Presented by:

Development and Testing of an 

Updated AGRO Model (AGRO-2014) for 

Predicting Aquatic and Benthic 

Pesticide Concentrations in Ponds

Lauren Padilla1

Michael Winchell1

Scott Jackson2

1. Stone Environmental 2. BASF Corporation

EMPM, April 28th, 2015
Background

 Historically, Exposure Analysis Modeling System (EXAMS) used to 

predict aquatic exposure concentrations in regulatory assessments

 Recently, Variable Volume Water Model (VVWM) replacing EXAMS 

to include important new features like varying volume, flow-through, 

burial

 A third model called AGRO, has additional dynamic features useful 

for exposure modeling, not included in EXAMS and VVWM, 

particularly for hydrophobic organic chemicals

 We will present a version of AGRO – AGRO-2014 – that has been 

calibrated for small ponds and modified for better comparison to 

EXAMS and VVWM

2
AGRO Model

 Developed by Canadian Environmental Modelling Centre 

(CEMC)/Trent University

 Used to assess chemical dynamics in lakes and rivers 

− Mackay, Paterson & Joy, 1983; Mackay & Diamond, 1989; Mackay & Hickie, 

2000; Arnot & Gobas, 2004; Webster et al., 2006

 Evaluated by 2008 Scientific Advisory Panel (SAP)

− Includes sediment burial as mechanism for system pesticide losses

− Limits lumped parameter modeling of benthic-limnetic pesticide mixing

− Formation of pure chemical reservoir when solubility exceeded and redissolution when concentrations fall below saturation recognized as 

conceptually correct

 Includes dynamic sediment mass algorithm for simulating 

fluctuations in suspended sediment concentration

− Important for predicting concentrations for hydrophobic organic chemicals 

(HOCs)

3
Comparison of Conceptual Models: 

System Inflows/Sources

1. If VVWM varying-volume with flowthrough option enabled

2. If VVWM constant volume flowthrough option enabled

System Input Processes AGRO VVWM EXAMS

A Precipitation x1

B Water inflow x x1 or 2

C Suspended sediment inflow x

D Dissolved and sorbed pesticide 

inflow x x x

E Direct application of sediment-sorbed 

pesticide to benthic (PRBEN) x x

F Pesticide drift deposition x x x

4
Comparison of Conceptual Models: 

System Outflows/Losses

System Loss Processes AGRO VVWM EXAMS

J Water Evaporation x1

K Water outflow x x1 or 2

L Suspended sediment outflow x

M Dissolved and sorbed pesticide outflow x x1 or 2

N Sediment burial x

O Pesticide burial x x3

System Loss Processes AGRO VVWM EXAMS

P Pesticide degradation in water x x x

Q Pesticide degradation in sediment x x x

R Pesticide volatilization (Henry's law) x x x

S Pesticide hydrolysis x x

T Pesticide photolysis x x

1. If VVWM varying-volume with flow-through option enabled

2. If VVWM constant volume flow-through option enabled

3. If VVWM burial option enabled

5
Comparison of Conceptual Models: 

System Internal Exchanges

Benthic-Limnetic Exchanges AGRO VVWM EXAMS

G Dissolved pesticide benthic-limnetic 

transfer x x x

H Sediment deposition and resuspension x

I Sediment-sorbed pesticide deposition 

and resuspension x

Equilibrium Partitioning Into Sub-compartments

Sub-compartment 

(L) - limnetic, (B) - benthic AGRO VVWM EXAMS

Dissolved (L) x x x

TSS-sorbed (L) x x x

DOC-sorbed (L) x x

Plankton/plant-sorbed (L) x x

Pore-water dissolved (B) x x x

Sediment-sorbed (B) x x x 6
AGRO-2014 – An updated version of AGRO

 Same sediment and pure chemical reservoir algorithms as AGRO

Model Attribute EXAMS VVWM AGRO AGRO-2014

Suspended sediment 

concentration in 

water column

30 mg/L 30 mg/L

Varies with erosion 

inputs 

(baseline/minimum 30 

mg/L)

