development of lake washington pcb fate and ... · figure 6. comparison of fate model‐predicted...

Development of Lake Washington PCB Fate and Bioaccumulation Models

March 2014

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Development of Lake Washington PCB Fate and Bioaccumulation Models

Prepared for: U.S. Environmental Protection Agency Region 10

Submitted by: Curtis DeGasperi, Jenée Colton, and Carly Greyell King County Water and Land Resources Division Department of Natural Resources and Parks

Funded by EPA Grant No. PC-J28501-1

Disclaimer:ThisprojecthasbeenfundedwhollyorinpartbytheUnitedStatesEnvironmentalProtectionAgencyunderassistanceagreementPC‐00J285‐01toKingCounty.ThecontentsofthisdocumentdonotnecessarilyreflecttheviewsandpoliciesoftheEnvironmentalProtectionAgency,nordoesmentionoftradenamesorcommercialproductsconstituteendorsementorrecommendationforuse.

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DevelopmentofLakeWashingtonPCBFateandBioaccumulationModels

KingCounty i March2014

Acknowledgements TheauthorsgratefullyacknowledgetechnicalsupportprovidedbyRichardJackoftheKingCountyWaterandLandResourcesDivision,ScienceSection(withinDNRP).ValuableadviceandfeedbackontechnicalaspectsofbothmodelswereprovidedbyGregPelletieroftheWashingtonStateDepartmentofEcology(Ecology).WethankGregPelletierandtheLakeWashingtonLoadingsprojectadvisorypanelmembersforreviewingthedraftmodelingreport.AdvisoryPanelMembers(inalphabeticalorder)were:

FredBergdolt,WADept.ofTransportation

BetsyCooper,KingCountyWastewaterTreatmentDivision

JonathanFrodge,SeattlePublicUtilities

JennyGaus,CityofKirkland

JoanHardy,WADept.ofHealth

RachelMcCrea,WADept.ofEcology

DougNavetski,KingCountyStormwaterManagement

AndyRheaume,CityofRedmond

RonaldStraka,CityofRenton

HeatherTrim,PeopleforPugetSound/FutureWise

BruceTiffany,KingCountyIndustrialWaste

PatrickYamashita,CityofMercerIsland

Citation KingCounty.2014.DevelopmentofLakeWashingtonPCBFateandBioaccumulation

Models.PreparedbyCurtisDeGasperi,JenéeColtonandCarlyGreyell.ScienceandTechnicalSupportSection,WaterandLandResourcesDivision,Departmentof

NaturalResourcesandParks.Seattle,Washington.


KingCounty ii March2014

Table of Contents ExecutiveSummary...............................................................................................................................................vii

1.0 Introduction.................................................................................................................................................1

1.1 ProblemDefinitionandBackground............................................................................................2

1.2 ProjectGoalsandObjectives............................................................................................................3

1.3 ModelingObjectives.............................................................................................................................4

2.0 StudyAreaandModelDomain............................................................................................................5

3.0 TheModels....................................................................................................................................................8

3.1 ContaminantFateModel....................................................................................................................8

3.2 EcosystemBioaccumulationModel..............................................................................................9

3.3 DataRequirements...............................................................................................................................9

3.3.1 Physical..............................................................................................................................................10

3.3.2 Chemical............................................................................................................................................10

3.3.3 Biological..........................................................................................................................................11

3.4 DataAcceptanceCriteriaandRules...........................................................................................12

3.5 ModelSensitivityandUncertaintyAssessments.................................................................13

4.0 FateModelInputData..........................................................................................................................15

4.1 RateConstants.....................................................................................................................................17

4.1.1 Lossesfromthewatercolumn................................................................................................17

4.1.2 Lossesfromtheactivesedimentlayer................................................................................21

4.1.3 Sedimentproperties....................................................................................................................23

4.1.4 Waterproperties...........................................................................................................................27

4.1.5 ExternaltPCBload........................................................................................................................28

4.1.6 ExportoftPCBthroughthelakeoutlet................................................................................28

5.0 BioaccumulationModelInputData................................................................................................29

5.1 LakeParameters.................................................................................................................................30

5.2 PCBChemistryParameters............................................................................................................32

5.3 FoodWebStructureandDietaryAssumptions....................................................................32

5.4 BiologicalParameters......................................................................................................................38

6.0 SensitivityandUncertaintyAnalysisApproach........................................................................45

6.1 Sensitivity..............................................................................................................................................45


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6.2 Uncertainty...........................................................................................................................................46

7.0 ResultsandDiscussion.........................................................................................................................49

7.1 FateModel.............................................................................................................................................49

7.1.1 Performance....................................................................................................................................49

7.1.2 Sensitivity.........................................................................................................................................54

7.1.3 Uncertainty......................................................................................................................................59

7.2 BioaccumulationModel...................................................................................................................62

7.2.1 Performance....................................................................................................................................62

7.2.2 Sensitivity.........................................................................................................................................65

7.2.3 Uncertainty......................................................................................................................................68

8.0 Conclusions................................................................................................................................................72

9.0 References..................................................................................................................................................74

Figures Figure1. LakeWashingtonWatershed......................................................................................................7

Figure2. SimplifiedFoodWebforLakeWashington.........................................................................12

Figure3. DiagramofPCBfateprocessesincludedinmodel.Source:Davis(2004).............15

Figure4. VisualillustrationsoflimitedmixingbyphysicalorbiologicalactivityinLakeWashingtonsediments:A)Cesium‐137,DDT,PCB,leadandcoppersedimentprofilesshowingpreservedcontaminantpeaks(VanMetreetal.2004),B)Radiographsshowingfineundisturbedsedimentlayers(EdmondsonandAllison1970),C)Photographofverticalsectionofcoreshowingundisturbedsedimentlayers–smallgraybaratbottomis10µm(Edmondson1991)...........25

Figure5. DetailedconceptualfoodwebforLakeWashington.......................................................33

Figure6. Comparisonoffatemodel‐predictedwaterandsedimentconcentrationstoestimatedaverageconcentrationsbasedonobserveddata........................................50

Figure7. HindcastmodelpredictionsoftotalanddissolvedtPCBconcentrationinlakewaterfrominitialsedimentandwaterconcentrationofzeroandasteadyloadingrateof0.672kgyr‐1.Alsoincludedarelinesrepresentingastatisticalsummaryoftheavailableobservedconcentrationdata(mean,25th,50th,and75thpercentiles)..............................................................................................................................51

Figure8. HindcastmodelpredictionsoftotalanddissolvedtPCBconcentrationinlakesedimentfrominitialsedimentandwaterconcentrationofzeroandasteadyloadingrateof0.672kgyr‐1.Alsoincludedarelinesrepresentingastatistical


KingCounty iv March2014

summaryoftheavailableobservedconcentrationdata(mean,25th,50th,and75thpercentiles)..............................................................................................................................51

Figure9. HindcastmodelpredictionsofthetotalmassoftPCBinwaterandsedimenthanwitinitialsedimentandwaterconcentrationofzeroandasteadyloadingrateof0.672kgyr‐1.AlsoincludedarelinesrepresentingastatisticalsummaryoftheavailableestimatesofobservedtPCBmass(mean,25th,50th,and75thpercentiles).....................................................................................................................53

Figure10. Hindcastmodelpredictionsoflossesthroughdifferentpathwaysbasedonaninitialsedimentandwaterconcentrationofzeroandasteadyloadingrateof0.672kgyr‐1......................................................................................................................................54

Figure11. Uncertaintyinmodel‐predictedtPCBconcentrationsinwatercomparedtouncertaintyinobservedconcentrationsbasedonrangeofpossibleinputsfortotaltPCBloadingandlogKow..................................................................................................60

Figure12. Uncertaintyinmodel‐predictedtPCBconcentrationsintheactivesedimentlayercomparedtouncertaintyinobservedconcentrationsbasedonrangeofpossibleinputsfortotaltPCBloadingandlogKow...........................................................61

Figure13. Model‐predictedtissueconcentrationsusingempirically‐derivedwaterandsedimentPCBconcentrationscomparedtoobservedtPCBtissueconcentrations.................................................................................................................................64

Figure14. Model‐predictedtissueconcentrationsusingwaterandsedimentconcentrationspredictedbyfatemodelcomparedtoobservedTotalPCBtissueconcentrations....................................................................................................................65

Figure15. LowandhighuncertaintyestimatesfortheLakeWashingtonbioaccumulationmodel................................................................................................................71

Tables Table1. Rateconstants(d‐1)forPCBsinLakeWashingtonbasedonbestestimatesof

modelinputdata.SanFranciscoBayandLakeOntariovaluesincludedforcomparison........................................................................................................................................16

Table2. Modelinputdata.............................................................................................................................18

Table3. ChemicalpropertiesofPCB‐118(3,3',4,4',5‐pentachlorobiphenyl)........................19

Table4. SummaryoftPCBconcentrationsmeasuredinLakeWashingtonsediments.....27

Table5. SummaryofmeasuredwatercolumntPCBconcentrations(pg/L).........................28

Table6. Bioaccumulationmodelparametervalues..........................................................................31

Table7. ChemistryparametersforPCB‐118.......................................................................................32

Table8. Dietfractionassumptionsformodeledtaxa.......................................................................36


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Table9. Generalbiologicalparameters..................................................................................................39

Table10. Phytoplanktonbiologicalparameters...................................................................................40

Table11. Daphniabiologicalparameters.................................................................................................40

Table12. Mysidbiologicalparameters......................................................................................................41

Table13. Copepodbiologicalparameters................................................................................................41

Table14. AmphipodandIsopodbiologicalparameters....................................................................41

Table15. BenthicInvertebratesbiologicalparameters.....................................................................41

Table16. Mollusksbiologicalparameters...............................................................................................42

Table17. CrayfishLarge(>90mmTL)biologicalparameters.......................................................42

Table18. CrayfishSmall(<90mmTL)biologicalparameters........................................................42

Table19. JuvenileSockeyeSalmonbiologicalparameters...............................................................42

Table20. Longfinsmeltbiologicalparameters......................................................................................43

Table21. Peamouthchubbiologicalparameters..................................................................................43

Table22. Pricklysculpinlarge(>74mmTL)biologicalparameters...........................................43

Table23. Pricklysculpinsmall(<75mmTL)biologicalparameters..........................................43

Table24. Yellowperchlarge(>224mmFL)biologicalparameters............................................43

Table25. Yellowperchsmall(<225mmFL)biologicalparameters............................................44

