Evaluating Atrazine Drinkin

Water Monitoring Data For Us

Human Health AssessmentHuman Health Assessment

Presentation to the FIFRA Scientific Advisory P

July 26, 2011

Nelson Thurman Nelson Thurman

Environmental Fate and Effects Division

USEPA Office of Pesticide Programs USEPA Office of Pesticide Programs
A Team Effort

• Mary Frankenberry – Comparative statis

analysis of monitoring frequency analysis of monitoring frequency

• Dr. James Hetrick and Dr. James Wolf –

Geostatistical analysis and stochastic 

applications

• Dr. Stephen Wente – Watershed mass 

bl dl a ance model

• Dr. R. David Jones, Donna Randall, and 

Corbin – technical peer review technical peer review
Topics

• Recap of monitoring issues and feedb

f A il d S t b 2010 SAP from April and September 2010 SAPs

• Addressing monitoring uncertainties r

to variability of pesticide concentratiowater:

• Day-to-day patterns (short-term tempora

variability)

• Year-to-year patterns (long-term tempor

variability)

• Spatial patterns
Monitoring Issues for SA

Consideration

• 2010 SAPs: Given a possible change in th

exposure duration for the human health leconcern, how well does the existing monitcharacterize shorter durations of exposurecharacterize shorter durations of exposure

• USEPA has evaluated methods to charact

the uncertainty in exposure estimates from the uncertainty in exposure estimates from

monitoring data collected at different samp

frequencies.

• Provide confidence bounds based on sampling 

frequency

• Address daily/seasonal, year-to-year, spatial v
Nature of Pesticide Occurre

Water

• Complex spatial patterns

• R l t d t ti id i f ll tt il/h 

R

ela

t

e

d

to pesti

cide use, rain

f

all patterns, soil/h

vulnerabilities

• Complex temporal patterns Complex temporal patterns

• Short-term variability: Often low or no detectab

with short-term (days to weeks to months) spikto timing of pesticide use and rainfall

• Year-to-year variation related to cropping patte

pesticide usage rainfall patterns climate chanpesticide usage, rainfall patterns, climate chan

• Often, pesticide concentrations measured on o

are related to the exposures measured on the and following days
Interpreting Between Sam

Events

MO-01, 2007

100

MO-01 Measu4-day avg

Measured 

daily time 

6080

L

series

Atrazine, ug/ 40 MO-01, 2007: 7-Day Sample Interval #6 100 MO-01 Measured Measured 4-day avg 7-da interval 6

linear 7-da interval 64 d li 7 d i t 6

MO-01, 2007: 7-Day Sample Interva

100

20

6080

ne, ug/L

4-day avg, linear 7-da int 6

6080

ne, ug/L

0

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

Date

20

Atrazi 40n

20

Atrazi 40n

04/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

Date

0

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 

Date
Key Recommendations of Pre

SAPs Re

gardin

g Monitorin

g A

Key Recommendations of Pre

SAPs Re

gg g

ardin

g Monitorin

g A

• Apr. 2010: To evaluate monitoring strategie

exposure estimation methods the USEPA exposure estimation methods, the USEPA 

• The toxicological exposure duration of concern• Intensive monitoring for representative range o

Intensive monitoring for representative range o

• Methods that can predict values that may be g

than those sampled

• Sept. 2010: Combining WARP with a deter

model, such as PRZM or SEAWAVE-Q off

most promising approach” with sparse mon

• It may be easier to use WARP in combination w

statistical approach such as krigingstatistical approach such as kriging
Topics

• Recap of monitoring issues and 

recommend ti f A il d S 

dations from April and Sep

2010 SAPs

• Addressing monitoring uncertainties r

to variability of pesticide concentratio

water:

• Day-to-day patterns (short-term tempora

variability)

• Year-to-year patterns (long-term tempor

variability)