Same as AGRO

Chemical burial in 

deep bed sediment None Optional dynamic process 

varying with erosion inputs

Dynamic process 

varying with erosion 

inputs

Same as AGRO

Sediment/sorbedchemical deposition 

to active bed

Chemical only 

modeled by PRBEN 

parameter, typically 

50% of incoming 

sorbed chemical

Chemical only modeled by 

PRBEN parameter, 

typically 50% of incoming 

sorbed chemical

Chemical and Sediment 

together at rate 

determined by erosion 

inputs and settling time

Same as AGRO

Sediment, water and 

chemical overflow None Depends on inflow rate and pond depth

Equal to inflow rate with 

chemical overflow at 

limnetic concentrations

Same as AGRO

Concentration over 

solubility threshold

Dissolved 

concentrations 

permitted to exceed 

solubility

Dissolved concentrations 

permitted to exceed 

solubility

Excess chemical is 

stored in a separate 

reservoir until dissolved 

concentrations decrease

Same as AGRO

7
AGRO-2014 – An updated version of 

AGRO

Model Attribute EXAMS VVWM AGRO AGRO-2014

Sediment settling 

time N/A N/A 7 days (90%) 3.4 days (90%)

Benthic-limnetic 

diffusive chemical 

exchange

Bulk process 

representing 

dissolved plus 

sediment-sorbed 

exchange

Bulk process representing 

dissolved plus sedimentsorbed exchange

Dissolved exchange 

only (0.0004 m/h 

diffusion coefficient)

Dissolved exchange only 

(0.05 m/h diffusion 

coefficient)

Chemical degradation 

rates as function of 

temperature

Yes Yes No Yes

Koc parameter Koc Koc Derived from log(Kow) Koc or derived from 

log(Kow)

Spray drift and runoff 

entry time

First time-step of the 

simulation day

First time-step of the 

simulation day

Over 24 simulation 

hours User-specified

 Calibrated for small ponds to observed data

− Settling time based on observed settling velocities (Chapra, 1997)

− Diffusive exchange coefficient based on pyrethroid drift mesocosm

 Added features to improve basis for comparison to EXAMS/VVWM 

8
AGRO-2014 Calibration of Diffusive 

Exchange Coefficient

 Spray drift mesocosm study 

(Leistra et al. 2003) with 

water column concentration 

time-series and multiple 

replicates 

 Suspended sediment 

concentration approximately 

constant

 Lambda-cyhalothrin in 0.43 

m3 aquatic mesocosms

 Chemical and environmental 

parameters set to match 

experimental setup

Simulation Leistra et al. 2003

Purpose observed data calibration

Exposure Pathways Drift

Chemical lambda-cyhalothrin

Water Degradation Half-life (days) 56.21

Sediment Degradation Half-life (days) 1001

Koc (mL/g) 2,941,3002

Solubility (g/m3) 0.0053

Molar Mass (g/mol) 449.93

Henry's Law Constant (atm-m3/mol) 1.90E-073

Vapor Pressure (Pa) 2.08E-073

Dose/Application mass 1.08E-5 g

Surface Area (m2) 0.865

Volume (m3) 0.43

Baseline TSS Concentration (mg/L) 5 to 16

Atmospheric Temperature (°C) 15.1

Inflow/Outflow negligible

1. Average of values from Meyer et al., 2012 as recalculated by Melendez, 2013

2. Mean corrected Solid Phase Micro-Extraction (SPME) value, B.J. Mason, 

personal communication, March 2014

3. Values from Melendez, 2010a 9
AGRO-2014 Calibration of Diffusive 

Exchange Coefficient

 Exchange 

coefficient based on 

molecular diffusion 

velocity was too low

 Increasing 

coefficient to 0.05 

m/h better 

represents benthiclimnetic exchange

 AGRO-2014 

response more 

closely matches 

observations than 

EXAMS/VVWM

10
Model Comparison – Validation with 

Pyrethroid Mesocosm Observations 

 Springer et al. 1996 simulated drift and erosion (slurry) mesocosms with 

water column concentration time-series and multiple replicates 

 Environmental and chemical inputs were set to match experimental setup but 

no calibration was conducted

Simulation Springer et al. 1996 Springer et al. 1996

Exposure Pathways Drift Slurry (Erosion)

