OFFICE OF CHEMICAL SAFETY

AND POLLUTION PREVENTION

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460

RE: Meeting between Dow AgroSciences (DAS) and EPA where DAS presented eight 

actionable concepts for refining predictions of surface water concentrations

List of Documents

1. Meeting Summary Page 1

2. Meeting Sign in Sheet Page 2

3. DAS PowerPoint Presentation Page 4

Meeting Summary

On Thursday, September 10, 2015, EPA met with DAS to discuss possible refinement elements 

available for drinking water assessments. DAS presented a proposal for data-driven refinement, 

which was not inconsistent with what EPA had presented in its December 2014 Revised Human 

Health Risk Assessment. Their proposal consisted of 8 actionable elements, which were (1) 

geographical vulnerability screen; (2) realistic PCA inputs; (3) product application timing inputs; 

(4) product use scenario inputs; (5) product use rate ranges inputs; (6) soil-type inputs; (7) 

environmental fate properties inputs; and (8) realistic “percent crop treated” inputs. EPA will 

evaluate whether the proposal presented by DAS is appropriate to support refinements to risk 

assessments.


Chlorpyrifos Drinking Water Risk Assessment:

A Proposal for Data-Driven Refinement

Surface Water Concentration Predictions

Discussion with US EPA

Sept 10, 2015

G Oliver, Dow AgroSciences
| 2

Outline

• Background

• 8 Elements - Surface Water Concentration Refinements

> Case Study: 15 Indiana Watersheds

• Path Forward

Objectives:

• Discussion

• Identify assessment needs – chart path and timeline to move forward

Introduction
| 3

Key Assumptions

p1 – highest Percent Crop Area 

factor and highest Percent Crop 

Treated assumptions.

p2 – All applications occur on the 

same day

p3 – Maximum label use rate 

and application type

p4 – Screening-level 

Environmental fate values

p5 – High-end runoff event

poverall = p1 x p2 x p3 x p4 x p5

Explains

1) The gap

2) The need to refine

Why Are Observed and Predicted So Different?

Shipman Index Reservoir
| 4

Important Principles

• Focus for Today - Prediction of surface water concentrations

• Only one aspect of dietary exposure assessment

• Refinement elements – are model inputs

• Key refinement elements - geographic-based.

“Although there are several exceedances of the DWLOC on a national basis, the incidence of high 

exposures is expected to be highly localized” (p. 5 Chlorpyrifos: Updated Drinking Water Assessment for 

Registration Review, 12/23/2015)

• Most refinement elements are broadly applicable

• Refined approach useful across US agriculture and technologies

• Refinements driven by 1) science, 2) data or 3) controllable variables

• Enables defensible assessments
| 5

Standard Index Reservoir

• Current screening-level approach utilizes a standardized watershed 

and CWS to represent America

• More than 55,000 US Community Water Systems (CWS)

> Each with unique characteristics

> Current approach is efficient.....but a significant contributor to the current 

overestimations. 
| 6

National Screen

Refined Model Inputs and Scenario Development

2. Realistic PCA inputs 

3. Product application timing inputs

4. Product use scenario inputs

5. Typical product use rate inputs

6. Soil-type inputs

7. Environmental Fate properties inputs

8. Realistic “percent crop treated” inputs

Data Derived Model Input 

Refinements

Surface Water Assessments – 8 Refinements Elements

1. Geographical vulnerability screen (HUC12 or better)

(runoff/erosion, crop density, product use) 
| 7

Information Sources – Best Available Data

• Element 1

> Pesticide vulnerability ranking – identify higher “risk” watersheds

• Publicly-available soils, weather, slope, cropping patterns available to rank runoff 

and transport potential

• (Giesy and Solomon, 2014)

> CWS-specific percent cropped area (PCA)

• Publicly-available Cropland Data Layer (CDL)

• Currently used by USEPA-EFED for assessments

> Product Use – percent crop treated (PCT)

• USEPA - BEAD

• USGS Survey Results 

• Registrant
| 8

Element 2 through 8 - Inputs

• Percent Crop Area (PCA)

• Percent Crop Treated (PCT)

• Application rate, type and timing

> Typical application rates

> Temporally distributed use rates

> Various application methods

• Local soil, elevation and weather data

• Refined Environmental Fate Endpoints (Giesy and Solomon, 2014)

> Relevant degradation pathways

> Parent- and metabolite-specific data from guideline and literature studies 

passing data quality screen
9

Case Study: Refinement - 15 Indiana CWSs
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The following case study has been developed for illustrative purposes to 

provide an understanding of how refinement of inputs in modeling can have 

an effect on the results.

For some inputs, multiple inputs may be valid alternatives (such as 

application type). For this presentation only one was selected for 

demonstration while in a full assessment, multiple values might be used 

with a probability of occurrence selected for each or as bounding values.
| 11

National Screen

Refined Model Inputs and Scenario Development

2. Realistic PCA inputs 

3. Product application timing inputs

4. Product use scenario inputs

5. Product use rate ranges inputs

6. Soil-type inputs

7. Environmental Fate properties inputs

8. Realistic “percent crop treated” inputs

Data Derived Model Input 

Refinements

Surface Water Assessments – 8 Refinements Elements

1. Geographical vulnerability screen (HUC12 or better)

(runoff/erosion, crop density, product use) 
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Identify a Sub-Set of Potentially Highly Vulnerable CWSs

National runoff and erosion potential Indiana runoff and 

erosion potential

Refined CWS 

watersheds

Element 1: Geographic Vulnerability Screen
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USGS DAS chlorpyrifos chlorpyrifos use use

Identify a Sub-Set of Potentially Highly Vulnerable CWSs

IN chlorpyrifos use

• Relatively high crop density (CDL) and use intensity (DAS sales or USGS)

