Quantification of Surface Water Monitoring Data Using an Integrative Spatial and Temporal Analysis Approach A Collaborative Effort September 18, 2017 Rochelle F. H. Bohaty, PhD Senior Scientist, EPA/OPP/EFED Matthew Bischof Natural Resource Scientist, WSDA/NRAS

Outline Background Project Summary Collaboration Questions

Background EPA/OPP’s Aquatic Exposure

OPP’s Measure of Exposure: Goal To derive reasonable upper bound pesticide concentrations Monitoring Data Modeling Data Direct measure Actual pesticide use for specific site Often limited in time, and may be representative of many sites Tends to underestimate frequency of occurrence and peak exposure Direct estimate Maximum or typical pesticide use Simulations over long time, based on a few standard vulnerable sites Daily concentrations and inputs can be adjusted to be more or less conservative Monitoring data can elucidate what is happening under current or past use practices (not necessarily current maximum label rates) and under specific conditions (may not be predictive of concentrations in other areas)

Monitoring Data Complex spatial patterns Complex temporal patterns Sources include federal, state, academic, and other sources All known monitoring data are considered in drinking water (human health) and ecological exposure assessments Qualitatively and quantitatively depending on the nature Data are analyzed and characterized based on study design and contextual information (i.e., ancillary data) Objective, design (strategy, frequency), pesticide use, pesticide fate properties, site locations, conditions, and analytical methods (preparation, LOD/LOQ) Generally monitoring data are NOT able to be used quantitatively in risk assessments; however, often it is used for characterization in a weight of evidence approach and to ground-truth modeling estimates Assess successful mitigation strategies Complex spatial patterns Related to pesticide use, rainfall patterns, soil/hydrologic vulnerabilities Complex temporal patterns Site vulnerability Often low or non-detectable levels Program design Infrequent sampling Year-to-year variation related to cropping patterns, pesticide usage, rainfall patterns, climate change Temporal autocorrelation

Challenges: Quantification of Pesticide Concentrations Based on Available Monitoring Data Quality Assurance/Control in Data Reporting Concentration unit errors (ng/L reported as µg/L) Merging Data Sets Duplicate sampling sites Non-Detections (in some cases a high number) Difficult to determine reason(s) for non-detections High LOD/LOQ Pesticide not used in watershed Sample frequency Temporal Sampling Low sampling frequency Biased low Low number of years of sampling at a site < 10 years Spatial Sampling Defining a target population Each pesticide has unique use patterns

Addressing Temporal Sampling Issues Bias Factors (BFs) Is a multiplicative factor used to adjust monitoring occurrence concentration (observed concentration * sampling bias factor) to ensure that, for example, a certain percentage of time the BF-adjusted value is equal to or higher than the true concentration Confidence intervalBoot-strap sampling process to determine develop BFs Lessons Learned: Requires daily or near daily monitoring data BFs are directly correlated with sampling interval BFs appear to be dependent on fate properties Extrapolation of BFs is difficult around the measured concentration value

Addressing Temporal Sampling Issues Explored different methods to develop sampling bias factors Hot Deck Imputation Kriging and Gaussian Sequential Stochastic Simulation USGS seawaveQ Regression Modeling Confidence intervalBoot-strap sampling process to determine develop BFs around the measured concentration value Lessons Learned: Requires daily or near daily monitoring data BFs are directly correlated with sampling interval BFs appear to be dependent on fate properties Extrapolation of BFs is difficult

Addressing Spatial Sampling Issues Evaluate relative vulnerability of monitoring sites Site vulnerability factors USGS WARP model Application intensity in watershed Water restrictive soil layer in surface 25 cm Precipitation in May and June

Summary ALL “available” monitoring data are considered; however, the extent of the analysis of monitoring data currently varies significantly. Quantification of monitoring data is a multivariate problem in a joint spatial and temporal domain There is a large effort to develop methods/tools that will allow the integration of pesticide water monitoring data quantitatively in risk assessment

Project Summary

USGS seawaveQ Regression Modeling Regression model for developing chemographs Relates measured pesticide concentrations with daily streamflow, seasonal wave, long-term trends Bounds of the model ≥ 12 samples/year with 3 years of data ≤ 75% censoring rate Produces multiple, equally-probable simulations Case-study national level: chlorpyrifos, atrazine, carbaryl, and fipronil, Site-specific: chlorpyrifos, carbaryl, imidacloprid, thiamethoxam, dinotefuran, oxamyl, atrazine, simazine, and metolachlor

Bias Factor Estimation Procedure for Daily Concentrations 7, 14, 21, and 28 days

Consideration of Watershed Characteristics Explore impact of 40 watershed properties to develop regression equations Examples USLE K (soil erodibility) factor Base Flow Index Canal Density Agricultural area with slopes >20% Urban Area Impervious Area

Integration Consider spatial and temporal distributions together Provide recommendation for improving risk assessment methodologies as well as monitoring program design to increase utility in exposure assessments 90th percentile site Sites Years 1 in 10 year Monitoring data can have inadequate spatial and temporal coverage to represent upper end exposure concentrations

Team Members EPA USGS WSDA Rochelle Bohaty Christine Hartless James “Trip” Hook Charles Peck Sarah Hafner Dana Spatz (management lead) USGS Skip Vecchia WSDA Matthew Bischof

