORALVILLE LAKE, 1976:
MASS FLUX TO SEDIMENT
t t t I 1 1 1 ! ! 1 1
JAM FEB1 MAR1 APR1 MAY 1 JUN1 JUL1 AUG1 SEPT OCT1 NOV1 DECT
Figure 15. Unsteady Mass Flux to the Sediment
328
-------
The mass flux to the sediment shown in Figure 15 provides information
about seasonal 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.
Historical Sediment Profiles
Sediments are historically contaminated with dieldrin, but the degree
of contamination has decreased since the ban in 1975. A typical upstream
sediment core is shown in Figures 16 and 17.
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 10%. In these locations one observes a maxi-
mum dieldrin concentration in surficial sediments (Figure. 16). 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 Figure 16 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.
CORALVILLE RESERVOIR
HWY 0, 16 FEB 1981
£
o
x"
1-
UJ
Q
5
10
15
20
OR
O
-
O
-
o
-
o
-
1 1 1 1 1
1234 0 10 20 30 40 50
SEDIMENT SEDIMENT
DIELDRIN /u.g/kg dry DIELDRIN ^g/kg VS
Figure 16. Sediment Core Near the Inflow to Coralvilie Reservoir
329
-------
CORALVILLE RESERVOIR
SEDIMENT CORE AT
HWY 0, 16 FEB 1981
10
t 15
ui
Q
20
25
0>
LJ
Q
0 2 4 6 8 10
% VOLATILE SOLIDS
0 2 4 6 8 10
% VOLATILE SOLIDS
Figure 17. Volatile solids in the Sediment Core Relationship
A point in contrast is the sediment concentration profiles in highly
depositing basins of the Reservoir. Figure 18 has been constructed from
field data and model simulations using sediment layers of ^6 cm depth. In
Coralville Reservoir, the annual average deposition is 5.8 cm/year. A
twenty year scenario is depicted in Figure 18 which shows the dieldrin
concentration on suspended particulate materials reaching a peak concentra-
tion in the late 1960's and then declining.
In depositional zones, the high rate of sedimentation of cohesive
sediments precludes a great degree of mixing. Field data, although it is
very sparse, supports the conclusion that some degradation of dieldrin is
occurring in 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 concentrations as shown. Otherwise the sediments
serve as a source of dieldrin to the water column by scour/resuspension,
and desorption/diffusion.
Bi concentration
The total storage of dieldrin in Coralville Reservoir is largely in the
sediment (^5 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.
330
-------
Apparently biomagnification is not a large problem since bottom feeding fish
have the highest biomass and residue levels (Figure 19).
There exists a strong correlation between insecticide residue levels in
fish and the percent oil or fat (petroleum ether extraction). To demon-
strate that the residue level of fish is directly related to their fat (oil)
content, Figure 20 was plotted with 1979 data for a variety of fish species
and seasons. Fish samples were analyzed according to US FDA procedures by
the University of Iowa Hygienic Lab. Figures 20 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 Figure 20 gives the residue level in fish oil
at the 1979 exposure levels. The annual average insecticide exposure con-
°
z fc •-•
5Z «? z
W IJ
3*1
20 r
K>
SEDIMENT CONC. ug/kg
10 20
o
I
I-
Q.
U
O
50
1960
1970
1980
100
no sediment mixing,
scour, diffusion, or
reactions
INTERSTITIAL CONC. ng/f
0 5 10 20
50
a.
u
o
100
SEDIMENT CONC. /ig/kg
0 5 10 20
50
a.
LJ
a
100
with mixing, scour
and diffusion—
no reactions
INTERSTITIAL CONC. ng/f
Q 5 10 20
SEDIMENT CONC. /xg/kg
0 5 K3 20
50
100
50
x
K
a.
100
with mixing, scour,
diffusion and degro-
datlon reactions
Figure 18.
Estimated Dieldrin Sediment Concentrations and
Loading Scenario in Highly Depositional Zones
of Coralvilie Reservoir
331
-------
centrations during this period were 0.0045 yg/1 dieldrin-, ^ 0.002 yg/1
chlordane (cischlordane plus trans-chlordane plus nonachlor), ^ 0.001 yg/1
DDE, and ^ 0.001 yg/1 heptachlor epoxide. Based on this residue and ex-
posure data, the oil-normalized bioconcentration factors (BCF-oil) expressed
as logio BCF yg/kg oil per yg/1 are 5.7, 6.0, 5.9, and 5.8 for dieldrin,
chlordane, DDE, and heptachlor epoxide respectively. Figure 21 shows the
log BCF-oil plotted for a number of insecticides in Iowa waters. Oil-
normalized BCFs correlate with the octanol water partition coefficients.
Alachlor in Lake Rathbun
Field data on alachlor was gathered on Lake Rathbun, a south-central
Iowa reservoir on the Chariton River, by the University of Iowa Hygienic
Laboratory. The study was conducted during a high flow runoff period. Under
these conditions, Lake Rathbun had a mean depth of approximately 29 feet, a
0.
PISCIVOROUS
FISH
-v- 5 mg/1 +
30 ug/kg wet
015 Ib (0.0067 kg)T
/
0.
SMALL
&MIN
^ 5 IT
30 ug/
015 Ib (
/
\
FISH
NOWS
9/1* +
kg wet
0.0067 kg)f
\
ZOOPLANKTON &
INSECTS
negligible
/
0.