Chemical fenpropathrin fenpropathrin

Water Degradation Half-life (days) 34.11 34.11

Sediment Degradation Half-life (days) 1691 1691

Koc (mL/g) 1,029,8734 1,029,8734

Solubility (g/m3) 0.01035 0.01035

Molar Mass (g/mol) 349.45 349.45

Henry's Law Constant (atm-m3/mol) 6.20E-075 6.20E-075

Vapor Pressure (Pa) 1.87E-065 1.87E-065

Dose/Application mass 1.061 g 1.82 g fenpropathrin, 30 L water, 118 kg sediment

Surface Area (m2) 960 960

Volume (m3) 870 870

Baseline TSS Concentration (mg/L) no data, defaults assumed no data, defaults assumed

Atmospheric Temperature (°C) 20 20

Inflow/Outflow negligible negligible

1. Average of values from Meyer et al., 2012 as recalculated by Melendez, 2013

4. Based on Mackay regression equation: Koc = 0.41*(10^log(Kow)) Mackay, 2001 with log(Kow) from Dix, 2014

5. Values from Melendez, 2010b

11
Model Comparison – Validation with 

Pyrethroid Mesocosm Observations

 The observed dissipation 

rate in the slurry 

experiment (0.7 day halflife) was twice the rate of 

the drift experiment (1.4 

day half-life)

 AGRO-2014 slightly 

overestimated observed 

maximum concentrations, 

simulated different 

dissipation rates well

 EXAMS and VVWM 

underestimated rates and 

predicted same rates for 

both experiments, 

PRBEN fraction lowered 

initial concentration

 AGRO-2014 run without allowing TSS to fluctuate was 

not in good agreement with observations

12
Model Comparison – Validation with 

Pyrethroid Mesocosm Observations

 Differences in sediment 

algorithms led to differences in 

partitioning between the two 

experiments

 Within the water column, the 

fraction adsorbed/dissolved 

chemical (determined by Koc)

was the same for all models 

but there was more chemical in 

the water column overall in 

AGRO-2014

 All models had more chemical 

in the water column than the 

benthic in the drift experiment 

compared to the corresponding 

 Total system losse slurry experiment s by degradation were greatest 

in AGRO-2014 – more chemical in water, faster 

degradation rate in water than sediment 

13
Model Comparison – Standard Scenarios

 Comparison of model 

predictions in response 

to drift and runoff inputs 

simulated by PRZM 3 for 

MS Cotton and CA 

Onion scenarios

 Assumed a hypothetical 

test chemical with a wide 

range of Koc values

Simulation EPA Standard Scenarios

Purpose standardized agricultural field loadings

Exposure Pathways PRZM simulated drift and runoff

Chemical hypothetical test chemical

Water Degradation Half-life 

(days) 12

Sediment Degradation Half-life 

(days) 70

Koc (mL/g) 50, 5000, 500000, 

or 5 million

Solubility (g/m3) 0.05

Molar Mass (g/mol) 420

Henry's Law Constant (atmm3/mol) 4.80E-07

Vapor Pressure (Pa) 2.00E-06

Dose/Application mass

1.12 g via drift entry 

6 times per year6, 

plus varied mass via runoff/erosion 

dependent on weather

Surface Area (m2) 10000

Volume (m3) 20000

Baseline TSS Concentration 

(mg/L) 30

Atmospheric Temperature (°C) varied daily with weather

Inflow/Outflow varied daily with runoff, min 5 m/s 

(AGRO-2014); none (VVWM, EXAMS)

