EP A/600/A-94/232
~
16
Fate and Exposure Modeling In Terrestrial
Ecosystems; A Process Approach
Sandra L. Bird
ABSTRACT
Pathways for exposure of birds to pesticides include soil, water, air, soil-dwelling
organisms, and insects. A process approach to avian exposure calculates transport and
transformation of agricultural chemicals through each of the exposure media. Differ-
ential equations calculating the time rate of change of chemical in each exposure
medium are solved in this approach. Development of terrestrial exposure algorithms at
the United States Environmental Protection Agency (U.S. EPA), Environmental Re-
search Laboratory, Athens, GA, draws on validated technology where well-established
methodologies do not exist.
Multimedia process models are data intensive. An integral part of developing a
usable and useful exposure calculation framework is the incorporation of supporting
databases in the system. Supporting data for soils, meteorology, crops and cropping
scenarios, and species distribution data are being developed on a regional scale based
on the 186 major land resource areas (MLRA) defined by the United States Depart-
ment of Agriculture (USDA).
This chapter further describes the process-oriented, mechanistically based approach
to avian exposure calculations, the supporting databases required for doing regional
analyses, and the interactive system design for accessing this information.
KEY WORDS
exposure assessment, pesticide transport, process modeling
INTRODUCTION
Relating the pesticide exposure pattern of birds to environmental characteristics of
the toxic chemicals involved is a prerequisite for predicting the long-term response
patterns of avian populations. Direct measurement of the exposure of birds in a field
situation is costly, and only a limited number of exposure scenarios will likely ever be
evaluated in this manner. Extrapolation techniques in the form of mathematical
models are required to supplement field and laboratory studies in performing compre-
hensive environmental exposure assessments and assisting in the choice of critical field
tests.
149

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150 WILDLIFE TOXICOLOGY AND POPULATION MODELING
Pathways for exposure of birds to pesticides include soil, water, air, soil-dwelling
organisms, plants, and insects. Transport and transformation of agricultural chemicals
through each of the exposure media, coupled with the behavioral and physiological
attributes of the bird, determine the ultimate exposure patterns in a field situation.
TEEAM, described by Dean et al.,' used a process-based approach in looking at the
fate of pesticides in the physical environment and their ultimate movement into the
biota. Processes originally represented in TEEAM included behavior of the spray,
water, and pesticide movement in the soil; evapotranspiration and volatilization; plant
growth and uptake; bioconcentration in soil organisms; and ingestion and inhalation
by terrestrial vertebrates. The TEEAM project provided the initial prototype for a
terrestrial exposure system that will be incorporated into the Pesticide and Industrial
Chemical Risk Analysis and Hazard Assessment (PIRANHA)' modeling system. The
current components of the PIRANHA system provide an exposure assessment method-
ology for aquatic systems.
The general approach of the PIRANHA system models is to combine the loadings,
transport, and transformation of a chemical into a set of differential equations using
the law of conservation of mass as an accounting principle. Generally, the process
descriptions for transport of chemicals between environmental compartments and
transformation of chemicals within those compartments are based on process-oriented
mechanistic constructs or accepted empirical relationships. The discussion in this chap-
ter describes the components to be incorporated into the PIRANHA system as part of
the U.S. EPA EcoRisk program, which will expand the methodology to terrestrial
exposure assessments.
SYSTEM ARCHITECTURE
Exposure assessment within PIRANHA is structured as a series of stand-alone pro-
grams linked together by input and output files. The modules are based on well-
developed and tested technology. The terrestrial exposure algorithm structure is a series
of four stand-alone components: (l) spray drift and deposition, (2) terrestrial exposure
media, (3) surface water, and (4) avian exposure, which are linked as illustrated in
Figure I. The spray drift and deposition component provides the spray deposition on
habitats and spray day inhalation concentrations. The terrestrial exposure media com-
FIGURE 1. Architecture ol a terrestrial exposure modeling system depicting the interactions between
spray drift and deposition, terrestrial exposure media (soil, plants, Invertebrates), surface
water, and avian uptake.