\
ALGAE*
2 mg/l +
B yg/kg
001 Ib (0.0004 kgV
BOTTOM
FEEDING FISH
47 tng/1* +
225 pg/kg wet
1.1 Ib (0.5 kg)f
A
ZOOBENTHOS
< 1 mg/1*
5 9/kg+
<0.01 Ib (<0.004kgV
\
DISSOLVED
WATER *
10 mg/1
0.004 Pg/l
0.42 Ib (0.19 kg)T
WA
SEDIMENT.
1.3 kg/1
2ug/kg+
•^lOOlb (45 kg)T
/
PARTICIPATES
80 mg/1* .
0.002 yg/1
0.21 Ib (0.09 kg)T
BIOMASS
"^DIELDRIN CONCENTRATION
fDIELDRIN MASS
Figure 19. Biomass, Dieldrin Concentration, and Dieldrin Mass in
Coralville Reservoir Ecosystem, 1980
332
-------
>_500
LJ
o»400
UJ30O
200
.
0 100
SUMMER
o BUFFALO
A CATFISH
7 CARP
D BASS
AUTUMN
SHADED
.750
% OIL IN EDIBLE FISH
Figure 20. Dieldrin Residues in Fish vs. Percent Oil Content, Coralville
Reservoir, 1979 Field Data. Equations for least squares regres-
sion: Y = 2640, X -10.8, r = 0.77.
mean hydraulic detention time of 162 days, and a mean volume of 4.33 x 108
cu. meters. Rathbun1s 535 square mile watershed is primarily in row crop
agriculture and pasture land. Grab samples were collected from four loca-
tions representing the two principal inflows from the Chariton River and
South Chariton River, a depth composite sample from Lake Rathbun, and a
downstream sample. Weekly samples of water, fish, and sediment were col-
lected.
7 -
log K
ow
Figure 21
Field Bioconcentration Factors in Fish Oil vs. Octanol/Water
Partition Coefficients. DIELD = dieldrin, H.F. = heptachlor
epoxide, TRI = trifluralin, CHLOR = chlordane
333
-------
Modeling efforts on Rathbun Reservoir in Iowa for the herbicides alach-
lor and atrazine are much different than for hydrophobic pollutants. Being
quite soluble, these herbicides are shown to undergo negligible sedimenta-
tion but to biologically degrade and to be transported out of the Reservoir
via the outflow. Figures 22-25 are the results for alachlor in Rathbun
Reservoir. Time-variable loadings and flow were required to accurately
calibrate the model to in-situ and outflow concentration data. The rate
of degradation was significant, and pseudo-first order rate constants
ranged from 0.03 - 0.05 per day, in relative agreement with laboratory
biotransformat!on measurements.
CONCLUSIONS
Fate and transport of the pesticides dieldrin, alachlor, and atrazine
have been simulated in Coralville and Rathbun Reservoirs, Iowa. The
soluble herbicides alachlor and atrazine have been shown to biologically
degrade and to be transported out of the Reservoirs in the outflow. Their
rate of degradation is significant, and first order biodegradation rate
constants range from 0.02-0.06 per day.
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
INFLOW HYDROGRAPH
STATION 11: CHARITON RIVER
1000 -
CO
U.
o
8OO
600
400 -
200
1
_L
_L
_L
0
t
MAY 18,
1978
10 20 30 40
>TIME, DAYS
50
60
70
Figure 22. Inflow to Rathbun Reservoir from the Chariton River
334
-------
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CO
en
IQ
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rt>
r\3
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INPUT CONCENTRATION,
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in -t)
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water, and less than 12%adsorped to suspended solids. Mean residues in the
edible tissue of bottom feeding fish have 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.
Field monitoring of bioconcentration factors (BCF) in fish indicated
that residues are directly proportional to fat content and dissolved
exposure concentration alone, regardless of diet, species, sex, weight,
length, portion of fish taken, or condition factor. For this situation
field BCFs corresponded quite closely with laboratory measurements when
normalized on a fat basis. In fact normalized field BCFs corresponded to
the octanol-water partition coefficient within a factor of ten.
ACKNOWLEDGEMENTS
The author thanks Mr. Thomas Barnwell, Dr. Lawrence Burns, and Dr.
Robert Swank of the USEPA Environmental Research Laboratory, Athens,
Georgia, for their helpful comments regarding this research. Financial
support for this study came from Grant No. R-806059-01,02, USEPA, Athens
Georgia.
0.4 -
, 0.3 -
g
£
0.2
LJ
O
I
LU
LAKE CONCENTRATION (MODEL)
LAKE CONCENTRATION (OBSERVED)
ZK = .04 DAY"1
Ks = .03 DAY"1
Kp = 260
M * 39 mg/P
>TIME, DAYS
Figure 25. Field Data and Model Output Concentrations from Rathbun
Reservoir for the Herbicide Alachlor (Lasso)
336
-------
REFERENCES
1. Kellogg, R. L.; R. V. Bulkey. 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. Donigan, 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., 19-7, 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.
11. Wolfe, N. L.; R. G. Zepp, D. F. Paris. Carbaryl, Propham and
Chloropropham: A Comparison of the Rates of Hydrolysis and Photo-
lysis with the Rate of Biolysis. Water Research. 1978, 12, 565-571.
337
-------
12. Steen, W. C.; D. F. Paris; G. L. Baughman. Effects of Sediment
Sorption on Microbial 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 Atrazine in Aqueous Fulvic
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. Spacie, A.; J. L. Hamelink. Dynamics of Trifluralin Accumulation
in River Fishes. Environmental Science and Technology, 1979, 13,
817-822.
16. Mackay, D.; 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, Colloidal, 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 partial 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.
338
-------
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.
This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication.