Collaboration OPP is working with USGS and Washington State Department of Agriculture Leverage resources Build proficiency Explore challenges of conducting surface water monitoring Explore, propose new risk assessment and monitoring program design strategies Utilize available monitoring data Explore site-specific examples (case studies)

Collaboration

WSDA Pesticide Monitoring Regions According to Elsner et al. 2010, Western Washington averages about 125.0 cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%). MN = 46-81 cm average precip (Northwest – Southeast) Analysis for pesticides is expensive, and often states have limited staff for collecting pesticides, therefore adequately covering the state is a logistical challenge. States with funding do their best to target “vulnerable” areas (areas that have a relatively higher probability of detecting pesticides). We would like to expand our monitoring program to cover more of the state, however, to do so we would have to redistribute funds (e.g. drop sampling site to pick up another, or take sampling events from one site and move to another site), we also do not want reduce the usability of our data for long term trend analysis or for EPA-OPP to use in their assessments.

WSDA Surface Water Monitoring Program Summary Sub-Basins Monitored Example of monitoring locations at the bottom of a subbasin – capture the entire watershed

WSDA Surface Water Monitoring Program Summary Cont. Existing Sampling Program Selected watersheds Ag cropping patterns & urban/mixed & salmonid co-occurrence Sample every week (March – August/October) Conduct education & outreach New Tiered Sampling Program Tier 1 – Sample every other week through application season 3 yrs – identify trends (Mar-August/Oct) Monitor additional yrs if trend suggests approaching LOC Move to Tier 2 if ≥1 detections exceed LOC Tier 2 – sample weekly during periods of expected exceedances Tier 3 – targeted monitoring – effectiveness of BMP Like most programs – resources are limited Trying to do more with what we have without sacrificing the quality of our data, and also identify ways to improve quality and usability for EPA-OPP

WSDA – Pesticides of Focus 2014-2016 Pesticides of Interest Case-study seawaveQ analysis Insecticides: Chlorpyrifos, Bifenthrin, Malathion, Pyridaben, Methiocarb, Diazinon, Etoxazole Herbicides: Metolachlor, Simazine, Chlorpropham, Diuron Fungicides: Azoxystrobin, Captan Insecticides: Chlorpyrifos, Carbaryl, Imidacloprid, Thiamethoxam, Dinotefuran, Oxamyl Herbicides: Atrazine, Simazine, Metolachlor Fungicides: None -left side – pesticides detected at WSDA-LOC. Safety factor is applied to WSDA data to ensure criteria is adequately protective of aquatic life = potential WQ issues are detected early on Not all pesticides have enough detections for seawaveQ analysis Need at least 3 years of data At least 12 samples collected/yr At least 25% of the samples having detectable pesticide concentrations -right side – chemicals identified for case-study, have enough detections for seawaveQ analysis - not all have been detected at a LOC, but have been detected consistently enough to ask “are we seeing an increasing or decreasing trend?” - bold chemicals have been detected at a WSDA-LOC - underlined-bold chemicals have been detected at or above an EPA LOC

Western Washington – case study sites Rainy side of state – Big Ditch – Tidally influenced, no continuous flow data Bertrand Creek – gaged stream = continuous flow/stage Precipitation & other weather data available for both sites According to Elsner et al. 2010, Western Washington averages about 125.0 cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%). MN = 46-81 cm average precip (Northwest – Southeast) Precipitation: use as a surrogate for flow data – assuming relationship between precipitation events (run-off) and chemical detections in stream Temperature or wind speed: Could show there is a relationship between pesticide application events and air temperature or wind speed. Pesticide applications occur during specific weather conditions. Metolachlor Imidacloprid Thiamethoxam Dinotefuran Metolachlor Imidacloprid Thiamethoxam Simazine

Eastern Washington – case study sites Dry side of state (receives ~25% of precip compaired to western WA. Sulphur Creek WW – gaged site = continuous flow/stage Marion Drain – no flow data available, USGS Granger Drain monitoring site located other side of Yakima River, allows for comparison. Granger Drain – USGS site (gaged, monitored for pesticides) - EPA is including in their Bias Factor analysis Precipitation & other weather data available for all sites Sites represent typical irrigated agriculture in eastern WA According to Elsner et al. 2010, Western Washington averages about 125.0 cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%). MN = 46-81 cm average precip (Northwest – Southeast) Atrazine Carbaryl Chlorpyrifos Imidacloprid Atrazine Carbaryl Chlorpyrifos Imidacloprid

Goals of State Participation Understand how EPA-OPP analyzes state data Learn seawaveQ – Identify statistical relationships in our data Long-term pesticide trends (increasing/decreasing) Increasing trend suggesting concentrations may approach LOC? Relate to – ESA listed Salmonid species Outreach to growers Extended monitoring Decreasing trend BMP is working? Outreach is working – growers adjusting their practices Decreasing trend in one pesticide = increasing trend in another? Phase out Change in pest pressure Increasing trend? Outreach to growers to address before it may become a problem – growers can maintain the use of the chemical in their toolbox

Contact Information EPA/EFED WSDA/NRAS Rochelle Bohaty Senior Chemist 703.305.6381 bohaty.rochelle@epa.gov Dana Spatz Branch Chief 703.305.6063 spatz.dana@epa.gov Matt Bischof Aquatic Biologist 509.895.9338 mbischof@agr.wa.gov Gary Bahr Section Manager 360.902.1936 gbahr@agr.wa.gov

Questions