A Comparison of Observed Pesticide Concentrations in Groundwater with Predictions by US Regulatory Models Used in Human Health Risk Assessments

Transcription

1 A Comparison of Observed Pesticide Concentrations in Groundwater with Predictions by US Regulatory Models Used in Human Health Risk Assessments Presented by: Michael Winchell 1, Tammara Estes 1, Scott Jackson 2 1. Stone Environmental, Inc. 2. BASF Corporation CLA/RISE Spring Conference April 23, 2015

2 Background An estimate of pesticide concentrations in groundwater used for drinking water is a key factor in the human health risk assessment process for the registration of pesticides by the U.S. Environmental Protection Agency. Regulatory models used to make pesticide concentration in groundwater predictions include: SCI-GROW ( ) PRZM-GW (2012 present) Since the introduction of PRZM-GW, the crop protection industry has found that predicted pesticide exposure in groundwater is occupying a larger portion of the risk cup in human health risk assessments.

3 Study Objectives Determine whether PRZM-GW is systematically predicting higher pesticide concentrations in groundwater than the SCI- GROW model. Determine whether a systematic bias exists between the PRZM- GW model predictions and observed concentrations in groundwater. Identify the causes for the PRZM-GW biases observed and propose approaches for reducing this bias.

4 Study design Topics for Discussion Model descriptions Modeling approach NAWQA data analysis Model comparison with NAWQA data approach Results and conclusions

5 Study Design Estimate groundwater concentrations for 66 pesticides using both SCI-GROW2.3 and PRZM-GW Extract all available pesticide monitoring data from groundwater wells in the USGS NAWQA database nationwide. Compare model predictions to high percentile NAWQA observed pesticide groundwater concentrations. Evaluate pesticide environmental fate characteristics to help explain observed model over- or under- prediction bias.

6 Model Descriptions: SCI-GROW 2.3 Empirical model based on observations from high vulnerability groundwater monitoring studies. Estimates chronic pesticide concentrations in groundwater used for drinking water. Developed in response to Food Quality Protection Act of Used as Tier I groundwater tool from 1997 to Allows for pesticide application and environmental fate inputs.

7 Model Descriptions: PRZM-GW 1.07 PRZM simulates leaching through the unsaturated zone. Since early 2012, used in Tier I and refined assessments. Currently, 6 high vulnerability standard scenarios Florida, Central Ridge: Citrus Florida, Jacksonville: Potato Georgia, Southwest: Peanuts Delaware, Delmarva Peninsula: Sweet Corn North Carolina, Coastal Region: Cotton Wisconsin, Central Sands: Corn 30-year geographical specific weather simulations, with extension to 100 years. More flexible input options for e-fate, applications, crop, soil, and aquifer characteristics. Post-breakthrough average concentration used as chronic EEC.

8 Model Descriptions: PRZM-GW 1.07, Conceptual Model Conceptual Model

9 Model Descriptions: PRZM-GW 1.07, Soil Degradation Degradation rate decreases (half-life increases) with depth in the unsaturated zone per table below. Layer Depth (cm) Half-Life Multiplier 1 0 to to to to to to to 900 No Degradation to 1000 No Degradation If pesticide has non-stable hydrolysis, soil half-life is reduced until it reaches the hydrolysis half-life, with only hydrolysis below 100 cm.

10 Model Descriptions: PRZM-GW 1.07, Effective Soil Half-Life The effective soil half life in the top 100 cm is 3.25 times higher than the conservative input soil half-life. Input Aerobic Soil Half- Life (days) Average Simulated Half- Life in top 100 cm (days) ,186 For pesticides with a longer half-life, this scaling effect decreases the amount of degradation significantly.

11 Modeling Approach A total of 66 pesticides with a range of e-fate characteristics were simulated with both SCI-GROW and PRZM-GW. Parameter Min Max Median Annual App. Rate (lb/ac) Koc (ml/g) , Aerobic Soil Half-Life (days) 3 2, Hydrolysis Half-Life (days) Application inputs assumed highest label rates. Environmental fate inputs were selected from EPA s PRZM-GW validation document; in 8 cases, soil half-lives independently set following EPA guidance. All 6 PRZM-GW standard scenarios were run for each pesticide. PRZM-GW simulations were run with the standard 30-year weather time series, assuming pesticide applications every year.

12 NAWQA Data Analysis: Data Source All NAWQA pesticide groundwater data from for the US, downloaded 11/24/2014. The dataset was filtered to exclude samples from sites that are not a drinking water source. These excluded types were: Water-supply treatment plant Wastewater-treatment plant Test hole not completed as a well Soil hole Groundwater drain Unsaturated zone Spring Samples analyzed by the enzyme-linked immunosorbent assay analytical method (ELISA) were excluded due to the low accuracy/high uncertainty associated with this method.

13 NAWQA Data Analysis: Data Summary Over 500,000 groundwater samples were included in this analysis of 66 pesticides. Parameter Min Max Median Total (All Pesticides) Number of Samples per Pesticide ,075 8, ,441 Number of Detections per Pesticide 0 4, ,751 % Detections per Pesticide Detections ranged from 0% to 29.3% of samples taken depending on the pesticide. Detection rates for the majority of pesticides were low, with a median detection rate of 0.3%.

