Improved Characterization of the Improved Characterization of the

Temporal and Spatial Variability of Potential Surface Water Drinking Water 

E b Ui E i tl d Exposure 

by 

U

sing 

Environmen

t

al an

d

Historic Monitoring Databases

Paul Hendley, Wenlin Chen, Robert Joseph, Ron Williams, Chris Harbourt & Paul Miller

Syngenta Crop Protection LLC, Product Safety, and Waterborne Environmental Inc.
Outline

● Atrazine Historic Monitoring Database● Statistical Analysis of Monitoring Data● Understanding and Classifying Community Water Systems ● Approaches to Designing Sampling Strategies Approaches to Designing Sampling Strategies● Conclusions

2
Take Home Messages

● Robust monitoring datasets define potential extreme high ce

chemograph patterns and year-to-year variation

● Surface Water CWS are highly individual but can be logicall● Monitoring y pg g q y 

 study sampling design must balance frequency /

- Various approaches exist to supplement moderate freque

monitoring data sets for different risk assessment needs

● Many conclusions from atrazine studies are potentially appl

general agrochemical monitoring study design

3
Th At i W t M it i The Atrazine 

W

a

ter 

Monitoring 

Database is Extensive
>340,500 

Surface water 

l *

The Atrazine Water Monitoring Database is Extensive

* Doe206 00

>200,000 

surface water 

samples

samples* 206,00

samples

14,875 

samples

15,100

samples

57,600 

samples

119,200 

samples

6 CWS

>500 

raw

6 CWS

>500 

finished

48,530 

finished sam

raw

samples samples

46,500 

raw samp

7-day frein season

5 * Includes around 3260 samples of finished water from PDP

in season
Database Analyses
Rich Temporal and Spatial Data Permit Detailed A

Compound Specific Trends Distributions and BeCompound Specific Trends, Distributions and Be

Database Size vs. Confidence

Confidence Level (100 q)

Centile (100 p)

90.0% 95.0% 97.5% 99.0% 99.9%

80.0

% 16 32 64 161 1609

90.0% 22 45 91 230 230295.0% 29 59 119 299 299597.5% 36 72 146 368 368899.0% 44 90 182 459 460399.9% 66 135 273 688 6905

7
AMP/VMP data provide high confidence of highe

atrazine concentrations in raw & finished water 

Water 

Source 

Type

Water 

Type

CWS 

Sampled

Days 

Sampled

Centile (ppb) 95% Confidence Int

P95 P99 P99.9 P95 P99 

All

Raw 226 46264 4.50 9.76 27.52 4.18, 4.85 8.80 , 11.3

Finished 201 46457 2.60 6.67 18.38 2.40, 2.80 5.84 , 7.7

223 22941 61 6 14 1 

April to 

July

Raw 223 22941 6.80 16.00 36.35 6.19, 7.6 14.93 , 17

Finished 197 23084 3.95 11.00 31.55 3.56, 4.42 9.27, 12.9

R 118 25791 4 60 10 00 28 20 4 03 5 16 8 80 12 9

Static

Raw 118 25791 4.60 10.00 28.20 4.03, 5.16 8.80, 12.9

Finished 118 28730 2.80 7.50 20.35 2.47, 3.40 5.92, 8.73 

Mi d

Raw 14 3072 4.11 8.07 19.48 3.93, 5.50 6.32, 18.8

Mixed

Finished 14 3305 2.64 4.69 17.64 2.35, 3.20 4.00, 7.07 

Flowing

Raw 56 10474 2.88 8.49 23.63 2.67, 3.24 7.71, 9.4

Finished 56 10572 2.30 6.00 17.74 2 00 2 60 5 33 6 60 

8

Finished 2.30 6.00 17.74 2.00, 2.60 5.33, 6.60 

Mosquin and Whitmore, 2010 (new enhanced version 2011)
U d t di d Diff ti ti C 

U

n

ders

tanding an

d Differenti

ating 

Com

Water Systems
New Database Characterizes CWS & Associated W

● 215 AMP/VMP CWS documented

- Source water types, off-channel reservoirs etc- 375 raw water intakes 375 raw water intakes

– 324 take surface water for proce

324 take surface water for proce

- 224 static – 100 flowing- 188 static intakes are “on-channel” - 36 “off channel” reservo

● Watersheds characterized by GIS Watersheds characterized by GIS

- Extent, soils, hydrology, land use, curve number, corn so

planting, estimates of atrazine use, time of concentration 

for 

p y otential atrazine vulnerabilit

y for surface water runoff

● Database includes CWS specific characteristics including at

monitoring summaries

● Allows categorization of CWS and subsequent analysis of ca

properties

10
Many CWS Have Multiple Intakes – Some Are Fo

11
CWS Categorization Example using Source Type and Wat

188 100

143 33 12 16 31 53

12
CWS Size Classes – Sizes and Finished Drinking Water At

Residue Characterization

Finished Water Atrazine Concentrati

AMP Type Size Category Size Range Time of Concentration Count Min Median 90th Pctile 95th Pctile 