Table26. Threespinesticklebackbiologicalparameters..................................................................44

Table27. Smallmouthbassbiologicalparameters...............................................................................44

Table28. Cutthroattroutbiologicalparameters..................................................................................44

Table29. Northernpikeminnowbiologicalparameters...................................................................44

Table30. Inputvaluesforparametersvariedinuncertaintyanalysis........................................48

Table31. ComparisonofmodeledandobservedtPCBexportviatheLakeWashingtonoutletatMontlakeCut(kgyr‐1)................................................................................................53

Table32. ComparisonSummaryofsensitivityanalysisresultsofthefatemodeltopredictedsteady‐statemassoftPCBinLakeWashington...........................................57

Table33. SummaryofsensitivityanalysisresultsofthefatemodeltopredictedpercentofinitialmassoftPCBinLakeWashingtonremainingin20yearsfollowinga50percentreductionfromthecurrentbestestimateloadingrate..........................58

Table34. Summaryoffatemodeluncertaintyanalysisresults......................................................59

Table35. Predicted/observedratiosforPCBtissueconcentrationsmodeledusingempirically‐derivedorfatemodel‐predictedmeansedimentandwaterinputconcentrations.................................................................................................................................63


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Table36. SensitivityofPredictedTissueConcentrationstoIndividualParametersContributingOver10%toVariance.......................................................................................67

Table37. SensitivityofBioaccumulationModeltoChangeinWaterandSedimentConcentrations.................................................................................................................................68

Table38. Rangeofuncertaintyinpredictedcomparedtoobserved(mean±SD)tissueconcentrations(µg/kgwet).......................................................................................................70

Appendices AppendixASolidsDataUsedinBioaccumulationModel

AppendixBVolume‐WeightedAverageParameterCalculations

AppendixCBioaccumulationModelSensitivityAnalysisResults(SeeseparatePDFfile)


KingCounty vii March2014

EXECUTIVE SUMMARY WashingtonDepartmentofHealthissuedafishconsumptionadvisoryforLakeWashingtonduetoPCBsin2006(WADOH2004)becausetheydeterminedLakeWashingtonfisharecontaminatedwithPCBsatlevelsunsafeforhumanconsumption.Toaddressthisfishadvisory,KingCountyproposedandwasawardedaU.S.EnvironmentalProtectionAgency(USEPA)PugetSoundScientificStudiesandTechnicalInvestigationsAssistanceGrantin2010to1)estimateloadingofpolychlorinatedbiphenyls(PCBs)andpolybrominateddiphenylethers(PBDEs)toLakeWashington,LakeUnionandPugetSoundand2)modelthepotentialdecreaseinLakeWashingtonfishtissueconcentrationsassociatedwithselectPCBloadingreductionscenarios.TheoverallprojectisconsideredafirststeptowardunderstandingtherelativeimportanceofmajorcontaminantloadingpathwaysthatcontributePCBstotheselakes,aswellasunderstandingtheirlongtermfateandthepotentialrecovery.

TheprimarygoalofthisprojectistodevelopamorecompleteunderstandingoftheprocessescontrollingtheultimatefateofPCBsandtoinformmanagementactionstoreducehealthrisksfromconsumingPCB‐contaminatedfishfromLakeWashington.ThestudywillalsoprovideabetterunderstandingonwhichtodevelopfuturemonitoringandmodelingeffortsneededtofullyaddresstheissueofhumanhealthrisksfromPCB‐contaminatedfishinLakeWashington.Thisreportdescribesindetailonecomponentofthisproject:thedevelopmentofafatemodelandabioaccumulationmodelforPCBsinLakeWashington.

ThisreportdescribesthedevelopmentoftotalPCB(tPCB)fateandbioaccumulationmodelsforLakeWashingtonandevaluateshowwellthemodelsperform.Italsodescribestheresultsofsensitivityanduncertaintyanalyses.ThefatemodeluseschemicalandLakeWashingtoninformationtopredictLakeWashingtonwaterandsedimentconcentrationsoftPCB.ThebioaccumulationmodeluseschemicalandLakeWashingtonfoodwebinformationtopredictconcentrationsinfishtissue.AlthoughloadingestimatesweredevelopedforPBDEs,developmentoffateandbioaccumulationmodelsforPBDEswasoutsidethescopeofthisproject.1

Theresultsofthefateandbioaccumulationmodeldevelopmentandtestingareasfollows.

Thefatemodelpredictedwaterandsedimentconcentrationsfromestimatesofcurrentloadingswellwhencomparedtoobservedwaterandsedimentconcentrations.Themodeltestingsupportstheassumptionsmadetodevelopthefatemodel,butmostimportantly,supportsthetPCBloadingestimatederivedaspartofthisstudyasareliablefirst‐approximationofthecurrenttPCBloadingratetothelake.

1ThefateandbioaccumulationmodelswereintendedtoanswerquestionspertainingtothePCBfishconsumptionadvisory.Currently,thereisnoPBDEfishconsumptionadvisoryforLakeWashington.Thus,thescopewaslimitedtoPCBsonly.


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ModelresultssuggestthattheresponsetimenecessaryforLakeWashingtonsedimentandwaterconcentrationstoreachequilibriumwithaconstantloadisapproximately40years.Themostrapidchangeinconcentrationsoccursinthefirst20years.Thus,thissuggestsanychangesreducingPCBloadtoLakeWashingtonwillrequireseveraldecadestobefullyreflectedinfishtissues.

TissueconcentrationspredictedusingwaterandsedimenttPCBconcentrationsfromfielddatacomparedtothoseusingthefatemodeloutputdemonstratedgoodperformanceofthebioaccumulationmodelwithboth,butbetterperformanceusingthesedimentandwaterconcentrationspredictedbythefatemodel.2

TestingindicatedthefateandbioaccumulationmodelsperformedwellandsimilarlytoapplicationsofthismodelinSanFranciscoBay,GeorgiaBasinandPugetSound.

Inconclusion,thefateandbioaccumulationmodelsweresuccessfullydevelopedforapplicationinthisprojecttoLakeWashington.Inthenextphaseofthisproject,thefateandbioaccumulationmodelswillbecoupledtoevaluatetotaltPCBloadingreductionscenariostoinformwaterqualitymanagersandstakeholdersonthemagnitudeofchangerequiredtoreachsafePCBtissuelevelsinLakeWashingtonfish.Theresultsofthesemodelsimulations,alongwithareviewofprojectfindingsandoverallrecommendationsforfuturework,willbepresentedinaseparateandfinalreportforthisproject.

2Fielddatawerelikelybiasedhighbecausesamplingoftenfocusedonareassuspectedofcontamination(e.g.,nearcombinedseweroverflowsorstormdrains)andincludedsamplesthatrepresentedupto10cmofsurfacesediment,whichpotentiallyincludeshighertPCBconcentrationsthatoccurredinsedimentsdepositedinthe1970s.Becauseofthisbiasandbettermodelfitofthefatemodelpredictedsedimentandwaterconcentrations,thelattersourcewasselectedforuseinmodelapplicationoccurringlaterintheproject.


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1.0 INTRODUCTION Polychlorinatedbiphenyls(PCBs)arepresentinLakeWashingtonfishatlevelsunsafeforhumanconsumption(WADOH2004).Inaddition,asimilarlypersistentandmoremodernchemical,polybrominateddiphenylethers(PBDEs)isalsobioaccumulatinginLakeWashingtonfish.ElevatedconcentrationsofPCBsandPBDEsarenotuniquetoLakeWashington,asevidencedbychemicalconcentrationsmeasuredinmarinemammalandfishtissuefromPugetSound(Rossetal.2000,Rossetal.2004,Krahnetal.2007,Westetal.2008,Sloanetal.2010,Cullonetal.2005).However,todatetherehavenotbeenanystudiesfocusedonhowthesechemicalsaregettingintoLakeWashingtonfishorthequantityofthesechemicalsenteringLakeWashington,LakeUnionandtheShipCanal(LakeUnion),andPugetSoundfromthiswatershed.PrimarygoalsofthisprojectaretohelpfillPCBandPBDEdatagapsfortheLakeWashingtonwatershedandPugetSoundbasinandprovideinformationandtoolsneededtodirectmanagementofPCBsandreducehealthrisksassociatedwithLakeWashingtonfishconsumption.

ThisprojectisconsideredafirststeptowardunderstandingtherelativeimportanceofmajorcontaminantloadingpathwaysthatcontributePCBsandPBDEstotheselakes,aswellasunderstandingtheirlongtermfateandthepotentialforrecovery.TheendresultofthisprojectisexpectedtobeamorecompleteunderstandingoftheprocessescontrollingtheultimatefateandthepotentialformanagementactionstoreducehealthrisksassociatedwithconsumingPCB‐contaminatedfishfromLakeWashington.Theprojectwillalsoprovideabetterunderstandingonwhichtodevelopfuturemonitoringandmodelingefforts.

Specifically,KingCountywasawardedaU.S.EnvironmentalProtectionAgency(USEPA)PugetSoundScienceStudiesandTechnicalInvestigationAssistanceGrantin2010to1)estimateloadingofPCBsandPBDEstoLakeWashington,LakeUnionandPugetSoundand2)modelthepotentialreductioninLakeWashingtonfishtissueconcentrationsassociatedwithselectPCBloadingreductionscenarios.Othercomponentsoftheprojectthathavebeencompletedbeforethisreportincludeafieldstudyin2011and2012(KingCounty2013a)andPCB/PBDEloadingsestimates(KingCounty2013b).ThefieldstudymeasuredPCBandPBDEconcentrationsinkeycontaminantloadingpathwaystoLakesWashingtonandUnionandintheexportpathwayleavingthelakesystemthroughtheHiramM.ChittendenLockstoPugetSound.Theloadingpathwayssampledincludedrivers,streams,stormwater,combinedseweroverflows,highwaybridges,andatmosphericdeposition.Fortheloadingsestimation,contaminantconcentrationdataforthesepathwayswerecombinedwithlongtermflowestimatestodevelopmassloadingestimatestoLakesWashingtonandUnion,andsubsequentexporttoPugetSoundfortotalPCB(tPCB)andtotalPBDE(tPBDE).

ThisreportdescribesthedevelopmentoftPCBmassbalance(fate)andbioaccumulationmodelsforLakeWashingtonaswellasmodelsensitivity,uncertaintyandperformance.



