1. 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

2. Outline
Background
Project Summary
Collaboration
Questions

3. Background
EPA/OPP’s Aquatic Exposure

4. 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)

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Project Summary

12. 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

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

14. 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

15. 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

16. 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

17. 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)

18. Collaboration

19. WSDA Pesticide Monitoring Regions
According to Elsner et al. 2010, Western Washington averages about cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%).
MN = 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.

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

21. 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

22. WSDA – Pesticides of Focus
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

23. 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 cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%).
MN = 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

24. 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 cm per year, compared to an annual average in Eastern Washington slightly above 31.0 cm (about 25%).
MN = cm average precip (Northwest – Southeast)
Atrazine
Carbaryl
Chlorpyrifos
Imidacloprid
Atrazine
Carbaryl
Chlorpyrifos
Imidacloprid

25. 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

26. Contact Information
EPA/EFED
WSDA/NRAS
Rochelle Bohaty Senior Chemist Dana Spatz Branch Chief
Matt Bischof
Aquatic Biologist
Gary Bahr
Section Manager

27. Questions