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Minutes of the March 20, 2012

Exposure Modeling Public Meeting

Office of Pesticide Programs (OPP) 4th Floor Conference Center

2777 South Crystal Drive – Potomac Yard Two, North

Arlington, VA 22202

Welcome and Introductions

The USEPA/OPP/EFED hosts biannual Exposure Modeling Public Meetings (EMPMs),

which provide a forum for exchanging information between OPP/EFED and 

stakeholders with similar technical expertise on current issues related to pesticide 

exposure modeling in support of risk assessment. Katrina White and Dirk Young 

chaired the March 20, 2012 meeting in the capacity of co-chairs of the Water Quality 

Technical Team. The minutes and presentation slides for the March 20, 2012 EMPM 

are posted in the docket (EPA-HQ-OPP-2009-0879), which can be accessed at 

http://www.regulations.gov/. The docket ID # is reported on the EMPM webpage: 

http://www.epa.gov/oppefed1/models/water/empm_top.htm

Brief Updates:

 EFED has updated the HUC8 percert cropped area (PCA) adjustment factors. They 

can be found in the guidance document titled, “Development and Use of Percent 

Cropped Area and Percent Turf Area Adjustment Factors in Drinking Water 

Exposure Assessments: 2012 Update” available at 

http://www.epa.gov/oppefed1/models/water/pca_adjustment_dwa.html

 Guidance for Evaluating and Calculating Degradation Kinetics in Environmental 

Media is now available at: 

http://www.epa.gov/oppfead1/international/naftatwg/guidance/degradation-kin.pdf

Presentations Abstracts and Notes:

1. NAFTA Guidance for Calculating Degradation Kinetics in Environmental Media

Dirk Young1, Ian Kennedy2

1Environmental Fate and Effects Division. United States Environmental Protection 

Agency

2Health Canada

The USEPA and Health Canada (NAFTA partners) have developed guidance for 

calculating degradation rates for transport model inputs (i.e., PRZM and EXAMS). The 

NAFTA guidance specifies the conditions under which a first-order model can be used 

to represent pesticide degradation data in NAFTA assessments and provides a 

procedure to determine a representative rate when the data deviate from a first-order 

concept. The NAFTA guidance is based on the premise that there is no "correct" 

transformation of non-first-order data into first-order model parameters; therefore, any 

parameter transformation will require a subjective decision based on an agreed-upon 

level of protectiveness. The emphasis of the NAFTA procedure is therefore on 
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standardization of a process rather than on statistics of determining "best fit" models. In 

this regard the NAFTA group has arrived at a comparatively simple method that has 

preserved some standard assumptions used by FOCUS such as use of a DT90 as a 

reference point.

Notes:

In his presentation, Dirk Young explained the NAFTA guidance for calculating 

degradation kinetics that USEPA and Health Canada (NAFTA partners) have developed 

for transport model inputs. Dirk explained how EPA and Health Canada used data from 

aerobic soil metabolism studies to describe the challenges when the data did not follow 

first-order kinetics. Therefore, the main idea behind the NAFTA project was to find a 

standardization process to determine a representative single first-order rate for 

modeling when the data deviate from first-order degradation. 

The first step in the NAFTA guidance is to determine whether the data is significantly 

non-first order. If data are non-first order, then the representative half-life using the 

indeterminate order rate equation (IORE) and the double first order in parallel (DFOP) 

equation (this is the DFOP slow rate) is obtained. A representative single first order halflife using the IORE equation is called the TIORE. The lower of TIORE or the DFOP slow 

rate is used as a representative single first-order model input value. The NAFTA project 

is currently completed; however, the implementation plan and technical evaluation of 

the R program and package to calculate kinetics is yet to be completed. 

Questions:

1. What is DFOP? Is it two-compartment decay? DFOP stands for Double FirstOrder in Parallel and it is a two-compartment decay.

