Mathematical Modeling Application to Environmental Risk Assessments

13

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Mathematical Modeling Application to Environmental Risk Assessments R. C. HONEYCUTT and L. G. BALLANTINE Ciba-Geigy Corporation, Agricultural Division, Greensboro, NC 27419

This gives an example o f fate modeling i n which the r i s k s o f an i n s e c t growth i n h i b i t o r , CGA-72662, in aquatic environments were assessed using a combination o f the SWRRB and EXAMS mathematical models. Runoff o f CGA-72662 from a g r i c u l t u r a l watersheds was estimated using the SWRRB model. The r u n o f f data were then used to estimate the loading o f CGA-72662 i n t o the EXAMS model f o r aquatic environments. EXAMS was used to estimate the maximum concentrations o f CGA-72662 that would occur i n v a r i o u s compartments o f the defined ponds and lakes. The maximum expected environmental concentrations o f CGA-72662 in water were then compared with acute and chronic t o x i c i t y data f o r CGA-72662 i n f i s h and aquatic i n v e r t e b r a t e s in order to e s t a b l i s h a s a f e t y f a c t o r f o r CGA-72662 in aquatic environments.

The major o b j e c t i v e o f t h i s p r e s e n t a t i o n i s t o i l l u s t r a t e h o w a n environmental r i s k assessment o f a chemical can be made u s i n g mathematical models which are a v a i l a b l e at the present time. CGA-72662, a CIBA-GEIGY i n s e c t growth i n h i b i t o r , i s used as an example to show how a r i s k assessment can be c a r r i e d out using the SWRRB r u n o f f model coupled to the EXAMS f a t e model. With any environmental r i s k assessment o f a chemical, there are three f a c t o r s : 1) The environmental fate o f a chemical and 2) the exposure to and 3) the t o x i c i t y o f the chemical to organisms i n h a b i t i n g the environment i n question. The environmental f a t e o f a chemical i s u s u a l l y a f u n c t i o n of many p h y s i c a l and chemical processes which the chemical may encounter from the time i t i s a p p l i e d u n t i l i t d i s s i p a t e s . Such processes i n c l u d e : P h o t o l y s i s on s u r f a c e s , i n s o l u t i o n or i n a i r , h y d r o l y s i s , b i o l y s i s , o x i d a t i o n , t r a n s p o r t by d r i f t , eros i o n ( r u n o f f ) and other means o f transport and d i s s i p a t i o n . H i s t o r i c a l l y , most r i s k assessments have emphasized the t o x i c i t y of a chemical s e p a r a t e l y without adequate c o n s i d e r a t i o n o f the amount o f exposure to a chemical which an organism might 0097-6156/83/0225-0249$06.00/ 0 © 1983 American Chemical Society

Swann and Eschenroeder; Fate of Chemicals in the Environment ACS Symposium Series; American Chemical Society: Washington, DC, 1983.