AlthoughPBDEloadingestimatesweredeveloped,includingthiscompoundinthefateandbioaccumulationmodelswasoutsidethescopeoftheproject.3

1.1 Problem Definition and Background PCBsarechlorinatedorganiccompoundsthatweremanufacturedforusesthatrequiredchemicalstabilityandlowflammability.CommercialPCBswereoriginallyproducedinNorthAmericaasmixtures“Aroclors®”bythemanufacturerMonsanto.SpecificAroclor®mixtureswerenamedusingafour‐digitnumber;thefirsttwodigitsrepresentthenumberofcarbonmolecules,whilethesecondtwodigitsrefertothepercentchlorinationbyweight(e.g.,Aroclor®1016,Aroclor®1254)(EPA2013).PCBsinclude209individualcompoundsknownascongenersthatvarytosomedegreeinphysical,chemicalandtoxicologicalpropertiesbasedprimarilyonthedegreeofchlorination.Duetotheirchemicalstabilityandlowwatersolubility,PCBsarepersistentintheenvironment,bindstronglytosedimentandsoilparticles,andbioaccumulateinaquaticorganisms,wildlife,andhumans.

ThebioaccumulationofPCBspresentsapotentialhealthrisktoaquaticlife,terrestrialwildlife,andhumans.In2004,theWashingtonDepartmentofHealth(WADOH)issuedafishconsumptionadvisoryforPCBsinLakeWashingtonforyellowperch,cutthroattrout,carpandnorthernpikeminnow(WADOH2004).4PCBconcentrationsinLakeWashingtonfishexceedboththeNationalToxicsRule5levelsforprotectionofhumanhealthandthe95th‐percentileofconcentrationsmeasuredinfishcollectedstatewide(SeidersandDeligeannis2007).

CommercialproductionofPCBsbeganinthe1920s,initiallyforuseasadielectricfluidinelectricaltransformers,capacitors,andelectricmotors.AfterWorldWarII,productionincreasedsubstantiallyandPCBusediversifiedtoincludeheattransferfluids,hydraulicfluids,plasticizers,carbonlesscopypaper,lubricants,inks,laminatingagents,paints,adhesives,waxes,additivesincementsandplasters,castingagents,sealingliquids,fireretardants,immersionoilsandpesticides(DeVoogtandBrinkman1989).PCBswerevoluntarilyphased‐outofproductioninthe1970sandintheUnitedStatesmanufactureandmostuseswerebannedin1979(44FR31514).6WhilethesaleandproductionofPCBshavebeenbannedforoverthreedecades,considerableamountsofPCBsremaininuse–primarilyasdielectricfluidin“closedsources”suchastransformersandcapacitorsandin“opensources”suchasbuildingcaulksandsealantsinolderstructures(Diamondetal.2010;Robsonetal.2010).

3ThefateandbioaccumulationmodelswereintendedtoanswerquestionspertainingtothefishconsumptionadvisoryforPCBs.Currently,thereisnofishconsumptionadvisoryforPBDEsonLakeWashington.Thus,themodelingscopewaslimitedtoPCBsonly.

4WashingtonStateDepartmentofHealthFishConsumptionAdvisories(see:http://www.doh.wa.gov/CommunityandEnvironment/Food/Fish/Advisories.aspx

5U.S.EnvironmentalProtectionAgency(USEPA)NationalToxicsRule(see:http://water.epa.gov/scitech/swguidance/standards/wqsregs.cfm)

6U.S.EnvironmentalProtectionAgency(see:http://www.epa.gov/history/topics/pcbs/01.html)



Ingeneral,haltingPCBproduction,eliminationofmanyuses,andadeclininginventoryofPCBsinusehasresultedindecreasingconcentrationsinenvironmentalmedia,includingfishtissueandsediments(Petermanetal.1990;VanMetreandMahler2005).However,studiesoffishtissueandsedimentconcentrationsinmanyareasoftheworldindicatethattheinitialrateofdeclineappearstohaveslowedorhaltedcompletely(VanMetreetal.1998;Hickeyetal.2006;Bhavsaretal.2007).

HistoricaldataonPCBlevelsinnon‐anadromousfishcollectedfromLakesWashingtonandUnionareinsufficienttoevaluatelong‐termtrendsinPCBconcentrations(McIntyre2004).Whileanadromousfishhavebeenstudied,thesefishgenerallyspendonlyaportionoftheirlifecycleintheselakesandmeasuredcontaminantconcentrationsaregenerallylowerthanthoseobservedinresident(non‐anadromous)fishspecies(McIntyre2004;Fletcher2009).

AsubstantialdeclineinsedimentPCBconcentrationsinLakeWashingtonhasbeendocumented;levelsarenowaboutathirdorlessofthepeakconcentrationsmeasuredintheearly1970s(Yake2001;VanMetreetal.2004;VanMetreandMahler2005;Furletal.2009Era‐Milleretal.2010).VanMetreandMahler(2005)collectedanddatedonecorefromthecentralbasinofLakeWashington.Theyreportedamedianconcentrationof199µg/kgdwforthe1965‐1975periodand59µg/kgdwforthe1990‐2000period.TheincreaseandsubsequentdecreaseinsedimentPCBconcentrationscoincidewithnationaltrendsinproduction,useandsubsequentuselimitationsandeliminationofproduction.InthecaseofLakeWashington,theincreaseanddeclinealsocoincidesgenerallywiththedevelopmentandgrowthofcitiesaroundthelakeandthesubsequentdiversionoftreatedwastewaterfromLakeWashingtontoPugetSoundthatwascompletedin1968(EdmondsonandLehman1981).

1.2 Project Goals and Objectives TheoverallprojectisintendedtoengageinthefirsteffortstounderstandwhyPCBsareaccumulatinginLakeWashingtonfishandidentifyactionsthatmayaddressthisproblem.Specifically,thisprojectwillfilldatagapsanddevelopmodelingtoolstohelpanswerthreemanagementquestions:

1. WhichtypesofloadingpathwaysarethehighestprioritiesforPCB/PBDEloadreduction?

2. Willpotentialloadingreductionsfromthesepathwaysreducechemicalbioaccumulationinfish,andcontributesubstantiallytowardsliftingthefishconsumptionadvisoryonLakeWashington?

3. Howlongmightittakethesystemtorespondtothesehypotheticalloadingreductions?

ThisreportdescribesthedevelopmentandtestingofcoupledmodelstoestimatecontaminantfateandbioaccumulationofPCBsinLakeWashington.



1.3 Modeling Objectives TheultimategoalofthedevelopmentoffateandbioaccumulationmodelsforLakeWashingtonistoreliablyforecastthereductionofPCBconcentrationsinfishtissueunderavarietyofpossiblewaterqualitymanagementscenarios.Thiseffortisconsideredtobeamulti‐phasedprocessinwhichthedevelopmentofarelativelysimplemassbudgetfatemodelcoupledtoanecosystembioaccumulationmodelisafirststep.Whilethisfirststepwillbeaccomplishedduringthegrantprojectperiodtheremainingphasesareexpectedtooccurbeyondthisperiod.

Thespecificobjectivesofthemodelingeffortareasfollows:

Developaquantitativeunderstandingofthelong‐termfateofPCBsinLakeWashington.

Providequantitativeestimatesofthetimeandmagnitudeoftheresponseoflakewater,sedimentandfishtissuetoreductionsinPCBloading.

Thesetwomodelingobjectivesaddressoverallprojectstudyquestions2and3above.



2.0 STUDY AREA AND MODEL DOMAIN ThestudyareaistheLakeWashingtonwatershedandencompasses1,550km2(598mi2)definedbytheLakeWashingtonoutletatMontlakeCut(Figure1).7Theareaexperiencesagenerallymildmaritimeclimatewithheaviestprecipitationoccurringinwintermonths,primarilyasrainatlowerelevationsandassnowathigherelevations.Elevationsaregenerallylessthan1,000m(3,281ft);however,totalannualrainfallisverydependentonelevationwhichrangesfromabout6mabovemeansealevel(msl)to1,700m(4,464ft).Thevariationinelevationresultsinarangeofannualprecipitationfromalmost1,000mm(39in)atlakeleveltoover2,500mm(100in)atthehighestelevations.Windsarehighlyvariable,butduringthewinter,majorstormsandassociatedwindstypicallyoriginatefromthesouthwest.

TwomajorriversdraintoLakeWashington.TheSammamishRiverdrainsLakeSammamishandtributariesintheSammamishRivervalleyandentersLakeWashingtonfromthenorth,providingabout30percentofthetotalinflowtotheLake.TheCedarRiverentersthesouthendofthelakeandcontributesabout50percentofthetotalinflow(Edmondson1977;KingCounty2003;Cercoetal.2004).Theremainderoftheinflowcomesfromanumberofcreeksandsmalltributarybasinsthatdraindirectlytothelake.LakeWashingtonthendrainsthroughtheMontlakeCuttoLakeUnion,whichdrainsthroughtheLakeWashingtonShipCanal(ShipCanal)andLockstoPugetSound.

Historically,LakesWashingtonandUnionwerenotconnected.By1916,acanalwascompletedbetweenthetwolakes,theoutletofLakeUnionwaswidenedanddeepenedandalockanddamsystemwasinoperation(Chrzastowski1983).Priortocanalandlockconstruction,themaininflowtoLakeWashingtonwasfromtheSammamishRiverandoutflowwasthroughtheBlackRiveratthesouthernendofthelake.Toprovidesufficientwaterforlockoperationandtoreduceflooding,theCedarRiver,whichhadpreviouslyjoinedtheBlackRivernearthesouthernendofthelake,wasdivertedtoLakeWashington(Chrzastowski1983).TheseengineeringchangesresultedinthesummerintrusionofsaltwaterfromPugetSoundthatentersthroughtheLocksandShipCanalintoLakeUnion,resultinginalayerofdensersalinewateratdepthinthelake,whichisthenentrainedandflushedfromthelakeduringwinterhighflows.TheextentofintrusionofsalinewaterislimitedtoLakeUnionthroughvariousmitigationmeasures,includingasaltwaterbarrierattheupstreamsideofthelargerofthetwolocksandasaltwaterdrainlocatedinadepressionattheheadofbothlocks.SalinityismonitoredcontinuouslyinsummerattheUniversityBridgeandisnottoexceed1ppt(173‐201AWAC).