2. Are you combining studies to do this data analysis? Because it is 

conservative as it takes the 90th percentile confidence. No, we are not 

combining studies. You can have different soil studies and some of them will 

follow Single First-Order (SFO), while others will not follow SFO. Therefore, you 

will be using this approach for soils that do not follow SFO.

2. Update: Implementation of PRZM-GW

Reuben Baris, Michael Barrett, Rochelle F. H. Bohaty, Marietta Echeverria, Ian 

Kennedy, Greg Malis, Dirk Young, and James Wolf

Environmental Fate and Effects Division. United States Environmental Protection 

Agency, Arlington, VA

Estimation of pesticide concentrations in groundwater is one of the considerations as 

part of the exposure assessment in the pesticide registration process. For this reason, 

Canada and the United States combined efforts as part of the North American Free 

Trade Agreement (NAFTA) to develop a harmonized groundwater modeling protocol. 

This included the development of a common conceptual groundwater modeling scenario 

for regulatory purposes designed to be protective of even the most vulnerable drinking 

water supplies. The Pesticide Root Zone Model (PRZM) was selected as the NAFTA 
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regulatory tool. Evaluation of PRZM was completed by comparing simulated pesticide 

concentrations to available targeted and non-targeted monitoring data. For the majority 

of chemicals tested, PRZM-predicted pesticide concentrations represent upper bound 

estimates of exposure in groundwater when conservative input parameters are 

used (e.g., maximum application rates, annually repeated applications, conservative 

degradation assumptions). There are some pesticide detections in monitoring data that 

are not captured by PRZM model estimates. This may be a result of physical 

processes that are not accounted for in PRZM (e.g., preferential flow or macroparticle 

transport). With site specific adjustments to the PRZM input values estimated pesticide 

concentrations compare very well to monitoring data (the same order or within an order 

of magnitude). The evaluation demonstrates that PRZM is a versatile assessment tool 

that can be used both as a screening tool and a refined site-specific tool in risk 

assessment.

Notes:

The presentation began by outlining the guidance documents under which PRZM-GW

and all EPA models are implemented: Guidance on the Development, Evaluation, and 

Application of Environmental Models produced by the Council for Regulatory 

Environmental Modeling (CREM) and Guidance for Quality Assurance Project Plans for 

Modeling produced by the Office of Environmental Information (OEI). A new guidance, 

Guidance on Quality Assurance Project Plans, combines the two aforementioned 

guidance documents. It was then explained how these guidances apply to the project.

The conceptual model is the same as the previous one presented to the EMPM, with 

the exception of a variable well screen length to replace a one-meter standard well 

screen length. Of the 19 models that were considered, 3 models were evaluated 

(PRZM, LEACHM, and PEARL), and PRZM was eventually selected. The data quality 

objectives for the model specify that non-targeted monitoring data should have lower 

values than the estimated value, and targeted monitoring results should have similar 

concentrations to simulations. Some observed concentrations in non-targeted studies 

were above simulated concentrations; however, this may be attributed to preferential 

flow and other unaccounted factors. In the National Alachlor Well-Water Survey, PRZM 

provided conservative estimates for wells. Even when the hydrolysis half-life was

estimated to be 4 years, large differences in EDWCs could be seen between a long 

hydrolysis half-life and stability to hydrolysis since PRZM-GW simulations run for 30 to 

100 years. Label rates and soil degradation differences also contribute significantly to 

changing EDWCs. The model is still in the evaluation phase within EFED, and 

guidance for consistent use among risk assessors is being developed concurrently.

Q: From Stuart Cohen: How do PRZM-GW concentrations compare to SCIGROW 

concentrations?

A: SCIGROW only models one year, whereas PRZM-GW models 30 to 100 years so 

results are not directly comparable. Also, SCIGROW is regression based whereas 

PRZM-GW is mechanistic. However, there are comparisons that will be released with 

other model documentation.
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Q: For a hydrolytically stable compound, how does PRZM-GW compare to monitoring 

and SCIGROW?