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PART 4,CHAPTER 16 151
poneni calculates the residues (soil, plants, invertebrates) to which the avian species
may be exposed. The surface water component provides exposure concentrations via
the drinking water.
There are several advantages to developing the terrestrial exposure system in this
modular format. The primary advantage is the ability to incorporate updated versions
of components easily and with a reduced risk of introducing errors in the integration
process. Thus, the user can be confident that the validity of the components is not
compromised in the transfer process and that the most recent versions of each compo-
nent are included in the package. Finally, a major emphasis in the PIRANHA develop-
ment is the linkage of the models to input databases. The mechanistic approach is data
intensive and use of the process modeling approach outside of a research environment
requires direct user access to parameter databases in developing input scenarios.
APPLICATION AND DEPOSITION
Evaluating the dynamics of toxicant application and deposition is the first step in
determining the fate of pesticides. Pesticides are introduced in several ways; spray
application to foliage or soil surfaces, incorporation into the soil, or deposition as time
release granules or on seeds treated prior to planting. The type of initial application
plays a large role in the type of environmental threat a pesticide may represent. Spray
applications may contaminate offsite wildlife habitat; treated seeds or granules may
provide a highly concentrated source of pesticide if ingested.
TEEAM used the USDA-Forest Service Spray Drift Model, FSCBG, to estimate
deposition and offsite drift from aerial spray applications,1 and the recent release,
FSCBG 4,0, is a candidate for inclusion in PIRANHA. Offsite drift is a function of a
variety of factors including aircraft type, speed, and application altitude; type and
configuration of spray nozzles; weather conditions; and chemical formulation. Spray
drift model algorithms calculate the spatial distribution of pesticide in the canopy and
on the soil surface and, in addition, provide an estimate of the aerosol concentration
that can be used to estimate inhalation dosage that could occur during the spray.
Wind tunnel tests provide the droplet size spectra, the most important equipment
parameter, for spray nozzles under different application conditions. One of the biggest
limitations in parameterizing spray drift models is estimating local meteorological
conditions. Turbulence (i.e., atmospheric stability) is the most difficult model feature
to accurately parameterize and lends the greatest uncertainty in modeling pesticide
drift.
Two candidate spray drift models for inclusion in the PIRANHA system are FSCBG
4.0, the updated USDA-Forest Service model, and the Dow-Elanco spray drift model.
Model testing efforts for FSCBG 4.0 have been reported previously,4 6 but have not
been oriented to performing exposure assessments. The Dow-Elanco model contains a
more simplified algorithm for near-aircraft spray behavior and is simpler to parameter-
ize. Both models require additional testing for use within a regulatory context. The
spray drift model selection for incorporation within the PIRANHA system is coordi-
nated with the spray drift task force, an industry coalition developing drift analysis
tools for regulatory evaluations.
In addition to simulating aerial spray drift, pesticide application in the soil/plant
component can be specified by the user as a direct application to the canopy, applica-
tion to the soil surface, incorporation to a specified depth of the soil, or release from
granules or treated seeds.

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152 WILDLIFE TOXICOLOGY AND POPULATION MODELING
TRANSPORT AND TRANSFORMATION IN SOIL
Following application, a combination of chemical properties, soil properties, crop-
ping practices, and meteorological conditions interact to determine whether a chemical
will move into surface water via runoff and erosion, leach into the groundwater,
volatilize into the atmosphere, move into the food chain via plants and soil-dwelling
organisms, or degrade in the soil surface layers.
The Pesticide Root Zone Model (PRZM) forms the basis in TEEAM for calculating
soil-associated movement of the pesticide.' PRZM is a one-dimensional compartmental
model for use in simulating vertical chemical movement in unsaturated soil systems
within and immediately below the plant root zone. PRZM processes are illustrated in
Figure 2. PRZM allows the user to simulate movement of potentially toxic chemicals,
particularly pesticides, that are applied to the soil or to plant foliage. Dynamic simula-
tions allow the consideration of pulse loads, the prediction of peak events, and the
estimation of time-varying mass emission or concentration profiles.
5«<#m inf
Soli
Core
and
Horizons
Depth
FIGURE 2. Process included in the pesticide root zone model (PRZM),

