Slide1 of16HighThroughputEcologicalExposureModelingHighThroughputEcologicalExposureModelingPresentedbyM.CraigBarber,Ph.D.NationalExposureResearchLaboratoryOfficeofResearchandDevelopmentPresentedbyM.CraigBarber,Ph.D.NationalExposureResearchLaboratoryOfficeofResearchandDevelopmentSlide2 of15PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch•ApplyingSEEMtoEcologicalExposure• ThirdGenerationSEEMAnalysisforHumanExposureFutureApplicationsSlide3 of15ForwardPredictionofExposureChemicalManufactureEnvironmentalReleaseFoodAir,Soil,WaterEcologicalFloraandFaunaDietary Far‐FieldMONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanDietaryDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)BiomarkersofExposureSlide4 of15EcologicalExposureModelingDevelopanintegratedecologicalexposuremodelingsystemforassessingpersistentbioaccumulative toxicants(PBTs)inaquaticecosystemsusingexistingpeerreviewedmodels.•Chemicalsarecharacterizedbytheirphysico‐chemicalpropertiesnottheirmodeofaction.•Modelsinclude:– ORD’sEFSchemicalpropertycalculator,– ORD’sEXAMSchemicalfate&transportmodel(steady‐stateanddynamic),– OPP’sKABAMsteady‐stateaquaticecosystembioaccumulationmodel,and– ORD’sBASSdynamicaquaticecosystembioaccumulationmodel.Challengesinclude:•Developastraightforward/welldefinedapplicationarchitecturethatcanbeappliedinanautomated,highthroughput‐manner•Develophighthroughputmethodsforassessingmultiplechemicalpropertieswithinmultipleenvironmentsandecologicalcommunities.Slide5 of15PreviouslyIdentifiedModelswithEcologicalComponentUSEtoxandRAIDARwereidentifiedasHTEmodelsbyExpoCastModelChallenge(Mitchell‐Blackwoodetal.,2013)Bothmodelspredictfateandtransportandbioaccumulation7834EDSPChemicalsUSEtoxorRAIDARPredictions(kg/kgemitted)Slide6 of15EXAMS‐KABAM‐BASSHigherTierExposureModelsEFSEXAMSProjectBuilder(EXAMS_prj_*.exe)CreatesbatchfilesforprojectexecutionEnvironmentsChemicalPropertiesKowHLCMPSwBAFDBExceldatatableofBAFsProjectsEnvironments+Chemicals+LoadingsOutputBuilder(EXAMS_report_ssv.exe)GeneratesasemicolonseparatedvaluefileforimporttoanExcelWorkbookEXAMSUDBChem.exaEnv.exaEnvironmentBuilder(EXAMS_river_environments_v5_*.xlsx)FacilitatescreationofEXAMS*exvfilesforinputintotheEXAMSUDBLoadingGeneratorDietary&DrinkingWaterAssessmentSHEDS‐HTKABAMBASSSlide7 of15AdaptingEXAMS‐KABAM‐BASSforHighThroughputExposureEstimationEXAMS‐KABAM‐BASSisacomplexsystemofintegratedmodelsToadaptthesemodelsforHTEestimation,wehavecreatedalinearized modelthatcanberapidlyparameterizedandrunforthousandsofchemicalsandscenarios:•Wesimulatedmultiple,representativechemicalsinmultiple,representativeEXAMSriverenvironments•Weusedthemaximum,minimum,mean,andmedianloadingratesestimatedusingSHEDS‐HT•WethenfittheresultingexpectedenvironmentalandbiologicalconcentrationstoaloglinearmodelEFSEXAMSProjectBuilder(EXAMS_prj_*.exe)CreatesbatchfilesforprojectexecutionEnvironmentsChemicalPropertiesKowHLCMPSwBAFDBExceldatatableofBAFsProjectsEnvironments+Chemicals+LoadingsOutputBuilder(EXAMS_report_ssv.exe)GeneratesasemicolonseparatedvaluefileforimporttoanExcelWorkbookEXAMSUDBChem.exaEnv.exaEnvironmentBuilder(EXAMS_river_environments_v5_*.xlsx)FacilitatescreationofEXAMS*exvfilesforinputintotheEXAMSUDBLoadingGeneratorDietary&DrinkingWaterAssessmentSHEDS‐HTKABAMBASSSlide8 of15Linearized EXAMS‐KABAM‐BASSHTEModel•CxistheEECorEBCofinterest,e.g.,dissolvedwater(mg/L),totalwater(mg/L),totalsediment(mg/kgdrywt),andwhole‐bodyfish(mg/kgwetwt)concentrations,•Qistheriver’smeanannualstreamflow(cfs),•Listhechemical’sloadingrate(kg/h),•Kowisthechemicaloctanol‐waterpartitioncoefficient,•HLCisthechemical’sHenryLawconstant(atm‐m3/mol),and•Swisthechemical’saqueoussolubility(mg/L).Wecanusethisregressionas:•AHTEmodelforestimatingEECsandEBCswithoutperformingfullEXAMS‐KABAM‐BASSsetupsandsimulations,•ToquantifytheparametersensitivitiesofthepredictedEECsandEBCs,•Toback‐calculateunknownorpoorlyknownloadingratestoriversofconcernbasedonobservedwater,sediment,and/orfishconcentrations,and•ToestimatepriorsforBayesianandotheradvancedstatisticalmodeling.10 0 1 10 2 10 3 10 OW 4 10 5 10log log log log log HLC logxwC a a Qa La K a a S    EEC:ExpectedEnvironmentalConcentrationEBC:ExpectedBiologicalConcentrationsSlide9 of15ChemicalsandEnvironmentalReleasesforLinearized ModelInitialEvaluationChemicals:74compoundsfromEDSPList1and2,withlogKow>3wereselectedInitialEnvironmentRelease:•Dischargesfrompubliclyownedtreatmentworks(POTWs)towadeablestreamsandlarge•Loadingratestothesewaterswereestimatedfor27oftheabovechemicalsusing–meandown‐the‐draindischarges(g/person‐day)estimatedbySHEDS‐HT,–POTWremovalefficienciesestimatedbyEPISUITE,and–theassumptionthatan“average”POTWservicesapproximately10,500people(e.g.,http://css.snre.umich.edu/css_doc/CSS04‐14.pdf).Resultingloadingratesvariedfrom2.23x10‐6to1.65kg/hr(mean=0.153;SD=0.419;median=4.18x10‐4)Slide10 of15AquaticEnvironmentsforLinearized ModelInitialAquaticEnvironments:nineexistingEXAMSriverenvironmentsdevelopedforOPPTworkplan chemicals[i.e.,chloroparaffins (MCCP)andcyclicsiloxanes (CS)].•USGS_01335754_Hudson_River_NY.exv(OPPTCSsite)•USGS_01483700_St_Jones_River_DE.exv(OPPTMCCPsite)•USGS_02217500_Middle_Oconee_River_GA.exv(ORDdevelopmentsite)•USGS_03277200_Ohio_River_KY.exv(OPPTCSsite)•USGS_04157000_Saginaw_River_MI.exv(OPPTCSsite)•USGS_04176000_River_Rasin_MI.exv(OPPTCSsite)•USGS_04196000_Sandusky_River_OH.exv(OPPTMCCPsite)•USGS_06710000_South_Platte_River_CO.exv(OPPTMCCPsite)•USGS_07174400_Caney_River_OK.exv(OPPTMCCPsite)InitialAquaticCommunities:•KABAM– fishrepresentedby3trophiclevelcompartments(fish_TL2,fish_TL3,andfish_TL4)•BASS– age‐structurefishcommunitywhosecompositionbytotalbiomassisapproximately67.4%redear sunfish(Lepomismicrolophus),19.2%largemouthbass(Micropterussalmoides),12.1%bluegillsunfish(Lepomismacrochirus),and1.34%channelcatfish(Ictaluruspunctatus).Slide11 of15StatisticalModelingofDynamicEXAMS‐BASSOutputsHTEmodelisbehavingasexpected1.0E‐111.0E‐101.0E‐091.0E‐081.0E‐071.0E‐061.0E‐051.0E‐041.0E‐031.0E‐021.0E‐011.0E+000 2 4 6 8 10

