A Discussion with the FIFRA Scientific Advisory Panel Regarding the Terrestrial and Aquatic Level II Refined Risk Assessment Models
Title Page, Acknowledgements, Table of Contents, Tables and Figures, and Executive Summary
Meeting Scheduled for
March 30 - April 2, 2004
March 4, 2004
Environmental Fate and Effects Division
Office of Pesticide Programs
U.S. Environmental Protection Agency
On this Page
|Function||Name of Author / Reviewer|
|Terrestrial Team||Edward Fite (Lead)|
Timothy Barry, Office of Economy and Environment
Henry Nelson, Office of Science Policy and Coordination
|Aquatic Team||Donna Randall (Lead)|
Timothy Barry, Office of Economy and Environment
|Additional Supporting Team Members||Douglas Urban (Deceased)|
|Additional Contributors||Christine Hartless|
R. David Jones
1.Scientists identified are in the Environmental Fate and Effects Division, Office of Pesticide Programs unless noted otherwise.
Table of Contents
Tables and Figures
Table 3-1. Generic Species for Level II Assessments
Table 3-2. Transition probabilities for various combinations of the long-run, on-field probability, π1, and Q, the bias factor.
Table 3-3. List of input parameters for puddle model.
Table 3-4. A Summary of Mixing Zone Depths from the Literature
Table 3-5. Inhalation model parameters.
Table 3-6. Dermal model parameters
Table 4-1. Level I and II Aquatic Risk Assessment Exposure Components
Table 4-2. Level I and II Aquatic Risk Assessment Acute Effects Components
Table 4-3. Parameters and standard values used in the current EXAMS-based model. Parameters are defined in the EXAMS user manual
Table 4-4. Parameter values for the current EXAMS-based model in terms of the VVWM definitions of the parameters.
Table 4-5. VVWM parameter equivalents to EXAMS parameters(a)
Table 4-6. Input Parameters for Short-lived Chemical, ChemA, and Long-lived chemical, ChemB
Table 4-7. Summary of PRZM/EXAMS and PRZM/VVWM Field Area and Water Body Size Variables and Their Values
Table 4-8. Identified Disparities Between the PE4 Shell and the RRA Shell for Generating PRZM Input Files and Any Subsequent Modifications Made to Address Issues
Table 4-9. Identified Incompatibilities of Standard Crop Scenarios and Corresponding Meteorological Files with RRA Shell and Subsequent Modifications Made to Address Issues
Table 4-10. Identified Disparities Between VVWM and EXAMS Static Water Body Model While Holding the Volume of the Water Body in VVWM constant and Equal to the Water Body Volume in EXAMS (20,000 m3) and Subsequent Modification Made to Address Issues
Table 4-11. Water body volume for a given depth and set surface area of 1 ha (10,000 m2)
Table 4-12. Field drainage area (ha) for a given water body depth and water body volume.
Table 4-13. Water body surface area for a given initial depth and set water body volume (20,000 m3).
Table 4-14. Field drainage area for a given depth and set water body volume (20000 m3).
Table 4-15. Field drainage area and water body size parameter values for option (1) setting surface area, SA, to 1 ha, (2) setting volume capacity, VC, to 20,000 m3, and (3) setting field area, DA, to 10, 20, 40, and 100 ha for crop scenarios CA fruit and FL sugarcane.
Table 4-16. Field area and water body size parameter values derived by setting field area to 10 ha and setting initial and maximum depth to the minimum, average and maximum of the range in Figure 4-20b for crop scenarios CA fruit and FL sugarcane.
Table 4-17. Proposed field and water body size parameter values for all standard crop scenarios for the Level II Exposure Model.
Table 4-18. Summary of simulated water volume and depth conditions and runoff volume associated with proposed crop scenario-specific conditions.
Table 4-19. Runoff Curve Numbers for soil cover complexes and soil groups (Antecedent Runoff Condition II and Ia = 0.2)
Table 4-20. Relationship of CNI CNII and CNIII
Figure 3-1. Conceptual Model
Figure 3-2. Hypothetical examples of the avian bimodal feeding pattern.
Figure 3-3. Examples of the betapert density used in the bimodal feeding pattern model and the range of shapes that it can assume.
Figure 3-4. Examples of feeding fractions based on bimodal feeding model.
Figure 3-5. The two-state, first-order Markov chain model for avian persistence.
Figure 3-6. Region of valid on-field to on-field transitional probability as a function of long-run, on-field probability, π1
Figure 3-7. Effect of Q on the shape of the triangle distribution of P11.
Figure 3-8. Change in avian on-field persistence (persistence) as it depends on long-term on-field probability and on Q.
Figure 3-9. Figures (a), (b), and (c) above illustrate the main features of the Level II bimodal feeding model.
Figure 3-10. Depiction of the area that contributes to runoff to the puddle.
Figure 3-11. Depiction of the hydrologic processes controlling puddle volume
Figure 3-12. Depiction of the field mixing zone concept.
Figure 4-1. Flowchart of the Level II Two-Dimensional Monte Carlo Risk Assessment Model
Figure 4-2. Conceptualization of the Varying Volume Water Body Model
Figure 4-3. Solute holding capacity as a function of Koc for the current (EXAMS-based) model.
Figure 4-4. Relative solute holding capacity of individual components in the littoral zone.
Figure 4-5. Relative solute holding capacity of individual components in benthic region
Figure 4-6. Effective half-life of pesticide due to washout in a water body as currently parameterized (1 ha area by 2 m deep).
