Slide 1 of 32EvaluationDataandStatisticalFrameworkforEcologicalExposurePredictionsEvaluationDataandStatisticalFrameworkforEcologicalExposurePredictionsPresentedbyR.WoodrowSetzerNationalCenterforComputationalToxicologyOfficeofResearchandDevelopmentPresentedbyR.WoodrowSetzerNationalCenterforComputationalToxicologyOfficeofResearchandDevelopmentSlide2 of32PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch•ApplyingSEEMtoEcologicalExposure• ThirdGenerationSEEMAnalysisforHumanExposureFutureApplicationsSlide3 of32GoalsTodevelopasextensiveadatasetaspossibletoevaluatemodelspredictingsurfacewaterconcentrationsofenvironmentallyrelevantchemicalsTodevelopmethodsforcomparingthatdatasettomodelpredictionsSlide4 of32EstimateUncertaintyEDSPChemicalsMonitoringData Measured Ecological ConcentrationsModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsWeightedHTEModelPredictionsDataset1Dataset2...SEEMforEcologicalExposureEcologicalmodelingisdifferentfromhumanmodelinginthattheexposures(concentrationinenvironmentalmedia)canbemorereadilydirectlyassayedratherthanindirectlyinferredfrombiomonitoring(e.g.,urine)data.Slide5 of32EcologicalMonitoringChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsSlide6 of32EcologicalMonitoringChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsSlide7 of32NationalWaterQualityMonitoringCouncilUSGS/EPAportalSlide8 of32DataStructureSlide9 of32DataStructure•SpatialStructure:•Samplingintensitycanbequiteheterogeneous,becausesiteswereestablishedfordifferentpurposes•Sitelocationisnotrandom•SitelocationNOTanalogoustoNHANESprobabilitysampling•Forexample,sitesestablished,e.g.,toevaluatecomplianceandsoareoftendownstreamofreleasepoint•Spatialvariationofsamplingintensitycancausebiasinestimates(e.g.,therearelotsmoresamplesinNewJerseythaninWyoming)•TemporalStructure:•Samplingfrequencyandtimesareheterogeneous•Temporalheterogeneitywillbedifficulttogeneralize•Itisnotoriouslydifficulttocapturepeakconcentrationswiththissortofgenerallylow‐frequencysampling.•Many<LODvalues•>700organicswithmultiplesamplesitesandtimesSlide10 of32SurfaceWaterSamplingSites,Cont.US>600,000surfacewatersitesinlower48> 700individualchemicalsGPS,date,andtimestampsLODindicationPlottedw/Randpackagesrgdal,sp,rgeos,&maptoolsSlide11 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide12 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide13 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide14 of32NestedHydrologicUnitsSlide15 of3203Level1:HUC2Slide16 of32Level2:HUC4Slide17 of320302Level2:HUC4Slide18 of32030201030202Level3:HUC6Slide19 of32Level4– HUC8Slide20 of32SamplingSitesareLocatedwithinHydrologicUnitsSlide21 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide22 of32HierarchicalNestedStatisticalModel



# is the national level log geometric mean:μ~N(M,Σ) (acrosschemicals)Slide23 of32HierarchicalNestedStatisticalModel



# is the national level log geometric mean:μ~N(M,Σ) (acrosschemicals)R,S,A,Ware effects at different spatial scales:Ri:ithregion:R~N(0, 

# R2)Sij:jthsubregion oftheithregion:S~N(0, 

# S2)Aijk:kthaccountingunitinthe...intheithregion:A~N(0, 

# A2)Wijkl:lthwatershed...:W~N(0, 

# W2)Slide24 of32HierarchicalNestedStatisticalModel



# is the national level log geometric mean:μ~N(M,Σ) (acrosschemicals)R,S,A,Ware effects at different spatial scales:Ri:ithregion:R~N(0, 

# R2)Sij:jthsubregion oftheithregion:S~N(0, 

# S2)Aijk:kthaccountingunitinthe...intheithregion:A~N(0, 

# A2)Wijkl:lthwatershed...:W~N(0, 

# W2)



# :deviationsofsamplesduetolocalspatialandtemporalvariation:



# ~N(0, 

# 2)Slide25 of32(simulateddata)EstimatemeanconcentrationsSlide26 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide27 of32(simulateddata)EvaluateModelPredictionsvsDataSlide28 of32OutlineofAnalysisStrategyChoosechemicalswithextensivegeographicandtemporalsamplesStratifydatatemporally:e.g.,seasonally,monthlyGroupdataforsinglechemicalsgeographically,usingnestedhydrologicunits:Estimatenational‐andlower‐levelgeometricmeansandspatialvariance.•Useahierarchicalrandomeffectsmodelforeffectsathuc2..huc8levels(maybeeventohuc12)•HandleLODswithcensoredlikelihoodsEvaluaterelationshipbetweenHTmodelpredictionsandestimatednational‐orlower‐levelgeometricmeansofwaterconcentrations.CalibrateHTmodelsand/oruseadditionalinformationaboutchemicalstoimprovepredictionandquantifypredictionuncertainty.Slide29 of32(simulateddata)VarianceunexplainedbymodelStep2:RegressonModelPredictionsSlide30 of32EstimateUncertaintyEDSPChemicalsMonitoringData Measured Ecological ConcentrationsModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsWeightedHTEModelPredictionsDataset1Dataset2...SEEMforEcologicalExposure:NextStepsSlide31 of32Summary•Thegoalistoevaluateandcalibrateecoexposuremodelsforsurfacewaterchemicalconcentrationsagainstexistingwatersampledata.•Wecantakeadvantageofthehierarchicalnatureofthehydrologyunittoestimategeographicvariationondifferentspatialscales.•Temporalvariationisproposedtobehandledbystratificationontime.•Observationslessthantheirlimitofdetectionaretreatedascensored,assumingalognormallikelihood.•Thegoalisachievedbyusingregressiontocomparemodelpredictionstoestimatesofwaterconcentrationsatanappropriatelevel.Slide 32 of 32Craig Barber, ORDPeter Eghehy, ORDKristin Isaacs, ORDWoody Setzer, ORDJohn Wambaugh, ORDExpoCast Ecological TeamElaine HubalCSS Deputy NationalProgram DirectorTina BahadoriCSS National ProgramDirectorAcknowledgements