TheimmediateareaaroundLakeWashingtonishighlydevelopedandincludesthemajorcities(i.e.,>50,000residents)ofSeattle,Bellevue,andRenton.AlthoughthedischargeofmunicipalwastewatertoLakeWashingtonwashaltedaround1968,therearestillapproximately40CSOsthatintermittentlydischargetolocationsalongtheSeattle

7Thiswatershedareaestimateincludesthesurfacesofalllakes,streamsandwetlandsinthewatershed.



shorelineoftheLake.LakeWashingtoniscrossedbytwofloatingbridges–StateRoute520(SR520)tothenorthandInterstate90(I‐90)tothesouth.

Thestudyareaalsoincludesrelativelyundeveloped,primarilyforested,areasintheheadwatersofthetwomajorriverbasins.TheheadwatersoftributariesalongthesoutheastshorelineofLakeWashingtonarealsorelativelyundeveloped.TheheadwatersoftheCedarRiverarewithinaprotectedwatershedfortheChesterMorsewatersupplyreservoirthatprovidesSeattlePublicUtilities(SPU)withaportionofitspotablewatersupply.

Withinthestudyarea,themodeldomainisdefinedasLakeWashington,whichisthesecondlargestnaturallakeinthestate.Thelakeisanelongatednorth‐southtrendingglacialtroughapproximately35km(21.7mi)longwithanaveragedepthof32.9m(108ft),amaximumdepthof65.2m(214ft),asurfaceareaof87.6km2(33.8mi2)andavolumeof2.884x109m3(2,338,000acre‐ft)(Anderson1954).8EdmondsonandLehman(1981)provideestimatesofannuallakehydraulicrenewaltimes,whichindicatethatonaveragethefractionoflakevolumerenewedeachyearwithincomingwater(correctedforevaporation)is0.43peryear.Thereciprocalofthisis2.3years–theaveragehydraulicresidencetimeofwaterinthelake.

8KingCountygeographicinformationsystem(GIS)dataindicatealakesurfacearea(includingUnionBay)closerto89km2(34.4mi2),butthismaybeduetotheexclusionofUnionBayfromtheearlierestimate.Also,EdmondsonandLehman(1981)reportatotallakevolumeof2.885x109m3(2,339,000acre‐ft).



Figure 1. Lake Washington Watershed.



3.0 THE MODELS BasedonthemodelselectioncriteriapresentedintheModelingQualityAssuranceProjectPlan(QAPP)developedforthisstudy(KingCounty2013c),thecontaminantfateandbioaccumulationmodelingframeworkdevelopedforSanFranciscoBay(Davisetal.2007)andmodifiedforuseinPugetSound(PelletierandMohamedali2009)wasalsoadaptedforuseinthisstudy.

Aspreviouslydiscussed,tPCBsencompass209congenersthatvarywidelyintheirchemicalandtoxicologicalproperties.TosimulatetPCBs,theframeworkforboththefateandbioaccumulationmodelsallowforeither(1)useofthechemicalpropertiesofasinglecongenertorepresenttheentirechemicalclass,or(2)separatesimulationsofanumberofdifferentcongenersorhomologueswhicharethensummedtodeterminethe“total”resultfortheentirechemicalclass.Davis(2004)andPelletierandMohamedali(2009)usedthefirststrategytosimulatemassfluxesoftPCBsaccordingtothechemicalpropertiesofasinglecongener,PCB‐118.PCB‐118wasselectedasthe“representative”congenerbasedonitsintermediatechemicalpropertiesandlevelofchlorination(penta),abundanceintheecosystem,chemicalsimilaritytothemosttoxiccongenertohumans(PCB‐126),anddataavailability.Thefirststrategy,useofasinglecongener,wasusedfordevelopmentoftheLakeWashingtonPCBfatemodel.

Incontrast,thebioaccumulationmodelasemployedbyCondon(2007)andPelletierandMohamedali(2009)usedthesecondstrategy,simulatingthemovementof57differentPCBcongenersthroughthefoodwebandthensummingtoget“total”concentrationsforthevariousorganisms.ThemodeledcongenerswerechosenbasedontheirpresenceinregionalsedimentandbiotaandbecausethosecongenerswereknowntocomprisethemajorityofthetPCBmass(andwerethusconsideredtobereasonablyrepresentativeofthebehavioroftheentirefamilyofPCBcongeners).However,PCBshaveonlybeenanalyzedasAroclors®inLakeWashingtonsedimentandasidefromfourfishfilletsamples9,congenerdataarenotavailableforbiota.Therefore,thefirststrategydescribedabove(e.g.,chemicalpropertiesofasinglecongener)wasusedtodeveloptheLakeWashingtonbioaccumulationmodel.

3.1 Contaminant Fate Model Thecontaminantfatemodelisatwo‐compartment(lakewaterandbottomsediment)fatemodeldescribedbyDavis(2004).Thestepsrequiredfordevelopmentandtestingofthemodelincludedthefollowing:

CompilationofwaterandsedimentPCBdataforLakeWashingtonprovidedthebasisfortestingmodelassumptions(currentestimatedloadsandparametervalues).Aspreviouslydiscussed,thesedatawerecollectedaspartoftheFieldStudyDataReportandDataReportSedimentAddendum(KingCounty2013aand2013d).

9Twosampleswerecutthroattrout,onewascarpandonewasnorthernpikeminnow.



Setupandtestthetwo‐compartmentboxmodeldevelopedforSanFranciscoBay(Davis2004)andadaptedforLakeWashington.

ModeltestingwasbasedonahindcastmodelingapproachusedinthedevelopmentoftheinitialSanFranciscoBayPBDEmodel(Orametal.2008).Fluvialandatmosphericloadingestimatesdevelopedaspartofthisstudy(KingCounty2013b)providedtheestimatedcurrenttPCBloadforhindcasttestingofthefatemodel.

3.2 Ecosystem Bioaccumulation Model ThebioaccumulationmodelwasoriginallydevelopedbyArnotandGobas(2004),andadaptedforuseinSanFranciscoBay(GobasandArnot2005).TheSanFranciscoBaybioaccumulationmodelwasalsoadaptedforuseinmodelingcontaminantaccumulationinbiotaatvarioustrophiclevelswithintheStraitofGeorgia(Condon2007)andsubsequentlyforselectPugetSoundbiota(PelletierandMohamedali2009).

Themodelpredictswholebodycontaminantconcentrationsinfoodwebcomponents(e.g.,phytoplankton,zooplankton,benthicinvertebrates,andfish)bycalculatingchemicaluptakefromingestionandrespirationaswellaseliminationfromrespiration,egestion,metabolism,andgrowthdilution.Themodelassumessteady‐stateconditions;i.e.,thechemicalconcentrationshavereachedequilibriuminwater,sediment,andbiota.ThemainimplicationofthisassumptionisthatpredictedbiotaPCBconcentrationsaredirectlyproportionaltosedimentandwaterconcentrationsatanypointintime.Thisassumptionisreasonableconsideringtherapidtypicalresponsetimeoforganisms(days)tochangesinexternalconditions(GobasandArnot2010).Incomparison,theresponseofsedimentconcentrationstoachangeinPCBloadisslow(i.e.,years).

ThestepsrequiredtodevelopandtesttheLakeWashingtonbioaccumulationmodelincludedthefollowing:

CompileexistinginformationonthefoodwebstructureofLakeWashingtonanddevelopaconceptualfoodwebmodelusingtheselectedtaxaofinterest.

Compiledatatoestablishreasonablemodelinputvaluesformodelparameters(e.g.,biotalipidcontent,concentrationofsuspendedsolids,organismwetweight).

CompilePCBconcentrationdatainLakeWashingtonbiotatoprovidethebasisfortestingmodelassumptions.

ModeltestingwasbasedontheapproachusedindevelopmentoftheSanFranciscoBayandPugetSoundmodels(GobasandArnot2005,Davisetal.2007,PelletierandMohamedali2009).Availablesediment,watercolumnandbiotatissuedatafortPCBprovidedthebasisformodeltesting.Fundamentally,thetestingofthebioaccumulationmodelwasbasedoncomparisonofmodel‐predictedbiotatPCBconcentrationstoobservedtissueconcentrations.

3.3 Data Requirements Avarietyofenvironmentaldataservedasinputsandboundaryconditionsforthemodels.Thedatarequiredformodeldevelopmentandtestingincludedphysical,chemical,and



biologicaldata.Thetypesofdatarequired,andsources,whereknownand/orapplicable,aredescribedbelow.ThedevelopmentofinputdataforeachmodelisdescribedinSections4.0(FateModel)and5.0(BioaccumulationModel).Thegeneraltypesofdataneededformodeldevelopmentaredescribedbelow.

3.3.1 Physical Thefatemodelrequiresavarietyofphysicaldataincludingthelakevolumeandwaterandsedimentsurfaceareas.Thesephysicalmeasurementswereobtainedfrompublishedsources(seeSection4.0).Dataforadditionalparametersincludedmeanoutflowrateandmeanlaketemperature,etc.Laketemperatureistheonlyphysicalparameterinthebioaccumulationmodel.Totheextentpossible,modelinputvalueswerederivedfromdatafoundinpublishedliteratureandsupplementedwithvaluespublishedforothersystemsiflocaldatawereunavailable.LakeWashingtonhasbeenthefocusofscientificresearchformanyyears.Therefore,thereisawealthofpublisheddatawasavailableforreviewtoidentifylake‐specificsourcesofmodelinputs.Forexample,themeanoutflowratewasbasedonthelong‐term(2002‐2011)lakeoutflowratederivedbyKingCounty(2013b).Wakehametal.(2004)andFurletal.(2009)providedthemostrecentestimatesofsedimentburialrates.

3.3.2 Chemical ThefateandbioaccumulationmodelsrequiredavarietyofambientPCBdataforwater,sediment,andbiotainLakeWashington,inadditiontoconcentrationdataforsuspendedsolids,particulateanddissolvedorganiccarbon(DOC)inthewatercolumn,andsedimentorganiccarboncontent.Thefatemodelalsorequiredlong‐termPCBloadingestimateswhichwerebasedondatacollectedaspartoftheprojectfieldstudy(KingCounty2013a).ThisfieldstudyalsoprovideddataonPCBsourcepathwaystothelake,includingCSOs,atmosphericdeposition,andriverandstreaminputswhichwereusedtodevelopcurrentlong‐term(2002‐2011)tPCBloadingestimatestoLakeWashingtonforuseasinputstothefatemodel(KingCounty2013b).

SummariesofavailablesedimentPCBdataforLakeWashingtonwerefoundinMoshenberg(2004).AdditionalsedimentPCBdatawereavailablefromEcology(e.g.,Era‐Milleretal.2010)andKingCounty(2008).