14
Model Comparison – Standard Scenarios

 A. Models had same peak 

concentration after first drift 

event, AGRO-2014 response 

most conservative

 B. Large eroded sediment 

load carrying adsorbed 

pesticide increased chemical 

mass in all models and 

increased TSS in AGRO2014

 Majority of chemical in 

AGRO-2014 water column 

adsorbed to increased TSS, 

reducing dissolved 

concentration

 Series of drift and erosion events for MS Cotton scenario, hypothetical chemical 

Koc = 5 million ml/g

15
Model Comparison – Standard Scenarios

 Series of drift and erosion events for MS Cotton scenario, hypothetical chemical 

Koc = 5 million ml/g

 C. After several days 

without additional 

runoff/erosion, TSS 

concentration in AGRO2014 decreased to base 30 

mg/L

 AGRO-2014 response to 

subsequent drift event had 

similar peak dissolved 

pesticide concentration and 

more conservative 

dissipation rate compared 

to other models

16
Model Comparison – Standard Scenarios

 Comparison of 90th percentile annual maximum EECs for hypothetical 

chemical with Koc = 50, 5000, 500,000, 5 million for MS Cotton and CA 

Onion scenarios

 Similar results in all 

models and scenarios at 

low Koc

 Koc had a more 

pronounced effect in the 

wetter-weather MS 

Cotton scenario

 EECs in relatively drier 

CA scenario more likely 

to be driven by spray 

drift, in which case 

AGRO-2014 is more 

conservative for higher 

Koc chemicals 17
Discussion

 Mesocosm comparison highlighted the differences in ability of each 

model to capture different dissipation rates for spray drift and slurry 

inputs

 The effect of instantaneous bed sediment loading (PRBEN fraction) 

in EXAMS/VVWM was limited to changing initial chemical 

concentrations immediately after dose, no impact on rate of benthiclimnetic chemical exchange

 Accounting for eroded sediment loading as TSS in AGRO-2014 

allowed for variable rate of exchange consistent with observations

 Standard scenario simulations showed dynamic sediment algorithms 

of AGRO-2014 became increasingly important as Koc increased

 For low-Koc compounds, AGRO-2014, VVWM, and EXAMS results 

were very similar

18
Summary/Conclusions

 AGRO-2014 which includes the same sediment dynamics as AGRO 

(2008) was calibrated for small ponds and updated to improve 

physical realism and make certain processes more comparable to 

VVWM and EXAMS

 Inclusion of dynamic sediment processes significantly impacted 

modeling results for high-Koc, hydrophobic organic compounds

 AGRO-2014 was the best model for predicting observed pyrethroid 

concentrations in drift and slurry mesocosm experiments

 All three models returned similar results for chemicals with low 

sorption to sediments

19
Questions?

 The latest version of AGRO-2014 (version 1.2) is publically available 

at http://www.stone-env.com/docs/PA5_v1.5b_Install_Beta_23.exe

 AGRO-2014 has been documented in the following technical reports 

submitted to EPA

− Padilla & Winchell 2014, PWG-ERA-03b, Development and Testing of an 

Improved AGRO Model (AGRO-2014) for use in Predicting Aquatic and Benthic 

Pesticide Concentrations in Ponds

− Desmarteau & Ritter 2014, PWG-ERA-07a, Sensitivity Analysis of Individual 

Parameters for Synthetic Pyrethroid Exposure Assessments to Runoff, Erosion 

and Drift Entry Routes for the PRZM, EXAMS and AGRO-2014 Models

 The following manuscript submitted to the Journal of Environmental 

Quality is currently in review

− Padilla, Winchell, & Jackson, Evaluation of AGRO-2014 for predicting 

hydrophobic organic chemical concentrations in ponds

20
References

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Envionmental Toxicology and Chemistry 23:2343-2355.

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References, continued

Melendez, J. L., 2010a. EFED registration review problem formulation for lambda-cyhalothrin and gamma-cyhalothrin. Rep. 

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