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Estimation of Upper Percentiles of Chlorpyrifos 

Surface Water Concentration from Yearly 

Monitoring Program Data

Paul Mosquin1

G. Gordon Brown1

Roy Whitmore1

Nick Poletika2

1. RTI International

2. Dow AgroSciences
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Overview

 Describe a method for obtaining point and interval 

estimates for ‘peak’ chlorpyrifos surface water 

concentrations.

– Use historical datasets, possibly few samples per site-year.

– Estimate 95th, 99th, 99.9th upper centiles.

 Apply method to compare centile estimates among 

groups of sites

– Change in chlorpyrifos usage due to label changes of 2001, 

specifically its withdrawal from household use.
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Overview (cont)

Issues

– Sampling is generally not from a probability design

– Potentially few observations per year at a given site

– Non-detects are common

Approach

– Estimate aggregate quantities across groups of site-years of 

interest.

– Assign weights to account for varying frequencies of sampling.

– Given sampling weights, use well-established survey design 

principles to find estimates and confidence intervals.

– Aggregate quantities tend to be larger than averages of site-year 

quantities.
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Historical Data

1. NAWQA/NASQAN data (USGS)

 National coverage

 Surrogate for drinking water intake locations

2. Ohio Streams Data (NCWQR)

 Regional coverage

 Targeted to storm hydrology

3. Pesticides in Salmonid-bearing Streams data 

(WSDA)

 Regional coverage

 Endangered species assessment
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National Water-Quality Assessment (NAWQA/NASQAN)

http://water.usgs.gov/nawqa/map/cyc1_browse.gif
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National Water-Quality Assessment (NAWQA/NASQAN)

http://water.usgs.gov/nawqa/map/cyc2_browse.gif
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National Center for Water Quality Research 

Water Quality Laboratory, Heidelberg College NCWQR Website 9/27/05
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WA State Departments of Ecology and Agriculture (2010) Pesticides in Salmonid-Bearing Streams 2006-2008 Triennial Report.

Washington Pesticides in Salmonid-bearing Streams
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Data Set Summary

Program Years Total 

Sites Total n

Median

(days per year)

Range

(days per year)

NAWQA/ 

NASQAN 1991 - 2010 2,098 27,551 3 1 to 77

NCWQR 1983 - 2008 11 13,391 111 1 to 204

Washington 

State 2003 - 2008 22 1,724 29 12 to 63
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Description of Approach

 Survey sampling approach (finite population)

– Randomness is due to sampling.

– Associate sampling weights with measured values to account for 

varying sampling frequency:

 If a probability design (systematic sampling) then use probability weights

 If not a probability design then use time-interval weights (ILSI, 1999; Larson 

et al, 2004)

 Point estimates:

– Directly from survey-weighted distribution function. 

– Quantity estimated is upper p

th percentile of aggregate data. 
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Description of Approach

 Interval Estimates:

– Standard survey weighted approach of Woodruff (1952)

– Performs very well for moderate to extreme tails of distribution 

(Sitter and Wu, 2001).

 Key Assumption: 

– Possible pseudo-random design.

– Time-interval weights. Give slight overestimation peak usage 

period and high frequency monitoring period coincide.
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Non-Detects / Level of Quantitation

 PROBLEM: Non-detects comprise majority of data, at 

times more than 95%

– Below level of detection (LOD, MDL, LT-MDL)

– Under level of quantitation (LOQ, LRL)

 Approach:

– Only require upper tail of estimated distribution

– Estimates under LOD marked as such

– Estimates under LOQ footnoted

 Calculated percentiles two ways:

– Below LOD set equal to i) zero, or ii) LOD

– Results did not show much difference 
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Results Using All Observations for Three Data Sets

Washington 95th 99th 99.9th

Percentile (L, P, U) (0.017, 0.023, 0.032) (0.050, 0.057, 0.077) (NA, NA, NA)