250

FATE

OF CHEMICALS

IN T H E ENVIRONMENT

encounter. However, when one considers f i r s t the use p a t t e r n and environmental fate o f a chemical and uses these to p r e d i c t the amount o f exposure o f an organism to the chemical, then a more r e a l i s t i c r i s k assessment i s achieved. For example, a chemical may be very t o x i c to f i s h . However, i f the chemical i s degraded r a p i d l y i n the environment or adsorbs r e a d i l y to s o i l sediment, i t may not pose a s i g n i f i c a n t r i s k to f i s h l i v i n g i n areas adjacent to i t s a p p l i c a t i o n . With the advent o f e n v i r o n mental models, one can assess the fate o f a chemical and couple these data to exposure and t o x i c i t y data to determine s a f e t y margins f o r b i o t a i n the environment. CIBA GEIGY Corporation i s p r e s e n t l y using models as an a i d to data i n t e r p r e t a t i o n f o r r i s k assessment. Our general p h i l o sophy i s to use the model as an a i d to r i s k assessment and not as a p r e d i c t i v e t o o l to e l i m i n a t e d e f i n i t i v e s t u d i e s . Hopefull y , environmental f a t e models w i l l be u s e f u l as a p r e d i c t i v e t o o l as they become v a l i d a t e d . CGA-72662 w i l l be used as an example to b r i e f l y i l l u s t r a t e an approach to the use o f models i n environmental r i s k assessment. CGA-72662 i s an i n s e c t i c i d e which i s being developed f o r use on c e l e r y i n F l o r i d a . The c e l e r y i s u s u a l l y grown on a high organic matter muck s o i l . The recommended a p p l i c a t i o n r a t e i s 0.125 l b s . ai/A. A maximum o f twelve a p p l i c a t i o n s at seven day i n t e r v a l s may be used f o r one crop o f c e l e r y f o r a t o t a l o f 1.5 l b s . ai/A. At f i r s t glance one might suspect that an e n v i r o n mental hazard might e x i s t from r u n o f f i n t o lakes or ponds a d j a cent to the a p p l i c a t i o n s i t e . As w i l l be demonstrated l a t e r , the SWRRB r u n o f f model was coupled to the EXAMS environmental fate model to f u r t h e r examine t h i s prospect by p r e d i c t i n g the f a t e of CGA-72662 and p r e d i c t i n g the exposure to aquatic organisms. The r e s u l t s showed very l i t t l e r i s k and a high s a f e t y margin f o r these organisms. Although, the r e s u l t s do not e l i m i nate the n e c e s s i t y to conduct appropriate environmental chemist r y s t u d i e s ; the r e s u l t s do give us much confidence that CGA72662 used i n t h i s manner does not pose a s i g n i f i c a n t e n v i r o n mental r i s k . The model w i l l a l s o help p r o j e c t the need f o r future long-term s t u d i e s . D e s c r i p t i o n o f the Runoff SWRRB and the EXAMS Models SWRRB - The Simulator f o r Water Resources on Rural Basins (SWRRB) was developed at EPA by R. C a r s e l and i s a m o d i f i c a t i o n of the USDA model CREAMS (I). I t was o r g i n a l l y developed to p r e d i c t d a i l y r u n o f f volume f o r small watersheds throughout the U.S. The b a s i c r u n o f f model i s based on the water balance equation: SM

t

= SM + P - Q - ET - 0 - QR


13.

Fate Model Application

HONEYCUTT AND BALLANTINE

251

SM i s the s o i l moisture at the beginning. SM i s the s o i l moisture t days l a t e r . P i s the amount o f r a i n f a l l . Q i s the amount o f runoff. ET i s the amount o f e v a p o t r a n s p i r a t i o n . 0 i s the amount o f p e r c o l a t i o n below the root zone. QR i s the amount o f r e t u r n flow during the t day period. t

Thus, the SWRRB model takes i n t o account many p h y s i c a l processes which c o n t r i b u t e to runoff. The p e s t i c i d e component o f SWRRB takes i n t o account the fate o f the chemical a p p l i e d under f i e l d c o n d i t i o n s : For example, the amount o f p e s t i c i d e a c t u a l l y reaching the ground a f t e r a p p l i c a t i o n over a plant canopy i s c a l c u l a t e d . Further, f i e l d d i s s i p a t i o n o f the chemical by p h o t o l y s i s on l e a f surfaces as w e l l as degradation i n the s o i l i s accounted f o r with the p e s t i c i d e component o f SWRRB. Leaching o f the p e s t i c i d e below the top 1cm o f s o i l i s a l s o computed and r u n o f f c o r r e c t e d f o r such l o s s e s . Further, adsorption o f the p e s t i c i d e to s o i l surfaces and sediment i s taken i n t o account by SWRRB. The automated p e s t i c i d e runoff model c o n s i s t s o f a set o f FORTRAN programs which c a l c u l a t e the amount o f p e s t i c i d e runoff from input o f r i v e r basin data, r a i n f a l l data, p e s t i c i d e charact e r i s t i c s , and a p p l i c a t i o n data. Table I shows the input r e quirements f o r the SWRRB model. Table I I shows the output data from the SWRBB model. Table I Input data for SWRRB P e s t i c i d e Name S o i l adsorption constant Washoff f r a c t i o n F o l i a r surface p h o t o l y s i s (t\/n = days) S o i l decay constant (K = d a y s ! ) Application efficiency I n i t i a l p e s t i c i d e on f o l i a g e ( l b s . ai/A) I n i t i a l p e s t i c i d e on ground ( l b s . ai/A) Enrichment r a t i o ( p e s t i c i d e contributed by sediment) A p p l i c a t i o n day ( J u l i a n Calendar) A p p l i c a t i o n r a t e ( l b s . ai/A) -

Table I I Output data f o r SWRRB 1. 2. 3. 4. 5. 6.