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PART 4, CHAPTER 16 153
PRZM has two major components: hydrology and chemical transport. The hydro-
logic component for calculating runoff and erosion is based on the Soil Conservation
Service curve number technique and the universal soil loss equation, Evapotranspira-
tion is estimated from pan evaporation data, or by an empirical formula if input pan
data are unavailable. Evapotranspiration is divided among evaporation from crop
interception, evaporation from soil, and transpiration by the crop. Water movement is
simulated by the use of generalized soil parameters, including field capacity, wilting
point, and saturation water content. Irrigation also may be considered. Dissolved,
adsorbed, and vapor-phase concentrations in the soil are estimated by simultaneously
considering the processes of pesticide uptake by plants, surface runoff, erosion, decay,
volatilization, foliar washoff, advection, dispersion, and retardation. Detailed descrip-
tions of equation development, numerical solution techniques, and input variables may
be found in the PRZM user's manual.7
PRZM performs calculations on a daily time step returning daily values of adsorbed,
dissolved, and vapor phase pesticide concentrations in vertical soil layers; runoff vol-
ume and pesticide concentration in the runoff; eroded soil volume and pesticide con-
centration in the eroded particles; and volatilization loss from the soil surface and
under canopy vapor concentrations. PRZM has been applied extensively and tested
against field data.110
TRANSPORT AND TRANSFORMATION IN PLANTS
Exposure of plants to pesticide can occur either through direct application to the
foliage or uptake of pesticide from the soil. On plants, the pesticide may wash off the
leaf surface, photodegrade, or move into the plant cells and be translocated throughout
the plant. Pesticides in soil water adsorb to root surfaces and move into the above-
ground plant parts in the transpiration stream. The chemical may be either degraded by
the plant or lost through the stomata to the atmosphere. Following application, pesti-
cide concentrations in plants decline not only due to washoff, degradation, and volatil-
ization but also due to growth dilution.
The plant growth simulations are based on the plant growth algorithm found in the
USDA model EPIC." Crop growth is calculated using crop-specific factors such as
maximum leaf area index, heat units required for crop maturity, energy conversion
efficiency, and optimal growth temperature along with environmental parameters such
as solar radiation, temperature, and available water. Crop growth models for corn,
wheat, soybeans, and other major field crops are relatively well developed and parame-
terized.
Uptake of pesticide from the soil and translocation into aboveground plant parts are
simulated using a simple compartment model (Figure 3) applicable to neutral organic
molecules passively transported by the plant. Uptake by the plant roots is parameter-
ized based on studies12 for a series of organic molecules with log Kow values ranging
from 1.2 to 5.0.
Individual components of the soil-plant transport system have been tested indepen-
dently, and the combined system has been recently tested using field data.10
DRINKING WATER EXPOSURE
Drinking water exposure of birds to pesticides can be considered through two routes.
When the soil surface layer is saturated following a rainstorm, this surface soil water is