339
-------
TECHNIQUE FOR PREDICTING RIVER WATER POLLUTION BY
DDT AND fl-BHC RESIDUES DURING SPRING FLOODS
Z.L. Sinitsyna
Institute of Applied Geophysics, USSR State
Committee for Hydrometeorology and Control
of the Natural Environment, Moscow
At present, despite the ban on DDT use in a number of de-
veloped countries, there exists a problem of global pollution
of the biosphere by this and other organochlorine pesticides
(OCP).
This is associated with the fact that OCP are persistent
toxicants which can be found almost in all natural environments
for a long time after their application.
In addition, some developed countries still produce pesti-
cides, DDT included, on a large scale and export a considerable
part of their production to other countries (12).
As a result of the atmospheric circulation, pesticides
evaporated from the soil are transported all over the world
(11) and found where they have never been applied (13).
A main route of the entry OCP into rivers is their wash-
out from a watershed in surface slope runoff waters (1,7)
formed as a result of snow melting and rainfall.
Watershed pollution by OCP is caused by their residues in
soils and cumulative build-up of precipitation from the atmo-
sphere.
The highest concentrations of OCP in river waters and
watercourses were observed during the period of surface run-
off (3,2).
Surface runoff of pesticides depends on many factors, the
main of them being, apparently, solubility, conditions of wa-
ter runoff, the caracter of pesticide distribution over a wa-
tershed and erosion properties of soil.
The latter factor is especially important when pesticides
are transported by runoff on soil particles (10).
Water runoff makes possible the migration of pesticides
and soil particles passed into the solution, thus being the
340
-------
determining factor for their washout from a watershed.
Maximum surface water runoff from watersheds of the low-
land rivers in the European part of the Soviet Union typically
occurs in spring.
During this period the water runoff of the rivers is
formed mainly due to the entry of melt waters from a watershed
into a river bed (70-9056) (6).
Therefore, river water pollution by DDT andfl-BHC in the
above-mentioned rivers in spring will be determined mainly by
the content of pesticides in slope waters flowing down from
the watershed.
The amount of pesticides entering the river bed during
the period of slope runoff is
u. = t±U±, [l]
where H. is the amount of the i-th pesticide accumulated in
the watershed by the time of snow melting;
f. is the portion of the pesticide entering the river bed,
or the runoff coefficient of the i-th pesticide.
In this case, the average flood-period concentration of
the i-th pesticide in the outlet (closed to the river mouth)
observation point of the watershed will be
where W is the volume of water runoff during the flood period
in the outlet observation point.
Expressing V/ as
\v - v w rvi
w ~ * "slope' lAJ
where Wslor)e is the volume of slope waters arriving from the
watershed and k is the factor of proportionality between the
water runoff of the river in the outlet observation point and
the slope runoff (k^1), we obtain
°±. *i*i M
k Wslope
The amount of pesticides accumulated in the watershed N.
and the runoff coefficient f. of DDT and fl-BHG are determined
experimentally, whereas the Volume of water runoff is predict-
ed.
The storage of DDT and % -BHC in the watershed before the
beginning of snow melting is the sum of their amount accumu-
lated in the snow cover and soil.
Pesticide storage in snow cover is determined before the
beginning of snow melting, and that in soil prior to the snow-
fall.
It should be noted that while determining the storage of
pollutants in the soil of a watershed for predicting the pol-
341
-------
lution of river waters, one encounters the element of uncer-
tainty.
As we discussed earlier (7,8), this uncertainty is asso-
ciated with a choice of the required depth of soil layer where
the storage is to be assessed.
The runoff coefficient of DDT and ft-BHC was determined
experimentally on real river watersheds and runoff plots (4,7,
9).
V/e determined the storage of DDT and # -BHC in the snow
cover of a given watershed or runoff plot, their storage in
the 0-20 cm layer and their washout from these watersheds in
runoff v/ater.
The runoff coefficient of DDT and )j-BHC can be expressed
in fractions of both the total storage and the storage in snow
cover alone.
The coefficient f. obtained under natural conditions ac-
tually reflects the process of pollution formation of melt wa-
ters flowing down from the watershed by DDT and %-BHC residues,
and also due to the underlying surface of the watershed.
The studies conducted showed that the -runoff coefficient
of pesticides is directly proportional to the volume of water
runoff, as are the previously obtained runoff coefficients of
other ingredients (5,9):
where y is the volume of waters flowing down from the watershed
which is expressed in terms of the runoff layer ( y =
_ 3ij0Pe , p - the area of watershed).
Index n is close to unity, and to a first approximation,
the relationship may be assumed to be linear.
In this case, proportionality factor f± is the normalized,
or reduced, runoff coefficient [5] which characterizes a rela-
tive washout of the i-th pesticide from the watershed with a
layer of water runoff 1 mm.
Substituting [5] in [4] gives
5± - -^ [6]
1 kF
The valuepof the reduced runoff coefficient for DDT and
X, -BHC is 1-10 and 7.9*10~°, respectively (taking no account
of the storage of pesticides in soil), or 2.3*10~~ and 4c10""-?»
respectively ( taking into account the storage of pesticides in
the 0-20 cm soil layer).
These values were obtained for a water content varying in
the range from 0.34 to 75 mm.
According to the observations of many years, the value of
water runoff for the rivers of the territory under study varies
in this range.
Consequently, the obtained runoff coefficients of pestici-
342
-------
des can be applied to the rivers in the middle zone of the Eu-
ropean part of the Soviet Union.
The technique considered was used for predicting the pol-
lution by DDT and % -BHC residues of the river in this territo-
ry with a watershed area of 800 km .
The discrepancy between the estimated and measured con-
centrations of the pesticides mentioned was 40-48%.