Washington: Pesticides in Salmonid-bearing Streams

Heidelberg 95th 99th 99.9th

Percentile (L, P, U) (<LOD, <LOD, 0.023) (0.049, 0.090, 0.191) (NA, NA, NA)

NAWQA 95th 99th 99.9th

Percentile (L, P, U) (0.007, 0.008, 0.009) (0.026, 0.030, 0.037) (0.129 , 0.237, 0.366)

Heidelberg, NCWQR

NAWQA/NASQAN

The 95th and 99th h percentiles are below LOQ.
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Example Using NAWQA/NASQAN

 Changes regarding use of chlorpyrifos in urban settings 

 Use covariates to group sites

– Land Use: Agricultural or Urban

– Time Period: 1991-2001 or 2002-2010

Type Period 95th 99th 99.9th

Ag 1991-2001 (0.009, 0.011, 0.014) (0.034, 0.047, 0.086) (0.265, 0.265 , 0.270)

2002-2010 (0.008, 0.011, 0.032) (0.008, 0.049, 0.196) (0.129, 0.209, 0.338)

Ur 1991-2001 (0.013, 0.017, 0.024) (0.038, 0.079, 0.196) (0.168, 0.295, 0.570)

2002-2010 (<LOD, <LOD, <LOD) (0.008, 0.009, 0.012) (0.015, 0.021, 0.024)

Comparison of Agriculture/Urban

Non-detects set to 0.000

95th and Urban 2002-2010 are below LOQ
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Example Using NCWQR

 Groups are:

– Agricultural, Drainage area <1000 sq. mi. [2 sites]

– Agricultural, Drainage area ≥1000 sq. mi. [6 sites]

– Mixed (Agricultural use above station <60%) [3 sites]

Comparison of Agriculture/Mixed. 

Type 95th 99th 99.9th

Ag <1000 mi2 (<LOD, <LOD, 0.007) 0.043, 0.071, 0.087) (NA, NA, NA)

Ag ≥1000 mi2 (<LOD, <LOD, 0.004) (0.044, 0.085, 0.143) (NA, NA, NA)

Mixed (<LOD,0.025,0.058) (0.042,0.189,0.50) (NA, NA, NA)

Non-detects set to 0.000
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Example Using Washington State

 Comparison agricultural vs urban watershed land use

Agricultural: 4 watersheds, 18 sites

Urban: 1 watershed, 4 sites

Non-detects set to 0.000

Type 95th 99th 99.9th

Agricultural (0.023, 0.034, 0.059) (0.059, 0.085, 0.400) (NA, NA, NA)

Urban (<LOD, <LOD, <LOD) (<LOD, <LOD, <LOD) (NA, NA, NA)
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Concluding Remarks

 A tool for estimation with large sample sizes but 

relatively few samples per year.

 Method provides point and interval estimates of upper 

percentiles at aggregate level.

 No parametric modeling assumptions.

 May assume pseudo-random sampling. 

 Identified differences due to historical changes in usage 

pattern.
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References

 ILSI, (1999) A framework for estimating pesticide concentrations in 

drinking water for aggregate exposures. An ILSI Risk Science 

Institute Working Group Report. Washington D.C.

 Larson, S.J., Crawford, C.G., and Gilliom, R.J., (2004) Development 

and application of watershed regressions for pesticides (WARP) for 

estimating atrazine concentration distributions in streams: U.S. 

Geological Survey Water-Resources Investigations Report 03-4047, 

68 p. 

 Sitter, R.S., and Wu, C. (2001) A note on Woodruff confidence 

intervals for quantiles. Statist. Prob. Lett., 52:353-358

 Woodruff, R.S. (1952) Confidence Intervals for medians and other 

position measures. J. Amer. Statist. Assoc.
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More Information

Paul Mosquin

Research Statistician

919.294.6105

mosquin@rti.org

G. Gordon Brown

Research Statistician

919.485.5647

ggbrown@rti.org
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