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154 WILDLIFE TOXICOLOGY AND POPULATION MODELING
Stem
Soli Gas
Root
Soil Organic
Carbon
FIGURE 3. A simple compartment model simulating uptake of xylem-transported pesticides from the
soil and translocation Into aboveground plant parts.
assumed to be available to the bird. Additionally, they may drink from ponds or
streams adjacent to treated areas.
EXAMS" is used to calculate pesticide concentration in streams or ponds. This
model combines the loadings, transport, and transformations of a chemical in the body
Of water into a set of differential equations using the laws of mass balance. Loadings to
the water body are measured from the runoff/erosion calculations of PRZM and direct
deposition to the body of water. The chemical kinetics expressions in EXAMS are
second order descriptions for transformations attributable to direct photolysis, hydrol-
ysis, biolysis, and oxidation reactions. The model input has been designed to accept
standard water quality parameters and system characteristics along with chemical data
sets required by EPA regulatory procedures.
TERRESTRIAL FOOD CHAIN
The simulation of pesticide movement in soil, plant, air, and water provide the media
concentrations for uptake by ecosystem fauna. Once environmental concentrations of
toxicants are known, the behavior and physiology of the individual species interact to
determine uptake and accumulation of the pesticide.
A simple Markov transition matrix is used to specify movement of animals between
environmental compartments. Soil organisms are allowed to move vertically between
soil horizons while aboveground dwellers move among the laterally defined habitats.
For each organism, an MxM transition matrix is specified where Mis the number of
possible locations (horizons or habitats), and the transition probability specified within
the matrix is the probability that an animal in a given location will move to a specific
location given its current location.
Formulation of equations to describe the uptake and accumulation of chemicals in
birds and other aboveground dwellers based on easily defined chemical properties and
organism physiology is the ultimate goal for the development of uptake and exposure

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PART 4, CHAPTER 16 155
algorithms. The pharmacokinetic approach used in the development of the Food and
Gill Exchange of Toxic Substances (FGETS) model14 to simulate accumulation of
neutral organics in fish serves as a prototype for estimating accumulation in terrestrial
species. An exposure model must be able to calculate internal concentrations to evalu-
ate impacts of time-varying dosages of the contaminant before extrapolations can be
made to population-level impacts. The original approach to modeling the uptake of
pesticides in TEEAM incorporated a calibration factor, often referred to as an assimi-
lation efficiency factor, to parameterize the amount of material retained in an organ-
ism following ingestion. A soil organism module recently developed for inclusion uses
this physiological approach.
Soil-dwelling organisms are assumed to remain in a single lateral habitat but may
move vertically between soil horizons. Initial development and testing of the uptake by
soil-dwelling organisms has centered on earthworms. Uptake of pesticide may be
through ingestion of soil material or diffusion of dissolved pesticide though the integu-
ment.
The two routes of uptake by the earthworm are illustrated in Figure 4. Chemical
dissolved in the soil water solution diffuses through the cuticle. Chemical ingested with
soil and litter is assumed to equilibrate between the gut and body of the worm as the
material passes through the worm's digestive tract. Equilibration is controlled by the
organic matter content of the soil and of the worm. Concentration in the organism may
decrease due to internal degradation and growth dilution. As of this writing, the
formulation is applicable only to neutral hydrophobic molecules, but plans are to
expand il to accommodate polar and ionic compounds.
A substantial database exists on accumulation of toxicants in earthworms because
they are an economically important species in agroecosystems as well as being an
important food source for many species of birds. Both mode! and field data indicate
that uptake of chemicals by earthworms is relatively rapid, with concentrations equili-
Slmpllfled Worm Model
Total
Soil
Ingttilon
Soil
mtw
Soil
*" Putlcln
Excntton
Organic
Carbon
FIGURE 4. Routes of uptake by the earthworm where Kp is the partitioning coefficient of chemical
between organic carbon and water.