14 Model Simulation Comparison with NAWQA Data Approach SCI-GROW and PRZM-GW predicted chronic groundwater concentrations compared with NAWQA daily concentration sample distributions. For PRZM-GW, maximum value of all 6 scenarios was chosen for all comparisons. National Comparisons: All pesticides combined, assessed model agreement, under-, and overprediction compared to NAWQA. Compared against NAWQA max, 99.9th %-ile, and 99th %-ile concentrations. Pesticide e-fate Evaluation: Compared pesticides independently based on sorption and degradation characteristics Grouped soil half-life into 3 ranges Grouped Koc into 3 ranges Distinguished between stable and non-stable hydrolysis

15 Results: National Comparison, NAWQA Maximum When breakthrough occurs, PRZM-GW concentrations are generally higher than SCI-GROW, and often significantly higher than maximum NAWQA.

16 Results: National Comparison, NAWQA 99.9 th %-ile SCI-GROW conservatively over-predicts NAWQA 99.9 th percentile concentrations for most pesticides, while PRZM-GW concentrations are often orders of magnitude higher.

17 Results: National Comparison with NAWQA, Summary Agreement between model simulation and NAWQA was assigned if the difference was less than 1 ppb. % Under- and Over- Prediction of NAWQA Observed Groundwater Concentrations NAWQA Value SCI-GROW2.3 % Under- Prediction % Over- Prediction PRZM-GW Year Simulation % Under- Prediction % Over-Prediction Maximum th Percentile th Percentile The under-prediction by PRZM-GW are dominated by cases where breakthrough does not occur. PRZM-GW over-predicts the maximum NAWQA value nearly 2 times more frequently than SCI-GROW.

18 Results: National Comparison with NAWQA Maximum, Summary Large over-predictions (of >100 ppb) are more common for PRZM-GW (45.5% of pesticide) compared to SCI-GROW (13.6% of pesticides). Under-prediction by PRZM-GW occurs only for pesticides with lower (< 10 ppb) differences between observed maximums. % Under- and Over- Prediction of NAWQA Maximum Observed Groundwater Concentrations SCI-GROW2.3 PRZM-GW Year Simulation Difference % Under- % Under- % Over-Prediction Prediction Prediction % Over-Prediction 1-10 ppb ppb ppb ppb ppb > 1000 ppb Total

19 Results: Effects of K OC on NAWQA Maximum Comparisons, Low K OC Predicted groundwater concentrations for pesticides with low K OC s are generally higher for PRZM-GW compared to SCI- GROW. Both models tend to predict higher than maximum observed.

20 Results: Effects of K OC on NAWQA Maximum Comparisons, Moderate K OC SCI-GROW predictions are generally closer to observed maximum NAWQA compared to PRZM-GW predictions. PRZM-GW predictions are generally higher than SCI-GROW.

21 Results: Effects of K OC on NAWQA Maximum Comparisons, High K OC PRZM-GW does not predict breakthrough for pesticides with K OC greater than 1000 ml/g. SCI-GROW predictions are higher than PRZM-GW.

22 Results: Effects of Half-Life on NAWQA Maximum Comparisons, Low Half-Life When breakthrough occurs, PRZM-GW has a slight tendency to over-predict maximum NAWQA concentrations. SCI-GROW slightly under-predicts maximum NAWQA.

23 Results: Effects of Half-Life on NAWQA Maximum Comparisons, Moderate Half-Life The over-prediction by PRZM-GW is more prevalent than was observed for the low half-life pesticides. SCI-GROW predictions are closer to maximum NAWQA.

24 Results: Effects of Half-Life on NAWQA Maximum Comparisons, High Half-Life As half-life increases, PRZM-GW over-prediction increases. SCI-GROW predictions do not show as much of this trend.

25 Results: Effects of Hydrolysis on NAWQA Maximum Comparisons, Non-Stable Hydrolysis PRZM-GW predictions show minimal bias compared to maximum NAWQA concentrations for pesticides with hydrolysis degradation.

26 Results: Effects of Hydrolysis on NAWQA Maximum Comparisons, Stable Hydrolysis For pesticides with breakthrough, PRZM-GW predictions show a high bias compared to maximum NAWQA concentrations and SCI-GROW predictions for pesticides with stable hydrolysis.

27 Conclusions PRZM-GW predictions of pesticide concentrations in groundwater are generally much higher than SCI-GROW. Exceptions include: Pesticides with high Koc (> 1000 ml/g) Pesticides with short soil aerobic half-life (< 10 days), often coinciding with active hydrolysis When compared to a large nationwide monitoring network, PRZM-GW predicted chronic pesticide concentrations are often (46% of pesticides) significantly higher (> 100 ppb) than the maximum observed daily concentration. The pesticides that are most significantly over-predicted have: Higher half-life, > 90 days Stable hydrolysis

28 Conclusions, Continued The high bias in PRZM-GW predictions is likely due to: The 3.25x reduction factor of degradation rate in the top 100 cm, on top of a conservative estimate soil aerobic half-life. No degradation below the 100 cm for pesticides with stable hydrolysis. Pesticides with K OC > 1000 ml/g, are not predicted to breakthrough to groundwater by PRZM-GW, yet detections exist in NAWQA. While not fully understood, these probably do not represent residues from leaching through the soil profile. Possible explanations include: Interaction with surface water, or preferential flow in unsaturated zone Faulty well seals, sample contamination, or analytical errors PRZM-GW offers much greater functionality and flexibility than SCI-GROW, and could be re-parameterized to predict more realistic, yet protective, pesticide groundwater concentrations.