• Spatial patterns 
Intensively Sampled Data Sets U

Evaluating Methods Evaluating Methods

Heidelberg U. NCWQR data for OH: Maumee

Syngenta atrazine

data for OH: Maumee ecological exposure

River, sampled in 199517,000 km

2 catchment 

ecological exposuremonitoring: MO-01, 30 km

2 catchment a

area

8090

100

MO-01, 2007

1416

Maumee River, 1995

50607080

8

ntration, ug/L 1012

ntration, ug/L

10203040

Atrazine Conce

246

Atrazine Conce

0

0 91 98 105 112 119 126 133 140 147 2

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189
Characterizing Uncertainty in Ex

Estimates Estimates Based on Sampling Fr

Estimates Based on Sampling Sampling Fr

Fr

• Approach: bootstrapping simulations of sa

frequencies to develop simple multiplicativfrequencies to develop simple multiplicativ

for exposure estimates

• Randomly sampled the daily time series fo

Randomly sampled the daily time series fo

14-, 28-day windows

• 10,000 simulations of the different sampling int

10,000 simulations of the different sampling int

• Estimated 1-day peak and maximum 4-, 7-

28-, and 90-da

y gg runnin

g avera

ges for each 

10,000 simulations of different sampling in

• Compared the true maximum concentrations a

th

5 percentile maximums of the distributions frosampling interval
Preliminary Multiplicative Bias 

Based on Different Sam

plin

g In

Preliminary Multiplicative Bias 

Based on Different Sam

p g lin

g In

Sam

ple 1-da

y 4-da

y 14-da

y 28-da

y 9

AEEMP 2007 MO-01

p

Interval

y

max.

y

avg.

y

avg.

y

avg.

4 days 4 3 2 2 

7 days

5

3

2

2

14 days 15 10 6 4 28 days 58 37 18 11 SampleIt l

1-day 4-day 14-day 28-day 9

NCWQR 1995 Maumee River

In

terval max. avg. avg. avg. 

4 days 2 1.4 1.2 1.1 7 days 2 2 1.4 1.2 

14 days 3 2.5 2 1.4 28 days 3 3 2 1.5 
Taking the Next Step: Estimatin

Series from Monitorin

g Da

• Sept. 2010 SAP: With uncertain critical ex

period the best course of action may be period, the best course of action may be

“t

capture the pattern of atrazine concentratithe source water of each CWS based on t

characteristics of that particular water syst

• The SAP recommended combining regres

b d d l h WARP ith ith base

d mo

d

els, suc

h as WARP, with either 

deterministic model or a geostatistical app

• Our analysis found that given sufficient Our analysis found that, given sufficient

sampling, conditional stochastic simulationusing variogram models may be sufficient 

ith t WARP

withou

t WARP
DETERMINE

APPROPRIATE POINT

ESTIMATION INFILL TIME S

NO

Analysis Strategy for MERGE

PROCEDURE

> 15 DATA POINTS IN

TIME SERIES?

WITH MO

ESTIMAT

VALUES

NO

Strategy for

Modeling 

PesticideStrategy for

Modeling 

Pesticide

MERGE

MONITORING

YES DATA

Pesticide USE WARP 

Time Series 

from

USE WARP

ESTIMATES

IN TIME

SERIES

PROCEED WITH

VARIOGRAM

ANALYSIS

from

Monitoring 

Data

ARRANGE

FLOW

PROCEED WITH 1‐

DIMENSIONAL

Data ORDINARY KRIGING PERCENTILES

WITH MERGED

DATA

PERCENTILES

ORDINARY KRIGING

STOCHASTIC

SIMULATION OF

EQUALLY PROBABLE

REALIZATIONS OF

TIME SERIES 
Focusing Modeling Analysis on 

Period

• Geostatistical methods 

require stationarity 14

16

Runoff stationarity

• assumes equal 

probability of occurrence 

entration (ug/L)

8

101214

regardless of location/time

Stationarity may not

Atrazine Conce

2468

Base-flow 

• Stationarity may not

hold true for annual distributions with Julian Day

0 100 200 

02

distributions with

seasonal trends

• Addressed by focusing

Analyses indicate statioapply during the runoff atrazine and other broaherbicides s ch as ace