L i s t i n g o f input data. River b a s i n parameters and p e s t i c i d e c h a r a c t e r i s t i c s Average d a i l y temperature and s o l a r r a d i a t i o n S o i l h y d r a u l i c p r o p e r t i e s by l a y e r R a i n f a l l data D a i l y p e s t i c i d e runoff values Average monthly and annual values f o r p e s t i c i d e r u n o f f


FATE OF CHEMICALS

252

IN THE ENVIRONMENT

EXAMS - The Exposure Analyses Modeling System was developed at EPA by Burns, C l i n e , and L a s s i t e r (2) The model i s based on the conservation o f the mass o f a chemical w i t h i n a dynamic aquatic environment. The f o l l o w i n g equation can be used to mathematically describe the model. ds — dt

-

= V + P

D

+ P

S

+ H + A + M + S e + D - L

where S - c o n c e n t r a t i o n o f the chemical i n the system V = volatilization PD = d i r e c t p h o t o l y s i s PS = s e n s i t i z e d p h o t o l y s i s H - hydrolysis A = breakdown by photo-autotrophs M = m i c r o b i a l degradation Se = exchanges with sediment r e s e r v o i r s D = dilution L = loadings o f chemical i n t o system The p a r t i c u l a r model can be viewed as composed o f s e v e r a l compartments as shown below f o r a lake i n F i g u r e 1.

1.

L

1 2.

B

L

8.

i 4.

B

6.

H

9.

B

F i g u r e 1. Compartments o f EXAMS. Key: L, L i t t o r i a l , t o p ; B, B e n t h i c , b o t t o m ; E, I p i l i m n i o n , u p p e r l a y e r o f w a t e r ; H, Hypolimnion, lower l a y e r o f water.


13.

HONEYCUTT AND BALLANTINE


253

Any compartment of the aquatic ecosystem can be represented as a p a r t i c u l a r volume c o n t a i n i n g water, p a r t i c u l a t e matter, b i o t a , d i s s o l v e d m a t e r i a l s , etc. Loadings and exports are represented as mass f l u x e s across the boundaries of the volume element (processes Se, D and L ) . Reactive processes are t r e a t e d as p o i n t processes centered w i t h i n the volume. Thus, the EXAMS model takes i n t o account both p h y s i c a l and chemical processes that a f f e c t the environmental f a t e of a p a r t i c u l a r chemical. The automated EXAMS model c o n s i s t s of a set of FORTRAN programs which c a l c u l a t e s the f a t e , exposure and d i s s i p a t i o n of the chemical from input environmental data such as: 1) G l o b a l parameters ( r a i n f a l l , i r r a d i a n c e , l a t i t u d e ) , 2) B i o l o g i c a l paramet e r s (biomass, b a c t e r i a l counts, c h l o r o p h y l l ) , 3) Depths and i n lows, 4) Sediment c h a r a c t e r i s t i c s , 5) Wind, 6) Evaporation, 7) A e r a t i o n , 8) Advective and turbulent i n t e r c o n n e c t i o n s , 9) Water flow, 10) Sediment flow, 11) pH and pOH, and 12) Temperature. A l s o c h a r a c t e r i s t i c s of the chemical are taken i n t o account such as h y d r o l y s i s p h o t o l y s i s , o x i d a t i o n , b i o l y s i s , and v o l a t i l i t y . Table I I I shows some of the input requirements f o r EXAMS. Table I I I Input Parameters f o r EXAMS 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Compound name Molecular weight (g/mol) S o l u b i l i t y (ppm) mg/L S o i l adsorption constant (mg/kg i- mg/L) Vapor pressure ( t o r r ) Quantum y i e l d Reference l a t i t u d e B i o l y s i s r a t e constant (g/hr. c e l l s ) P h o t o l y s i s r a t e constant ( h r . ~ l ) H y d r o l y s i s r a t e constants (e.g., a c i d hr.~"l/M) D i s s o c i a t i o n constants ( a c i d , base, n e u t r a l ) Table IV shows the types of output data from EXAMS: Table IV Output Parameters for EXAMS

1. 2. 3. 4. 5. 6. 7. 8.