PCBdataforLakeWashingtonbiotawereavailablefromMcIntyre(2004),Ecology(Johnsonetal.2006,SeidersandDeligeannis2007)andKingCounty(2013e).However,someofthesedatarepresentedPCBconcentrationsinfillettissueandcouldnotbeused;onlywholebodytissuedatafromMcIntyre(2004)andKingCounty(2013e)wereusedtotestthemodel.Useoffillettissuedatawouldhaverequiredconversiontowhole‐bodyestimatesandregressionofempiricalfilletandwhole‐bodyPCBconcentrationstodevelopapredictiverelationship.However,thesetypesofdataarenotavailableforLakeWashingtonandthenumberofwhole‐bodyresultswasdeemedadequateformodeltesting.

InadditiontotPCBdata,dataforadditionalparameters(e.g.,sedimentorganiccarboncontent,andsuspendedsolidsandorganiccarboninwater)wereobtainedprimarilyfrom



theKingCountyLaboratoryInformationManagementSystem(LIMS)environmentaldatabasethatcontainsdatageneratedbytheKingCountyEnvironmentalLaboratoryfollowingproject‐specificQAPPsandpublishedlaboratoryStandardOperatingProcedures(SOPs).

3.3.3 Biological Avarietyofbiologicaldatawererequiredtodevelopthebioaccumulationmodelincludingfoodwebstructureanddietaryfractionsforthemodeledtaxonomicgroupsortaxa.TheLakeWashingtonfoodwebstructureusedinthebioaccumulationmodelwasdevelopedbystartingwithasimplifiedfoodwebandexpandingintoamoredetailedonebasedoninformationavailablefrompublishedstudiesexaminingtheLakeWashingtonfoodweb(McIntyre2004,Mazur2004)anddietarypreferences(e.g.,Brocksmith1999,FayramandSibley2000,McIntyre2004,Taboretal.2004,Taboretal.2007).Thesimplifiedfoodwebincludedmajortaxonomicgroups:detritus,phytoplankton,zooplankton,benthicinvertebrates,forage(herbivorousandomnivorous)fish,andpiscivorousfish(Figure2).

Oncetheconceptualfoodwebmodelwasestablished,informationondietarycomposition,solidandlipidcontent,andbodyweightwerecompiledfromsourceswithapreferenceforLakeWashingtonspecificdata.WhenLakeWashingtonspecificdatawereunavailable,assumptionsguidedbystudiesfromotherwaterbodiesweremadetoselectappropriatevalues.



Figure 2. Simplified Food Web for Lake Washington

3.4 Data Acceptance Criteria and Rules Thefollowingacceptancecriteriawereappliedtoalldatausedformodeldevelopmentandtesting:

DataReasonableness.Thequalityofexistingdatawasevaluatedgraphicallyandthroughreviewofavailablewrittenreports.Datawereassessedformodelrelevanceandbiaseddatapointswereremoved,suchaschemistryresultsfromdeepsubsamplesofsedimentcoresandtissuedatafromspeciesnotmodeled.

DataRepresentativeness.Datathatwerereasonablycompleteandrepresentativeoftypicalconditionsatthelocationunderconsideration(e.g.,modelregion,watercolumnlayer,andwatershed)wereused.Datafromcontaminated“hotspots”wereincludedbecausetheyrepresentedcurrentconditions.

DataComparability.Long‐termwaterqualitymonitoringprogramsoftencollect,handle,preserve,andanalyzesamplesusingmethodologiesthatevolveovertime,particularlyforhighlyregulatedorrecentlybannedchemicals.Inrecentdecadesadvancesinanalyticalmethodshaveimprovedthecapabilityofdetectingextremelylowcontaminantconcentrations.OlderPCBAroclor®analyticalmethodsfrequently



resultedinnon‐detectedconcentrations,particularlyforsurfacewater,butwithdetectionlimitsmuchhigherthancurrentstate‐of‐the‐artanalyticalmethods.Bestprofessionaljudgmentwasusedtodeterminewhetherdatafromthevarioussourceswerecomparable.Theaddendatothestudydatareport(KingCounty2013dand2013e)detailedtheassumptionsthatweremadewhenusingdatacollectedwithdifferingsamplingoranalysistechniques.

AtleastoneAroclor®orcongenerwasdetectedineverywaterorsedimentsampleusedformodeling(KingCounty2013aand2013c).However,Aroclors®werenotdetectedinsomeofthefishtissuesamples.Duetotherelativelysmallsamplesizes(n<20)thesesampleswereexcludedfromthedataanalysisbecauseanysubstitutionmethod(e.g.,fullorhalfthedetectionlimit)wasseenaspresentingsubstantialbiastoanycalculatedstatistics.OneexceptiontothisrulewasmadewhennodetectedPCBresultsforanysamplesofaspecieswereavailable.Inthesecases,thehighestdetectionlimitofthenon‐detectresultswasusedtoprovidesomeindicationoftheobservedtissueconcentrationformodeltesting.

SummationofPCBcongenerandAroclor®dataintotPCBconcentrationsforuseinmodeldevelopmentandtestingrequireddevelopmentofsummingrulesthatincludedrulesdevelopedaboveforhandlingnon‐detectdata.Ingeneral,tPCBwascalculatedbysummingthedetectedvaluesasreportedforindividualcongeners.RulesdevelopedforsummingPCBcongenersorAroclors®forexistingdatausedinmodeldevelopmentandtestingweredocumentedintheaddendatothestudydatareport(KingCounty2013dand2013e).GuidanceinPelletierandMohamedali(2009)andOsterbergandPelletier(2012)providedthefoundationoftheproposedapproach.

ItisacknowledgedthatPCBcongenersumsaregenerallyconsideredmoreaccuratethanAroclor®sumsandthatasumofAroclors®canover‐orunderestimatetPCBs.PCBcongenerandAroclor®concentrationsfromsplitsedimentortissuesamplesfromLakeWashingtonarenotavailabletoquantifythispotentialbias.However,comparisonsoftPCBsbasedoncongenerandAroclor®datafortissueintheLowerDuwamishWaterwayRemedialInvestigation(Windward2010)andforsedimentandtissueintheEastWaterwayOperableUnitSupplementalDraftRemedialInvestigation/FeasibilityStudy(WindwardandAnchorQEA2012)concludedthatthesumsweresimilar;noconsistencyinover‐orunder‐predictionwasobserved.Forthisproject,itisassumedthatdifferencesintPCBsumsbasedoncongenersandAroclors®arenotcollectivelysignificant.

3.5 Model Sensitivity and Uncertainty Assessments

Toevaluatemodelperformanceandvariabilityofresults,sensitivityanduncertaintyanalyseswereconducted.Uncertaintycanarisefromanumberofsourcesthatrangefromerrorsintheinputdatausedtocalibratethemodel,toimpreciseestimatesforkeyparameters,tovariationsinhowcertainprocessesareparameterizedinthemodeldomain.Regardlessoftheunderlyingcause,itisgoodpracticetoevaluatetheseuncertaintiesandreducethemifpossible(EPA2009a,Taylor1997,Beck1987).Byinvestigatingthe“relativesensitivity”ofmodelparameters,ausercanbecomeknowledgeableoftherelativeimportanceofparametersinthemodel.Byknowingtheuncertaintyassociatedwith



parametervaluesandthesensitivityofthemodeltospecificparameters,auserwillbemoreinformedregardingtheconfidencethatcanbeplacedinthemodelresults(EPA2009a).

Modelsensitivitydescribesthedegreetowhichresultsareaffectedbychangesinselectedinputs.Sensitivityanalysiscanhelpimproveunderstandingoftherelativeimportanceofmodelparameters,identifyingwhichparametersdonotsignificantlyaffectmodeloutputsandwhichparametersandprocessesstronglyinfluenceresults.

Modeluncertaintyisusedtodescribeincompleteorimperfectknowledgeaboutparameters,dataandassumptions.Uncertaintycanarisefrommanysources,includingmeasurementandanalyticalerrorsformodelinputdataandimpreciseestimatesforkeyparameters.Uncertaintyanalysesinvestigatehowthemodelresultsareaffectedbythislackofknowledgeofthetruevaluesofcertaininputsandparameters.

ThedetailsoftheapproachusedtoevaluatethesensitivityanduncertaintyofthefateandbioaccumulationmodelsareprovidedinSection6.0.



4.0 FATE MODEL INPUT DATA Mathematically,thefatemodeliscomprisedoftwoequationswhichdeterminethegainsandlossesofPCBsfromthelakewaterandbottomsediment(Figure3):

ΔMW/Δt=L+kSW1MS+kSW2MS–kVMW–kOMW–kWRMW–kWS1MW–kWS2MW (1)

ΔMS/Δt=kWS1MW+kWS2MW–kSW1MS–kSW2MS–kBMS–kSRMS (2)

where,

MW = totalmassofPCBinlakewater

L = totalexternalloadofPCBtolake

MS = totalmassofPCBinsediment

andtherateconstantsaredefinedinTable1.

Figure 3. Diagram of PCB fate processes included in model. Source: Davis (2004).



Table 1. Rate constants (d-1) for PCBs in Lake Washington based on best estimates of model input data. San Francisco Bay and Lake Ontario values included for comparison.

Rate Constant (day-1)

Notation Applies to mass in…

Lake Washington

San Francisco Bay a

Lake Ontario b

Outflow kO Water 0.00117 0.0054 0.00042

Volatilization kV Water 0.00171 0.0044 0.00093

Solids settling kWS1 Water 0.00656 0.1854 0.0011

Water-to-sediment diffusion

kWS2 Water 0.0000207 0.000035 0.0000082

Degradation in water kWR Water 0.000034 0.000034 0.000034

Solids resuspension kSW1 Sediment 0.0001569 0.001133 0.00014

Sediment-to-water diffusion

kSW2 Sediment 0.0000007 0.0000012 0.000047

Burial kB Sediment 0.0001764 0.0 0.00059

Degradation in sediment

kSR Sediment 0.000034 0.000034 0.000034

a Davis (2004) b Gobas et al. (1995) as cited by Davis (2003)

TherateconstantsinTable1describethefractionalchangeinthemassofPCBsinwater(orsediment)perday.Forexample,ifthemassofPCBsinthewatercolumnonaparticulardayis10kgandthevolatilizationrateconstantis0.00171d‐1,then0.0171kg(10kgx0.00171d‐1)willbelostoverthedayviavolatilizationfromthelaketotheatmosphere.Overall,ninerateconstantsdescribethegainsandlossesofPCBsfromthewaterandsediment.Theoutflow,volatilization,degradationinwater,degradationinsediment,andsedimentburialrateconstantsdescribethelossofPCBsfromthelakeecosystem.Theremainingfourrateconstants(solidssettling,solidsresuspension,water‐tosedimentdiffusion,sediment‐to‐waterdiffusion)areusedtocalculatetheexchange(gainsandlosses)betweenthewatercolumnandsediment.Thedeterminationoftheserateconstantsisdescribedinmoredetailbelow.