Addressed by focusing

on runoff season

herbicides

s

uch as ace

not for insecticides succhlorpyrifos
Variograms: 1995 Maumee 

(a) 4-day sampling intervals (b) 7-day sampling i

e (μg/L)2

e (μg/L)2

Variance

Variance
Realizations of Atrazine Time Se

1995 Maumee Data 1995 Maumee Data

1416

Estimate Mean (SD)  Actual Data 

(a) 4-day intervals

1416

Estima Actual 

(b) 7-day intervals

zine Conc (ug/L)

68

1012

zine Conc (ug/L)

68

1012

100 120 140 160 180 200 220

Atraz

0246

100 120 140 160 180 

Atraz

0246

Julian Day

100 120 140 160 180 200 220

L)

121416

Estimate Mean (SD)  Actual Data 

L)

121416

Estima Actual 

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

Julian Day

100 120 140 160 180 

Atrazine Conc (ug/L

468

10

Atrazine Conc (ug/L

468

10

Julian Day

100 120 140 160 180 200 220

024

Julian Day

100 120 140 160 180 

024
Variograms: 2007 MO-

(a) 4-day sampling intervals (b) 7-day sampling i

e (μg/L)2

e (μg/L)2

Variance

Variance
Realizations of Atrazine Time Se

2007 MO

-01 Data

100

Estimated Mean (SD)

2007 MO 01 Data

(a) 4-day intervals 100

Estimated Mean (SD)Actual Data 

(b) 7-day intervals

Concentration (ug/L) 406080

( )

Actual Data 

Concentration (ug/L) 406080

100 120 140 160 180

Atrazine C

0

20

100 120 140 160 

Atrazine C

0

20

(ug/L)80

100

Estimated Mean (SD) Actual Data

Julian Day

100 120 140 160 180

(ug/L)80

100

Estimated Mean (SD) Actual Data

(c) 14-day intervals

(d

Julian Day

100 120 140 160 

azine Concentration ( 4060

azine Concentration ( 4060

Julian Day

100 120 140 160 

Atra

0

20

Julian Day

100 120 140 160 180

Atra

0

20
Estimates for 1995 Maumee R

Sampled at Weekly Interva

Estimates for 1995 Maumee R

Sampled at Weekly Interva

16 p y 1610121416

n, ug/L

Daily Time Series Sim 95th %ile

Sim 5th %ileActual

10121416

ion, ug/L

Sim 95th %ile 4-Day AveragesSim 5th %ileActual

468

10

razine Concentration

468

10

Atrazine Concentrati

024

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

Atr

Julian Day

02

91 98 105 112 119 126 133 140 147 154 

A

Julian Day

16 16 Si 95th %il 28 D A10121416

ion, ug/L

Sim 95th %ile 14-Day AveragesSim 5th %ileActual

101214

tion, ug/L

Si

m 95th %ile 28-Day 

Averag

Sim 5th %ileActual

468

10

Atrazine Concentrati

468

Atrazine Concentrat

024

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

A

Julian Day

02

91 98 105 112 119 126 133 140 147 15

Julian Day
Estimates for 2007 MO

-01 Sam

Weekly Intervals

100 100

708090

n, ug/L

Daily Time Series Sim 95th %ile

Sim 5th %ileActual

708090

100

on, ug/L

Sim 95th %ile 4-Day AverageSim 5th %ileActual

30405060

razine Concentration 30405060

trazine Concentratio

0

1020

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

Atr

Julian Day

0

1020

91 98 105 112 119 126 133 140 147 15

A

Julian Day

100 100

708090

on, ug/L

Sim 95th %ile 14-Day AveragesSim 5th %ileActual

708090

100

on, ug/L

Sim 95th %ile 28-Day AveragSim 5th %ileActual

30405060

Atrazine Concentratio 30405060

Atrazine Concentratio

0

1020

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

A

Julian Day

0

1020

91 98 105 112 119 126 133 140 147 15

A

Julian Day
DETERMINE

APPROPRIATE POINT

ESTIMATION INFILL TIME S

NO

What Happens If MERGE

PROCEDURE

> 15 DATA POINTS IN

TIME SERIES?

