Key Environmental and Physicochemical Parameters Influencing PRZM-GW Predicted Groundwater Residues

Transcription

1 Key Environmental and Physicochemical Parameters Influencing PRZM-GW Predicted Groundwater Residues Timothy Negley & Andrew Newcombe (ARCADIS U.S., Inc.) Dirk Young (USEPA, EFED) American Chemical Society, August 29, 2011, Denver, Colorado Evaluating Agrochemical Aquatic Exposure Modeling in Relation to Risk Evaluator Needs Imagine the result

2 Outline PRZM-GW Overview Case Studies & Observations USEPA Weather Extender Overview Summary & Conclusions Further Research & Development Questions & Comments 2

3 USEPA Tier I Model SCI-GROW Regression Model Based on peak 90-day average residues from PGW studies conducted on sandy, permeable soils with shallow aquifers (e.g., 2-8 meter) Requires aerobic soil degradation half-life, adsorption coefficient (Koc), and the annual application rate Represents conservative, high-end exposure values Does not account for heterogeneity of soil textures, organic carbon content, weather patterns, cropping practices, or various pesticide application regimes 3

4 USEPA Tier II Model Pesticide Root Zone Model for Groundwater (PRZM-GW) Intended to refine exposure estimates - incorporates meteorological, crop, biological, chemical, and management processes that affect the transport from soil to groundwater Represents vulnerable private drinking water wells adjacent to treated fields 4 Post processing and final summary statistics yet to be determined by USEPA EFED

5 Primary PRZM-GW Inputs (Pre-release) Parameter Aerobic soil metabolism half-life Hydrolysis half-life Koc Soils Weather Irrigation Crops Note Linearly decreasing degradation from 0-1 m Used beyond 1 meter From laboratory studies Bulk density, maximum water capacity, wilting point, organic carbon content Standard PRZM meteorological files (daily values) Type, rate, depletion threshold Rooting depth, canopy coverage, cropping dates, residue status after harvest 5

6 Casestudies PRZM-GW evaluations based on nine selective herbicides Eight crop uses assessed including turf, corn, soybeans, and several vegetables Six states in the west, southeast, Midwest, and south central USA Generally high mobility based on low Koc (<100 ml/g) Surface soil half-lives ranged from several days to stable Most compounds evaluated were stable to hydrolysis Soils ranged from silty clays to coarse sands All results are based on a pre-release version of PRZM-GW, courtesy of Dirk Young, USEPA. 6

7 Maximum Water Capacity (MWC) 1. Soil texture dependent 2. Drives the rate of water movement through the unsaturated zone in PRZM-GW 3. Influences both the timing and magnitude of peak residues predicted by PRZM-GW 4. Can increase or decrease predicted residues depending on pesticide degradation properties 5. May be initially estimated as a soil s field capacity (FC), although empirical data suggest MWC is greater than FC 6. Knowledge of appropriate maximum water capacity by soil depth and for the modeled soil type is KEY 7

8 MWC and ASM vs. Peak Residues Maximum Water Capacity Effects Hydrolysis = 10,000 days (stable) Koc = 20 ml/g OC < 0.1% Sands Loamy Sands MWC Stable 1000 days 365 days 75 days 25 days MWC is sensitive to pesticide degradation properties For 2x increase in MWC: Sandy Loams Silt Loams Long half-lives: 60-70% residue increase Short half-lives: 30-60% residue decrease Silty Clays For coarse soils, MWC had less impact on residues, especially when ASM exceeded 1 year. 8 *Based on a one-time, single application at the maximum rate.

9 Aerobic Soil Metabolism on Coarse Soils Hydrolysis = 10,000 days (stable) Koc = 20 ml/g OC < 0.1% ASM vs. Peak Residues by MWC ,000 MWC 0.3 (Silt Loam) MWC 0.2 (Sandy Loam) MWC 0.1 (Loamy Sand) MWC 0.05 (Sand) 10, ,000 MWC influences residues across a range of hydrolysis half-lives, especially short-lived compounds: 1,000, Short half-lives: + MWC = - C GW (Some cases >10x reduction) 9 Long half-lives: + MWC = + C GW For any MWC, ASM half-lives > days has little impact on residues

10 Hydrolysis ASM = 10,000 days (stable) Koc = 20 ml/g OC < 0.1% Hydrolysis vs. Peak Residues by MWC ,000 MWC 0.2 (Sandy Loam) 10,000 MWC 0.1 (Loamy Sand) MWC 0.05 (Sand) 100,000 MWC influences residues across a range of hydrolysis half-lives 1,000, Short half-lives: + MWC = - C GW (Some cases >10x reduction) Long half-lives + MWC = + C GW For any MWC, hydrolysis half-lives > 10,000 days ( 30 year run duration) have little impact on residues 10