Chemical input data Parameters d e s c r i b i n g environment Maximum, average and minimum concentrations of chemical at steady state Degree to which each chemical and p h y s i c a l process e f f e c t s dissipation D i s t r i b u t i o n of chemical between water, sediment and b i o t a D a i l y accounting of chemical concentrations i n water and sediment Degradation rates for each process Exposure a n a l y s i s summary i n c l u d i n g p e r s i s t e n c e e v a l u a t i o n


254

FATE OF CHEMICALS IN THE

ENVIRONMENT

Use o f SWRRB and EXAMS t o Assess the Hazard of CGA-72662 t o an Aquatic Environment SWRRB The SWRRB runoff model was used to determine the amount o f CGA72662 that would runoff of a h y p o t h e t i c a l 3.2 acre watershed with a predominant muck s o i l type. The following were the SWRRB input data. 1. 2.

3. 4. 5. 6. 7. 8. 9. 10.

Chemical name = CGA-72662 S o i l adsorption constant = K(j - 49.5 (The organic matter content of muck s o i l i n F l o r i d a i s about 80%.) The washoff f r a c t i o n = 1.00. F o l i a r surface p h o t o l y s i s = t = 1 day. S o i l decay constant = 0.008 day"" . A p p l i c a t i o n e f f i c i e n c y = 0.65 (65% of CGA-72662 reached the ground.) 0 l b s . ai/A on f o l i a g e before a p p l i c a t i o n . 0 l b s . ai/A on ground before a p p l i c a t i o n . 12 a p p l i c a t i o n s at 0.125 l b s . a i / A / a p p l i c a t i o n s at 7 day intervals. River Basin = Watkins 2 3.2 acres. 1

/

2

1

The runoff for 1974 and 1975 was c a l c u l a t e d by SWRRB to be 0.001 lbs. ai/A for each year. This runoff figure was then used to c a l c u l a t e the amount of CGA-72662 that could enter the EXAMS aquatic environments due t o runoff during one season. SWRBB-EXAMS Interconnections - C a l c u l a t i o n f o r Load Input i n t o EXAMS Pond Using the f o l l o w i n g equations from Reinert (_3), the expected environmental concentration i n water (EEC ) due to runoff into the EXAMS pond can be c a l c u l a t e d : W

W

w

= Weight

4

W = 2 X 10f* M lb.

3

X

w

3

1 f t . X 62.4 l b . / f t . 0.028 M*

W

= weight of sediment

W

= 6.75 X 10 Kg

s

w

= load that w i l l

= 4.46 X 10

6

= 1.49 X 10 l b . 453.6

Z

3

i n pond.

5

s

3

of water i n EXAMS pond of volume 2 X IO M

kg

partition

into water of pond.


7

13.


HONEYCUTT A N D BALLANTINE

W (Kd)+W s

255

Z - load W = weight of water W = weight of sediment = s o i l adsorption constant

w

w

s

= 49.5

Z = 0.001 l b s . ai/A runoff X 3.2 acres 2 watershed Z = 0.0032 l b s . load 7

^

m

0.0032 (4.5 X IO )

=

q

o

q

i

into

^

2

6

(1.5 X 10 )(49.5)+4.5 X 10 The

C G A

7

_

6 4 2 5 ( )

pond water

Expected Environmental Concentration

(EEC ) i n water W

is: E E C w

ppm

- ^JLiOl .

0.0012 X 106

W

4.46 X 10

w

=

2

j

x

i Q

_

5

7

The non point source flow rate (NPSFL) into the EXAMS pond i s 5.1 X 10 kg/hr. C a l c u l a t i o n (3) of the non point source loading rate (NPSLDG) i n t o the EXAMS pond i s then: 3

6

NPSLDG = E E C X NPSFL X 10" kg/hr. = 2.7 X 10" X 5.1 X 10 X IO" kg/hr. NPSLDG = 1.38 X IO" kg/hr. W

5

3

6

7

This loading rate i s then input i n t o EXAMS pond environment. The non point source loading rates (NPSLDG) for an Eutrophic Lake or an O l i g o t r o p h i c Lake can be s i m i l a r l y c a l c u l a t e d using the Reinert - (3) Approach. Use of EXAMS Ponds and Lakes t o Determine Environmental Fate of CGA-72662 The f o l l o w i n g data were input i n t o the EXAMS model t o determine the fate of CGA-72662 r e s u l t i n g from runoff (0.001 l b s . a i / A ) into ponds or lakes. 1. 2. 3. 4. 5. 6. 7. 8. 9.