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156 WILDLIFE TOXICOLOGY AND POPULATION MODELING
brating within the worm in 10 to 20 days with a three- to tenfold organism concentra-
tion relative to soil concentration.
Aboveground organisms may move between different lateral habitats and take in
pesticide from a variety of sources including water, air, plants, soil, pesticide granules,
and prey. Daily drinking volumes and respiratory volumes are combined with water
and air concentrations, respectively, to calculate dosage via the water and inhalation
routes.
Total daily feeding rates along with preference factors for each food source are
combined with each food type to calculate dosage via ingestion. In addition to food
(plants, soil organisms, and prey animals), soil, pesticide granules, and coated seeds
may be ingested. Capture of prey by a predator is simulated by basing capture on the
probability that predator and prey are in the same habitat and a probability of capture
for each predator/prey pair.
Development of a pharmacokinetic approach to estimating internal organism con-
centrations for bird is a precondition for adequately predicting impacts of field expo-
sures on birds. Previous bioaccumulation modeling for birds' used an empirical assimi-
lation factor, which has limited use in extrapolating between conditions, individuals,
and species. The pharmacokinetic approach analogous to that used in FGETS, based
on physiology of the organism and the properties of the chemical, is necessary to
adequately predict the impact on a mixed population under multiple stresses.
INPUT DATA GENERATION
The input data requirements for performing mechanistic-based exposure calcula-
tions are extensive and include soil characteristics, chemical properties, crop and crop-
ping practice information, meteorological conditions, animal behavior and physiology,
and food chain interactions. Development of a utilitarian tool requires linkage of the
model to databases for development of user input sequences. The PRZM Input Colla-
tor, Version 1.0 (PIC:VI)15 is the first step in developing a comprehensive database
support system for all elements of a terrestrial exposure system.
The geographical data in PIC:V1 are organized based on the 186 MLRAs defined by
the U.S. Soil Conservation Service.16 The delineation of MLRAs is based primarily on
soils, climate, and hydrology of individual regions. The county-scale soil and crop data
contained in NSSAD/SIRS (National Soil Survey Area Database/Soil Interpretations
Record Database)11 was overlaid onto the MLRA scale data.
PIC:VI allows the user to enter the data generator by selecting either a crop or an
MLRA. If a crop is selected, the program returns a list of MLRAs where this crop is
potentially grown for user selection. Alternately, if an MLRA entry is selected, the
program returns the list of potential crops grown in that area.
Based on crop and region selected, a list of soil names sorted by areal extent in the
MLRA is returned to the user along with the number of hectares of the particular soil,
hydrologic group, and soil textural class. The user then simply selects the soil of
interest for input generation.
Selection of the MLRA also identifies the appropriate first order National Oceanic
and Atmospheric Association (NOAA) meteorological station for the area. PIC:VI
contains a utility that allows the user to review 20 to 30 years of rainfall records to
easily identify a specified 30-day period of interest. The user can then select the applica-
tion date that represents the critical period for a particular evaluation.
Selection of the crop, MLRA, and soil specifies all PRZM input variables except the
chemical properties and application scenarios. PIC:VI requests input of the chemical

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PART 4, CHAPTER 16 157
properties and application parameters. Following specification of these parameters, a
PRZM input sequence is generated.
Incorporation of additional databases including information such as plant growth
parameters, feeding rates and food preferences, physiological parameters, and aircraft
and spray nozzle characteristics will expand preprocessing to exposure calculation for
terrestrial species. Development of typical farm pond scenarios within each MLRA for
use with the EXAMS model is an additional component of the PIRANHA system.
Specific scenario generation is another approach to increasing model accessibility.
This approach has been successfully used in the inclusion of the canonical series devel-
oped for use with the EXAMS model.
MODEL VALIDATION
Mathematical models and computer simulation codes designed to aid in risk assess-
ment must be verified and validated before they can be used with confidence in a
decisionmaking context. The builders and users of ecological models are rightfully
concerned that the models be valid, because decisions based on them have significant
economic, public health, and ecological consequences. Structuring a system such as
PIRANHA to incorporate, where available, developed technological components
(e.g., EXAMS and PRZM), each characterized by a substantial history of validation
testing and maintained to the extent possible in a stand-alone context — allows the
package to be updated easily as the components are updated, with reduced risk of
introducing errors in the process.
Individual process models developed for incorporation in PIRANHA must be sub-
jected to validation testing. In addition to process-level testing, the model as a whole
must be evaluated. This overall system testing includes use of the databases and param-
eter estimation or measurement techniques when the model is used in a risk assessment
context. This level of validation will be an integral part of the continuing development
of the terrestrial exposure model.
One of the difficulties in evaluating terrestrial exposure, whether through models or
through field-based evaluations, is the lack of local homogeneity of the soil medium
itself, of the pesticide distribution in the system, and of the population of organisms.
The potential variability of pesticide concentration must be considered as it moves
through all the components of the system. Model results must be evaluated within the
context of this concentration variability. Field studies for use in testing model validity
must be carefully designed with consideration for this sample variability.10
Objective criteria for model validity must be defined. Models are, due to inherent
assumptions, incapable of predicting exactly the "true" values and can only be expected
to get close to them. A model's validity must be considered in the context of producing
values that are sufficiently close to true values. Hence, defining what is meant by
"sufficiently close" in the context of the user's application is the essence of model
validity. One way to pose the model validity question is using a criterion statement of
the form of whether the model is capable of predicting within a factor (e.g., 2) of the
true value in a given application context. This type of statement can be easily translated
into a hypothesis statement that can be subjected to statistical tests.
SUMMARY
Calculating the exposure of terrestrial biota to pesticides is the first step in evaluating
possible ecological impacts from the application of these pesticides in terrestrial sys-