Prediction of river water pollution by pesticide residues
during spring flood was performed for the river with a water-
shed area of 3240 km .
DDT and ^-BUG concentrations in the river water v/ere es-
timated twice: taking arid not taking into account the storage
of these pesticides in the soil.
Respectively, two above-mentioned values of the reduced
runoff coefficients of pesticides v/ere used.
The obtained concentrations of each pesticide v/ere almost
the same (to an accuracy of 1050.
The estimated average flood-period concentrations of pes-
ticides v/ere compared with the concentrations measured over
four samples.
The predicted concentrations v/ere twice as high as the
measured ones, which appears to be associated with the fact
that the water samples where the content of pesticides was mea-
sured, v/ere collected at the end of the spring flood.
However, in detailed studies of the river water pollution
by pesticides during the spring season (daily sampling), an
increase in their content over the period of large water dis-
charges was observed (3).
From these data the following relation for DDT was obtain-
ed:
where C(t) is the concentration of DDT in the river water at
time t;
C is the average concentration of DDT in the river water
during the flood period;
(j>(t) is the water discharge in the river at time t;
w is the average water discharge during the flood;
P is the proportionality factor.
From the above it follows that the average concentration
of pesticides during the spring flood was higher than the con-
centration obtained over four samples, according to the re-
sults of comparison.
Relation [7} can be used for predicting the daily concen-
trations of pesticides in river water during the spring season.
343
-------
LITERATURE CITED
I. 7rochinsky,K.K. The routes of pesticide entry and their
content in the water of water sources. GidrobioloKiches-
kii zhurnal 1976, 12,98-101 / in Russian/.
2. Vrochinsky,K.K. Pesticide content in surface runoff,
watercources and ground waters in Rural area. In "Sur-
face water formation and Duality control";Kiev°, Publi-
shing House "Naukova Dunka" 1976,Issue 3,109-112 / in
Russian/ .
3. Morozova,G.K.; Sinitsyna,Z.L.j Cherkhanov,Yu.P. Distri-
bution of organochlorine pesticides in natural waters
in the basin of the Moskva-River. Transactions of the
Institute of Applied Geophysics 1979, Issue 31, 28-32
/ in Russian /.
4. Bobovnikova,Ts.I., Virchenko,E.P., Morozova,G.K., Sini-
tsyna,Z.L., Cherkhanov,Yu.P. Estimation of organochlo-
rine pesticide loss in surface runoff waters. In "Envi-
ronmental transport and transformation of pesticides",
Proceedings of the USA-USSR Symposium,Tbilisi,1976.
EPA-600/9-78-003,February 1978, 103-107.
5. Rovinsky,F.Ya., Morozova,G.K., Sinitsyna,Z.L., Sini-
tsyn,N.M. The transfer to water and migration capacity
of radionuclides in peaceful uses of atomic energy. In
"Rddioecology of aquatic organisms. Distribution and
migration of radionuclides in fresh-water and marine
biocenoses"; Rigaj Publishing House "Zinatne" 1972,
20-29 in Russian/.
6. Surface Water Resources in the USSR.v.10, book I.Mos-
cow j Publishing House "Gidrometeoizdat",I973 , 32-37
/ in Russian .
7. Rovinsky,F.Ya, Sinitsyna,Z.L. Surface runoff from a wa-
tershed and its role in river and water body polluti-
on. Transactions of the Institute of Applied Geophysics.
1979, Issue 31,5-14 , in Russian,
8. Rovinsky,F.Ya., Sinitsyna,Z.L. Prediction of river wa-
ter Duality during spring flood. Meteorologiya i gidro-
logiya 1979, 6, 74-77 in Russian/.
9. Sinitsyna,Z.L., Cherkhanov,Yu.P., Koloskov,I.A. On
the runoff of some pollutants from a watershed in spring.
Transactions of the Institute of Experimental Meteorolo-
gy. 1978. 9(82^ 55-56 / in Russian/ .
344
-------
10. Hann,C.T. Movement of pesticides by runoff and erosion.
Transactions of the ASAE I971, 14, 445-449.
II. Junge,C.E. Transport mechanism for pesticides in the
atmosphere. Pure and Appl.Chem. 1975,42, 95-104.
12. Monod,M.L. Les residus pesticides dans I1enviroime-
ment. Trav. Soc.Pharm.Montpellier 1977, 37, I\ro.lb,9-14
13. Peel,D.A. The study of global atmospheric pollution in
Antarctica. Polar Res. 1975, 17, No.III,639-643.
345
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REGULARITIES OF PESTICIDE ACCUMULATION AND MIG-
RATION IN THE ECOSYSTEMS OF LOWLAND RESERVOIRS
L.P.Braginsky, F.Ya.Komarovsky,
A.Ya.Malyarevskaya
Institute of Hydrobiology,
Ukrainian Academy of Sciences,
Kiev
Extensive use of pesticides in agriculture has resulted
in their migration in terrestrial and aquatic ecosystems. At
present world literature on this problem has become quite im-
mense (I-II).
Nevertheless, there are certain gaps in the great bulk of
information on the problems of pesticide turnover and migration
in the biosphere. In particular, the problem of pesticide inter-
action with aquatic ecosystems under regulated flow conditions
is still not clearly understood. Meanwhile, reservoirs are be-
ing intensively constructed in all developed countries, which
inevitably affects the nature of pesticide migration in water-
ways.
It is well Jtnown that pesticides washed out from agricul-
tural lands with soil runoff migrate to the very mouth of main
rivers and accumulate in deltas and estuaries, especially in
their biota. This regularity observed back in 5>0s-60s in the
United States ( in particular, when studying the Mississippi
River) has been confirmed by our studies on the Danube, a great
European river.