A: The models are not directly comparable with respect to hydrolysis alone because 

SCIGROW does not include hydrolysis as an input. Long term simulations like PRZMGW need accurate hydrolysis estimates. Chemicals with pH- dependent hydrolysis 

half-lives can have high monitoring detections in sites highly acidic or highly alkaline 

because extreme pH values are not modeled. 2,4-D is an example of this.

Q: PMRA: How is soil hydrolysis accounted for relative to aerobic soil metabolism?

A: Degradation from aerobic soil metabolism is restricted to the top one meter in the soil 

profile. Degradation from hydrolysis is active in the full soil profile.

Note from Cal DPR: With respect to monitoring data, California Department of Pesticide 

Regulation has not found alachlor parent in samples, only its associated degradates. 

Additionally, CalDPR is finding concentrations 3-4X higher in run-off areas as opposed 

to groundwater. 

Q: Cal DPR: The diuron example shows that the application rate is important. How was 

the application rate determined? It should be noted that diuron is used as a desiccant at 

2 lb/acre.

A: The application rate was calculated over the full 25 mi2application area.

Q: Syngenta: How is over-prediction accounted for in the model evaluation?

A: EFED is working with BEAD to better characterize use data and we have a number 

of other refinements that can be made.

3. Measuring and Estimating Concentrations in Drinking Water: A Historical 

Perspective

Russell L. Jones

BayerCrop Science

After the adoption of the Food Quality Protection Act in 1996, risk assessments for 

registration had to include the contribution of residues in drinking water to dietary 

assessments. This triggered work modeling and monitoring work by both EPA and 

industry to determine satisfactory methods for estimating and measuring concentrations 

in drinking water from both ground and surface water. This presentation reviews the 

developments in modeling and monitoring of drinking water from surface water over the 

past 15 years, including identification of the most sensitive drinking water intakes, 

SWMI, WARP, and the Index Reservoir as well as the evolution in surface water 

monitoring studies. The results of the past research will be used to suggest potential 

approaches to obtaining appropriate estimates of drinking water based on either 

modeling or monitoring.

Notes:

The Food Quality Protection Act of 1996 required risk assessments for registration to 

include the contribution of residues in drinking water to dietary assessments. This 
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triggered modeling and monitoring activities by both EPA and industry to determine 

satisfactory methods for estimating and measuring concentrations in drinking water from 

both ground and surface water. The focus of this talk is on surface water assessments,

which historically have been the driving exposure pathway for most drinking water risk 

assessments. Improvements made over the years include the addition of the index 

reservoir and percent crop area (PCA) factors in drinking water exposure assessments. 

The assessment methods have not changed significantly in the last 10 years. Often the 

predicted concentrations are much larger than monitored results, which “demonstrates 

the overly conservative nature of drinking water assessments.”

Examples were given from aldicarb, bromoxynil, carbaryl, ethoprophos, oxadiazon, and 

iprodione exposure assessments, using OPP models versus monitoring observations in 

raw and finished drinking water samples. Usually model estimates were orders of

magnitude greater than measured concentrations*. (EPA Comment: However, it is true 

that it is often easy to miss peak concentrations at a site by a significant factor with all 

but the most intense monitoring programs.) Current OPP models for rice and 

cranberries may be especially conservative. One major factor increasing the likelihood 

of overestimation of drinking water concentrations is that the highest use intensities are 

usually in small watersheds where the small streams are not directly used for drinking 

water. Regression models such as the Surface-Water Mobility Index (SWMI) and 

Watershed Regressions for Pesticides (WARP) have advanced our ability to predict 

moderate- and long-term exposure levels by identifying input factors responsible for 

most of the variability in observed data – with emphasis on model inputs that can be 

generated from commonly available data sources in the US. EPA was involved in the 

early development of WARP but was unable to continue supporting its development for 

drinking water assessments “due to non-technical reasons.”

