Approaches to EvaluatingApproaches to EvaluatingWater Sampling StrategiesWater Sampling Strategiesand Frequency of Monitoringand Frequency of MonitoringPresentation to Science Advisory PanelApril 26-29, 2010Nelson Thurman and Mary FrankenberryOffice of Pesticide ProgramsMichael Messner, Ph.D.Office of Ground Water and Drinking Water2



# Why We ’ re Visiting Sampling Frequency re Visiting Sampling FrequencyzBased on the best available data for the 2003 atrazineIRED, EPA identified a human health level of concern basedon a 90-day exposure period. If a shorter duration ofexposure is considered:How well will the existing CWS monitoring design characterizeshorter durations of exposure?Does the existing monitoring program need to include more frequentsampling or is it sufficient fora shorter duration of exposure?zFor atrazine and other pesticides in drinking water:How much can we derive from existing, less frequent sampling?How frequent does sampling needto be in order to adequatelycharacterize various exposure durations?3 

# Overview: Sampling Frequency Overview: Sampling FrequencyzContext for water sampling frequencyevaluationsConsiderations in evaluating monitoring and samplingfrequenciesOverview of approaches for analyzing monitoring datazExamples of approaches / applicationsEvaluating the Performance of Sampling Strategies forEstimating Maximum Concentrations of Different DurationsArtificial Neural Network Modeling of Atrazine OccurrencePatternszWhat’s next?Planned approach to evaluate methods / modelsIssues for the SAP4 

# Nature of Pesticide Occurrence in Water Nature of Pesticide Occurrence in WaterzComplex spatial patternsRelated to pesticide use, rainfall patterns, soil/hydrologicvulnerabilitieszComplex temporal patternsVariability within seasonsYear-to-year patternszTemporal autocorrelation – pesticide concentrationsmeasured on one day are related to the exposuresmeasured on the previous and following dayszCensored data - pesticide concentrations below theLOD/LOQzInfrequent sampling5



# Nature of Nature of Atrazine Atrazine Occurrence in CWS: Occurrence in CWS: Spatial Patterns Spatial Patterns6MO-01, 20080204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/L 

# Temporal patterns: within a single year Temporal patterns: within a single yearzSingle seasonalpulse, relativelyshort durationzCensored data(below the LOD/LOQ)7 

# Temporal patterns: different year Temporal patterns: different yearMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LzTwo seasonalpulses, extendedtailDifferentapplicationperiods, rainfalleventszAutocorrelation8MO-02, 20060204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/L 

# Temporal patterns: different year Temporal patterns: different yearzBroader seasonalpulses, extendedtailzMultiple peaks9MO-01, 2004-2009020406080100120J-04J-05J-06J-07J-08J-09Date

Atrazine, ug/L 

# Temporal patterns: year Temporal patterns: year - to - year variations year variationsMO-02, 2004-2009020406080100120J-04J-05J-06J-07J-08J-09Date

Atrazine, ug/L10 

# Sampling Frequency Considerations Sampling Frequency ConsiderationszAccounts for spatial, temporal patterns in exposureTargeted monitoring focuses onwhere the pesticide is used, whenit is usedAccounts for fate/transport of pesticide, nature of use, expectedweather patterns, water body/sourcezExposure duration of concern for adverse health outcomeand the concentrations / durations associated with itMore intensive sampling for shorter exposure durationszConsequences of false negatives (failing to detect highexposures) and false positives (false alarm)zCost11 

# Previous Recommendations on Sampling Previous Recommendations on Sampling

Frequency FrequencyzILSI (1999) recommendations for estimating peakexposures:Minimum of 100 samples per yeartargeting the pesticide useseasons (beginning of application, maximum use/runoff, postapplication) at a single CWSLess frequent sampling, more CWS for regional, nationalassessmentszCrawford (2004) evaluated various sampling strategies,recommended concentrating samples during likely use/runoff periodzBoth noted the number, frequency of sampling dependon the time frame of concern12 

# Analyzing and Interpreting Monitoring Data Analyzing and Interpreting Monitoring DatazBasic approachesDistribution of measured values onlyLinear or stair-step interpolation between sampling pointszStatistical methodsBootstrappingArtificial neural networksKriging / nearest neighborzModeling methodsRegression-based or deterministic13 

# Interpreting Between Sampling Events Interpreting Between Sampling EventsMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LOriginalchemographMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 MeasuredMO-01 7-da intervalsSampled at 7-day intervalsMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 Measured7-da sample intervalsstair-step (7-da)Stair-stepinterpolationbetween pointsMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 Measured7-da sample intervalslinear interpolation (7-da)Linearinterpolationbetween points14 

# Broader Sampling Intervals Broader Sampling IntervalsMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 Measured7-da sample intervalsMO-01 14-da intervalsSampling at14-dayintervalsMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 Measured7-da sample intervalsMO-01 14-da intervalslinear interpolation (14-da)Loss ofinformationwith linearinterpolation15 

# Basic Interpolation Approaches Basic Interpolation ApproacheszAdvantagesQuick, simpleMay be all that’s needed for certain exposure durations, samplingfrequencieszDisadvantagesAssumes no concentrations greater than the maximum measuredLikely to underestimate peak or short-term exposures; depending onfrequency, may overestimate longer-term exposuresUse only the distribution of monitoring data or fill in the distributionzHow well they characterize actualconcentration profiles depends ontiming, sampling frequency, shape of the true chemograph16 

# Statistical Analyses Statistical AnalysesMO-01, 20070204060801004/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine, ug/LMO-01 Measured7-da sample intervalsCurve-fittingUpper CI17



# Kriging Kriging / Nearest Neighbor Approach / Nearest Neighbor ApproachzKrigingBased on variance at different lagtimeslinear interpolation withappropriate weighting fromnearest neighborszNearest NeighborEvaluation of correlation (orvariance) of pesticideconcentration at different lagseparation times (days)Variogram for Atrazine Data00.0020.0040.0061 2 3 4 5 6 7 8 9 10Lags (Days)

ppb218 

# Preliminary Evaluations with Preliminary Evaluations with Atrazine AtrazineVisual Inspection of DataDescriptive StatisticsVariogram AnalysisCross-ValidationKriging StochasticSimulationJulian Days80 100 120 140 160 180 200 220 240 260

Concentration (ug/L)012345619 

# Statistical Approaches Statistical ApproacheszAdvantagesCan be used to provide probabilistic distributions, confidencebounds around existing monitoring dataDo not necessarily assume greatest exposure has been detectedNon-subjective estimate of missing values; can account fortemporal correlationzDisadvantagesMore complex modeling than basic interpolationsMay underestimate high exposures with data smoothingMay not capture underlying explanatory variableszUtility may depend on both quality/frequency of availablemonitoring data, toxicity exposure duration of concern20 

# Modeling Methods Modeling MethodszRegression-based modelsWatershed Regression on Pesticides (WARP)- developedusing atrazine monitoring data to predict pesticide concentrationsin unmonitored/ inadequatelymonitored streams. Providesestimates of selected percentiles, annual mean, annual maximum,and annual maximum moving-average (21-, 60-, and 90-daydurations) concentrations.zDeterministic modelsFor the 2007 SAP on the atrazine ecological exposure monitoringprogram (AEEMP), Syngenta investigated the potential for in-fillingof AEEMP chemographs with edge of field exposures using thePesticide Root Zone Model (PRZM)zFlow-adjustments or combination of flow / timing ofplanting / rainfall events21 

# Examples of Approaches / Applications Examples of Approaches / ApplicationszEvaluating the Performance of Sampling Strategiesfor Estimating Maximum Concentrations of DifferentDurations – Mary FrankenberryUse of bootstrapping methods to evaluate different samplingfrequencies with different pesticide concentration profileszArtificial Neural Network Modeling of AtrazineOccurrence Patterns – Michael MessnerUse of ANN to simulate pesticide concentration pattern andevaluate monitoring strategiesEvaluating the Performance ofEvaluating the Performance ofSampling Strategies for EstimatingSampling Strategies for EstimatingMaximum Concentrations of DifferentMaximum Concentrations of DifferentDurationsDurationsPresentation to Science Advisory PanelApril 26-30, 2010Mary J. FrankenberryEnvironmental Fate & Effects DivisionOffice of Pesticide Programs23



# Introduction IntroductionzRecent work by Crawford (2004) and othershas evaluated the performance of differentsampling strategies for estimating the upperpercentiles of “true” chemograph profiles.zIn the SAP issue paper OPP carried out twobootstrapping methods that were variationson the Crawford approach, one of which willbe presented today.24



# Background BackgroundzCrawford’s work created “true”chemographs using linear interpolationto augment less than daily sampling to365-day profiles.zOPP chose results from 30 CommunityWater Systems (CWS) which weresampled weekly or biweekly andaugmented linearly to 365 days .25



# Background BackgroundzWhile earlier work looked at the upperpercentiles of single day concentrations, OPPis concerned with the maximum value forseveral different durations, ranging fromsingle day values to 3-, 7-, or 90-dayaverages.zSimilar to earlier methods, OPP looked at theperformance of different sampling strategiesfor computing estimates of the “true” valuesfor these maximum running averages.26



# Methodology MethodologyzSampling was carried out using the interpolated CWSprofiles according to schedules which ranged fromquarterly to sampling every 4 days.zEach sampling strategy was implemented over 4,500times with replacement following a random start.Each resample was interpolated to 365 days, and themaximum single day, maximum 3-day, 7-day, etc.average of each run was compiled, with the rangeand percentiles of these maximum values reported.27 

# Matrix of Sampling Strategy v. Concentration Matrix of Sampling Strategy v. Concentration Duration for Simulations Duration for SimulationsConcentrationDurationSingleDay peak3-day average 7-day average 90-dayaverageSamplingIntervalDaily SeasonalXX X XEvery 4 daysXX X XEvery 7 daysXX X XBiweeklyXX X XWeekly/Biweekly Seas.XX X XQuarterlyXX X X28



# CWS Chemograph Chemograph with a Moderate Duration Peak and with a Moderate Duration Peak and

extended tail extended tail29 

# Percentiles of Maximum Daily Concentrations for CWS #21 Percentiles of Maximum Daily Concentrations for CWS #21

Sampled at 4 Sampled at 4 - , 7

- , or 14 , or 14

- day Intervals day Intervals30



# Percentiles of Maximum 7 Percentiles of Maximum 7 - Day Average Concentrations for Day Average Concentrations for

CWS #21 Sampled at 4 CWS #21 Sampled at 4 - , 7

- , or 14 , or 14

- day Intervals day Intervals31



# Results for CWS #21 Results for CWS #21zResults in these tables show that for CWS#21 (and consistent with all systems run) :Accuracy is best for strategies employing the mostfrequent samplingAccuracy generally improves for longer termaverages (e.g., 7 days or 90 days) over shorterterm averages--single day or 3–day, for instance32



# CWS Chemograph Chemograph with a Single, relatively Short with a Single, relatively Short

Duration (narrow) peak Duration (narrow) peak33



# Chemograph Chemograph with a Broad or Extended Series of Peaks with a Broad or Extended Series of Peaks34Maximum 3Maximum 3-Day Average Concentrations for All CWSDay Average Concentrations for All CWSSampled at 4Sampled at 4-day Intervalsday Intervals35Maximum 3Maximum 3-Day Averages for All CWS SamplingEvery Day (April thru August);Day Averages for All CWS SamplingEvery Day (April thru August);Every 14 Days (balance of year)Every 14 Days (balance of year)36Maximum 90Maximum 90-Day Averages for All CWS SamplingEvery 7 Days (April thru AugusDay Averages for All CWS SamplingEvery 7 Days (April thru August);Every 14 Days (balance of year)Every 14 Days (balance of year)37 

# What do these indicate? What do these indicate?zAs expected, an endpoint with a shorterduration will require more frequent samplingthan a longer one to capture correspondingmonitoring values with similar accuracy.zThe current sampling scheme ofweekly/biweekly monitoring appears toperform adequately for estimating 90-dayaverages with reasonable to good accuracy.38



# Indications (cont. Indications (cont.

’ )zSeasonally targeted sampling strategiesgenerally perform better than allocatingsamples equally throughout the year.zSampling strategies appear to performdifferently according to the shape of thechemograph.zIt may be useful to examine the shapeproperties of annual profiles in the subset ofCWS with the highest peaks over the years.39



# Atrazine Atrazine Monitoring Program Data for Monitoring Program Data for Atrazine Atrazine in

Raw Water* Raw Water*YearTot.Samples/Tot. CWSInd Peaks> 12 ppbInd Peaks> 20 ppbInd Peaks> 30 ppbCWS w.peak(s) >12 ppbCWS w.peak(s) >20 ppbCWS w.peak(s) >30 ppb20044429/ 141 19713010496 2 15 5 220054779/ 14222 7 27 12 620064817/ 15111 2 20 10 120074521/ 1371 0 7 1 020083927/ 13419 10 23 14 9* Source: Raw data submitted by Syngenta annually to EPA40 

# Characterizing Uncertainty Characterizing UncertaintyzThe Agency seeks the Panel’s feedbackon methods for characterizinguncertainty:for the population of systems from year toyearfor individual systems within a given yearwith missing values from less than dailysampling41



# Summary of Results Summary of ResultszResults of this preliminary exercise agree withCrawford and others in pointing to ‘smart’sampling strategies that can target bothspatial and temporal patterns among theCWSzMore intense seasonal sampling amonghigher concentration systems in vulnerableareas may be a good allocation of samplingresources42ArtificialNetworkArtificialNeuralNetworkModelingModelingofAtrazineAtrazineOccurrencePatternsOccurrencePatternsPresentation to Science Advisory PanelApril 26, 2010Michael J. Messner, Ph.D.Standards and Risk Reduction BranchOffice of Ground Water and Drinking Water43•Mathematical Form of ANN model•Fitting Occurrence Data•Simulation to Assess Performance•Some Pros & Cons



# Outline Outline4344 

# Mathematical Model Mathematical Model•Above, the “smooth” part of a four-node model.•The first parameter, b0, establishes the baseline logconcentration – before any node begins to have aneffect•Each fraction is called a “node,” and two are requiredto describe a peak (one for the increase and anotherfor the decrease in concentration)•Parameters in numerators are the total effects of the nodes•Parameters in the denominators determine when (day ofyear) and how quickly (slope)the increases and decreasesoccur44lnCdayb0b11 eb5−b6 day⋅−++b21 eb7−b8 day⋅−++b31 eb9−b10 day⋅−++b41 eb11−b12 day⋅−++:=45 

# Estimating Model Parameters Estimating Model Parameters1.With basic understanding of the parameters,set initial values.2.Calculate log likelihood, given initial values.3.Use some algorithm to maximize the loglikelihood.–MS Excel’s Solver Add-In–MathCAD’s Maximize function–R function mle in stats4 package45lnCdayb0b11 eb5−b6 day⋅−++b21 eb7−b8 day⋅−++b31 eb9−b10 day⋅−++b41 eb11−b12 day⋅−++:=46 

# Finding Initial Values for Maumee 1998 Finding Initial Values for Maumee 1998Intercept+FirstNode+SecondNode+ThirdNode+FourthNode47 

# Maumee 1998, a Best Maumee 1998, a Best - Fit ANN Model Fit ANN Model4748 

# Maumee 1998 Three Nodes Maumee 1998 Three Nodes AutoCorrelation AutoCorrelation

Function (ACF) Function (ACF)4849 

# Model for Maumee 1998 Model for Maumee 1998zThree nodeszInclude autocorrelation (AR2)49Preddayb0b11 eb4−b5 day⋅−++b21 eb6−b7 day⋅−++b31 eb8−b9 day⋅−++:=Var0.05895229:=α00.3807437:=α10.195229:=50 

# Summary Statistics Summary StatisticszGiven the model parameters, can derive somesummary statistics.MeanMedianOther percentiles of interestzBut what if we’re concerned about errors(false pos and false neg) when occurrence ismuch higher or lower than this??? 

# Adjust the

model.5051 

# Adjusting the Model Adjusting the ModelzAdding a constant K to b0Daily concentrations increase by factor of exp(K)If K < 0, the result is an decrease.If K > 0, the result is an increase.Mean, median and other percentiles increase byexp(K)Error structure (lognormal with serial correlation)is preserved5152



# Illustration: One Illustration: One Sim Year (same random number seed, but different intercepts) (same random number seed, but different intercepts)5253



# Many (10,000) Many (10,000) Sim YearszSimulate one year of occurrence, and foreach year, apply the monitoring strategySelect concentrations for days on whichmonitoring is planned andEmploy the rule for deciding whether occurrenceis high or lowz10,000 -> Good estimates of error rateszRepeat, using different levels to “trigger” apositive5354 

# Error Rates as Functions of Critical Value Error Rates as Functions of Critical Value55 

# This kind of outcome is This kind of outcome is “ Normal ”zDecision-makers initially express great concern for both F(+) and F(-)errors, then find thatRates can be difficult to meetwith an affordable strategySatisfying initial objectives mayrequire large number of sampleszWhen this happens, need to reconsider things.Can we live with greaterF(+) and F(-) error rates?Was the simulated occurrence pattern“typical,” or challenging? Ifchallenging, what are F(+) and F(-)rates for more typical occurrencepatterns? Can we live with that?5556



# Summary SummaryzANN Models can be used to simulate occurrencepatterns of concern...Low occurrence levels, forwhich concern is for F(+)High occurrence levels, with concern for F(-)High-variability patterns thatare difficult to assess with fewsamples.Low-variability patterns that are easy to assess with fewsamples.z... and assess candidate monitoring strategies,evaluating in terms of their F(+) and F(-) rates.5657 

# Some Advantages and Disadvantages of Some Advantages and Disadvantages of

Using ANN Models Using ANN Models58zContext for water sampling frequencyevaluationsConsiderations in evaluating monitoring and samplingfrequenciesOverview of approaches for analyzing monitoring datazExamples of approaches / applicationsEvaluating the Performance of Sampling Strategies forEstimating Maximum Concentrations of Different DurationsArtificial Neural Network Modeling of Atrazine OccurrencePatternszWhat’s next?Planned approach to evaluate methods / modelsIssues for the SAP 

# Overview: Sampling Frequency Overview: Sampling Frequency59 

# What We Know So Far What We Know So FarzPesticide concentrations vary spatially and temporallyzTiming and frequency of sampling needed to characterizepesticide exposures at a given location depend not onlyon the temporal patterns but also on the exposureduration for the toxicological endpoint of concernzThe shape of the exposure chemograph also influencesthe frequency of sampling neededzThe complexity of the approach needed to provideexposure estimates depends on the quality of themonitoring data, exposure duration of concern, level ofcertainty needed60 

# Planned Approach to Evaluate Methods Planned Approach to Evaluate MethodszUse the robust monitoring data sets (with daily /intensive sampling during use/runoff season) torepresent the three general exposure shapes (short-,moderate-, and long-duration/multiple peaks).zSample these data sets at different intervals (e.g., 7-,14, 30- day sampling intervals).zUse the various approaches described in Section 5.4.1 toestimate the exposures between sampling points foreach of the sampled intervals.zGenerate estimated exposures for varying rollingaverage periods (1, 3, 7, 14, 30, 60, 90 days) andcompare those estimates against the actual values.61 

# Ultimate Focus Ultimate FocuszHow do we use this in drinking water exposureassessments?zTaking into account...The spatial and temporal nature of pesticide concentrations indrinking water sourcesThe duration of exposure forthe toxicological endpoint ofconcernzHow frequent does sampling need to be in order toadequately characterize various exposure durations witha specified confidence interval?zHow much can we derive from existing, less frequentsampling?62 

# Issues for the SAP Issues for the SAPzGiven the nature of the temporal patterns ofpesticide occurrence in surface waters, how do webest determine confidence bounds on exposureestimates from monitoring data?Converse of this: how do you design a monitoring study toprovide exposure estimates with a given level of confidencezWhat are the advantages and disadvantages of thevarious approaches for estimating pesticideconcentrations in drinking water over short,moderate, and long durations?When is ‘simple’ good enough and when do we need to lookat more complex methods?