# Presented by 

# : 

# Development and Testing of an U pdated AGRO Model (AGRO -2014) for Predicting Aquatic and Benthic Pesticide Concentrations in Ponds

Lauren Padilla 1 

# Michael Winchell 1 

# Scott Jackson 2 

### 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

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# 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 re dissolution 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 flow
through option enabled 2. If VVWM constant volume flow
through option enabled 

### System Input Processes AGRO VVWM EXAMS A Precipitation x 1 

### B Water inflow x x 1 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 x 1 K Water outflow x x 1 or 2 L Suspended sediment outflow x M Dissolved and sorbed pesticide outflow x x 1 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/sorbed chemical 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

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# 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 sediment sorbed 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

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# 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 m 3 

# 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) 56.2 1 Sediment Degradation Half- life (days) 100 1 Koc (mL/g) 2,941,300 2 Solubility (g/m3) 0.005 3 Molar Mass (g/mol) 449.9 3 Henry's Law Constant (atm - m3/mol) 1.90E - 07 3 Vapor Pressure (Pa) 2.08E - 07 3 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 m ore 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) 34.1 1 34.1 1 Sediment Degradation Half- life (days) 169 1 169 1 Koc (mL/g) 1,029,873 4 1,029,873 4 Solubility (g/m3) 0.0103 5 0.0103 5 Molar Mass (g/mol) 349.4 5 349.4 5 Henry's Law Constant (atm - m3/mol) 6.20E - 07 5 6.20E - 07 5 Vapor Pressure (Pa) 1.87E - 06 5 1.87E - 06 5 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 slurry experiment 

#  Total system losse 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 (atm m3/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 AGRO 2014 

#  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

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# 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 AGRO 2014 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

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# Model Comparison – Standard Scenarios



#  Comparison of 90 th 

# 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 benthic limnetic 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
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# 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

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# 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

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# References

Arnot, J., and F. Gobas . 2004. A food web bioaccumulation model for organic chemicals in aquatic ecosystems. Envionmental Toxicology and Chemistry 23:2343
- 2355. Chapra, S. C. 1997. Surface water
- quality modeling. WCB/McGraw
-Hill, Boston, Massachusetts . Dix, M.E., 2014. Determining the partitioning coefficient (n
- Octanol/Water) of nine pyrethroids by the slow
- stirring method following OECD guideline 123. Pyrethroid Work Group Rep. PWG
-ERA
- 04a. Smithers Viscient, Wareham, MA. Leistra, M., A.J. Zweers , J.S. Warinton , S.J.H. Crum, L.H. Hand, W.H.J. Beltman , and S.J. Maund . 2003. Fate of the insecticide lambda
- cyhalothrin in ditch enclosures differing in vegetation density. Pest Management Science. 60:75
- 84 . Mackay, D. 2001. Multimedia environmental models: The fugacity approach. Lewis, Boca Raton, FL. Mackay, D. and M. Diamond. 1989. Application of the QWASI (Quantitative Water Air Sediment Interaction) fugacity model to the dynamics of organic and inorganic chemicals in lakes. Chemosphere. 18:1343
- 1365. Mackay, D., and B., Hickie . 2000. Mass balance model of source apportionment, transport and fate of PAHs in Lac Saint Louis, Quebec. Chemosphere. 41:681
- 692. Mackay, D., M. Joy, and S. Paterson. 1983. A quantitative water, air, sediment interaction (QWASI) fugacity model for describing the fate of chemicals in lakes. Chemosphere. 12:981
- 997. Mackay, D., S. Paterson, and M. Joy. 1983. A quantitative water, air, sediment interaction (QWASI) fugacity model for describing the fate of chemicals in rivers. Chemosphere. 12:1193
- 1208.

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# References, continued
Melendez, J. L., 2010a. EFED registration review problem formulation for lambda
- cyhalothrin and gamma
- cyhalothrin . Rep. EPA
-HQ
- OPP
- 2010
- 0479
- 0005. Environmental Fate and Effects Division, Office of Pesticide Programs, USEPA, Washington, DC. Melendez, J. L., 2010b. EFED registration review problem formulation for fenpropathrin. Rep. EPA
- HQ
- OPP
- 2010
- 0422
- 0008. Environmental Fate and Effects Division, Office of Pesticide Programs, USEPA, Washington, DC. Melendez, J. L., 2013. EFED review of aerobic and anaerobic aquatic metabolism studies for eleven pyrethroids in three sediments from California. Rep. MRID 48762908. Environmental Fate and Effects Division, Office of Pesticide Programs, USEPA, Washington, DC. Meyer, B.N., C. Lam, S. Moore, and R.L. Jones. 2013. Laboratory degradation rates of 11 pyrethroids under aerobic and anaerobic conditions. J. Agric. Food Chem. 61:4702
-4708 . Springer, T., D. Anderson, H. Krueger. 1996. An evaluation of the impact of fenpropathrin insecticide exposure on aquatic mesocosm ecosystems: Amended report. Lab Project Number: 263
- 125: VP
- 10056: 263/031292/DT2A/CYA9. Rep. MRID 43958801. Wildlife International, Ltd. Easton, MD . Webster, E., L. Lian , D. Mackay, and C. Harvey. 2006. Application of the quantitative qater air sediment interaction (QWASI) model to the Great Lakes. Report to Ontario Ministry of the Environment. CEMC Rep. 200501. CEMC, Trent University, Peterborough, Ontario, Canada.

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