The accuracy of drinking water assessments can be improved greatly by obtaining data 

to refine inputs to better reflect real world exposure scenarios. A major area for 

refinement is in the area of use intensity where improved representation of percent crop 

area, percent crop treated, and typical application rates is needed; all have been used 

by OPP in cumulative risk assessments. Pesticide sales data is often a good surrogate 

source for refining usage inputs when direct localized usage information is not available. 

Acute exposure estimation from monitoring data can also be improved by techniques 

like the use of “bias factors.” One should try to determine whether the underestimation 

bias from insufficiently frequent sampling to capture peak exposure is as important as 

the overestimation from conservative assumptions in modeling. An example of this was 

illustrated from the carbaryl data. The sampling schedule used for carbaryl should have 

been more than adequate to accurately determine the 70th and 90th percentile 

concentrations. Since the modeling was overestimating these concentrations by 1-3 

orders of magnitude, then one could not attribute the difference between the modeled 

and measured concentrations at higher percentiles, but only to missing the peaks.

* Examples of acute and annual mean exposure modeling estimates and targeted 

monitoring result ratios:

Aldicarb: 1.5 x to 30x
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Bromoxynil: 35x

Carbaryl: 100x to 2000x

Ethoprophos: 100x to 1000x

Oxadiazon: 300x

Iprodione: 600x

3,5-DCA: 5000x

[Above were the ratios of model predicted acute concentrations to observed 

concentrations, chronic (annual mean) concentrations were also compared.]

4. Improved Characterization of the Temporal and Spatial Variability of Potential 

Surface Water Drinking Water Exposure by Using Environmental and Historic 

Monitoring Databases

Paul Hendley1, Wenlin Chen1, Robert Joseph1, Ron Williams1, Chris Harbourt2 & 

Paul Miller2

1Syngenta Crop Protection LLC, Greensboro, NC

2Waterborne Environmental Inc., Champaign, IL

The extensive atrazine surface water monitoring database totals over 300,000 samples 

including ~120,000 drinking water samples from high frequency seasonal monitoring 

programs covering more than 15 years and >210 Community Water Systems (CWS). 

This wealth of frequent monitoring data sampled across a wide range of years and sites 

allows for robust conclusions to be drawn with very high statistical certainty regarding 

the highest centiles of potential atrazine exposures. To complement the monitoring 

data, site-specific details have also been accumulated on CWS watersheds, source 

waters and water handling procedures; this information has been used to better 

understand the expected temporal concentration patterns at individual CWS. We will 

outline how these and related data (e.g. daily flow and chemical monitoring data from 

Heidelberg University) have been used to examine approaches for optimizing sampling 

strategies for assessing potential drinking water concentrations in surface water. 

Scenarios with different sampling frequencies have been simulated and the results have 

been compared to measured data for each year and site. This talk will demonstrate how 

lower frequency monitoring data can be used to estimate potential maximum shorter 

duration concentrations.

Notes:

For larger sampling sizes (e.g.,> 7000), there is a greater confidence in capturing the 

highest centile concentration (99th%-tile) compared to smaller sampling sizes.

Two considerations/enhancements to the database for capturing peaks in monitoring 

(i.e., did you miss peaks?)

1) biased factors and error ratios

2) supplement monitoring data with model estimates (PRZM-hybrid) 

Because the atrazine dataset is so large, it may be possible to apply conclusions from 

these studies to the general monitoring study design (e.g., baseline for using year-toyear variation). 
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Questions:

1) What is the effect and applicability of chemical specific biased factors? Can they be 

applied across chemicals?

- Potentially, but this would need some additional research.

2) How much data are available across water systems?

- 150 or greater water systems. 

