&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-

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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;

-------
      -  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
 I.   Braginsky,L.P. Pesticides and the life of water bodies.
      Publishing House "flaukova Dumka:" Kiev; 1972,227 p.
      /in Russian/.

 2.   Vrochinsky, M.A. et al. Hydrobiological migration of
      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
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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,
     18,No.7,29-35 /in Russian/.

9.   Melnikov,N.N. Pesticides and environmental protection.
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     1978,13,208-214- /in Russian/.

10.  Melnikov,N.N.;Volkov,A.I.;Korotkova  ,O.A. Pesticides
     and the environment. Publishing House "Khimija:"Moscow;
     1977,24-0  p. /in Russian/.

II.  Golovleva,L.A. et al. Microbial degradation of herbici-
     des ordram and 2,4- D in a water body. Izv.AN SSSR,
     Biology Series 1977,723-732 /in Russian/I

12.   Metelev,V.V.; Kanaev,A.I.;Dzasokhova,N.G. Aquatic
      toxicology.  Publishing House "Kolos": Moscow; 1971,
      24-7 P.  /in Russian/.

13.   Kovda,V.A.;Glazovskaya,M.A.; Sokolov,M.S.;Strekozov,
      B.P.  Izv.AN SSSR. Biology Series 1977,No. 1,120-124-
      /in Russian/.

I4-.   Rodin,L.E.iBazilevich,N.I. Dynamics of organic matter
      and biological turnover of substances in the main
      types of vegetation. Publishing House "Nauka": Moscow-
      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

-------
 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

-------
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

-------
    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

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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

-------
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
   
-------
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  50.  Quistad,  G.  B.,  Staiger,  L.  E., Milligan,  L. E., and Schooley, D. A.
      (1980) "Abstracts of  Papers", Second Chemical Congress of  the North
      American  Continent, (180th National Meeting of the American Chemical
      Society), Las Vegas, Nevada, August 1980;  American Chemical Society,
      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

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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


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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:


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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


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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

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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

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     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

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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


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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

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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.


LITERATURE CITED


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10.   van Genuchten, M.Th.; Cleary, R.W.  Movement of solutes  in  soil:
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38.  Green, R.E.; Rao, P.S.C.; Corey, J.C.  Solute transport in aggregated
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             •

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52.  van Genuchten, M.Th.; Wierenga, P.O.  Mass transfer studies  in sorbing
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65.  Hamaker, J.W.; Thompson, J.M. Adsorption.  In "Organic Chemicals  in  the
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 78.   Green, R.E.; Davidson, J.M.; Biggar, J.W.  An assessment of methods for
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 79.   Green, R.E.; Corey, J.C.  Pesticide adsorption measurement by flow
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 80.   Cheung,  M.W.  Equilibrium and kinetic processes of the interactions of
      4-amino-3,5,6-trichloropicolinic acid (picloram) and o,o-diethyl-
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      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
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 82.   Beese, F.; van der Ploeg, R.R.; Richter, W.  Test of a soil water model
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 83.   Leistra,  M.; Smelt, J.H.; Verlaat,  J.G.; Zandvoort, R.  Measured and
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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.

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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

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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

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 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

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 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

-------
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

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     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

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 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

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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

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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

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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

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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

-------
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

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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

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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

-------
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

-------
 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

-------
    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

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 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

-------
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

-------
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

-------
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.

Donigian, A.S.  Jr.,  and  N.H.  Crawford.   1976a.    Modeling
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.

International  Business  Machines,  Inc.   1974.    Structured
Programming Textbook and Workbook.  Independent Study Program.
   /
Johanson, R.C., J. C. Imhoff and H.H. Davis, Jr., 1980.  Users
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.

Negev, M.  1967.  A Sediment  Model  on  a  Digital   Computer.
Department  of   Civil   Engineering,   Stanford   University,
Stanford, California.  Technical Report No. 76.  109 pages.