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158 WILDLIFt TOXICOLOGY AND POPULATION MODELING
terns, A series of models that calculate transport of pesticides in the exposure media
and into the terrestrial biota is necessary in developing population effects modeling of
field applications. In addition to algorithm development, parameter estimation utilities
and validation testing are key requirements for success of a terrestrial exposure model
in a risk assessment framework and use in a regulatory setting.
REFERENCES
1.	Dean, J. D., K. A. Voos, R. W. Schang and B, P. Popenuck, Terrestrial Ecosystem Expo-
sure Assessment Model (TEEAM), EPA/600/3-88/038, U.S. Environmental Protection
Agency, Athens, GA, 1989.
2.	Bums, L. A., Ed., PIRANHA: Pesticide and Industrial Chemical Risk Analysis and Hazard
Assessment, Version 2.0, U.S. Environmental Protection Agency, Athens, OA, 1992.
3.	Bjorklund, J. R., C. R. Bowman and G. E. Dodd, User Manual, Forest Service Aerial
Spray Computer Model, FPM 89-1, USDA, Forest Service, Forest Pest Management,
Davis, CA, 1989.
4.	Rafferty, J. E., J. M. White, J. F. Bowers and J. W. Barry, Comparison of Program Wind
Phase 111 Deposition Measurements with FSCBG2 Model Predictions, U.S. Army Material
Command, Technical Analysis and Information Office, U.S. Army Dugway Proving
Ground, Project 1M4657I0DO49, Joint Chemical/Biological (CB) Contact Point and Test
(Project D049), 1988.
5.	Teske, M. E., J. W. Barry and R. B. Ekblad, Canopy Penetration and Deposition in a
Douglas-Fir Seed Orchard, Paper No. 901019, Proc. American Society Of Agricultural
Engineers, Columbus, OH, June 24-27, 1990.
6.	Rafferty, J. E., and J. F. Bowers, Comparison of FSCBG2 and FSCBG3 Aerial Spray
Model Predictions with Field Measurements, FPM 90-2, USDA Forest Service, Davis, CA,
1990,
7.	Carsel, R. F., C. N. Smith, L. A. Mulkey, J. D. Dean and P. Jowise, User's Manual for the
Pesticide Root Zone Model (PRZM), EPA/600/3-84/109, U.S. Environmental Protection
Agency, Athens, GA, 1984.
8.	Carsel, R. F., W. B. Nixon and L.G. Ballantine, Comparison of pesticide root model pre-
dictions with observed concentrations for the tobacco pesticide metalaxyl in unsaturated
zone soils, Environ. Toxicol. Cftem., 5, 345-353, 1986.
9.	Jones, R. L., G. W. Black and T. L. Estes, Comparison of computer model predictions with
unsaturated zone field data for aldicarb and aldoxycarb, Environ. Toxicol. Chem., 5,
1027-1037, 1986.
10.	Smith, C. N., R. S. Parrish and D. S. Brown, Conducting field studies for testing pesticide
leaching models, Int. J. Environ. Anal. Chem., 39, 3-12, 1989.
11.	Williams, J. R., C. A. Jones and P. T. Dyke, A model for assessing the effects of erosion on
soil productivity, in Analysis of Ecological Systems: State-of-the-Art in Ecological Model-
ing, W. K. Lauenroth, G. V. Skogerboe and M. Flug, Eds., Elsevier, New York, 1983.
12.	Briggs, G. G., R. H. Bromilow and A. A. Evans, Relationship between lipophilicity and
root uptake and translocation of non-ionised chemicals by barley, Pestic. Sci., 13,495-504,
1982.
13.	Burns, L. A., and D. M. Cline, Exposure Analysis Modeling System (EXAMS): Reference
Manual for EXAMS II, EPA/600/3-85/038, U.S. Environmental Protection Agency, Ath-
ens, GA, 1985.
14.	Barber, M. C., L. A. Suarez and R. R. Lassiter, Modeling bioaccumulation of organic
pollutants in fish with an application to PCBs in Great Lakes salmonids, Can. J. Fish. Aq.
Sci., 48, 318-337, 1991.
15.	Bird, S. L., PRZM Input Collator, Version 1 (PIC:V1), in PIRANHA: Pesticide and Indus-
trial Chemical Risk Analysis and Hazard Assessment, Version 1.0, U.S. Environmental
Protection Agency, Athens, GA, 1990.

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PART 4, CHAPTER 16 159
16.	U.S. Soil Conservation Service, Land Resource Regions and Major Land Resource Areas of
the United States Agriculture Handbook 26, U.S. Department of Agriculture, Washington,
DC, 1981.
17.	U.S. Soil Conservation Service, User Manual for Interactive Soils Databases: National Soil
Survey Area Database, Soil Interpretations Record Database and Plant Name Database,
U.S. Department of Agriculture, Fort Collins, CO, 1985.

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TECHNICAL REPORT DATA
|	(Please read Instructions on the reverse before complet
1, REPORT NO. 2.
EPA/600/A-94/232
3.
4. TITLE AND SUBTITLE
FATE AND EXPOSURE MODELING IN TERRESTRIAL
ECOSYSTEMS: A Process Approach
5. RECUH i i c
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Sandra L. Bird
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Research Laboratory
U.S. Environmental Protection Agency
960 College Station Road
Athens GA 30605
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory - Athens, GA
Office of Research and Development
U.S. Environmental Protection Agency
Athens GA 30605-2700
13. TYPE OF REPORT AND PERIOD COVERED
Book Chapter
14. SPONSORING AGENCY CODE
EPA/600/01
15.	SUPPLEMENTARY NOTES
IN; Wildlife Toxicology and Population Modeling; Integrated Studies of Agroecosystems.
R.J, Kendall and T.E. Lacher, Jr. (Eds.). Lewis Publishers, Boca Raton FL. 1993.
p. HQ-KQ	
16.	ABSTRACT
Pathways for exposure of birds to pesticides include soil, water, air, soil-
dwelling organisms, and insects. A process approach to avian exposure calculates
transport and transformation of agricultural chemicals through each of the exposure
media. Differential equations calculating the time rate of change of chemical in
each exposure medium are solved in this approach. Development of terrestrial exposure
algorithms at the United States Environmental Protection Agency (U.S. EPA), Environ-
mental Research Laboratory, Athens, GA, draws on validated technology where well-
established methodologies do not exist. Multimediak process models are data intensive.
An integral part of developing a usable and useful exposure calculation framework is
the incorporation of supporting databases in the system. Supporting data for soils,
meteorology, crops and cropping scenarios, and species distribution data are being
developed on a regional scale based on the 186 major land resource areas (MLRA) de-
fined by the United States Department of Agriculture (USDA). This chapter further
describes the process-oriented, mechanistically based approach to avian exposure
calculations, the supporting databases required for doing regional analyses, and the
interactive system design for accessing this information.-
17.	KEY WORDS AND DOCUMENT ANALYSIS
a, DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATl Field/Group
Terrestrial Pollution
Avian Exposure
Fate Modeling


18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
12
20. SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Fwm 2220-1 (Rev. 4-77) previous edition is obsolete

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