5. Estimating Upper Centile Pesticide Concentrations and Sample Size 

Requirement

Wenlin Chen1, Paul Mosquin2

1Syngenta Crop Protection, LLC

2RTI International

A survey sampling approach is presented for estimating upper centiles of aggregate 

distributions of surface water pesticide concentrations obtained from monitoring with 

large sample sizes but variable sampling frequency. It is applied to three atrazine 

monitoring programs of Community Water Systems (CWS) that used surface water as 

their drinking water source: 1. the nationwide Safe Drinking Water Act (SDWA) data, 

2.the Syngenta Voluntary Monitoring Program (VMP), and 3. the Atrazine Monitoring 

Program (AMP). Requisite sample sizes are determined using statistical tolerance 

limits, relative standard error, and the Woodruff interval sample size criterion. These 

analyses provide 99.9% confidence that the existing data include the 99.9th centile 

atrazine concentration for CWS raw and finished water in the Midwest atrazine high-use 

areas and in the nationwide SDWA dataset, assuming no bias due to nonrandom 

selection of CWS and monitored days during these years. The general validity of these 

approaches is established by a simulation that shows estimates to be close to target 

quantities for weights based on sampling probabilities or time intervals between 

samples. 

Notes:

Additional detail on the analysis is included in Mosquin et al., 2012 (JEQ publication)

There are two approaches to focus monitoring design (precision and sample size) on 

confidence in capturing upper centile concentration and relative standard error (Slides 

12 and 13, respectively)

Question:

1) How is right-handed bias handled in Woodruff; how is it used? What is the Woodruff 

method?

-Woodruff is the skewness of a binomial approximation

-Enough samples as defined by Woodruff allow you to have good coverage at Xcentiles, and at x-sample size.
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6. Estimation of Upper Percentiles of Chlorpyrifos Surface Water Concentration 

from Yearly Monitoring Program Data

Paul L. Mosquin1, G. Gordon Brown1, Roy W. Whitmore1, Nick Poletika2

1RTI International, Department of Statistics & Epidemiology, Research Triangle Park, 

NC, 27709, United States 

2Dow AgroSciences LLC, Department of Field Exposure and Effects, Indianapolis, 

IN, 46268, United States 

This talk presents a finite population method to estimate annual 95th, 99th and 99.9th

centiles of surface water chlorpyrifos concentrations and associated confidence 

intervals using historical surface water pesticide monitoring programs with large number 

of measurements. The method provides reliable estimates, accounting for infrequent 

sample collection, measurements below reporting limits, and the strong seasonality 

present in these datasets. Data from three monitoring programs are considered: the 

USGS National Water-Quality Assessment Program (NAWQA), the Heidelberg 

University National Center for Water Quality Research (NCWQR) monitoring data, and 

the Washington State Department of Agriculture/ Department of Ecology surface water 

monitoring program. Results for temporal trends will be provided, including a 

comparison of estimates before and after chlorpyrifos was withdrawn from residential 

use in 2001.

Notes:

The authors discussed a finite population method to estimate annual 95th, 99th and 

99.9th centiles of chlorpyrifos concentrations in surface water and associated confidence 

intervals using historical surface water monitoring data from three different monitoring 

programs. The three monitoring programs included: the USGS National Water-Quality 

Assessment Program (NAWQA), the Heidelberg, University National Center for Water 

Quality Research (NCWQR) monitoring data, and the Washington State Department of 

Agriculture/ Department of Ecology surface water monitoring program. In their talk, the 

authors presented a comparison of chlorpyrifos concentration estimates before and 

after the removal of residential chlorpyrifos uses as well as a comparison of chlorpyrifos 

concentrations in agricultural and urban watersheds. 

The following issues were highlighted by the presenters: 1) the monitoring sampling is 

generally not from a probability design; 2) there are potentially only a few observations 

per year at a given site; 3) and non-detections are common. 

The correlation of the monitoring datasets and actual chlorpyrifos use—application 

timing, crops, etc.—was not discussed. Therefore, the representation of the annual 

95th, 99th and 99.9th centiles of chlorpyrifos concentrations in surface water was not 

clear. 

7. Sampling Plans for Water Quality Assessment

John W. Green, Ph.D. 

