Evaluating Atrazine Drinking 

Water Monitoring Data For Use in 

Human Health Assessments

Environmental Fate and Effects Division

USEPA Office of Pesticide Programs

1
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

2
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

4

Chlorpyifos (Low Frequency)

Julian Days

0 100 200 300

Concentration (ug/L)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Acetochlor (Moderate Frequency)

Julian Days

0 100 200 300

Concentration (ug/L)

0

1

2

3

4

5

Atrazine (High Frequency)

Julian Days

0 100 200 300

Concentration (ug/L)

0

2

4

6

8

10

12

14

16
Intensively Sampled Data Sets Used in 

Evaluating Methods

Heidelberg U. NCWQR 

data for OH: Maumee 

River, sampled in 1995

17,000 km2 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 km2 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

5
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

7

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

Julian Day

0 100 200 300 400

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

Base-flow Base-flow

Runoff 

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/L2)

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 correlation

MO-01 2007

Grab samples

4-day2 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- day2 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

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

(a) 4-day intervals

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

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

(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
Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data 

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD) 

Actual Data

Realizations of Atrazine Time Series for 

2007 MO-01 Data

(a) 4-day intervals

(c) 14-day intervals

(d) 28-day intervals

Julian Day

100 120 140 160 180

Atrazine Concentration (ug/L)

0

20

40

60

80

100

Estimated Mean (SD)

Actual Data 

(b) 7-day intervals
Estimates for 1995 Maumee River 

Sampled at Weekly Intervals

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

Julian Day

Daily Time Series Sim 95th %ile

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 4-Day Averages

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 14-Day Averages

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 28-Day Averages

Sim 5th %ile

Actual
Estimates for 2007 MO-01 Sampled at 

Weekly Intervals

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

Julian Day

Daily Time Series Sim 95th %ile

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 4-Day Averages

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 14-Day Averages

Sim 5th %ile

Actual

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

Julian Day

Sim 95th %ile 28-Day Averages

Sim 5th %ile

Actual
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

Julian Day

120 140 160 180 200

Atrazine Concentration (ug/L)

0

5

10

15

20

25

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
20

Illustration of Covariate (Acetochlor) to 

Estimate Daily Time Series

Sampling 

Frequency N Intercept Slope R2

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)
21

Illustration of Covariate (Acetochlor) to 

Estimate DailyTime Series

Julian Day

100 120 140 160 180 200

Atrazine Concentration (ug/L)

0

2

4

6

8

10

12

14

16

 Daily Samples 

4-day Samples 

 7-day samples 

Actual 
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

23