However, these examples refer to the rivers with practical-
ly natural flow. It is impossible to find out without special
research what occurs on a river with one or more reservoirs.
The difficulty of such a research lies primarily in a great
variety of sources of reservoir pollution by pesticides which
can enter from both global and local sources. Among these
latter are agricultural lands and the industries processing
agricultural vegetative raw materials; forests treated with
insecticides; cities discharging domestic pesticide residues
as part of sewage waters, etc.
In running water, pesticide settling to bottom sediments
is insignificant. It is different in case of regulated flow
346
-------
where vast zones of silting begin to form and water mass move-
ment along the river channel ceases. Under these conditions
pesticide residues sorbed on suspended particles settle to the
bottom, in particular in silted estuarine reaches of backwater
tributaries where overgrown higher aquatic plants intensively
develop. Many aquatic plants, especially reed, can extract pes-
ticide residues from water, accumulate, metabolize and even
store them up in rhizomes.
Overgrown zones act as buffers limiting the entry of pesti-
cides from tributaries which collect their residues directly
from drainage areas, into the main water area of reservoirs.
Regularities of DDT accumulation and migration have receiv-
ed the most study. Average concentration of DDT residues found
in the organs and tissues of submersed macrophytes is typically
about 10"* mg/kg, whereas in rhizomes of reed and mace reed it
may be as great as 10' - 10** mg/kg (I).
Under conditions of extensive application of pesticides in
agriculture even strong buffer zones such as overgrown reed
and mace reed do not.protect reservoirs against pesticide conta-
mination. This showed up most vividly as a result of an exten-
sive use of DDT in 50s - 60s. Remote aftereffects were observed
in early 70s as a noticeable increase in the content of DDT
residues in all links of the trophic chains of the studied
reservoir ecosystems, especially in higher links(predatory fish-
zander, pike, perch).
Studies revealed that in these cases DDT residues are dist-
ributed among the elements of biota in the same way as in the
other ecosystems, i.e. according to the principle of biological
magnification. Increase in concentrations by this principle can
be traced in food chainst zooplankton-plankton-eating predatory
fish, silt - higher aquatic plants, silt - zoobenthos - bentho-
phage fish (Table I) (2,3).
In some biotic components of the lowland reservoirs DDT
residues metabolize to various levels depending on the species
specificity and pollution duration. Metabolites o,p'- and p,p'~
DDT (DDE and DDD) are in different proportions. In the last
few years,however, there has been a shift towards prevailing
metabolite DDE that is indicative of the extensive processes
of metabolic transformations and a long time passed since the
entry of pesticides into the reservoirs studied.
Experimental and field studies made it possible to estab-
lish some regularities of DDT accumulation and formation of its
metabolites in hydrobionts at various trophic levels. In model
experiments it was found that the transfer of DDT residues from
water to tissues of invertebrates (Daphnia and Oligochaetae)
occurred during a very short period of time - mostly in the
first 24 hours. At a DDT concentration in water of 1-200AS/1
Daphnia accumulated 0.25 to 18.02 mg/kg of the compound. Bottom-
dwelling invertebrates (Oligochaetae) accumulated during the
first day 4.3 mg/kg, 3 days - II.7 mg/kg and 6 days - 27.2 mg/kg.
The lowest rate of accumulation of DDT and its metabolites was
observed in mollusks of Dreissena polymorpha which contained
to 2.18 mg/kg in their tissues. In the organisms of Daphnia,
347
-------
Table I. DDT content in the main components of the aquatic
ecosystems studied
Groups of hydrobionts DDT concentration,
Higher aquatic plants (stems and
leaves of Phragmites communis,
Tipha latifolia, Potamogeton
perfoliatus, etc.) 0.02 - 0.08
Zooplankton 0.01 - 0.04
Fish fry 0.01 - 0.02
Plankton-eating fish 0.2 - 0.02
(roach, sardelle)
Fish-benthophages 0.4 - 0.6 to 1.8
(bream, sazan)
Predators (zander, pike) 1.5 - 6.0 to 30
Reed rhizomes ~ IO"2
Oligochaetae and Dreissena, mostly metabolite ODD was formed.
Systematic study of the level of* DDT residue content in wide-
spread fish species dwelling in reservoirs (.zander, pike, bream,
sazan, perch, carp, etc.) revealed a regular distribution of
accumulated DDT and its metabolites in the organs and tissu-
es (4) . These compounds were found to accumulate mainly in
fatty and brain tissues. In the internal organs( liver, stomach,
intestines), prevalent were metabolites DDD and DDE, whereas
DDT accumulated to a lesser extent. Minimum amounts of pestici-
de residues were found in muscular tissue. Experiments oti feed-
ing fish with hydrobionts poisoned by DDT showed that DDT meta-
bolites were distributed- in the organs and tissues in a simi-
lar way(Tables 2 and 3). On prolong feeding with DDT-contain-
ing nutriment, the amount of DDT increased in the lipoid -
containing tissues, metabolites DDE and DDD being prevalent(5),
At the same time, clinical signs of cumulative toxicosis
developed, such as loss of equilibrium and motor coordination,
weakening or complete loss of defensive reflexes, uncoordinat-
ed performance of fins, convulsions; fish swimmed on its side
or belly up and was not mobile; in the state of agony its body
was arched and gills wide-open, etc. (6, 7, 8). Toxicosis can
proceed in two ways:
a) slow extinction of one or another predatory population
(dead fish comevto the surface;
348
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Table 2. DDT accumulation in carp yearlings during its
transfer through the food chain
Duration of
experiment,
days
Average content, mg/kg
DDE
DDD
DDT
Total DDT
Control
2
5
10
15
22
0.035
O.III
0.460
0.885
1.302
not
0.0^9
0.081
0.330
0.64-7
1. 212
detected
0.024
0.081
O.II?