Atime‐varyingsolutionapproach10,usingadailytimestep,isusedtocalculatethechangeinwaterandsedimentPCBconcentrations(andmassofPCBinthelake)overtime.Whenthemodelisrunwithinitiallakewaterandsedimentconcentrationsofzeroandaspecified

10Anexplicitfirst‐orderEulermethodwasusedasthenumericalintegrationsolution.



steadyloadingrateforasufficientlylongtimeperiod,theresultingsteady‐statewaterandsedimentPCBconcentrationcanbedetermined.ThisprovidesameansoftestingthemodeltodeterminehowwellthemodelmatchestheobservedtPCBlakeconcentrationusingtheestimatedcurrenttPCBloadingrate.Thesamemodelcanalsobeusedtoestimatethelake’sresponsetovariousloadingreductionscenariosgiventheinitialsteady‐statewaterandsedimentconcentrations.

4.1 Rate Constants Althoughthemodelisrelativelysimple,thereare19inputvaluesusedtocalculatethemodelrateconstants(Table2).Theseinputvaluesdescribethesizeandcharacteristicsofthewaterandsedimentcompartments(physicalandchemical).TheinputvaluesinTable2representthebestestimatesforeachparameterbasedonsitespecificdataforLakeWashington,ordatafromthescientificliteraturethatdescribesthefateofPCBsinaquaticsystems.

BecausethemodelcurrentlyonlysimulatesthefateofonePCBcongeneratatime,asinDavis(2004),thechemicalpropertiesofPCB‐118(3,3',4,4',5‐pentachlorobiphenyl)wereusedasdefaultvaluesbecauseithasanintermediatelevelofchlorinationand,therefore,intermediatechemicalproperties.ThechemicalpropertiesofPCB‐118arealsosimilartoPCB‐126,whichisthemosttoxiccongenertohumans.PCB‐118wasdetectedwithsomefrequencyinLakeWashingtonwatersamples;however,PCB‐126wasnot.PCB‐126wasalsonotfrequentlydetectedinSanFranciscoBaywater,butitwasthemostsignificantcontributortohumanandwildlifedioxin‐equivalenttoxicexposurefrombayfish(Davis2004).

Table3identifiesfouradditionalmodelinputsthatdescribethechemicalpropertiesofPCB‐118;thesevaluesrepresentthebestavailableinformationforthiscongener.

Thederivationoftherateconstantsandtheinformationusedtodeterminethebestestimatesfortheinputdataaredescribedbelow.

4.1.1 Losses from the water column

4.1.1.1 Outflow

Outflow: kO=F/(1,000*VW)(d‐1)

TheoutflowrateconstantdescribesoutflowfromLakeWashingtonthroughtheMontlakeCut,whichexportsdissolvedandparticulatesorbedPCBsoutofLakeWashingtontoLakeUnion.Thebestoutflowratewasbasedonthelong‐term(2002‐2011)estimateinKingCounty(2013b).ThetotalmassofPCBslostthroughoutflowwascalculatedbymultiplyingkObythetotalmassofPCBsinthewatercolumn.



Table 2. Model input data.

Parameter (units) Symbol Best

Estimate Source

tPCB load (kg yr-1) L 0.672 King County (2013b)

Water surface area (m2) SAW 8.90E+07 Anderson (1954)

Sediment surface area (m2) SAS 8.90E+07 Assumed equal to water surface area (SAW)

Active sediment layer depth (m) DS 0.025 Gobas et al. (1995)

Volume of water (m3) VW 2.90E+09 Anderson (1954)

Volume of sediment (m3) VoS 2.23E+06 Determined from SAS*DW

Average water temperature (oC) TW 10.9 Based on long-term monitoring data

Average wind speed (m s-1) WS 3.3 Based on long-term monitoring data

Water outflow rate (L d-1) F 3.40E+09 King County (2013b)

Suspended particulate matter (kg L-1) CPW 1.0E-06 Based on long-term monitoring data

Sediment solids concentration (kg L-1) CSS 0.12 Based on lake sediment coring data

Water POC concentration (kg L-1) Xpoc 3.0E-7 Based on long-term monitoring data

Water DOC concentration (kg L-1) Xdoc 3.1E-6 Based on long-term monitoring data

Sediment organic carbon content (fraction) OCSS 0.055 King County (2013d)

Density of sediment organic carbon (kg L-1) dOC 1.0 Gobas et al. (1995), Davis (2004)

Solids settling rate (m d-1) Vs 1.0 Gobas et al. (1995)

Water-to-sediment diffusion coefficient (m d-1) Vd 0.0024 Gobas et al. (1995), Davis (2004)

Sediment burial coefficient (m d-1) Vb 4.41E-06 Wakeham et al. (2004) a

Degradation rates in water (d-1) KWR 0.000034 Gobas et al. (1995), Davis (2004)

Degradation rates in sediment (d-1) KSR 0.000034 Gobas et al. (1995), Davis (2004)

aAdjusted for sediment focusing.



Table 3. Chemical properties of PCB-118 (3,3',4,4',5-pentachlorobiphenyl).

Property Symbol PCB-118a Source

No. of chlorines -- 5

Molecular formula -- C12H5Cl5

Log10KOW @ 25 oC (@ 10.9 oC)

KOW 6.65 (6.86)b Schenker et al. (2005)

Henry’s Law constant (Pa m3 mol-1)

H298 10.821 Calculated using equation 6-15 in Schwarzenbach et al. (1993)

Enthalpy of Kow (J/mol) const_EnthalpyKow -24,500 Schenker et al. (2005)

Molar Mass (g mol-1) Mol Wt 326.43 From molecular formula

Molar Volume at Boiling Point (cm3 mol-1)

MVB 289.1 LeBas method (14.8*12+3.7*5+24.6*5-15-15)

a PCB-126 has the same chemical formula, so it has the same molar mass and volume as PCB-118. The Henry’s Law constant and Log10KOW for PCB-126 are 21.3 Pa m3 mol-1 and 6.60, respectively. b Kow reported at 25 oC was adjusted to a temperature of 10.9 oC using the approach in Pelletier and Mohamedalli (2009) This approach uses the Van’t Hoff equation to calculate the change in Kow as a function of temperature and the standard enthalpy of change of the process.

4.1.1.2 Volatilization

Volatilization: kV=SAW*FDW*VE/VW(d‐1)

whereVEisthevolatilizationmasstransfercoefficient,

1/VE=1/VEW+1/(KAW*VEA)

andVEWandVEAarethewater‐sideandair‐sidemasstransfercoefficients,respectively,andKAWisthetemperature‐adjustedHenry’sLawconstantforthemodeledPCBcongener.

Thevolatilizationrateconstant(kV)describesthetransferofPCBsfromtheirfreelydissolvedforminwaterthroughthesurfacewaterinterfaceandintotheair.Theapproachusedisbasedontwo‐filmmodeltheorythatiscommonlyusedtoestimatePCBlosstotheatmosphereacrosstheair‐waterinterface(Chapra1997).AnunderlyingassumptionisthatthePCBairconcentrationissolow,thatonlytransferfromwatertoairneedstobeconsidered.ConsiderationofatmosphericabsorptionwouldrequireestimatesofPCBconcentrationsintheairoverlyingthelakesurface(forexampleseeTottenetal.2001).

TheMatlabcodefortheSanFranciscoBaymodelusedasthebasisfortheLakeWashingtonmodeldidnotincluderoutinestoestimatethewater‐side(VEW)andair‐side(VEA)masstransfercoefficients;inthatmodelthesevariablesweredeterminedexternally.Toprovidethecapabilitytoestimatetheseparameterswithinthemodelcode,therelevantroutinesdescribedinJohnson(2010)andwritteninRwereadaptedforuseintheLakeWashington



model.Theseroutinesuseinputsofaveragewindspeedacrossthelakesurface,averagelakewatertemperatureandthechemicalcharacteristicsofthemodeledPCB(e.g.,Henry’sLawconstant,molarvolume,andmolarweight)tocalculatethevolatilizationmasstransfercoefficient.Thelong‐term(2002‐2011)averagewindspeedof3.3ms‐1wasdeterminedfrompublishedhourlyobservationsatnearbySeattle‐TacomaInternationalAirportandthemeanwatertemperatureof10.9oCwasdeterminedfromlong‐term(2002‐2012)volume‐weightedaveragesoftemperatureprofiles(AppendixB)collectedfromthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject(KingCounty2013a).

AnothermodificationtotheSanFranciscoBaymodelappliedhereinvolvedcalculationofthefreelydissolvedfractionofPCBsinthewatercolumn.ThiscalculationaffectsdeterminationofthemassofPCBsinthewatercolumnthatwillbeaffectedbyvolatilization.TheSanFranciscoBaymodelconsideredtwo‐phasepartitioninginthewatercolumn;partitioningbetweenparticulateorganiccarbon(POC)andthefreelydissolvedphase(Davis2004).However,understandingofpartitioningofnonionicpolarorweaklypolarorganicchemicalshasevolvedfromconsiderationoftwo‐phasestothreeormorephases(Greeneetal.2013).Thethree‐phasemodelconsiderspartitioningtodissolvedorganiccarbon(DOC)andhasbeenincorporatedintofatemodelsofPugetSound(PelletierandMohamedali2009),theHudsonRiver(Farleyetal.1999),theDelawareRiver(Totteneta.2001,Roweetal.2007)andinlakebioaccumulationmodels(ArnotandGobas2004).