Onishi,  Y,.  and  S.E.  Wise.   1979.   Mathematical   Model,
SERATRA, for Sediment-Contaminant Transport in Rivers and  its

                              155

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Application  to  Pesticide   Transport  in  Four  Mile  and   Wolf
Creeks   in   Iowa.    Batelle  Pacific  Northwest  Laboratories,
Richland, Washington.

Toffaleti, F.B.  1968.   A   Procedure  for  Computation  of   the
Total River  Sand Discharge and Detailed Distribution,  Bed  to
Surface.   Tech.   Report    No.   5,   Committee   on   Channel
Stabilization,   U.S.   Army  Corps  of  Engineers,   Vicksburg,
Mississippi.

Toffaleti,   F.B.   1969.    Definitive  Computations   of    Sand
Discharge in  Rivers.   J.  of Hydraulics Div.,  ASCE,  Vol.   95,
No. HY1, pages  225 -  248.

U.S. Army Corps  of Engineers.   1956.  Snow Hydrology,  Summary
Report of the Snow Investigations.   North  Pacific  Division,
Portland, Oregon  437  pages.

Wischmeier,  W.H.  and  D.D.  Smith.  1978.   Predicting   Rainfall
Erosion  Losses.    U.S.   Dept.  of  Agriculture,   Agriculture
Handbook No.  537.   58  pages.

Yalin, Y.S.   1963.  An Expression for Bedload  Transportation.
J. of Hydraulics Division,  ASCE,  Vol.  89,  No.  HY3,  pages
221-250.
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

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     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

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  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

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   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

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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

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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

-------
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

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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

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           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

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            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

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                                                        [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

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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

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(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

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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

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 (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

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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

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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

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    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

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    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

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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

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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

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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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     ment. In "Proceedings of the Vlth All-Union Symposium
     on the Present Problems of Water Body Self-Purification
     and Water Quality Monitoring";Tallin; 1979;Part 1,105-
     107 / in Russian/.

56.  Behavior, transformation and analysis of pesticides
     and their metabolites in soil. Scientific Center of
     Biological Research,USSR Academy of Sciences: Puschino;
     1973; 209 p. / in Russian/ .

57.  Popovich,N.A. Hygiene of herbicide application in rice
     growing. Synopsis of thesis for academic degree;Lvov
     Hydrometeorological Institute: Lvov ; 1972; 24 p.
     / in Russian/.

58.  Tarasov,M.N.; Korotova,L.G.; Demchenko,A.S.;Brazhniko-
     va,L.V. Hexachlorocyclohexane, metafos /methyl parathi-
     on/ and chlorofos /dipterex/ decomposition in soil, and
     their migration with the waters of surface runoff. In
     "Symposium on Environmental Transport and Transforma-
     tion of Pesticides"; EPA-600/9-78-003; 1978; I08-II6.

59.  Rubinchik,E.A.; Kaplin,V.T. The behavior of BHC (hexa-
     chlorocyclohexane) in natural waters. Gidrokhimicheskie
     materialy / Hydrochemical Materials/ 1979, 70,100-105
     / in Russian/ .

60.  Kovtun,V.G.; Yurchenko,A.I.; Kivshik,L.S.;Shandybin,
     V.E. Self-purification of irrigation waste waters in
     rice irrigation systems of anti-cereal herbicides. In
     "Proceedings  of the Vlth All-Union Symposium on the
     Present Problems  of Water Body Self-Purification and
     Water Quality Monitoring"; Tallin; 1979; Part I, 108-
     110 / in Russian/.
                             227

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61.  Simonovich,A.I.; Kolesnikova,T.Kh.  The role of hydro-
     bionts in the transformation of some thiocarbamate pes-
     ticides. In "Proceedings of the VIth Ill-Union Sympo-
     sium on the Present Problems of Water Body Self-Puri-
     fication and Water Quality Monitoring"; Tallin; 1979;
     Part I, 152-153 / in Russian/.