DuPont Applied Statistics Group
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Sampling plans for monitoring chemical concentrations in rivers were investigated for 

two California streams of differing order to assess the relative merits of daily sampling 

versus less frequent sampling. The assessments were based on the ability to estimate 

time-weighted total and peak concentrations on a monthly or annual basis and to detect 

the presence of spikes in these concentrations. A secondary purpose was to determine 

the feasibility of predicting spikes in chemical concentrations from the river discharge 

rate. In these streams, some less frequent sampling plans were shown to provide 

almost the same utility as daily sampling for the first purpose and discharge rate was 

shown to have little correlation with chemical concentration.

Notes:

Sampling plans for monitoring chemical concentrations in rivers were investigated for 

two California Rivers (Orestimba Creek at River Road near Crows Landing and

Sacramento River at I Street Bridge) to assess the merit of daily sampling versus less 

frequent sampling. The assessments were based on the ability to estimate timeweighted total and peak pesticide concentrations on a monthly or annual basis and to 

detect the presence of spikes in these concentrations. The feasibility of predicting 

spikes in chemical concentrations from the river discharge rate was also investigated. 

The presenter suggested that for these datasets, less frequent sampling plans provide 

almost the same utility as daily sampling and that pesticide concentrations did not 

correlate with the discharge rate.

8. Update on Development of Drinking Water Intake Watershed PCAs

Jim Carleton, Rochelle Bohaty, Marietta Echeverria, Nelson Thurman, Michelle 

Thawley, Katrina White, Mohammed Ruhman

Environmental Fate and Effects Division. United States Environmental Protection 

Agency, Arlington, VA

EFED uses percent crop area (PCA) adjustment factors in its drinking water 

assessments to account for the fact that watersheds supplying surface drinking water 

sources may not be devoted entirely to agriculture. The first PCAs were developed in 

the late 1990s and mid 2000s, based on cropped acreage within 8-digit HUCs. More 

recently, EFED has developed delineations for watersheds associated specifically with 

drinking water intakes that supply community water systems. EFED is planning to use 

this new dataset to develop drinking water intake watershed-specific PCAs. This talk 

will discuss the intake watershed dataset and PCA development methodologies in 

general terms, along with unique issues and challenges associated with the use of 

these new data for PCA development.
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Notes:

Drinking water intake (DWI) percent cropped area (PCA) adjustment factors are being 

developed to represent delineated watersheds with surface water intakes serving 

community water systems (CWS). These PCAs use the same crop distribution data as 

other PCA projects (Ag Census and NLCD), but are based on the 4,840 DWI

catchments judged “reasonable” by the USGS. Some DWIs were not included because 

they are not associated with NHD+; however, surrogate PCAs can be used in these 

instances. The catchments vary greatly in size (1/3 are less than 10 mi2and 1⁄4 are 

greater than 1,000 mi2). These PCAs are limited by the resolutions of the datasets 

involved (AgCensus only provides data at the county level), and there is temporal 

variation in cropland data. Also, there are concerns with releasing drinking water intake 

data as it relates to homeland security. Next steps in this project are to develop draft 

PCAs, validate the GIS process, evaluate draft PCAs, finalize PCAs, and finalize the 

report and guidance document.

Q: Will drinking water intake PCAs replace HUC-derived PCAs?

A: This is a policy decision that will be made in the future. However, a HUC PCA may 

be more appropriate in some cases as DWIs are not available for all localities.

Q: The analysis being done for DWI PCAs seems very similar to an approach currently 

underway for assessing endangered species. Are these efforts duplicative?

A: These efforts are not the same as there are pixel value differences between the data 

sets being used and the land cover data is being used for very different purposes (e.g., 

proximity analysis versus percent of a watershed with a specified land cover).

Q: Is any work being done beyond GIS work to determine the appropriateness of 

including water storage reservoirs among the DWI PCA catchments?

A: No, only GIS analysis is being used to identify catchments. EFED does not have the 

resources to individually verify all water storage reservoirs.

Q: Will a specific representative DWI catchment be identified as being protective and 

representative for specific crop(s)?

