# Evaluating Atrazine Drinking Water Monitoring Data For Use in Human Health Assessments Environmental Fate and Effects Division USEPA Office of Pesticide Programs

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# A Team Effort

• Nelson Thurman - Team Leader • Mary Frankenberry – Comparative statistical 

# analysis of monitoring frequency • Dr. James Hetrick and Dr. James Wolf – Geostatistical analysis and stochastic applications 

# • Dr. Stephen Wente – Watershed mass balance model • Dr. R. David Jones, Donna Randall, and Mark 

# Corbin – technical peer review

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# Nature of Pesticide Occurrence in



# Water

• Complex spatial patterns
• Related to pesticide use, rainfall patterns, soil/hydrologic 

# vulnerabilities 

# • Complex temporal patterns
• Short-term variability: Often low or no detectable levels with short-term (days to weeks to months) spikes related to timing of pesticide use and rainfall
• Year-to -year variation related to cropping patterns, pesticide usage, rainfall patterns, climate change
• Often, pesticide concentrations measured on one day are related to the exposures measured on the previous and following days 3

# Nature of Pesticide Occurrence in 

# Water

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# Intensively Sampled Data Sets Used in



# Evaluating Methods Heidelberg U. NCWQR data for OH: Maumee 

# River, sampled in 1995 17,000 km 2 

# catchment area

0 2 4 6 8 10 12 14 16

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189 Atrazine Concentration, ug/L Maumee River, 1995 

# Syngenta atrazine ecological exposure 

# monitoring: MO - 01, 2007 30 km 2 

# catchment area

0 10 20 30 40 50 60 70 80 90 100

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189 Atrazine Concentration, ug/L MO-01, 2007



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# Characterizing Uncertainty in Exposure 

# Estimates Based on Sampling Frequency

• Approach: bootstrapping simulations of sampling frequencies to develop simple multiplicative factors 

# for exposure estimates
• Randomly sampled the daily time series from 4 - , 7 - , 14 - , 28 - day windows
• 10,000 simulations of the different sampling intervals 

# • Estimated 1 - day peak and maximum 4 - , 7 - , 14 - , 28 - , and 90 - day running averages for each of 10,000 simulations of different sampling intervals
• Compared the true maximum concentrations against the 5 th 

# percentile maximums of the distributions from each sampling interval 6

# Preliminary Multiplicative Bias Factors 

# Based on Different Sampling Intervals Sample Interval 

## 1 -day

max. 

## 4-day

avg. 

## 14-day

avg. 

## 28-day

avg. 

## 90-day

avg. 4 days 4 3 2 2 1.5 7 days 5 3 2 2 2 14 days 15 10 6 4 4 28 days 58 37 18 11 7

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## Sample 

## Interval 1 -day



## max. 4-day



## avg. 14-day



## avg. 28-day



## avg. 90-day



## avg. 4 days 2 1.4 1.2 1.1 1.1 7 days 2 2 1.4 1.2 1.2 

## 14 days 3 2.5 2 1.4 1.3 28 days 3 3 2 1.5 1.5



# AEEMP 2007 MO - 01

NCWQR 1995 Maumee River

# Analysis Strategy for 

# Modeling Pesticide Time Series 

# from Monitoring Data MERGE MONITORING DATA

ARRANGE FLOW PERCENTILES WITH MERGED DATA PERCENTILES DETERMINE APPROPRIATE POINT ESTIMATION PROCEDURE > 15 DATA POINTS IN TIME SERIES?

PROCEED WITH 1- DIMENSIONAL ORDINARY KRIGING INFILL TIME SERIES WITH MODEL ESTIMATED VALUES
YES NO

USE WARP ESTIMATES IN TIME SERIES PROCEED WITH VARIOGRAM ANALYSIS USE COVARIATE TO ESTIMATE CONCENTRATION

STOCHASTIC SIMULATION OF EQUALLY PROBABLE REALIZATIONS OF TIME SERIES

# Focusing Modeling Analysis on Runoff 

# Period

• Geostatistical methods require stationarity
• assumes equal probability of occurrence regardless of location/time 

# • Stationarity may not hold true for annual distributions with seasonal trends
• Addressed by focusing on runoff season 9 

## Analyses indicate stationarity may apply during the runoff season for atrazine and other broad - use herbicides such as acetochlor, but not for insecticides such as chlorpyrifos

# Variograms Analysis

Data Sampling

Frequency N Nugget (μg/L2) Model Sill (μg/L 2) Range

(Days) Maumee95 Actual Data 100 0.10 Spherical 0.97 16 4 - day 25 0.00 Spherical 3.70 52 7 - day 15 0.00 Spherical 3.20 61 14 - day 8 0.30 Gaussian 3.20 80 28 - day 4 2.2 no temporal correlation MO - 01 2007 Complete Time Series 4 - day 25 0.00 Spherical 3.60 27 7 – day 15 0.00 Spherical 3.80 27 14 –day 8 0.50 Spherical 3.80 32 28 - day 4 3.80 no temporal correlatio n MO - 01 2007 Grab samples 4 - day 2 27 0.2 Spherical 4.0 27 7 – day3 15 0.2 Gaussian 2.9 25 8 – day2 15 0.5 Spherical 3.5 33 14 –day3 8 0.3 Spherical 6.0 30 16 - day