WITH MO

ESTIMAT

VALUES

NO

Happens If

We Have 

MonitoringHappens If

We Have 

Monitoring

MERGE

MONITORING

YES DATA

Monitoring USE WARP 

with 

Sampling

USE WARP

ESTIMATES

IN TIME

SERIES

PROCEED WITH

VARIOGRAM

ANALYSIS

Sampling

Intervals > 

7 Days?

ARRANGE

FLOW

PROCEED WITH 1‐

DIMENSIONAL

ORDINARY KRIGING 7 Days? PERCENTILES WITH MERGED

DATA

PERCENTILES

ORDINARY KRIGING

STOCHASTIC

SIMULATION OF

EQUALLY PROBABLE

REALIZATIONS OF

TIME SERIES 
Illustration of Combined WAR

Krigin

g

Approach for Maumee Ri

Illustration of Combined WAR

Krig g in

g

Approach for Maumee Ri

Variogram generated from 28-day sampling intervals of 1995 M R ith

Error analysis from realizations of estim

1995

Maumee 

R. with ti i f th 

WARP parameters from OH-03 monitoring site

time series 

from the 

monitoring intervals WARP modeling

g/L)20

25

2

Concentration (ug 1015

ance (μg/L) 2

Atrazine C

5

10

Vari

Julian Day

120 140 160 

0
Topics

• Recap of monitoring issues and 

recommend ti f A il d S 

dations from April and Sep

2010 SAPs

• Addressing monitoring uncertainties r

to variability of pesticide concentratio

water:

• Day-to-day patterns (short-term tempora

variability)

• Year-to-year patterns (long-term tempor

variability)

• Spatial patterns 
Characterizing Year-to-Year Va

in Pesticide Concentratio

7080 IL-29, 2004-09 30405060

T, ug/L (ppb)

0

1020

Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 

TCT

• Analysis of AMP monitoring for TCT in CWS found 

range in maximum detections

• For >100 CWS with 6 years of monitoring, 90th percentile 

between lowest and highest maximum across years for tsystem was 20X for source water, 13X for treated water

• Increasing the number of years monitored increases the 

capturing a wider range of conditions 
Estimating Potential Year-to

Variability in Atrazine Concent

• PRZM Hybrid Model

• R PRZM ith t h d Run PRZM with wa

ters

h

e

d-specifi i t i 

ific inpu

ts, rain

for that year

• Adjust percentile concentrations using monitor

Adjust percentile concentrations using monitor

accounting for sampling frequency uncertaintie

• Run for additional weather years for range

• WARP

• Vary temporal parameters – May/June precipit

at i raz ne use i t it lik l f l in

tensity over lik

ely range o

f value

• Uncertainties in estimation of atrazine use inte

dominate variabilit

y in WARP estimations
Topics

• Recap of monitoring issues and 

recommend ti f A il d S 

dations from April and Sep

2010 SAPs

• Addressing monitoring uncertainties r

to variability of pesticide concentratio

water:

• Day-to-day patterns (short-term tempora

variability)

• Year-to-year patterns (long-term tempor

variability)

• Spatial patterns 
Addressing Spatial Variability in P

Concentrations

• CWS in AMP fall in areas predicted to be more vuln• No inference No inference

to other CWS in vulnerable 

areas 

• Vulnerability 

assessmentsfor eco-risk will help identify other potentially vulnerable CWS
Monitoring Interpretation Iss

SAP Co

n

s

ide

ratio

n

Monitoring Interpretation Iss

S Co s de at o AP Co

n

s

ide

ratio

n

• Given the temporal and spatial patterns of 

pesti id i f t h 

ti

cide occurrence in sur

face wa

ters, 

ho

we best put confidence bounds on exposu

estimates from monitoring? estimates from monitoring?

• Approaches for day-to-day variability and estim

time series from monitorin

g p

 at different sam

plin

frequencies

• Approaches for characterizing year-to-year var• Pl i CWS i i i l Placing CWS sites in a spatial context