Element 1: Geographic Vulnerability Screen
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Identify a Sub-Set of Potentially Highly Vulnerable CWSs

* 2014 Cropland Data Layer

Element 1: Geographic Vulnerability Screen

• Relatively high crop density or high use intensity, runoff/erosion potential

CWS_I

D

Corn

PCA*

Soybean

PCA*

Screenin

g-level

PCA = 1

1 32 29 100

2 1.7 1.3 100

4 2.4 12 100

8 19 17 100

11 2.5 2.5 100

13 22 24 100

18 38 45 100

23 21 26 100

25 7.4 9.3 100

26 12 11 100

30 0.32 0.30 100

32 0.16 0.05 100

33 - - 100

34 7.5 6.4 100

35 11 0.67 100

CWS_I

D

CPY (lb) 

Applied 

2012 

USGS

Average 

CPY (lb) 

Applied 

(2010 & 

2011) DAS

Screeninglevel EFED 

maximum

Corn/Soy rate 

(lb); PCA = 1.0

1 25 210 1281

2 1 <1 1281

4 0 0.00 1281

8 43 93 1281

11 5 0.73 1281

13 58 108 1281

18 496 521 1281

23 219 397 1281

25 1 <1 1281

26 1 <1 1281

30 0 <1 1281

32 0 <1 1281

33 0 <1 1281

34 0 <1 1281

35 0 <1 1281

CWS_I

D

Runoff

and 

Erosion 

Potential 

Ranking

1 76-90%

2 51-75%

4 76-90%

8 >90%

11 76-90%

13 >90%

18 76-90%

23 51-75%

25 76-90%

26 76-90%

30 51-75%

32 76-90%

33 76-90%

34 76-90%

35 76-90%
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Screening Level - IN corn scenario

• USEPA Current Assessment 

> IN corn standard scenario, Shipman Reservoir (IR) , PCA=1.0

Assumes:

PCA = 100%

PCT = 100%

Maximum Application Rate

Worse-case application method

All applications occur on single day

Screening-Level Efate properties

3rd highest 30-year 21 day avg. conc.
| 16

PCA factors for 15 IN CWS watersheds from 2014 NASS CDL data

CWS_ID Corn Soybean

2013 2014 2013 2014

1 30.03% 32.06% 29.68% 28.79%

2 1.58% 1.68% 1.29% 1.28%

4 5.34% 2.44% 7.79% 12.03%

8 17.96% 19.07% 17.91% 17.44%

11 3.00% 2.54% 1.93% 2.48%

13 24.19% 21.54% 21.73% 24.30%

18 41.06% 38.34% 41.74% 44.87%

23 26.02% 21.17% 21.42% 26.34%

25 6.95% 7.36% 1.32% 9.31%

26 15.76% 12.16% 6.81% 10.82%

30 0.12% 0.32% 0.48% 0.30%

32 0.18% 0.16% 0.06% 0.05%

33 - - - -

34 7.55% 7.50% 5.62% 6.37%

35 4.97% 11.39% 6.84% 0.67%

Element 2: Realistic PCA Inputs
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Element 2: Actual PCA Information

PCA factors for 15 IN CWS watersheds derived from NASS CDL data
| 18

Element 3: Product Application Timing Inputs

• Product Applications occur over a period of days
| 19

Element 4: Product Use Scenario Inputs

> Chlorpyrifos is applied via various application scenarios

> e.g., assume T-Band application scenario
| 20

Element 5: Typical Product Use Rate Inputs

• Foliar ground application - 1.05 kg a.i./ha

> fields treated on different days
| 21

Element 6: Soil-Type Inputs

• USEPA IN corn standard scenario - slow infiltration assumed

• Actual - CWS34 (moderate infiltration), CWS35 (very slow infiltration) 
| 22

Element 7: Environmental Fate Properties Inputs

Property

Screening Level

Environmental fate 

property used by 

USEPA (2014)

Refined Level

Value used in refined 

modeling*

Water solubility (20oC) (mg/L) 1.4 0.73

Henry’s law constant (20oC) 

(dimensionless) NA 0.5 x10-5

Sorption Coefficient, KOC (ml/g) 6040 8216

Soil Half-life (days) 170.6 28.2 

Water Column Metabolism Halflife (days) 91.2 50.8

Foliar Half-life (days) 0 3.28

Foliar Washoff (1/cm) 0.5 0.1

*Giesy and Solomon (2014)
| 23

Element 7: Environmental Fate Properties Inputs

• Refined environmental fate model inputs - chlorpyrifos
| 24

Element 8: Realistic “Percent Crop Treated” Inputs

• DAS—county sales data

• USGS—imputed county level use data 
| 25

National Screen

Refined Model Inputs and Scenario Development

2. Realistic PCA inputs 

3. Product application timing inputs

4. Product use scenario inputs

5. Typical use rate inputs

6. Soil-type inputs

7. Environmental Fate properties inputs

8. Realistic “percent crop treated” inputs

Data Derived Model Input 

Refinements

Surface Water Assessments – 8 Refinements Elements

1. Geographical vulnerability screen (HUC12 or better)

(runoff/erosion, crop density, product use) 
| 26

Proposal

Obtaining Refined Surface Water Concentration Estimates:

• Element 1: Define which US Community Water Systems require 

additional surface water assessments

• Elements 2-8: Assess utilizing the refinement elements

• Result of Refinement: Develop a detailed, localized understanding of 

surface water concentrations. Compare distribution of model 

predictions to monitoring data (i.e., bridge the gap).

Utilize Refined Surface Water Concentration Estimates: 

• Conduct exposure and risk assessment

• Options: modeling, monitoring, and/or mitigation elements