Molecular weight 166.19 (grams/mole) S o l u b i l i t y 15,000 ppm k = 49.5 Vapor pressure = 10~ t o r r Reaction quantum y i e l d = 0.3 D i r e c t p h o t o l y s i s rate = 6.93 X 15" h r . " Reference L a t i t u d e = 32 H y d r o l y s i s (none at pH 5,7,9 - 30-70°C for 28 days) 2nd order rate constant for bottom b i o l y s i s = 1.7 X 10 lOOg/hr. c e l l s d

6

2

1


256 10. 11. 12.

FATE OF CHEMICALS IN THE ENVIRONMENT

7

NPSLDG f o r pond = 1.38 X 10~ kg/hr. NPSLDG for Eutrophic Lake = 3.6 X IO" kg/hr. NPSLDG f o r O l i g o t r o p h i c Lake = 3.6 X IO" kg/hr. 6

6

The output from EXAMS gives the environmental fate of CGA-72662 and shows what the exposure l e v e l s of CGA-72662 are to aquatic organisms i n h a b i t i n g ponds and lakes adjacent to an a p p l i c a t i o n site. These data are shown i n Table V.

Table V Environmental

Environment

Exposure Levels of CGA-72662

Maximum Concentration in Water PPm

Maximum Concentration in Sediments PPm

Half-Life i n Days

Pond

1.6 X IO"

6

1.5 X IO"

6

12.6

Eutrophic Lake

1.4 X 10~

6

8.5 X IO"

7

61.2

Oligotrophic Lake

5.2 X IO"

1.7 X IO'

7

4.8

7

SelfPurification Time Mo. 9 12

The data i n Table V i n d i c a t e that runoff of CGA-72662 from 12 a p p l i c a t i o n s would r e s u l t i n extremely low concentrations of CGA-72662 i n ponds and lakes. The water column i n a l l cases would contain a l l of the chemical, the sediment l i t t l e or no CGA-72662. I t follows from these data that exposure of CGA72662 to aquatic organisms would be low. The data i n Table V also shows that CGA-72662 would be p e r s i s t e n t only i n eutrophic lake environments. A f t e r the load i s removed, the h a l f - l i f e of CGA-72662 i n ponds, eutrophic lakes and o l i g o t r o p h i c lakes was 13, 62, and 5 days r e s p e c t i v e l y . S e l f p u r i f i c a t i o n times were 9, 12, and 3 months r e s p e c t i v e l y . Table VI shows the f i n a l r i s k assessment of CGA-72662 to aquatic organisms.


13.

HONEYCUTT

AND BALLANTINE


257

Table VI Risk Assessment CGA-72662

Aquatic C

Safety Factors ( L C / M E C ) Eutrophic Oligotrophic Lake Lake 5Q

W

^ 50 (ppm)

Pond

B l u e g i l l Sunfish (Lepomis macrochirus)

>90

5.6 X 10

7

6.4 X 10

7

1.8 X 10

8

Rainbow Trout (Salmo g a i r d n e r i )

>88

5.5 X 10

7

6.3 X 10

7

1.8 X 10

8

Channel C a t f i s h (Ictalurus punctatus)

>92

5.8 X 10

7

6.6 X 10

7

1.8 X 10

8

Freshwater Invertebrate (Daphnia magna)

93

5.8 X 10

7

6.6 X 10

7

9.3 X 10

8

Species

x

The t o x i c i t y of CGA-72662 to f i s h and daphnids was determined from aquatic laboratory t e s t s . The L C was then compared to the maximum environmental concentration of CGA-72662 expected (from EXAMS) i n ponds and lakes. The r a t i o of L C / M E C i s c a l l e d the aquatic safety f a c t o r . Aquatic s a f e t y f a c t o r s ranged from 5.5 X 10 f o r rainbow trout i n ponds to 9.3 X 10 f o r daphnia i n lakes. These data emphasize that exposure l e v e l s of CGA-72662 are low and must be taken into account f o r a r i s k assessment. Although the p e r s i s tence of CGA-72662 i n eutrophic lakes i s r e l a t i v e l y long, the exposure i s extremely low and of no environmental consequence. O v e r a l l , use of SWRRB runoff and EXAMS models show CGA-72662 to be very safe i n aquatic h a b i t a t s when used on vegetables i n F l o r i d a muck s o i l . 5 Q

5Q

W

7

8

L i m i t a t i o n s of SWRRB and EXAMS Models No d i s c u s s i o n of the use of runoff and environmental fate models would be complete without p o i n t i n g out t h e i r l i m i t a t i o n s and pitfalls.