Thethree‐phasemodelinArnotandGobas(2004)11wasincorporatedintotheLakeWashingtonmodel:

FDW=1/(1+Xpoc*Dpoc*alpha_poc*KOW+Xdoc*Ddoc*alpha_doc*KOW)

where,XpocandXdocaretheconcentrations(inkg/L)ofPOCandDOCinthewatercolumn.DpocandDdocarethedisequilibriumfactorsforPOCandDOCpartitioning(unitless).Disequilibriuminpartitioningbetweenwaterandorganicmatterhasbeenobservedinvariousstudies(i.e.,hysteresisinthesorption/desorptionprocess),butthesevaluesaredifficulttodetermine.Disequilibriumvaluesof1.0wereusedinthemodel,whichrepresentsstandardequilibriumpartitioning.

Thefactorsalpha_pocandalpha_doc(L/kgOC)relatetheestimatedpartitioningbetweenwaterandoctanol(KOW,unitless)topartitioningtoPOCandDOC.ThevaluesusedbyArnotandGobas(2004)of0.35and0.08foralpha_pocandalpha_doc,respectively,wereselectedforuseintheLakeWashingtonmodel.ThisimpliesthatforagiveninputKOWvalue,theequivalentpartitioningtoPOCandDOCwillbesomewhatlessandthatPCBwillpartitionmorestronglytoPOCthanDOC.

TheaverageconcentrationsofwatercolumnDOCandPOCwerebasedonvolume‐weightedmeasurementsofDOCandtotalorganiccarbon(TOC)madebetween2002and2008(DOCandTOCanalyseswerediscontinuedafter2008)atthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject.Volume‐weightedmeanconcentrationsofTOCandDOCwere3.4and3.1mg/L,respectively(AppendixB).The

11ThispartitioningmodelisusedinthePugetSoundmodel(PelletierandMohamedali2009).



meanconcentrationofPOCwasestimatedbythedifferencebetweenTOCandDOC–equalto0.3mg/L.

4.1.1.3 Solids settling

SolidsSettling: kWS1=SAW*VS*[1–(FDW+FOW)]/VW(d‐1)

TheparametersthatcontrolthesolidssettlingrateconstantarethesolidssettlingrateandthefractionofwatercolumnPCBssorbedtosettlingorganicsolids.Thebestestimateofthesolidssettlingrate(Vs)was1.0md‐1,whichwasthevalueusedintheLakeOntario(Gobasetal.1995)andSanFranciscoBay(Davis2004)models.

Asaresultoftheincorporationofathree‐phasepartitioningmodel,calculationofthefractionofPCBsorbedtosettlingorganicsolidsrequireseitherthedirectdeterminationofthefractionofwatercolumnPCBsorbedtoPOCorthefractionsorbedtoDOC.ThemodelusesthelatterapproachtocalculateFOW,thefractionofwatercolumnPCBsorbedtoDOC:

FOW=(Xdoc*Ddoc*alpha_doc*KOW)/(1+Xpoc*Dpoc*alpha_poc*KOW+Xdoc*Ddoc*alpha_doc*KOW)

ThefractionofPCBinwaterintheparticulatephaseisthen:1–(FDW+FOW).

4.1.1.4 Water-to-sediment diffusion

Water‐to‐sedimentdiffusion: kWS2=SAS*VD*FDW/VW(d‐1)

Water‐to‐sedimentdiffusiontransfersfreelydissolvedPCBsfromthewatercolumntotheactivesedimentlayerandisafunctionofthefractionoffreelydissolvedPCB(FDW,previouslydescribed)andthewater‐to‐sedimentdiffusionmasstransfercoefficient(VD).Thewater‐to‐sedimentdiffusionmasstransfercoefficientusedinthemodelwas0.00024md‐1,whichwasthevalueusedintheLakeOntario(Gobasetal.1995)andSanFranciscoBay(Davis2004)models.Davis(2004)indicatedthatthemodelwasrelativelyinsensitivetolarge(ordersofmagnitude)changesinthisparameter.

4.1.1.5 Degradation in water

Degradationinwater: kWR=3.4x10‐5(d‐1)

ThisvaluewasalsousedintheLakeOntario(Gobasetal.1995)andSanFranciscoBay(Davis2004)modelsandcorrespondstoahalf‐lifeof56years.Thisdegradationrateisintendedtoaccountforallwatercolumndegradationpathways,includinghydrolysis,photolysis,biodegradation,andreductivedechlorination.ThemodelalsoassumesthatPCBsindissolvedandsorbedphasesaresubjecttothesamedegradationrate.

4.1.2 Losses from the active sediment layer

4.1.2.1 Burial

Burial: kB=SAS*VB*(1–FDS)/VoS(d‐1)



Thesedimentburialrateconstantisafunctionofthesurface‐area‐to‐volumeratiooftheactivesedimentlayer(SAS/VoS),theparticleboundfractionofPCBinsediment(1‐FDS)andthesedimentburialmasstransfercoefficient(VB).

Atwo‐phasemodelwasusedtoestimatethefractionofactivesedimentlayerPCBsthatwouldbefreelydissolvedinthesedimentporewater(FDS).ThesedimentpartitioningequationusedinthePugetSoundmodel(PelletierandMohamedali2009)wasincorporatedintotheLakeWashingtonmodel:

FDS=1/OCSS*dOC/(alpha_poc*KOW)

WhereOCSSisthefractionofsedimentorganiccarbon,dOCisthedensityofPOC(kgOCL‐1).Thetermsalpha_pocandKOWwereintroducedaboveandresultinanestimatedKOC(LkgOC‐1).ThebestestimateofthefractionofsedimentorganiccarbonwasbasedontheaverageconcentrationofOCSSmeasuredinsurfacesedimentscollectedfromLakeWashington(KingCounty2013d).Thedensityoforganiccarbon(dOC)wasassumedtobe1.0kgL‐1,whichisconsistentwiththevalueusedfortheLakeOntariomodel(Gobasetal.1995).

Thesedimentburialmasstransfercoefficientwasbasedonthesedimentationrateof~0.25cmyr‐1reportedbyWakehametal.(2004)foracorecollectedin2000fromthedeepermainbasinofthelake.Becausesedimentationratesreportedforthedeepestlocationsoflakesarelikelytobehigherthantheaveragesedimentationrateovertheentirelakeduetosedimentfocusing–i.e.,resuspensionandtransportfromsteeperareasalongtheshorelineandsubsequentlong‐termtransporttothedeeperandgenerallyflatterareasofthecentralbasin–anadjustmenttothereportedsedimentationratewasdevelopedfollowingthemethodoutlinedinHäkansonandJansson(2002).Thebottomslope(inpercent)wascalculatedfromthedigitallakebathymetryusingtheArcGIS10.0RasterSlopeToolboxandtheratioofthelakeareawithbottomslopeslessthan4percenttothetotallakeareawascalculated.Thisanalysisindicatedthat63percentofthelakebottomhadaslopeoflessthan4percent.Therefore,themodelsedimentburialmasstransfercoefficient(assumingSAS=SAW)was4.41x10‐6md‐1(0.25cmyr‐1~=7.0x10‐6md‐1;7.0x10‐6md‐1x0.63=4.41x10‐6md‐1).

4.1.2.2 Solids resuspension

Solidsresuspension: kB=(ResFlux/CSS)*(1–FDS)/(1,000*VoS)(d‐1)

where ResFlux=SetFlux–BurFlux

SetFlux=1,000*CPW*VSS*SAW

BurFlux=1,000*CSS*VB*SAS

SedimentsolidsresuspensiontransfersPCBsfromtheactivesedimentlayertothewatercolumn.AsintheSanFranciscoBay(Davis2004)andLakeOntariomodels(Gobasetal.1995),theresuspensionfluxisdeterminedbythedifferencebetweenthesettlingsolidsflux(SetFlux)andtheburialflux(BurFlux),whichiscontrolledprimarilybythesolidssettlingrate(VSS)andthesedimentburialmasstransfercoefficient(VB).Solidsresuspensionisalsodependentontheconcentrationofsedimentsolids(CSS)andthetotalvolumeofactivesediment(VoS).Whilesedimentresuspensionwasdeterminedtobean



importantprocessinSanFranciscoBay,therelativelylowresuspensionrateconstantdeterminedforLakeWashingtonismoreconsistentwiththeonedeterminedforLakeOntario(Table1).

Thesettlingflux(SetFlux)isafunctionofthemeanconcentrationofsettlingparticulatematterinthewatercolumn(Cpw),theaverageparticlesettlingvelocity(Vss)andthelakesurfacearea(Saw).Themeanconcentrationofsettlingparticleswasdeterminedfromtheaveragetotalsuspendedsolids(TSS)concentrationmeasuredbetween2002and2012atthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject.Thevolume‐weightedmeanconcentrationofTSSwas1.0mg/L(AppendixB).Thesettlingrateusedinthemodelwas1.0md‐1,whichwasthevalueusedintheLakeOntariomodel(Gobasetal.1995).

4.1.2.3 Sediment-to-water diffusion

Sediment‐to‐waterdiffusion: kSW2=SAS*VD*FDS/VoS(d‐1)

Aswithsolidsresuspension,sediment‐to‐waterdiffusionalsotransfersPCBsfromsedimenttowater.EventhoughtheLakeWashingtonsolidsresuspensionrateconstantisanorderofmagnitudelowerthantheconstantdeterminedfortheSanFranciscoBaymodel(Davis2004),itisstillaboutthreeordersofmagnitudegreaterthanthesediment‐to‐waterdiffusionrateconstant(Table1).Thesediment‐to‐waterdiffusionrateconstantisalsolowerthanthewater‐to‐sedimentdiffusionrateconstantbyabouttwoordersofmagnitude.ThesedifferencesaredeterminedmainlybytheverylowsedimentporewaterdissolvedPCBconcentrationsandmuchhigherwatercolumndissolvedPCBconcentrationsthatarepredictedbythesedimentandwatercontaminantpartitioningmodels.

4.1.2.4 Degradation in sediment

Degradationinsediment: kSR=3.4x10‐5(d‐1)

ThisvaluewasusedintheLakeOntario(Gobasetal.1995)andSanFranciscoBay(Davis2004)modelsandcorrespondstoahalf‐lifeof56years.Thisdegradationrateisintendedtoaccountforallactivesedimentdegradationpathways,includinghydrolysis,biodegradationandreductivedechlorination.ThemodelalsoassumesthatPCBsindissolvedandsorbedphasesaresubjecttothesamedegradationrate.

4.1.3 Sediment properties Therearefundamentallythreeimportantsedimentpropertiesthatmustbespecifiedinthemodel:depthofactivesedimentlayer,sedimentsolidsconcentrationandthesedimenttPCBconcentration.Theselectionofthebestvaluesfortheseparametersisdescribedbelow.