Figure 4-7. Effect of Henry's Law Coefficient and wind speed (measured at 6m) on effective volatilization half-life of aqueous phase.
Figure 4-8. Effect of Henry's Law Coefficient and temperature on effective volatilization half-life of aqueous phase for the current (EXAMS-based) model. The lack of temperature sensitivity is a result of not considering the effect of temperature on Henry's Law Coefficient. Wind speed = 1 m/s, MW=100.
Figure 4-9. Comparison the volatilization mechanisms of the proposed VVWM and EXAMS for conditions of pesticide solubility = 100 mg/L, MW = 100, vapor pressure = 0.1 torr, Koc = 1 mL/g, wind speed = 1 m/s, temperature = 25 °C, and an input mass of 0.02 kg to the littoral zone.
Figure 4-10. Comparison of proposed VVWM with EXAMS for the conditions of MW = 100, solubility = 100 mg/L, vapor pressure of 0.01 torr, aerobic half-life of 10 days, anaerobic half-life of 100 days, Koc of 100 mL/g, wind speed of 1 m/s, temperature of 25 °C, and arbitrarily selected PRZM input fluxes.
Figure 4-11. Flow chart of Level II exposure modeling approach compared to Level I
Figure 4-12. RRA shell launched PRZM output compared to PE4 shell launched PRZM output, (a) mass loading in runoff (RFLX) and (b) mass loading in erosion (EFLX).
Figure 4-13. RRA Level II Exposure model (PRZM/VVWM) output compared to PRZM/EXAMS exposure model where all transformation processes were set to zero except aerobic metabolism, which was set to an 80 day half-life.
Figure 4-14. RRA Level II Exposure model (PRZM/VVWM) output compared to PRZM/EXAMS exposure model where all transformation processes were set to zero except benthic metabolism, which was set to an 80 day half-life.
Figure 4-15. RRA Level II Exposure model (PRZM/VVWM) output compared to PRZM/EXAMS exposure model where all transformation processes were set to zero except hydrolysis, which was set to an 80 day half-life.
Figure 4-16. RRA Level II Exposure model (PRZM/VVWM) output compared to PRZM/EXAMS exposure model where all transformation processes were set to zero except photolysis, which was set to an 80 day half-life.
Figure 4-17. RRA Level II Exposure model (PRZM/VVWM) output compared to PRZM/EXAMS exposure model where all transformation processes were set to zero except volatilization, vapor pressure was set to 1e-4 torr.
Figure 4-18. EXAMS and VVWM simulated daily concentrations for (a) short-lived ChemA and (b) long-lived ChemB and water body volume for CA almond crop scenario.
Figure 4-19. EXAMS and VVWM simulated daily concentrations for (a) short-lived ChemA and (b) long-lived ChemB and water body volume for FL sugarcane crop scenario.
Figure 4-20. (a) USDA recommended minimum depth of water for a small, permanent surface water supply (e.g., pond) in the U.S. (USDA, 1997).
Figure 4-21. (a)USDA guidelines for estimating the size of drainage area (in acres) required for each acre-foot of storage in an embankment or pond (USDA, 1997).
Figure 4-22. The effect of setting water body surface area, initial volume or drainage area on (a) daily concentration and (b) water body volume for ChemB at a semi-arid site corresponding to the CA fruit crop scenario (metfile W93193, DA/VC = 50 acre/acre-ft, D0 = Dmax = 2.4 m).
Figure 4-23. The effect of varying drainage areas on (a) daily concentration and (b) water body volume for ChemB in semi-arid site corresponding to standard scenario CA fruit (metfile W93193, DA/VC ratio = 50 acre/acre-ft, and D0 = Dmax = 2.4 m.
Figure 4-24. The effect of using the minimum (min), average (avg), and maximum (max) initial water body depth (D0) on daily concentration for (a) ChemA and (b) ChemB and (c) water body volume in semi-arid site corresponding to standard scenario CA fruit, metfile w93193.
Figure 4-25. The effect of using the minimum (min), average (avg), and maximum (max) initial water body depth (D0) on daily concentration for (a) ChemA and (b) ChemB and (c) water body volume in humid site corresponding to standard scenario FL sugarcane, metfile w12844.
Figure 4-26. Comparison of VVWM using standard water body conditions and VVWM using crop scenario-specific water body conditions for (a) ChemA, (b) ChemB, and (c) volume for CA fruit scenario, met w93193.
Figure 4-27. Comparison of VVWM using the standard water body conditions, and VVWM using the crop scenario-specific water body conditions for (a) ChemA, (b) ChemB and (c) volume for FL sugarcane.
Figure 4-28. Plot of the curve numbers that represent the 10th and 90th percentile exceedence frequencies for 14 watersheds as presented by Hjelmfelt (1991).
Figure 4-29. PRZM's relationship of curve number to soil moisture. FC is field capacity, and WP is wilting point.
Figure 4-30. Curve numbers implied by the data of Wauchope et al. (1999) plotted as a function of soil moisture.
Figure 4-31. A comparison of curve numbers derived from field data with those generated by the probabilistic curve number and those generated by PRZM.
Figure 4-32. Curve numbers generated probabilistically and by PRZM shown as a function of soil moisture.
Figure 4-33. A depiction of the runoff/rainfall relationship of the measured field data and PRZM 3.12 simulated values.