258

FATE OF CHEMICALS IN THE ENVIRONMENT

SWRRB L i m i t a t i o n s and P i t f a l l s 1.

Since the e x i s t i n g watersheds i n the model are based on c o l l e c t e d f i e l d data, choice of a p p l i c a t i o n dates are c r i t i c a l since the i n t e n s i t y of r a i n f a l l i s important. Reali s t i c dates must be chosen to c o i n c i d e with recommended a p p l i c a t i o n times before or during the growing season. E.g., choosing a date j u s t p r i o r to a 4" r a i n w i l l be a worst case scenario, but may be the wrong time of year.

2.

Choice of s o i l type and adsorption constants are less t i c a l than choice of a p p l i c a t i o n dates.

3.

The a p p l i c a t i o n e f f i c i e n c y must be determined carefully.

4.

The p h o t o l y s i s and s o i l degradation constants must be r e a l i s t i c f o r the compound i n question. Laboratory or f i e l d studies are u s u a l l y needed to confirm these numbers.

5.

Choice of watershed must be r e a l i s t i c should have p e r t i n e n t crops on i t .

cri-

or chosen

and the watershed

Table VII shows a s e n s i t i v i t y a n a l y s i s on the SWRRB model. I t can be seen that the i n t e n s i t y of the r a i n f a l l i s one of the most important parameters a f f e c t i n g r u n o f f . EXAMS L i m i t a t i o n s and P i t f a l l s 1.

P e s t i c i d e input data must be accurate and r e a l i s t i c f o r the chemical i n question. E.g., minor changes i n input load may r e s u l t i n major changes i n output data.

2.

The EXAMS model was designated f o r point source p o l l u t i o n examination. However, m o d i f i c a t i o n for non point source p o l l u t i o n can be done.

3.

EXAMS may not take into account other important t r a n s f o r mation or transport processes that occur i n n a t u r a l aquatic environments. Thus, v a l i d a t i o n i s important.

Table VIII shows a s e n s i t i v i t y a n a l y s i s on the EXAMS model. Changing the input load d r a m a t i c a l l y changes the concentration of chemical i n both water and sediment. P h o t o l y s i s rates appear to e f f e c t the model less than input loads. Changing the s o i l type e f f e c t s the p u r i f i c a t i o n time of the system and not so much the water concentrations of the chemical i n d i c a t i n g the i n f l u ence of chemical adsorption to degradation.


Swann and Eschenroeder; Fate of Chemicals in the Environment ACS Symposium Series; American Chemical Society: Washington, DC, 1983. 3

a

14.9 7.2 7.2 7.2 7.2 14.9 7.2 4.5 3.4

0.023 0.001 0.001 0.001 0.001 0.004 0.001 0.061 0.001

CGA-72662 Run-off lbs. ai/A

^ 1974 - Light

r a i n period

( a p p l i c a t i o n s between J u l i a n days 240-317).

( a p p l i c a t i o n s between J u l i a n days 01-12, 300-

( a p p l i c a t i o n s between J u l i a n days 240-317).

1974 - Light r a i n period 365).

d

0

0

0

0

0

INTENSE LIGHT LIGHT LIGHT LIGHT INTENSE LIGHT INTENSE^ LIGHT

Rainfall in"

0

65 65 65 65 10 10 65 65 65

Rainfall Intensity

1975 - Intense r a i n period

0.008 0.138 0.138 0.008 0.008 0.008 0.008 0.008 0.008

% Onto Soil

D

1.0 20.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

-1

Soil Degradation (Days' )

1974 - Intense r a i n period 4.26 inches on day 178 when one a p p l i c a t i o n was made ( a p p l i c t i o n s between J u l i a n days 143-220).