Category Range Range (hrs) Pctile

Flowing Small 0‐50 0.6 ‐ 14.1 16 <LOD 0.33 2.51 3.84 

Flowing Medium 50‐800 12.5 ‐ 127.1 31 <LOD 0.24 1.42 2.40 

Flowing Large >800 25.4 ‐ 248.1 53 <LOD 0.34 1.86 3.03 

Static Small 0‐50 0.0 ‐ 19.0 143 <LOD 0.41 2.10 3.20 Static Medium 50‐1000 11.0 ‐ 41.2 33 <LOD 0.43 1.83 2.50 

Static Large >1000 50.4 ‐ 248.1 12 <LOD 0.60 1.53 2.17 

● Chemograph shapes will differ

- Statics/Flowing- Small/Lar

ge watersheds

● Therefore optimal monitoring sampling strategies can differ

13
B l i S li F d B

alancing 

Sampling 

Frequency an

d

Uncertainty in Monitoring Study Design 
Factors Influencing Study Design

● Endpoint of Interest

- Duration, magnitude 

● Anticipated exposure patterns

- Transport routes

- Agrochemical uses timings and locations Agrochemical uses, timings and locations- Water body types

- Chemograph shapes and return frequencies

- M d li t M

o

d

eling ou

tcomes

● Cost– Sampling and Analysis● Availability of relevant EXISTING monitoring data

- Can they address study objectives?

NAWQA States earlier company programs

15

- NAWQA, States, earlier company programs.....
Study Design Options

● Conduct new monitoring study at (current) desired frequency● Supplement existing data of lower Supplement existing data of lower-than-desired frequency desired frequency

- Tiered approach -increasing realism -

1. Apply conservative bias factors reflecting sample frequency 

- M k ti ti b t b f “ i 

Make conservative assumptions about numbers of “miss

2. Use physically based model that conservatively estimates “m

when a rainfall event happened in the watershed

16
Bias Factor Approach 

(see Wenlin Chen/Paul Mosquin presentation)
Developing 

Bias Factors

DecT“c

b

Generate 7

“sets

” of

Three daily / near-daily data sets used to generate bias factors- 34 Atrazine eco monitoring sites (1-2 yrs data)

1 large CWS on Missouri river (7 yrs data) 

1RA

Generate

7 sets of

simulated 7-day sampling regimes

-

1 large CWS on Missouri river (7 yrs data)

- 4 watersheds in Ohio (Heideleberg/NCWQR) (15+ years of data)Generate “true” 4, 14, 28 da

y Rollin

g Avera

ges 

ecbd

95th centile of error ratios “Bias Factors” for 

Various Watershed Area 

R i

4-day Average 14-day Average 

ygg

from Near-daily Data d

Averaging Periods

Range sq.mi.

(site years) Sampling Interval 4 day 7 day 4 day 7 day 

34 AEMP Sites (Non drinking water source) 4.5 – 388 (1-2) 1.82 3.04 1.52 2.14 

Rock Creek (Non drinking water source) 34 (15) 2.00 2.47 1.50 1.92 

Compare each simulated rolling average with true values to develop a set of 7 error ratios. 

th

Honey Creek (Non drinking water source) 149 (16) 1.68 2.73 1.44 1.87 

Maumee River (Potential raw water source) 6330 (16) 1.30 1.91 1.19 1.50 

Sandusky River (Potential raw water source) 1251 (15) 1.38 1.90 1.22 1.48 

St. Louis CWS (finished water only) 524,000 (7) 1.54 1.68 1.12 1.20 

Estimate 95th centile of pool of 

data 
AEMP Sites Demonstrate Decreasing 

Bias Factors When Moving from 4- To 14-to 28-day Estimates Based on 7-day Si l t d M it i Simula

t

e

d

Monitoring

MO-01 2007 value 3.4 from white paper

19
Error Ratios Similar 

For Raw and 

Finished Water Finished Water

● Open circles reflect error 

ratios > group 95th centile ( f) Bias factors 2 60/2 61 for group of 6 CWS (i.e. bias factor).

● No significant differences 

apparent between raw & finished samples

Bias factors 2.60/2.61 for group of 6 CWS

finished samples.

● Bias factors fall largely 

within range for watersheds of comparable area

20
Overall Variation Reduced 

in Larger NCWQR 

Watershed

● Open circles reflect 

error ratios > group 95th

Bias factor 2.73 for Honey Creek (149 sq. mi.) 