2 7 0.1 Linear 6.0 41 16 - day2,4 6 0.2 Gaussian 3.6 45
1 Grab samples collected every 4 days from 04/01/07 to 07/19/07; to additional samples were collected on 8/24/07 and 8/28/07 and are not included. 2 Grab samples: 4, 8, and 16 day sampling interval. 3 Estimated from kriged samples. 4 Highest data value removed to assess impact on variogram model

# Variograms: 1995 Maumee River



# Atrazine (a) 4 - day sampling intervals (b) 7 - day sampling intervals Variance (μg/L) 2 Variance (μg/L) 2

# Realizations of Atrazine Time Series for



# 1995 Maumee Data (a) 4 - day intervals

(c) 14 - day intervals (d) 28 - day intervals (b) 7 - day intervals

# Variograms: 2007 MO-01 (a) 4 - day sampling intervals (b) 7 - day sampling intervals Variance (μg/L) 2 Variance (μg/L) 2

# Realizations of Atrazine Time Series for



# 2007 MO-01 Data (a) 4 - day intervals

(c) 14 - day intervals (d) 28 - day intervals (b) 7 - day intervals

# Estimates for 1995 Maumee River

Sampled at Weekly Intervals

# Estimates for 2007 MO-01 Sampled at



# Weekly Intervals

# What Happens If 

# We Have Monitoring with 

# Sampling Intervals > 7 Days? MERGE MONITORING DATA

ARRANGE FLOW PERCENTILES WITH MERGED DATA PERCENTILES DETERMINE APPROPRIATE POINT ESTIMATION PROCEDURE > 15 DATA POINTS IN TIME SERIES?

PROCEED WITH 1- DIMENSIONAL ORDINARY KRIGING INFILL TIME SERIES WITH MODEL ESTIMATED VALUES
YES NO

USE WARP ESTIMATES IN TIME SERIES PROCEED WITH VARIOGRAM ANALYSIS USE COVARIATE TO ESTIMATE CONCENTRATION

STOCHASTIC SIMULATION OF EQUALLY PROBABLE REALIZATIONS OF TIME SERIES

# Illustration of Combined WARP and 

# Kriging Approach for Maumee River Data Variogram generated from 28 - day sampling intervals of 1995 Maumee R. with 

# WARP parameters from OH - 03 monitoring site Error analysis from 10 realizations of estimated time series from the 28 - day 

# monitoring intervals and WARP modeling

18 Variance (μg/L) 2

# What Happens If 

# We Have Monitoring with 

# Sampling Intervals > 7 Days? MERGE MONITORING DATA

ARRANGE FLOW PERCENTILES WITH MERGED DATA PERCENTILES DETERMINE APPROPRIATE POINT ESTIMATION PROCEDURE > 15 DATA POINTS IN TIME SERIES?

PROCEED WITH 1- DIMENSIONAL ORDINARY KRIGING INFILL TIME SERIES WITH MODEL ESTIMATED VALUES
YES NO

USE WARP ESTIMATES IN TIME SERIES PROCEED WITH VARIOGRAM ANALYSIS USE COVARIATE TO ESTIMATE CONCENTRATION

STOCHASTIC SIMULATION OF EQUALLY PROBABLE REALIZATIONS OF TIME SERIES

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# Illustration of Covariate ( Acetochlor

) to 

# Estimate Daily Time Series

Sampling Frequency N Intercept Slope R 2 F Test Probability Complete Time Series 100 1.90 (0.32) 2.03

(0.28) 0.3555 5.94E - 11* 4- day 25 1.46 (0.66) 2.846243

(0.60) 0.4906 9.66E - 5* 7 - day 15 1.87 (0.91) 2.26

(0.80) 0.3785 0.0146* 14 - day 8 3.11 (1.36) 0.25

(1.42) 0.0051 0.866 28 - day 4 2.50 (7.06) - 0.12

(13.66) 3.83E - 5 0.99 ( ) = Standard error * - Significant (P<0.05)

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# Illustration of Covariate ( Acetochlor

) to 

# Estimate DailyTime Series

# Key Findings

• The bias factor for estimating pesticide concentrations from monitoring data is dependent on the exposure interval and sampling interval.


## • Bias factor will decrease with an increase in exposure interval.
• Bias factor will increase with an increase in sample interval.
• Temporal correlation was observed for corn herbicide at 4 - , 7 - , 

## and 14 - day sampling intervals.
• Variograms show substantial autocorrelation at lag times up to 60 days
• Temporal correlation is dependent on pesticide and sampling interval


## • Conditional stochastic simulation of time series of 4 - and 7 - day interval monitoring data predicted reasonable uncertainty bounds around actual time series of daily, 4 - day average,14 - day average, and 28 - day average concentration time series. 22

# Key Findings

• Regression models can be used to simulate a time series of daily data.
• Dependent on availability and statistical relationship of covariate data
• Hybrid approach of merging WARP data with existing data is dependent on numerous factors
• Ability to realign data into temporal sequence


## • Ability to find temporal correlation



# • Waiting for SAP written comments

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