EXAMS C wdRiverIDDissolvedwaterconc.(Cwdmg/L)‐12‐10‐8‐6‐4‐20‐12‐10‐8‐6‐4‐2 0

EXAMS log C wdRegressionlogCwd(SE=0.12)Dissolvedwaterconc.(Cwdmg/L)Slide12 of15StatisticalModelingofDynamicEXAMS‐BASSOutputs‐10‐8‐6‐4‐202460 2 4 6 8 10

BASS log C fRiverIDWhole‐bodyfishconc.(Cfmg/kg)‐10‐8‐6‐4‐20246‐9‐41 6

BASS log C fRegressionlogCf (SE=0.26)Whole‐bodyfishconc.(Cfmg/kg)HTEmodelisbehavingasexpectedSlide13 of15ComparisonofECHA(2005)MCCPAssessmentwithEXAMS‐KABAM‐BASSSteady‐stateModeling:MedianLow‐endLoading0.010.11101000.010.1110100ECHACf KABAMCf BASSCf ECHACs EXAMSCs

C f (mg/kg ww) or C s (mg/kg dw)ECHA(2005)vsEXAMS‐KABAM‐BASSSlide14 of15ResultsofStatisticalAnalysisInvestigatedEXAMS‐KABAM‐BASSoutputsfor27chemicalsacross9riverenvironmentsusingloadingsfromPOWTandSHEDS‐HTEnvironmentsofconcernmatter•EECsandEBCsareinverselydependentonriverstreamflow.Loadingratesmatter•EECsandEBCsarelinearlydependentonloadingrates.WaterandsedimentEECshavesignificantlydifferentlogKowdependencies.GiventhesameEDCloadingrates,dynamicEECsaresignificantlyhigherthantheirsteady‐statecounterparts•TheratioofdynamicEECstotheircorrespondingsteady‐statecounterpartsaveraged1.37(median=1.29;SD=0.264;min=1.06,max=3.30).EEC:ExpectedEnvironmentalConcentrationEBC:ExpectedBiologicalConcentrationsSlide15 of15SummaryAdaptinghighertierEPAmodelsEXAMS‐KABAM‐BASStoaugmentRAIDARandUSEtoxecologicalpredictionsHTEXAMS‐KABAM‐BASSlinearregressionmodel:•ServesasHTEmodelforotherchemicals,rivers,andloadingscenarioswhoseparametersareboundedbythoseusedinthecurrentprototype,•Canbeusedtoback‐calculatemorerefinedloadingratesfromobservedfielddata,•Canbeusedtoestimatepriorsforadvancedstatisticalmodeling,and•CanbeusedtoquantifyparametersensitivitiesofpredictedEECsandEBCs.Slide16 of15BroadenlinearmodelwithmorechemicalsandEXAMSenvironmentsPerformSEEManalysiswithwaterconcentrationdataEstimateUncertaintyEDSPChemicalsMonitoringData Measured Ecological ConcentrationsModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsEXAMS‐KABAM‐BASSHTEPredictionsDataset1Dataset2...DatafromNationalWaterQualityMonitoringCouncilUSGS/EPAportalNextsteps