11 11 PRZM-GW vs. Soil-pore Water Results Data from a Terrestrial Field Dissipation (TFD) study with a deep leaching module (suction lysimeters and bromide tracer) was evaluated in PRZM-GW Parameter Compound Class Hydrolysis half-life Koc Surface soil half-life Soils (Loamy Sand) Weather Irrigation Crops Value Selective herbicide stable 30 ml/g 30 days Bulk density: 1.51 to 1.56 g/cc Field Capacity: to Field Capacity (Adj.): 0.12 to 0.19 Wilting Point: to Site specific weather file including irrigation inputs Sprinkler irrigation Bare soil

12 0.9 m 1.8 m Bromide MWC Example 1: Bromide Soil-pore Water Results Cold Run (---): MWC set to field capacity from 0-1 meters ( 0.05) Earlier breakthrough predicted than TFD data Higher predicted residues than TFD data 2.7 m Adjusted Run ( ): MWC set to the MWC for a loamy sand (0.125 at bar) Predicted breakthrough time and residue magnitude for soil-pore water closely matched TFD data Months After Treatment Whiskers represent minimum and maximum plot values for each date

13 0.9 m 1.8 m CHEM X MWC Example 1: ChemX Soil-pore Water Results Cold Run (---): MWC set to field capacity from 0-1 meters ( 0.05) Earlier breakthrough predicted than TFD data Higher predicted residues than TFD data 2.7 m Adjusted Run ( ): MWC set to the MWC for a loamy sand consistent with bromide adjusted run (0.125 at bar) Predicted breakthrough and peak residues closely matched TFD data Months After Treatment Whiskers represent minimum and maximum plot values for each date

14 Addressing Situations of Incomplete Residue Breakthrough Previous examples and analyses were based on mobile pesticides and demonstrated complete pesticide breakthrough. Certain compounds with high retardation factors (such as high Koc values) or where rainfall is generally lower, may not completely breakthrough the saturated zone during the 30 year simulation. 14

15 Residue Breakthrough Breakthrough Observed Incomplete Breakthrough Koc = 100 Throughputs = 13.5 Koc = 5000 Throughputs =

16 Peak Residues as a Function of Rainfall Heterogeneity Annual Precipitation 16 1,070 PRZM-GW runs based on 5 runs at each location after selecting a random application date in March.

17 Weather Extender Create extended weather file by looping weather file to 100 years No Breakthrough Breakthrough now apparent Relevant concentration not well defined Concentration better defined 17

18 Summary PRZM-GW Tier II modeling approach represents the scenario of a vulnerable private drinking water well adjacent to treated fields. PRZM-GW incorporates factors not directly accounted for or that can be set in SCI- GROW, including meteorological, crop, biological, chemical, and crop management processes. A pre-release version of PRZM-GW was used to evaluate the sensitivity of expected key inputs for a number of scenarios. 18

19 Summary (cont d) Results indicated that proper characterization of maximum water capacity, which can vary both spatially and by depth, is KEY to making robust and relevant PRZM-GW predictions. For mobile compounds at any given field capacity, the influence of aerobic soil metabolism becomes inconsequential at around 500 days, but hydrolysis can be important to half-lives of roughly 10,000 days. Maximum water capacity significantly influences both the timing and magnitude of peak residues, as demonstrated by the bromide and ChemX example. 19

20 Summary (cont d) Some compounds may not completely breakthrough the saturated zone during the 30 year PRZM-GW simulation. Incomplete breakthrough can result from compounds with high retardation factors (such as high Koc values) and/or scenarios where rainfall is generally lower. USEPA has developed a weather extender to extend the standard PRZM metfile from 30 years to 100 years to allow for the examination of a complete concentrationtime profile. 20

21 PRZM-GW Further Research and Development Continued efforts to validate predictions with actual field data are key. Continued efforts using tracer data to corroborate PRZM-GW simulation of leaching for various soil textures. Continue to evaluate additional parameters not summarized in this presentation, including weather effects, irrigation, and cropping practices. Development, evaluation, review, and testing of standardized scenarios. PRZM-GW update at the next Exposure Modeling Public Meeting (EMPM) (9/20/2011). 21

22 Acknowledgements Appreciation is extended to the following individuals and organizations that contributed to the development of this presentation: Dr. David Gustafson, Monsanto Company Dr. Patrick Havens, Dow AgroSciences, LLC Mr. Jonathan Janis, Arysta LifeScience North America, LLC Dr. Natalia Peranginangin, Syngenta Crop Protection, LLC Dr. Ian van Wesenbeeck, Dow AgroSciences, LLC Dr. Dirk Young, USEPA Ms. Kacie Gehl, ARCADIS Acetochlor Registration Partnership Contact Information Timothy L. Negley 6723 Towpath Road Senior Environmental Scientist Syracuse, NY Risk Assessment and Ecological Sciences ARCADIS U.S., Inc. phone: