EvaluatingAtrazine DrinkingWater Monitoring Data For Use inHuman Health AssessmentsHumanHealthAssessmentsPresentation to the FIFRA Scientific Advisory PanelJuly 26, 2011Nelson ThurmanNelsonThurmanEnvironmental Fate and Effects DivisionUSEPA Office of Pesticide ProgramsUSEPAOfficeofPesticideProgramsA Team Effort•Mary Frankenberry – Comparative statisticalanalysis of monitoring frequencyanalysisofmonitoringfrequency•Dr. James Hetrick and Dr. James Wolf –Geostatistical analysis and stochasticapplications•Dr. Stephen Wente – Watershed massbl dlaance model•Dr. R. David Jones, Donna Randall, and MarkCorbin–technical peer reviewtechnicalpeerreview2Topics•Recap of monitoring issues and feedbackf A il d S t b 2010 SAPfromAprilandSeptember2010SAPs•Addressing monitoring uncertainties relatedto variability of pesticide concentrations inwater:• Day-to-day patterns (short-term temporalvariability)• Year-to-year patterns (long-term temporalvariability)• Spatial patterns3Monitoring Issues for SAPConsideration•2010 SAPs: Given a possible change in theexposure duration for the human health level ofconcern, how well does the existing monitoringcharacterize shorter durations of exposure?characterizeshorterdurationsofexposure?•USEPA has evaluated methods to characterizethe uncertainty in exposure estimates fromtheuncertaintyinexposureestimatesfrommonitoring data collected at different samplingfrequencies.• Provide confidence bounds based on samplingfrequency4• Address daily/seasonal, year-to-year, spatial variabilityNature of Pesticide Occurrence inWater•Complex spatial patternsR l t d t ti id i f ll tt il/h d l i•Relatedto pesticide use, rainfallpatterns, soil/hydrologicvulnerabilities•Complex temporal patternsComplextemporalpatterns• Short-term variability: Often low or no detectable levelswith short-term (days to weeks to months) spikes relatedto timing of pesticide use and rainfall• Year-to-year variation related to cropping patterns,pesticide usage rainfall patterns climate changepesticideusage,rainfallpatterns,climatechange• Often, pesticide concentrations measured on one dayare related to the exposures measured on the previous5and following daysInterpreting Between SamplingEventsMO-01, 2007100MO-01 Measured4-day avgMeasureddaily time6080

Lseries40

Atrazine, ug/MO-01, 2007: 7-Day Sample Interval #6100MO-01 MeasuredMeasured 4-day avg7-da interval 6linear 7-da interval 64 d li 7 d i t 6MO-01, 2007: 7-Day Sample Interval #4100MO-01 MeasuredMeasured 4-day avg7-da interval 4linear 7-da interval 44 d li d i 4206080

n e, ug/L4-day avg,linear7-daint66080

n e, ug/L4-day avg,linear 7-daint404/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date2040

Atrazi n2040

Atrazi n604/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date04/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19DateKey Recommendations of PreviousSAPs RegardingMonitoringAnalysisKey Recommendations of PreviousSAPs RegardingMonitoringAnalysisg gy•Apr. 2010: To evaluate monitoring strategies orexposure estimation methods the USEPA needs:exposureestimationmethods,theUSEPAneeds:• The toxicological exposure duration of concern•Intensive monitoring for representative range of sitesIntensivemonitoringforrepresentativerangeofsites• Methods that can predict values that may be greaterthan those sampled•Sept. 2010: Combining WARP with a deterministicmodel, such as PRZM or SEAWAVE-Q offers “themost promising approach” with sparse monitoring• It may be easier to use WARP in combination with astatistical approach such askriging7statisticalapproachsuchaskrigingTopics•Recap of monitoring issues andd ti f A il d S t brecommendationsfromAprilandSeptember2010 SAPs•Addressing monitoring uncertainties relatedto variability of pesticide concentrations inwater:• Day-to-day patterns (short-term temporalvariability)• Year-to-year patterns (long-term temporalvariability)• Spatial patterns8Intensively Sampled Data Sets Used inEvaluating MethodsEvaluatingMethodsHeidelberg U. NCWQRdata for OH: MaumeeSyngenta atrazineecological exposuredataforOH:MaumeeRiver, sampled in 199517,000 km2catchmentecologicalexposuremonitoring: MO-01, 200730 km2catchment areaarea8090100MO-01, 20071416Maumee River, 199550607080

ntration, ug/L81012

ntration, ug/L10203040

Atrazine Conce246

Atrazine Conce9091 98 105 112 119 126 133 140 147 154 161 168 175 182 1890291 98 105 112 119 126 133 140 147 154 161 168 175 182 189Characterizing Uncertainty in ExposureEstimates Based on Sampling FrequencyEstimatesBasedonSamplingFrequencyEstimatesSamplingFrequency•Approach: bootstrapping simulations of samplingfrequencies to develop simple multiplicative factorsfrequenciestodevelopsimplemultiplicativefactorsfor exposure estimates•Randomly sampled the daily time series for 47Randomlysampledthedailytimeseriesfor4-,-,14-, 28-day windows•10,000 simulations of the different sampling intervals10,000simulationsofthedifferentsamplingintervals•Estimated 1-day peak and maximum 4-, 7-, 14-,28-,and 90-dayrunningaverages for each ofgg10,000 simulations of different sampling intervals• Compared the true maximum concentrations against theth105percentile maximums of the distributions from eachsampling intervalPreliminary Multiplicative Bias FactorsBased on Different SamplingIntervalsPreliminary Multiplicative Bias FactorsBased on Different SamplingIntervalsgSample 1-day4-day14-day28-day90-dayAEEMP 2007 MO-01pIntervalymax.yavg.yavg.yavg.yavg.4 days 4 3 2 2 1.57 days5322214 days 15 10 6 4 428 days 58 37 18 11 7SampleIt l1-day 4-day 14-day 28-day 90-dayNCWQR 1995 Maumee RiverIntervalmax. avg. avg. avg. avg.4 days 2 1.4 1.2 1.1 1.17 days 2 2 1.4 1.2 1.21114 days 3 2.5 2 1.4 1.328 days 3 3 2 1.5 1.5Taking the Next Step: Estimating TimeSeries from MonitoringData•Sept. 2010 SAP: With uncertain critical exposureperiod the best course of action may be“toperiod,thebestcourseofactionmaybecapture the pattern of atrazine concentrations inthe source water of each CWS based on thecharacteristics of that particular water system”•The SAP recommended combining regressionb d d l h WARP ith ithbasedmodels, suchasWARP, witheither adeterministic model or a geostatistical approach•Our analysis found that given sufficientOuranalysisfoundthat,givensufficientsampling, conditional stochastic simulationsusing variogram models may be sufficient with orith t WARP12withoutWARPDETERMINEAPPROPRIATEPOINTESTIMATIONINFILLTIMESERIESNOAnalysisStrategy forMERGEPROCEDURE>15DATAPOINTSINTIMESERIES?WITHMODELESTIMATEDVALUESNOStrategyforModelingPesticideStrategyforModelingPesticideMERGEMONITORINGDATAYESUSEWARPUSECOVARIATEPesticideTime SeriesfromUSEWARPESTIMATESINTIMESERIESPROCEEDWITHVARIOGRAMANALYSISUSECOVARIATETOESTIMATECONCENTRATIONfromMonitoringDataARRANGEFLOWPROCEEDWITH1‐DIMENSIONALORDINARYKRIGINGDataPERCENTILESWITHMERGEDDATAPERCENTILESORDINARYKRIGINGSTOCHASTICSIMULATIONOFEQUALLYPROBABLEREALIZATIONSOFTIMESERIES13Focusing Modeling Analysis on RunoffPeriod•Geostatistical methodsrequirestationarity1416Runoffstationarity• assumes equalprobability of occurrence e ntration (ug/L)8101214regardless oflocation/timeStationaritymay not Atrazine Conc e2468Base-flowBase-flow•Stationaritymaynothold true for annualdistributions withJulian Day0 100 200 300 40002distributionswithseasonal trends•Addressed by focusingAnalyses indicate stationarity mayapply during the runoff season foratrazine and other broad-useherbicides s ch asacetochlorb tAddressedbyfocusingon runoff season14herbicidessuchasacetochlor,butnot for insecticides such aschlorpyrifosVariograms: 1995 Maumee River(a) 4-day sampling intervals (b) 7-day sampling intervals

e (μg/L)

2 e (μg/L)

2

Varianc e Varianc e15Realizations of Atrazine Time Series for1995 Maumee Data1995MaumeeData1416Estimate Mean (SD)Actual Data(a) 4-day intervals1416Estimate Mean (SD)Actual Data(b) 7-day intervals

z ine Conc (ug/L)681012 z ine Conc (ug/L)681012100120140160180200220

Atra z0246100120140160180200220

Atra z0246Julian Day100120140160180200220

L )121416Estimate Mean (SD)Actual Data L )121416Estimate Mean (SD)Actual Data(c) 14-day intervals (d) 28-day intervalsJulian Day100120140160180200220

Atrazine Conc (ug/ L46810 Atrazine Conc (ug/ L46810Julian Day100 120 140 160 180 200 220024Julian Day100 120 140 160 180 200 22002416Variograms: 2007 MO-01(a) 4-day sampling intervals (b) 7-day sampling intervals

e (μg/L)

2 e (μg/L)

2

Varianc e Varianc e17Realizations of Atrazine Time Series for2007 MO-01 Data100Estimated Mean(SD)2007MO01Data(a) 4-day intervals100Estimated Mean (SD)Actual Data(b) 7-day intervals

C oncentration (ug/L)406080( )Actual Data C oncentration (ug/L)406080100120140160180

Atrazine C020100120140160180

Atrazine C020

( ug/L)80100Estimated Mean (SD)Actual DataJulian Day100120140160180

( ug/L)80100Estimated Mean (SD)Actual Data(c) 14-day intervals(d) 28-day intervalsJulian Day100120140160180

a zine Concentration (4060

a zine Concentration (4060Julian Day100 120 140 160 180

Atr a020Julian Day100 120 140 160 180

Atr a02018Estimates for 1995 Maumee RiverSampled at WeeklyIntervalsEstimates for 1995 Maumee RiverSampled at WeeklyIntervals1616p y10121416

n , ug/LDaily Time SeriesSim95th%ileSim5th%ileActual10121416

i on, ug/L4-Day AveragesSim95th%ileSim5th%ileActual46810

r azine Concentratio n46810

A trazine Concentrat i02491 98 105 112 119 126 133 140 147 154 161 168 175 182 189

At rJulianDay0291 98 105 112 119 126 133 140 147 154 161 168 175 182 189

AJulianDay161628D ASi95th%il10121416

i on, ug/L14-Day AveragesSim95th%ileSim5th%ileActual101214

t ion, ug/L28-DayAveragesSim95th%ileSim5th%ileActual46810

A trazine Concentrat i468

Atrazine Concentra t02491 98 105 112 119 126 133 140 147 154 161 168 175 182 189

AJulianDay0291 98 105 112 119 126 133 140 147 154 161 168 175 182 189JulianDay19Estimates for 2007 MO-01 Sampled atWeekly Intervals100100708090

n , ug/LDaily Time SeriesSim95th%ileSim5th%ileActual708090100

o n, ug/L4-Day AveragesSim95th%ileSim5th%ileActual30405060

r azine Concentratio n30405060

trazine Concentrati o0102091 98 105 112 119 126 133 140 147 154 161 168 175 182 189

At rJulianDay0102091 98 105 112 119 126 133 140 147 154 161 168 175 182 189

AJulianDay100100708090

o n, ug/L14-Day AveragesSim95th%ileSim5th%ileActual708090100

o n, ug/L28-Day AveragesSim95th%ileSim5th%ileActual30405060

A trazine Concentrati o30405060

A trazine Concentrati o0102091 98 105 112 119 126 133 140 147 154 161 168 175 182 189

AJulianDay0102091 98 105 112 119 126 133 140 147 154 161 168 175 182 189

AJulianDay20DETERMINEAPPROPRIATEPOINTESTIMATIONINFILLTIMESERIESNOWhatHappens IfMERGEPROCEDURE>15DATAPOINTSINTIMESERIES?WITHMODELESTIMATEDVALUESNOHappensIfWe HaveMonitoringHappensIfWe HaveMonitoringMERGEMONITORINGDATAYESUSEWARPUSECOVARIATEMonitoringwithSamplingUSEWARPESTIMATESINTIMESERIESPROCEEDWITHVARIOGRAMANALYSISUSECOVARIATETOESTIMATECONCENTRATIONSamplingIntervals >7 Days?ARRANGEFLOWPROCEEDWITH1‐DIMENSIONALORDINARYKRIGING7Days?PERCENTILESWITHMERGEDDATAPERCENTILESORDINARYKRIGINGSTOCHASTICSIMULATIONOFEQUALLYPROBABLEREALIZATIONSOFTIMESERIES21Illustration of Combined WARP andKrigingApproach for Maumee River DataIllustration of Combined WARP andKrigingApproach for Maumee River DatagVariogram generated from28-day sampling intervals of1995 M R ithError analysis from 10realizations of estimatedti i f th 28d1995MaumeeR. withWARP parameters fromOH-03 monitoring sitetime seriesfromthe28-daymonitoring intervals andWARP modeling

g /L)2025

2 C oncentration (u g1015

ance (μg/L)

2

Atrazine C510

Vari22Julian Day120 140 160 180 2000Topics•Recap of monitoring issues andd ti f A il d S t brecommendationsfromAprilandSeptember2010 SAPs•Addressing monitoring uncertainties relatedto variability of pesticide concentrations inwater:• Day-to-day patterns (short-term temporalvariability)• Year-to-year patterns (long-term temporalvariability)• Spatial patterns23Characterizing Year-to-Year Variabilityin Pesticide Concentrations7080IL-29, 2004-09TCT Source30405060

T , ug/L (ppb)01020Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09

TC T•Analysis of AMP monitoring for TCT in CWS found widerange in maximum detections• For >100 CWS with 6 years of monitoring, 90th percentile of ratiobetween lowest and highest maximum across years for the samesystem was 20X for source water, 13X for treated water• Increasing the number of years monitored increases the likelihood ofcapturing a wider range of conditions24Estimating Potential Year-to-YearVariabilityin Atrazine Concentrations•PRZM Hybrid ModelR PRZM ith t h difi i t i f ll d t•RunPRZMwithwatershed-specificinputs, rainfalldatafor that year•Adjust percentile concentrations using monitoring data,Adjustpercentileconcentrationsusingmonitoringdata,accounting for sampling frequency uncertainties• Run for additional weather years for range•WARP• Vary temporal parameters – May/June precipitation,t ii t it lik l f larazne useintensity overlikely range ofvalues• Uncertainties in estimation of atrazine use intensity maydominate variabilityin WARP estimations25Topics•Recap of monitoring issues andd ti f A il d S t brecommendationsfromAprilandSeptember2010 SAPs•Addressing monitoring uncertainties relatedto variability of pesticide concentrations inwater:• Day-to-day patterns (short-term temporalvariability)• Year-to-year patterns (long-term temporalvariability)• Spatial patterns26Addressing Spatial Variability in PesticideConcentrations•CWS in AMP fall in areas predicted to be more vulnerable•No inferenceNoinferenceto other CWSin vulnerableareas•Vulnerabilityassessmentsfor eco-risk willhelp identifythoerpotentiallyvulnerable27CWSMonitoring Interpretation Issues forSAPConsiderationMonitoring Interpretation Issues forSAPConsiderationCo s de at o•Given the temporal and spatial patterns ofti id i f t h dpesticide occurrencein surface waters,howdowe best put confidence bounds on exposureestimates from monitoring?estimatesfrommonitoring?• Approaches for day-to-day variability and estimatingtime series from monitoringat different samplingpgfrequencies• Approaches for characterizing year-to-year variabilityPl i CWS i i i l•PlacingCWSsitesin a spatialcontext28