62.  Simonovich,A.I.; Likhovidova, T.P.;Svat,P.M. The role
     of some factors in the self-purification of natural
     waters of thiocarbamate herbicides. In "Proceedings
     of the Vlth Ail-Union Symposium on the Present Problems
     of Water Body Self-Purification and Water Quality Mo-
     nitoring"; Tallin; 1979; Part I, I54-I56/ in Russian/.

63.  Sokolov,M.S.;Knyr,L.L. Photolysis, sorption and migra-
     tion of pesticides in soils and landscapes. Khimija v
     selskom khozjaistve / Chemistry in Agriculture/ 1^73»
     No.9,43-48 /in Russian/.

64.  Pesticide handbook. Medved, L.I.,Ed.; Publishing House
     "Urozhai": Kiev ; 1979; 448 p.  / in Russian/.

65.  Spynu,E.I. On the role of experimental hygienic studies
     in predicting the behavior of pesticides in the environ-
     ment. Gigiena i sanitarija / Hygiene and Sanitation/
     1978, No.II, 6-9 / in Russian/.

66.  SpymijE.I.; Ivanova,L.P. Mathematical prediction and
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67.  Strekozov,B.P. The study on xenobiotic transformation
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68.  Strekozov,B.P. Propanil decomposition in water depending
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     Biological Research, USSR Academy of Sciences: Puschino;
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69.  Khaskin,B.A. Quaternary ammonium and phosphonium salts
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     vel Pesticides". Publishing House "Mir":Moscow; 1970;
     218-222 / in Russian/.

70.  Chemical means for plant protection. Published by
     VNIIKhSZR / All-Union Research Institute of Chemical

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     Means for Plant Protection/; Moscow; 1970; Issue I,
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71.  Khokhryakova,V.S.; Shustova,V.P. DDT breakdown by soil
     yeast cultures in liquid media. Khimina y selskpm kho-
     z.laistve / Chemistry in Agriculture/ I974,No.8,26-28
     /in Russian/.

72.  Tsiprijan,V.I.j Stefansky,K.S.;Perevozchenko,I.I. Per-
     sistence of organochlorine and organophosphorus pesti-
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     229-239 / in Russian/ .

73.  ChmiljV.D.Chromatographic methods for pesticide residue
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     Union Research Institute of Chemical Means for Plant
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74.  Shokodko,T.I.; Merezhko,A.I.;Malinovskaya,M.V. DDT
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     "Formation and quality Control of Surface Waters";
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75.  Yurovskaya,l'.M.; Zhulinskaya,V.A. The behavior of or-
     ganophosphorus insecticides in soil. Khimi.la y selskom
     khoz.jaistve 1974,No.5, 38-41 / in Russian/.

76.  Yurchenko,A.I.; Kovtun,V.G. The study on pesticide
     sorption by soils as a factor of self-purification
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     185-186 / in Russian/ .

77.  Yurchenko,A.I.; Sobina, N.A. The study on DDT and lin-
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     Issue 3, 28-34 / in Russian/.
                              229

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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

-------
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

-------
                       • 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

-------
             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

-------
          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

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         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.

                                     249

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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

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                                     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

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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

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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

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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

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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

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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-
   ment, U.S. Environmental Protection Agency, Cincinnati, Ohio, April,
   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

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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

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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

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 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

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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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         o
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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


      -KPM = 0.25

             KPM = 0.50

                 -KPM=1.00

                       -KPM = 2.00
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

-------
CO
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                  DIELDRIN IN EDIBLE TISSUE
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SEDIMENT DIELDRIN
           DRY
        c+ ro
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          -s
TOTAL  DIELDRIN
IN WATER, /xg/jf
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                                                                                 w   *  en   

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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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SEDIMENT  DIELDRIN
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  TOTAL DIELDRIN CONCENTRATION  (p.q/1)

P        o         P       P        P
2        b         b       o        o
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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
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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
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      0.10
    (f)
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      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
         c

         rt>

         r\3
                                                                            n>
                   INPUT  CONCENTRATION,
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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

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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

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 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

-------
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

-------
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

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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

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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;
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   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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