OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D.C. 20460

RE : Meeting between Dow AgroScience s (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 Asses sment. 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 p 1 – highest Percent Crop Area factor and highest Percent Crop Treated assumptions. p 2 – All applications occur on the same day p 3 – Maximum label use rate and application type p 4 – Screening - level Environmental fate values p 5 – High - end runoff event p overall = p 1 x p 2 x p 3 x p 4 x p 5 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” inputsData Derived Model InputRefinements 

# 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 screen9 

# Case Study: Refinement - 15 Indiana CWSs| 10 

### 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” inputsData Derived Model InputRefinements 

# Surface Water Assessments – 8 Refinements Elements
1. Geographical vulnerability screen (HUC12 or better) (runoff/erosion, crop density, product use)| 12 

# 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| 13 

## USGS chlorpyrifos use DAS chlorpyrifos 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| 14 

# 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 Screening level 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%| 15 

# 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 3 rd 

### 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| 17 

# 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 , K OC 

### (ml/g) 6040 8216 Soil Half - life (days) 170.6 28.2 Water Column Metabolism Half life (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” inputsData Derived Model InputRefinements 

# 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