0.212
0.646
O.II8
0.274
0.907
1.744
3.160
Table J>. DDT accumulation in organs and tissues of perch
in the model experiment
Duration of Organs,
experiment, tissues
days
Control
10-12
13-20
Liver
Stomach +
Intestines
Muscles
Liver
Stomach +
Intestines
Brain
Liver
Stomach +
Intestines
Brain
Muscles
Average content, mg/kg
DDE
0.185
0.170
DDD
0.053
0.090
DDT Total DDT
0.052
0.090
0.280
0.330
Traces
0.520
0.483
0.330
0.360
5.570
1. 122
0.080
0.010
0.461
0.456
0.270
1. 310
1. 015
0.030
0.02
0.345
0.205
0.038
1.790
0.563
0.040
0.532
1.289
0.991
0.668
8.670
2.700
0.150
b) sudden mass fish mortality (in stress situations such as
water overheating,spawning, etc.).
On feeding fish with poisoned natural nutriment, toxicosis
develops due to a material cumulation of DDT and its metaboli-
tes. This pesticide cumulates most intensively in the brain
tissues of fish reaching critical values(4). Pish mortality-
occurs at a total DDT content in the brain tissue of about
3 mg/kg and over,which is in agreement with the results of the
studies on similar phenomena in warm-blooded, animals and
349
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birds(8,9). In this case, the most typical change is cerebral
hemorrhage (9)•
In experimental modeling, the clinical and pathological-
anatomical picture of cumulative toxicosis can be reproduced
fairly rapidly and unambiguously, the behavior of fish and
clinical picture of toxicosis being similar to those of acute
poisoning.
The conducted model experiments enabled us to establish
the principles of DDT residue distribution in the organs and
tissues of hydrobionts at various trophic levels, study cumu-
lative toxicosis in fish, and determine characteristic featu-
res of accumulation of DDT and its metabolites depending on
the functional significance of tissues and species specificity
of hydrobionts.
Biochemical studies showed that the intensity of pestici-
de accumulation depends on the level of substance exchange
typical of one or another species. Predatory fish with a higher
level of metabolism have maximum accumulation. DDT metabolites
are formed most intensively in the organs and tissues with
a high content of lipids(liver, internal fat, brain, stomach,
intestines).
Along with the effect of pesticide cumulation on high
trophic links that can be shown using physiological-biochemical
methods, pesticides can affect lower links of the trophic
chains as well, in particular, planktonic biocenoses which
response to the presence of organochlorine pesticides by chang-
ing the dominating forms of phyto- and zooplankton. In benthos,
the phenomena of mass mollusk mortality were observed.
Structural changes of biohydrocenoses indicate the chang-
es of metabolism in hydrobionts under conditions of intoxica-
tion. Experiments designed to reveal metabolic disturbances
in various hydrobionts under conditions of DDT intoxication
showed that the main mechanism of disturbances under the action
of this pesticide and some other toxicants is the change in
the content of regulators of the metabolic processes,such as
vitamin Bj and nicotinamide coenzymes. For example, the content
of vitamin By in tissues of mollusks and chironomids increased
by I24-I56j% under the influence of DDT. In this case, under
conditions of intoxication the anaerobic processes dominated
in the tissues of Dreissena and mollusks(2), as evidenced by
an increasing level of the reduced forms of nicotinamide coen—
zymes(6).
The studies are currently being continued on the levels
of accumulation of organochlorine pesticide residues in the
organs and tissues of hydrobionts of lowland reservoirs. There
is a distinct tendency for a sharp decrease in the levels men-
tioned and an increase in the numbers of food fish populations,
especially predatory ones, due to the growing fry. This tenden-
cy is indicative of the improved ecological-toxicological situ-
ation in the reservoirs resulting from the effective measures
on the environmental protection.
350
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LITERATURE CITED
I. Alekseev, V.A.jLesnikov, L.A. Pesticides and their influ-
ence on aquatic organisms. Transactions of the Research
Institute of Lake and River Fishery, 1977, issue 121,
8-93 (in Russian).
2. Birger,T.I. Metabolism of aquatic invertebrates in the
toxic environment. Kiev, Publishing House "Naukova Dum-
ka", 1979, 190 pp.(In Russian).
3. Braginsky,L.P. Pesticides and life in water bodies. Kiev,
Publishing House "Naukova Dumka", 1972,236 pp.(in Russian).
4. Braginsky, L.P., Komarovsky,F.Ya., Merezhko, A.I. Persis-
tent pesticides in the ecology of fresh waters. Kiev,
Publishing House "Naukova Dumka", 1979,14-3 pp. (in Russian).
5. Komarovsky, F.Ya., Metelev, V.V.v Pischolka,lu.K. DDT and
its metabolites in organs and tissues of fish. In: "Sur-
face water formation and quality monitoring." Kiev,
Publishing House "Naukova Dumka", 1975,74-79 (in Russian).
6. Malyarevskaya, A.Ya. Metabolism of fish under conditions
of anthropogenic eutrophication of water bodies. Kiev,
Publishing House "Naukova Dumka", 1979,254- pp.(in Russian).
7. Brawn, A. Ecology of pesticides.-Awiley-Interscience Pub-
lication. New-York- Toronto,1978,560 pp.