A: For the HUC PCAs, specific crops and regions are identified that address this issue; 

however, identifying crops and regions for DWI PCAs cannot be done easily as many of 

the catchments are nested within large catchments complicating which catchment is 

representative.

Q: What are the minor crops in consideration for crop specific PCAs?

A: The same groups as for HUC PCAs: all agriculture, corn, soybean, cotton, wheat, 

turf, orchard/vineyard, vegetable, and rice

Q: What is the measure of success for this project?

A: When verification, comparison to monitoring, comparison to old PCAs, and 

implementation for use in EFED is completed, the project will be considered a success.

Q: What is included in the turf layer?
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A: The turf layer used to calculate the HUC8, HUC10, and HUC12 PCAs recently 

completed covers residential turf and sod farms. There was uncertainty for that turf 

layer for sod farms as many acres of sod farms are not reported in Nationall Agricultural 

Statistis (NASS) data. A new turf layer has been created that is specific to residential 

turf (e.g., did not account for sod farm turf) and that layer was used with the DWI-PCA 

calculation. The all agricultural PCA is expected to be used for sod farms for PCAs 

completed in the future.

9. Exposure Assessment for Pronamide Drinking Water Residues in California 

Central Coast Lettuce Production Areas 

Nick Poletika1, Brian Bret2, Mike Winchell3

1Dow AgroSciences LLC

2Dow AgroSciences LLC, Regulatory and Government Affairs NAFTA

3Stone Environmental, Inc. Applied Information Management

Regulatory pesticide human exposure models are intentionally designed to be 

protective of individuals in populations. Consequently, they tend to be quite 

conservative in early assessment tiers, and predictions are generally refined with data in 

subsequent assessments. For drinking water obtained from surface water, pesticide 

exposure model concentration estimates for agricultural use generally are in the μg L

-1

to tens of μg L-1range, which can lead to concern if human health endpoints for a 

specific pesticide are low values. Currently US EPA OPP accepts only targeted 

monitoring studies for refinement of model estimated concentrations. We present an 

alternative assessment method to targeted monitoring for a pesticide with a limited 

range of label crops and geographic regions of use that relies on proximity analysis of 

use sites and drinking water intakes. We also address national security issues related 

to sensitive intake location data and consider the general utility of the approach.

Notes:

Using the case study of pronamide on leaf lettuce in the central California coastal 

region, the presentation focused on an approach for evaluating drinking water exposure 

by examining public records and data sets to assess potential contamination of surface 

water sources. By using historical surface water drinking water intake locations, SDWIS 

data, Internet resources (e.g., Google Earth), and discussions with community water 

system (CWS) employees, a dataset was compiled of active drinking water intakes. 

The exposure potential for the active drinking water intake was assessed by using the 

ArcGIS tool NHDFlowline and use data from California’s Pesticide Use Reports. Sites 

were eliminated from future consideration if the pesticide was not used in the county, if 

there was no hydrologic connection (as determined by NHDFlowline), or if the intake 

was located upstream of the use sites. One revelation of the research was that in the 

central California coast region a number of CWS receive raw water from remote 

sources, which tend to be in the elevated regions of the State, as opposed to the valley 

regions where lettuce is grown. Based on the pronamide case study, of the 233 

locations evaluated, none had the potential for pronamide contamination and exposure. 

The recommendation was made that this approach could be extended to other regions 

of California, but that routine updates would need to be conducted to demonstrate that 
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no exposure changes had occurred. As drinking water intake locations are considered 

sensitive information, it was also recommended that EPA work with the USGS to more 

easily conduct this type of analysis in the future. 

Q: Can this analysis be done for other crops, or is it only applicable for lettuce? 

A: Using the PUR data in general, this analysis could be conducted for other crops.

10.Hybrid PRZM:”Filling in the Gaps” in Field Sampling Data using Realistic 

Simulation Modeling

Paul Miller, PhD1, Chris Harbourt, PhD1, William J. Northcott, PhD1, Paul Hendley, 

PhD2, and Jessie J. Prenger1

1Waterborne Environmental, Inc.

