# Screening approaches for 

# predicting pesticide concentrations in groundwater Dazhi Mao 1 

### , W. Martin Williams 1 

### , J. Mark Cheplick 1 

### , and Robert Morris 2 1 Waterborne Environmental, Inc., Leesburg, Virginia, USA 2 FMC Corporation, Ewing, New Jersey, USA2



# Objective • Use the results of prospective ground -water monitoring (PGW) studies to evaluate the ability of three screening models to predict the fate and 

## transport of pesticide residues through the soil profile and into shallow groundwater3 

# Modeling Approaches Modeling approaches evaluated:

– SCI - GROW (Screening Concentration in Ground Water)


### – “NMC” (N - Methyl Carbamate Cumulative Analysis)
– PRZM -ADAM (Pesticide Root Zone Model – Aquifer Dilution Assessment Model)4 

## Screening Concentration in Ground 

## Water (SCI -GROW) • EPA Tier I model • Empirical model for predicting “conservative high - end concentrations” of pesticides in groundwater • Regression based on prospective groundwater monitoring (PGW) studies available at the time • Simple user- input5



## N - Methyl Carbamate (NMC) 

## Cumulative Risk Assessment • Based on PRZM model • Field capacity set to porosity to simulate a saturated zone • Soil horizons divided uniformly in the top 1 m • Environmental fate linearly declining with depth until the water table • Concentration averaged vertically 1 m below water table (assuming 1 m well screen) USEPA (2005). Preliminary N - methyl Carbamate Pesticides Cumulative Risk Assessment, Appendix IID: Drinking water exposure, 35pp.6



# PRZM -ADAM • PRZM provides water and chemical flux into aquifer model, ADAM (aquifer dilution assessment model) • The water balance algorithm includes displacement from recharge and lateral transport using Darcy’s law:
• Chemical algorithms account for degradation, adsorption, and advection. • Ability to simulate a single parent chemical and up to four degradation products Qlat = K

h * I* A K h = hydraulic conductivity (m/day) I = the hydraulic gradient (m/m) A i= cross - sectional area (5 m depth)7 

# Methods and Materials • Set up a model representation of five prospective 

## groundwater monitoring (PGW) studies

– SCI - GROW: application rates, number of applications
– PRZM: application rates, dates, soil properties, meteorology, irrigation, soil properties
– ADAM: bulk density, porosity, hydraulic gradient, hydraulic conductivity, 5 - m mixing depth 

## • Compare predicted concentrations (without calibration) of bromide and pesticide residues to values measured in field studies8 

## Prospective Ground -Water Monitoring Study • 1 - 2 hectare plot • Apply pesticide and tracer • 120% normal monthly rainfall or crop water demand • Sample soil, soil - pore water, and ground water for 2 - 3 year period9



# Study Sites Location IN95 IN85 IN75 NC4L GA1L Crop Soybean Soybean Soybean Soybean Sweet corn Soil Sand Sand Sand Loamy sand Sand loam GW depth (m) 5.7 9.7 6.4 3.1 4.3 Bromide rate (kg/ha) 168 159 168 150 150 IN85 GA1L NC4L IN75 IN9510 

# Results (IN95)

SCI -GROW: 2140 ppm11 

# Results (IN85)

SCI -GROW: 2140 ppm12 

# Results (IN75)

SCI -GROW: 2140 ppm13 

# Results (NC4L)

SCI -GROW: 1910 ppm14 

# Results (GA1L)

SCI -GROW: 1740 ppm15 

# Results (GA1L, pesticide)

SCI -GROW: 0.27 ppb LOQ = 0.1 ppb16 

# Bromide Conclusions • Surface soil predictions with PRZM

– Concentration magnitude and timing match observed data fairly well 

### • Soil pore -water predictions with PRZM

– Tend to flush chemical through soil profile too quickly 

### • Groundwater Predictions

– SCI - GROW tends to over- predict maximum concentrations by ~2 orders of magnitude
– NMC approach over- predicted observed median concentrations by 4 - 8 times
– ADAM predictions similar to observed median concentrations17 

# Pesticide Conclusions • Soil predictions with PRZM:

– Matched maximum concentrations measured in the surface soil at GA1L 

### • Soil -water predictions with PRZM

– Over predicted soil - pore water concentrations 

### • Groundwater predictions:

– SCI - GROW predictions similar to observed detections
– NMC under predicted groundwater detections
– ADAM under predicted groundwater detections 

### But most groundwater samples <LOQ!18



# Further Research • Examine pesticide performance at additional 

## study sites • Examine pesticide performance after calibrating bromide timing • Sensitivity analysis of PRZM -ADAM and NMC

– Compartment size in PRZM


### – Aquifer thickness/mixing zone (ADAM vs NMC)
– Pesticide degradation assumptions19 

# ADAM is a component of PLUS Pesticide Leaching in the U.S.20



# Acknowledgements • FIFRA Environmental Model Validation Task Force (FEMVTF) • Amy Ritter, Waterborne Environmental, Inc. • Katie Betz, Waterborne Environmental, Inc. • Kristen Umbaugh, Waterborne Environmental, Inc.21



# Questions?