8. Dane, W.j Daines, T.^ Hayer, W. Poisoning by DDTs rela-
tion between clinical signs and concentration in rat
brain. Science, 1963,142, No.3598, 1474-1479.
9. Indwig, J.$ Ludwig, C. The effect of starvation on insec-
ticide contaminated herring gulls removed from a lake
Michigan. Proc. I2th Conf.Gr.Lakes Res., Annual Arbor
(Mich.),1969,185-192.
10. Melanby, K. Pesticides and pollution. London,I967,22I pp.
II. Muirchead-Thomson, R. Pesticides and freshwater fauna.
London-New-York, Acad.press.,1971, 248 pp.
351
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MATHEMATICAL MODEL OF IESTICIDE EFFECTS ON
AQUATIC ECOSYSTEMS
by
V.V.Alekseev
Department of Physics,Moscow State University,
Moscow
The problem of pesticide effects on the environment has
received widespread attention in the last few years. There is
an extensive scientific literature on water and soil pollution
by pesticides. Experiments are being carried out to study pes-
ticide effects on individual species such as bacteria, algae,
mollusks, fish, mammals, etc., as well as on biocenoses as
a whole. It has been found that pesticides can cause biocenolo-
gical changes affecting differently various organisms.
In addition to the experimental studies, attempts have
been made to simulate mathematically the processes occurring
in biocenoses in the presence of pesticides. For example, the
authors of ( 7 ) have developed a mathematical model of the
change in biological productivity of the World Ocean from 1970
to I960. They investigated the effect of DDT on phytoplankton
assuming that DDT substantially inhibits the mineral metabolism
of microphytic algae, in particular that of nitrogen and phos-
phorus. It was evident from the model that as a result of the
direct DDT effect alone at a concentration of 1.5 J* 6/lf the
reproduction of feed resources and,therefore,fish take would
decrease thirtyfold for ten years. Such a conclusion was drawn
because of the fact that the model took no account of detoxi-
cation and possible increase in the number of competitor due
to degradation of a particular species if the former was more
resistant to toxins.
It has been shown experimentally ( 5 ) that pesticides
increase mortality and affect motor abilities, food consump-
tion rate and other physiological functions of the organism.
According to the data presented in ( 5»6 ) > the dependence of
mortality on pesticide concentration can be approximated by
a linear function in the area of low concentrations. At these
concentrations of pesticides their effect on motor activity,
food consumption rate and other functions in most cases is
352
-------
still insignificant and can be neglected.
In this paper we consider the model illustrating the dyna-
mic processes in a biogeocenosis to determine qualitative chan-
ges which can occur in the living component of biogeocenoses on
exposure to pesticides.
Research procedure is similar to that used previously to
study water body eutrophication(2). We consider a model bioce-
nosis consisting of two species of producers and two species
of consumers, assuming that the system is closed with respect
to the limiting biogenic element. M,, and M,j are biomasses of
producers, and M2 and M^ are the biomasses of consumers. We
assume that the biomasses of all species are expressed in units
of (normalized to) the biomass of the limiting biogenic element,
for example,nitrogen,phosphorus,etc. M0 is the concentration
of the limiting biogenic element in the water body. Thus, the
closed system condition with respect to the substance will take
the following form:
/ / ,
MX-I-MH-I-MO^MO + MO = M = const
1 -2 -g 0
The set of equations describing the dynamics of the model
biogeocenosis has the form
[2]
Here £-L are the coefficients of the mortality; £> are
the photosynthetic coefficients; y12 and d" ^are the coefficients
of producer uptake by consumers; ®y ., and (^ are the coeffici-
ents of consumer growth. 0
Clearly, the qualitative behavior of the ecosystem is
determined largely by the value M . When M is small, that is
M < mln (£,/]}, £;/j3j ,
a trivial solution M,= 1^1=1^2 = 1^2=0 turns out to be the
stable solution to the set of equations jj[l and f~2l .
As M increases, with
e'Jp > tjf> and M < e1/^-HniLn(e2/jj21- , e'z/$) ,
the stable stationary state with nonzero biomass M, has the
form M1 = M-syp . As M continues to increase, the systems
with two,three(and so on) species turn out to be stable. A de-
tailed study of the analogous four-species system is given
353
-------
in (2,4) . Thus, we can simulate qualitatively the process of
succession with weak eutrophication of the system.
Suppose there is a water body which is not inhabited at
the initial moment of time and contains zero amount of biogens.
Assume also that there .occurs its slow enrichment with nutri-
ents, and that small number of species M1 , M^ , M2 ancl M'2 can
migrate to this water body. Then various stable ecosystems will
occur in it.
We will assign numbers to the species so that
21 -
From the former inequality it follows, in accordance with the
Volterra-Gauze theorem, that in the absence of other species,
M1 wins M, by competition. From the latter inequality it
can be inferred, based on the same theorem, that in the absen-
ce of other relations, species M2 wins by competition species
M'2 when they feed on the same resources M1 . The Volterra-
Gauze theorem can be applied to the particular case given by
the following set of equations;
= p M, M0 -
dM,'/dt = £>' M/M0 -(TM/Ms . [3]
After integrating, we can easily obtain from [3]
i
"to ,
From this it follows, inasmuch as M-, and M., are limited,that
at i^/Xi2> P>'/<^' species M1 is forced out.
In the general case.depending on relations between the
coefficients of the set j_2] , various succession series can
be realized (their total number for the biocenosis studied is
16). Each of the series shows the change of stable states cha-
racterized by a certain number of species, as particular criti-
cal values of M are reached. We shall use a schematic repre-
sentation of the series suggested by M.S.Polyakova (4 ) . The
limiting biogenic element MO always present in the system is
designated by a rectangle placed at the bottom of the diagram
which describes the stationary state of the system. If producers
are present in the biocenosis we designate them by squares and
place in the diagram directly above M0 , and if consumers are
present we designate them by squares and place above producers.