2Syngenta

The Hybrid-PRZM approach can be used to combine observations from field monitoring 

with results from simulation modeling based on local rainfall for the same period to “fill 

in” gaps between sampling occasions. The approach uses Pesticide Root Zone Model 

(PRZM) simulations based on local rainfall data together with site-specific watershed 

soil and agronomic characteristics. The model is used to estimate edge-of-field atrazine 

concentrations occurring on days with rainfall; the simulation results are scaled to 

watershed conditions without calibration. The PRZM model results supplement field 

monitoring data with estimates of potential runoff events on days when runoff inducing 

rain falls in a watershed between sampling occasions. This technique captures the 

strengths of field and simulation approaches to develop conservative daily time series 

using quality sampling results and appropriate simulation assumptions. Significant 

developments are presented here that result in improved exposure simulation results; 

these include the use of NEXRAD local precipitation data combined with a land 

workability algorithm to develop appropriate distributions of application dates across a 

watershed.

Notes:

The hybrid PRZM techniques for estimating peak concentrations / acute exposure levels 

in surface waters are being developed at Waterborne Environmental, Inc. in cooperation 

with Syngenta Crop Protection. 

Why is this needed? There’s a concern that monitoring programs may be missing peak 

concentrations, in some cases by quite a large margin. 

The use of bias factors is one promising method for extrapolating from moderatefrequency monitoring data to conservative estimation of what the peak daily exposures 

might look like if higher frequency monitoring data were available for the pesticide of 

interest. 

The author proposed a more “realistic” way of estimating high frequency data (i.e.,

acute exposure levels). The method utilizes regionally specific data for inputs into the 
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PRZM modeling system to set up simulations that are calibrated (but with no “model 

specific” calibration required) with the available monitoring data to fill in the exposure 

distribution. This method has been applied to predict site and event specific 

concentrations of atrazine at the edge of field. The local information includes:

 Nexrad rain temp solar data 

 Planting date, crop maturity 

 Soil characteristics 

 Cropland data layer, 

 Historical and current weather (Growing degree days, rainfall etc.)

 Crop yield and soil moisture models

 Etc.

Locally measured weather and pesticide application data are used. The watershed PCT 

(percent crop treated) calculations are based on Cropland Data Layer (CDL) land-use 

data from the NASS and grower survey data (local but not specific – see presentation 

authors for details). The atrazine example presented crop reporting district - based 

application rates.

SSURGO soils data is used for site-specific inputs on corn and sorghum in the example 

presented.

Algorithms have been developed to simulate farmer behavior and agronomic timing 

based on local conditions to give more realistic inputs on a daily basis. The method 

accounts for daily changes in land workability and distribution of practices over the 

watershed – there is a LOCAL response regarding whether land can be worked. The 

reluctance of farmers to plant too early is accounted for.

The first planting date is determined from 14 day moving average of air temperature 

when 55 degrees and soil moisture is less than 0.85 and date is April 1 or later then 

planting is possible (for Midwest?)

Several adjustments to the inputs were made to improve the precision and accuracy of 

modeling of runoff events. The modeled window for potential applications was started 7 

days prior to the accumulation of 630 GGD (growing degree days). If local variation is 

not considered, predictions are still acceptable. 

Conclusions:

PRZM hybrid shows considerable promise for supplementing 7-day interval monitoring 

data.

The method predicts the relative magnitude of peaks but over predicts (the overall 

peak?) by >20 ppb- The method reflects reality in that peaks are predicted only during 
14

real runoff periods. The new land workability approach provided improved estimates of 

atrazine application timing.

The authors will be evaluating larger community water supply watersheds as well - the 

examples in this presentation were for watersheds of 10 to 1000 square miles that were 

part of the atrazine ecological monitoring program. Instead of a mechanistic approach, a 

WARP-type (USGS model) regression approach for larger watersheds may be used. 

Probabilistic approaches will also be investigated.