49.5 49.5 49.5 49.5 49.5 49.5 0.74 14.1 14.1

MUCK MUCK MUCK MUCK MUCK MUCK SAND MUCK MUCK

Photolysis Half-Life (Days)

a

Kd

Soil Type

SENSITIVITY ANALYSIS OF SWRRB MODEL

Table VII


49.5 49.5 49.5 49.5 0.74 0.74 0.74 0.74

1,.38 8..67 1,.38 8,.67 1,.38 8..67 1..35 8..67 6

7

7

6

7

7

6

6

X 10' * X 10~ ** X 10" X 10~ X 10" X10~ X 10" X 10"

Input Load To Pond kg/hr. 10 10 100 100 10 10 100 100

1.6 1.0 4.2 2.7 1.6 1.0 4.2 2.7

6

2

1

6

6

4

6

4

4

4

X 10" X IO"* X IO"" X IO" X IO" X IO" X 10~ X IO"

Max. Cone. i n water Of Pond (ppm)

6.93 X 10~ h r . " .

hrs.*** hrs. hrs. hrs. hrs. hrs. hrs. hrs.

Photolysis t 1/2

* from 0.001 l b s . a i / A runoff. ** from 0.061 l b s . ai/A runoff. *** t 1/2 = 10 hours rate constant

MUCK MUCK MUCK MUCK SAND SAND SAND SAND

Soil Type

VIII

1.5 9.2 3.9 2.5 3.4 2.2 9.2 5.8

6

5

7

7

4

5

5

6

X 10" X IO"" X IO"" X IO"* X IO" X 10" X 10" X IO"

Max. Cone. i n Sediment Of Pond (ppm)

SENSITIVITY ANALYSIS OF EXAMS POND MODEL

Table

9 mos. 9 mos. 13 mos. 13 mos. 1 mo. 1 mo. 3 mos. 3 mos.

SelfPurification Time (mo.)

13.


HONEYCUTT A N D BALLANTINE

261

Table IX i s a summary of the s e n s i t i v i t y of SWRRB and EXAMS t o change i n inputs. These data are taken from Tables VII and VIII. I t can r e a d i l y be seen that SWRRB i s s e n s i t i v e to r a i n f a l l i n t e n s i t y while EXAMS i s s e n s i t i v e to input load changes.

Table IX EFFECTS ON SWRRB AND EXAMS DUE TO SENSITIVITY E f f e c t s (Fold Change) Parameter Amount of Cone, i n Changed Change Runoff EXAMS POND lbs. ai/A (ppm) S o i l Type Total R a i n f a l l

67X*

NONE

5X

NONE

I n t e n s i t y of R a i n f a l l

NONE

23Xt

S o i l Degradation Rate

17X

NONE

Photolysis

Rate

20X

NONE

Photolysis

Rate

10X

3X+

6IX

160X+

Input Load From Runoff *

49.5 = 67 0.74

Summary 1.

Environmental models which are a c c e s s i b l e for exposure assessment of p e s t i c i d e s .

today can be used

2.

For a r e a l i s t i c r i s k assessment, the environmental f a t e , exposure l e v e l s and t o x i c i t y of the compound must be considered i n an integrated fashion.

3.

The SWRRB runoff model coupled to the EXAMS fate model can be used to p r e d i c t exposure l e v e l s of chemicals to aquatic organisms. Safety f a c t o r s can then be c a l c u l a t e d .

4.

L i m i t a t i o n s do e x i s t with each model. Care must be taken to describe both the environments and chemical c h a r a c t e r i s t i c s i n a r e a l i s t i c manner.


262

F A T E OF

C H E M I C A L S IN

THE

ENVIRONMENT

Acknowledgments Assistance of Dr. Bob C a r s e l at EPA i n Athens, Georgia i s g r a t e f u l l y acknowledged. Dr. C a r s e l was instrumental i n g e t t i n g our models to a p r a c t i c a l state of usage at CIBA-GEIGY. Literature Cited 1.

C a r s e l , R. F. P e s t i c i d e Runoff Simulator User's Manual, Computer Sciences Corporation, 1980.

2.

L a s s i t e r , R. R.; Baughman, G. L.; Burns, L. A., State-of-the-Art i n E c o l o g i c a l Modeling, 1978, 7 219-245. Int. Soc. E c o l . Mod., Copenhagen.

3.

Reinert, J . L., "Estimating the Maximum Concentration of P e s t i c i d e s i n the Environment as a Consequence of S p e c i f i c Events" October 1, 1980, Environmental Fate Branch, U.S. EPA.

R E C E I V E D April 29, 1983.


Mathematical Modeling Application to Environmental Risk Assessments

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