354
-------
Links between the elements of the biocenosis are designated by
straight lines. Delations between the coefficients also can be
represented schematically by replacing with arrows the lines
which indicate the links between the elements. Upward arrows
designate "uptake" and downward arrows designate "pressure".
For example, corresponding coefficients for "uptake" of biogenic
element MO by, producers M1 and M.J are £.,/£> and £\ / $' ,
respectively; for"uptake" of producer M1 by consumers M2 and
M2 " £2/^2iand £2/0"'* yfor "pressure"exerted on M, by consumers
M2and Mj-p/j^ and p/cr » and so on.Thickness of the arrows show
the relations between the coefficients indicating which or
the species has better "uptake" or exerts greater "pressure" on
a particular species. An example of schematic representation of
succession series is given in Fig.I. As seen from the figure,
ATA
M3
M'
M
Mn
H
a
m
0 Mf M*
Fig.I. Examples of succession series for
/\P'"l"X/i2)>((b+^)(Pl/~l~(f/) . The top series corresponds to
0 Jfn » ' ^1 >\'i\ • ^e ^°'fc'tom series corres-
1 pcrnds to ^ > Y21 , (^
-------
two possibilities correspond to one and the same diagram of
coefficients in accordance with^two additional conditions for
coefficients (T, , $( , Y21 and Y21 . The first possibility is
characterized by the fact that as M increases, the number of
species initially increases and then decreases. Characteristic
of the second possibility is the existence of triggers( dashed
areas) when realization of the state depends on the initial
conditions. We shall not dwell on studying the initial stage
of oligotrophic-eutrophic succession, since it is discussed in
some detail in ( 4 ) .
We now turn to the description of the model of pesticide
effects on the change in the qualitative structure of ecosys-
tems. As previously noted, mortality coefficients are assumed
to be linearly dependent on pesticide concentration P :
where £0L and oCL are the constants characterizing properties
of the i-th species. There are three types of inequalities
which can vary with increasing pesticide concentration:
M
since the quantities £ assumed to be dependent on P enter
into these inequalities only.
With a small number of species(less than three), the cri-
tical values of M linearly depend on 6 and,thertjf ore , on
P (I) .Consequently, we have a fan of divergent straight
lines in the plane ( M , P ) that demarcate stable states. In
this case, if the character of inequalities [>] does not vary,
"degradation" of the system goes through the same states with
increasing P , as it went through with increasing M . This
process is shown in Fig. 2. r
With increasing p , however, inequalities |_4 J can vary,
and then "degradation" follows a different path. One example
is given in Fig. 3. Here very interesting situations are possib-
le. In the example considered, the stationary state with the
maximum number of species occurs twice. Of interest are the
examples presented in Fig. 4, where, with increasing pesticide
concentration, a sharp increase in the number of species
occurs or no change in species diversity is observed for
a long time.
Thus, a very complex change of biocenosis can be observed,
as pesticide concentration increases. When added to the system,
small doses of pesticides can cause not only a decrease, but
also an increase in the number of species.
356
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Fig.2. The process of "degradation" of the ecosystem,
as the pesticide concentration increases, for
d, <. Y21 and invariable inequalities [4-J
LITERATURE CITED
Alekseev,V.V. Biogeocenoses as autogenerators and
triggers. Zhurnal Obschei Biologii/Journal of General
Biology/ 1976,57, No.5,728-744- /in Russian/.
Alekseev,V.V.; Polyakova,M.S. A simple model of the
initial stage of oligotrophic-eutrophic succession in
fresh water bodies. Ecologia /Ecology/ 1978,9, No.I,
5-10 /in Russian/.
Braginsky, L.P.; Komarovsky F.Ya.; Merezhko,A.I.
Persistent pesticides in the ecology of fresh waters.
Publishing House "Naukova Dumka",Kiev, 1979; 14-1 p.
/in Russian/.
Alekseev,v.V.:, Kryshev ,1.1; Polyakova, M.S.; Sazyki-
na,T.G. Dynamics and statistical mechanics of biogeo-
cenoses with the fixed mass of limiting biogenic ele-
ment. In "Man and Biosphere"; Publishing House of Mos-
cow State University,Moscow;I978/; Issue 2, 42-102
/in Russian/.
357
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Fig. 5. The process of "degradation" of the ecosystem,
as the sign of inequalities £4] reverses. The
additional conditions for coefficients are as
follows: d;
-------
5. Klimanuskene, V.P. Effect of the dairy wastewater on
Daphnia Magna Straus. Izvestia GosNIOEKh/Transactions
of the State Research Institute' of Lake and River
Fishery/ 1974, 98, 144-148 /in Russian/.
6. Korostylev, M.V. Effect of diuron,dilor/dihydroheptach-
lor/ and methyl nitrophos /fenitrothion/ on chironomids,
Izvestia GosNIORKh /Transactions of the State Research
Institute of take and River Fishery/ 1977,121, 161-
164 /inRussian/.
7. Morgan, R.E.; Weinberg,"R. Computer simulation of world
systems biogeochemical cycles. Int.J.Environ.Studies
1972, 3,No.2, I05-II8.
Fig.4. Examples of succession series, as the ecosystem
is poisoned by pesticides. The diagrams of coef-
ficients correspond to p<^>ch* Unlike Fig.3,
the ecosystem travels in the same line, as M
and P change.
359
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