Bias fRiver 

centile (i.e. bias factor).

● Shows that the larger 

watershed has some years with very tightyears with very tight

distributions

21
Apparent Relationship 

Between Group Bias Factor and Watershed Areas

Bias factor 3.04 for group of 34 AEMP sites

Large osquarecentile corresp

and Watershed Areas

● Open circles reflect 

error ratios > group 95thcentile (i e bias factor)

Bias grou

centile (i.e. bias factor).

● Supports expectation 

that appropriate bias factors for largerwatersheds will be lower than for smaller watersheds

6 Daily 2011 CWS 

Watershed Areaa

(mile2)

6805

405537311614

227

22

109
Conservative Bias Factors Characterized For Flowing Wa

● Bias factors generated for 110 AEMP/NCWQR site-years and 

sites with daily monitoring in 2011.

- Systematic sampling is appropriate for AMP dataset

- Raw and finished show similar ratios- Bias factors decrease as averaging period increases- Bias factors decrease when watershed area increases- Use bias factors appropriate for CWS based on watershed 

- <800 sq.mi.=2.6 and >800 sq. mi.=2.0 (4 day averages from - Chemo

g p p g 

ra

phs should not be extra

polated to values 

greater th

bounds based upon atrazine database statistics

● Bias factors defined for flowing water watershed, data do not 

static water sources

- Generally agreed static sources are less “flashy” than flow- Flowing water bias factors will be conservative for static w

● Note that data used to generate bias factors are mostly from 

d i ki

t d il CWS d t b i 

23

d

rinking wa

ter sources 

– more 

d

aily CWS

d

a

ta 

b

eing genera
Bias Factors for Creating Synthetic Chemograph

● A t i ff t d b t Assume atrazine runoff events occurre

d

b

etween every pai f li d ir o

f sampling 

d

- Generates unrealistically large number of elevated atrazine peaks - disto

● Magnitude 3 times higher than maximum of adjacent measured residues

- Should consider applying upper bound “cap” based upon atrazine data

avoid simulation of highly improbable values (see example below)

Region of highly improbable estimates

24
Hi h Ti A h Hi

g

her Tier 

Approac

h – Ph i ll b 

Physically 

base

PRZM-Hybrid modeling
PRZM-Hybrid is Site & Local Event Specific*

● Uses PRZM code

- Atrazine Efate data

● Regional data

- Crop reporting district based 

atrazine application rate

● Watershed & year specific 

local data

- SSURGO soils- NEXRAD rainfall- % Crop (NASS CDL (NLCD))

● Output 

- Uses directly available data -

No model specific calibration

- Conservative edge-of-field 

concentrations

26

● Uncertainties defined
PRZM-Hybrid Uses Local Soils/Rainfall to Drive Realistic 

Simulations of Potential Events between Monitored Data. 

A

C

nfall

2

B

verted Rain

1

4

3

5

ow and Inv

D

Fl

Time (t) l

PRZM‐Hyb Interpolated Line PRZM‐Hyb Conc.

Monitored Sample Conc. Flow

Sample Interpolated Line

Rainfall Event

27
How many pg Years should a program last??

Year-to-Year Variation Very Well Defined for Atrazine - Database Invaluable for General Monitoring Design.
Year-to-year Variation Very Well Defined for Atra

● Atr

a e s ea zin

e i

s near-idea case l

- High fraction of major crop treated every year at uniform rates- Large historical monitoring database- Wide range of Midwest agronomy/climates Wide range of Midwest agronomy/climates

● Therefore atrazine data 

- Provide a direct answer for atrazine- Provide optimal basis to assess options for other molecules Provide optimal basis to assess options for other molecules

● Atrazine residue and range of bias factors vary across years 

- Primary driver is inter-relationship between runoff inducing rainfall e

application timing application timing

● When extensive data do not already exist, medium-term prospe

monitoring may not provide reasonable bounds on year-to-year a reasonable 

period

● Therefore, where monitoring is being planned, one approach w

validation of PRZM-Hybrid with 2 – 3 years of monitoring data fprobabilistic modeling of 30+ years of weather variation and cro

29

- MODITORING
Probabilistic PRZM-Hybrid to Evaluate Year-to-year 

30
Conclusions

● Robust monitoring datasets define potential extreme high ce

chemograph patterns and year-to-year variation

● Surface Water CWS are highly individual but can be logicall● Monitoring y pg g q y 

 study sampling design must balance frequency /

● Various approaches exist to supplement existing moderate 

monitoring data to address different risk assessment needs

- “Bias factor” approach

- Conservative “synthetic chemograph” using worst case ass

- Watershed specific “hybrid” modeling

● Many conclusions from Atrazine studies are applicable to ge

agrochemical monitoring study design

31
Thank You.