4.1.3.1 Depth of active sediment layer

Davis(2004)indicatedthatthedepthoftheactivesedimentlayerisoneofthemostpivotalparametersinthemodel.Thisparameter,inconjunctionwiththeareaofbottomsedimentandsedimentsolidsconcentrationdeterminesthemassofsedimentavailableforexchange



withthewatercolumn.AlthoughtheresearchonSanFranciscoBaydescribedbyDavis(2004)suggestedthatthebestestimateoftheaveragedepthoftheactivesedimentlayerwas10cm(0.010m),SanFranciscoBayismuchshallower,andmorephysicallyandbiologicallydynamic,thanLakeWashington.PublishedsedimentgeochronologystudiesofLakeWashingtonindicateverylittledisturbanceofdepositedsedimentswiththinseasonaldepositionlayerspreservedinthesediments(EdmondsonandAllison1970,Edmondson1975,Edmondson1991).Thelogarithmicdeclineinsurfacesedimentlead‐210profilesandanoxicconditionsimmediatelybelowthesedimentwaterinterfaceindicateminimalphysicalorbiologicaldisturbance(WakehamandCarpenter1976,Furlong1986).Thewell‐preservedpeakinPCBconcentrationsatdepthinthesedimentisconsistentwithpeakhistoricalinputsandservesasanotherindicationthatsedimentdisturbanceisminimal(Furletal.2009,Era‐Milleretal.2010).VisualillustrationsoftheevidenceforlimitedsedimentmixingandarelativelyshallowactivesedimentlayerareprovidedinFigure4;andsuggestthattheactivesedimentlayermaybelessthan2.5cm.Basedonthisinformation;adepthof2.5cm(0.025m)wasselectedastheactivesedimentlayerforuseintheLakeWashingtonmodel.ThisvalueisthesameastheoneusedintheLakeOntariomodel(Gobasetal.1995).



Figure 4. Visual illustrations of limited mixing by physical or biological activity in Lake

Washington sediments: A) Cesium-137, DDT, PCB, lead and copper sediment profiles showing preserved contaminant peaks (Van Metre et al. 2004), B) Radiographs showing fine undisturbed sediment layers (Edmondson and Allison 1970), C) Photograph of vertical section of core showing undisturbed sediment layers – small gray bar at bottom is 10 µm (Edmondson 1991).

A

B C



4.1.3.2 Sediment solids concentration

Sedimentinvestigationsthatfocusonsurfacecontaminantstypicallyincludemeasurementofpercentsolids(toconvertwetsedimentanalysisresultstocontaminantconcentrationsonadrysedimentweightbasis),butnotmeasurementsofsedimentsolidsconcentration.However,sedimentgeochronologystudiesinvolvecollectionofcoresthataretypicallysectionedandsubsampledtodeterminesedimentsolidsconcentrationtoaccountforsedimentcompactionwithdepth.Fortunately,manysedimentcoreshavebeencollectedandanalyzedfromLakeWashingtonovertheyearsaspartofvariousstudiesconductedbyresearchersattheUniversityofWashington.Althoughnotallofthesedimentsolidsconcentrationprofilesfromthesestudieshavebeenpublished,unpublishedUniversityofWashingtondatafrom14coresweremadeavailablebyArniLitt(personalcommunication)foruseinthisstudy.PublisheddataincludethreecorescollectedbyBirch(1976)andsixcorescollectedbyGriffithsandEdmondson(1975).

Themeansolidsconcentrationinthetop2.5cmofthe23sedimentcoreswas0.12kgL‐1.Themedian(50thpercentile)concentrationwas0.11kgL‐1andthe25th‐and75th‐percentileconcentrationswere0.10and0.15kgL‐1,respectively.

4.1.3.3 Average Sediment PCB Concentration

AveragetPCBconcentrationsintheactivesedimentlayerdefinethemassoftPCBinlakesediments.BecausePCBsaregenerallyhydrophobicandsorbstronglytoorganicsolids,themajorityofPCBsinlakesandestuariesisstoredinsedimentsandsediment‐waterinteractionslengthentheresponsetimetoreductionsinPCBloading(Schwarzenbachetal.1993,Gobasetal.1995,Davis2004).

SedimentPCBconcentrationdatawereavailablefor69samplinglocationsdistributedthroughoutthelake(Table4)(seeKingCounty2013dfordetails).Thedatawereobtainedfromavarietyofsamplingprogramswitharangeofobjectives,someofwhichfocusedoncharacterizingconcentrationsnearsuspectedcontaminantsources.Differentobjectivesandprotocolsalsoresultedindifferenttargetsamplingdepthsinanyparticularprogram.However,themajorityofsamplesrepresentconcentrationsinthetop10cm.Fewerprogramstargetedthetop2cmofsediment.Theavailabledataarelikelybiasedhighduetoinclusionofsamplesfromprogramsspecificallyattemptingtoidentifynearshorecontaminantsourcesandduetoinclusionofdatafromthetop10cmofsediment,whichlikelyincorporatedhigherPCBconcentrationsthatareburieddeeperinthesediments(Furletal.2009,Era‐Milleretal.2010).IntheabsenceofaspatiallystratifiedrandomdesignsuchasthoseimplementedforcharacterizingthedistributionofSanFranciscoBaysedimentPCBconcentrations(Davisetal.2007),theavailabledatawereusedtoprovideafirstapproximationofaveragesedimenttPCBconcentrationsforuseinmodeldevelopmentandtesting.

Concentrationsweregenerallyhigheralongtheshallowermarginsofthelakeandlowerindeeperoffshoreareas(Table4).Thespatially‐weightedaveragebasedontheaveragetPCBconcentrationsinshallow(<30ft)anddeeper(>30ft)lakeareaswas55µg/kgdrysediment.



Table 4. Summary of tPCB concentrations measured in Lake Washington sediments.

Location Number

of Samples

Total PCBs (µg/kg dry)

Min Max Mean 25th Median 75th

<30ft depth 41 3.3 577.2 71.2 17.6 29.0 56.5

>30ft depth 28 4.6 184.6 31.8 10.4 15.2 22.5

All locations combined 69 3.3 577.2 55.2 11.4 22.0 53.2

4.1.4 Water properties Althoughthewaterpropertydataselectedforinputtothemodelhavebeendocumentedabove,thedataaresummarizedagainbelow.Inaddition,thewatercolumntPCBdatausedinmodeltestingarealsodocumentedbelow.

4.1.4.1 Water temperature

Themeanwatertemperatureof10.9oCwasdeterminedfromlong‐term(2002‐2012)volume‐weightedaveragesoftemperatureprofilescollectedfromthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject(KingCounty2013a).

4.1.4.2 Particulate and dissolved organic carbon concentrations

TheaverageconcentrationsofwatercolumnDOCandPOCwerebasedonvolume‐weightedmeasurementsofDOCandTOCmadebyKingCountybetween2002and2008(DOCandTOCanalyseswerediscontinuedafter2008)atthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject.Volume‐weightedmeanconcentrationsofTOCandDOCwere3.4and3.1mg/L,respectively(AppendixB).ThemeanconcentrationofPOC(0.3mg/L)wasestimatedbythedifferencebetweenTOCandDOC.

4.1.4.3 Suspended solids concentrations

ThemeanconcentrationofsettlingparticleswasdeterminedfromtheaverageTSSconcentrationmeasuredbetween2002and2012atthesamethreecentrallakestationssampledduringthefieldstudyconductedforthisproject.Thevolume‐weightedmeanTSSconcentrationwas1.0mg/L(AppendixB).

4.1.4.4 Water column tPCB concentration

LakewatertPCBdatawerecollectedaspartofthisstudy(KingCounty2013a),whichwereusedtocalculateanaveragewatercolumntPCBvalueforuseinmodeldevelopmentand



testing.ThetPCBresultsfromthesamplesintegratedfromthreelakestations(separatelyfromtheepilimnionandhypolimnionorfromthewholelakedependingonwhetherthelakewaswellmixedornot)andthatwerecollectedeveryothermonthforoneyear(atotalofsixsamplingevents)wereusedtocalculateanannualvolume‐weightedmeantPCBconcentration(Table).12Thecalculatedannualmeanvolume‐weightedtPCBconcentrationwas92pg/L.

Table 5. Summary of measured water column tPCB concentrations (pg/L).

Statistic tPCB (pg/L)

Volume-weighted mean 92

25th-percentile 51

Median (50th-percentile) 61

75th-percentile 118

4.1.5 External tPCB load TheexternaltPCBloadtothelake,includinginputsfromrivers,streams,bridgerunoffandtheatmospherebasedondatacollectedaspartofthisstudyisdocumentedinKingCounty(2013b).ThebestestimateoftPCBloadingtothelakewas0.672kgyr‐1.Upperandlowerestimateswerealsoprovidedforthevarioussources,whichallowcalculationofarangethatbracketsthebestestimate.Theseestimatesrangefrom0.333to0.889kgyr‐1.TheseloadingestimatesprovideanindicationoftheuncertaintyintPCBloadingandinputdatawithwhichtotesttheeffectofuncertaintyintPCBloadingonfate(andbioaccumulationmodel)predictions.

4.1.6 Export of tPCB through the lake outlet TheamountoftPCBexportedfromLakeWashingtonviathelakeoutletwasalsodeterminedbasedonmeasuredoutlettPCBconcentrationsandalakewaterbudgetdocumentedinKingCounty(2013b).TheestimatedaveragetPCBexportwas0.140kgyr‐1.Thisestimatecanbecomparedtothefatemodelpredictiontotesttheabilityofthemodeltoestimatethemagnitudeoflossviathispathway.Areasonablematchbetweenobservedwaterandsedimentconcentrationsandlossthroughtheoutletwouldsuggestareasonablygoodpredictionofthelossviavolatilizationfromthesurfaceofthelake.

12SeeKingCounty(2013a)fordetailsregardingsampleintegrationmethodsandtPCBconcentrationdata.



5.0 BIOACCUMULATION MODEL INPUT DATA

ThebioaccumulationmodelmathematicallyrepresentsthebalancebetweenPCBuptakefromingestionandrespirationandeliminationfromrespiration,egestion,metabolism,andgrowthdilutioninalinearandsteadystaterelationship.Asasinglealgorithm,themodelcanbeviewedasthefollowingforinvertebratesandfishes(Condon2007):

∗ ∗ ∗ , ∗ , , ∗ ΣPi ∗ CD,J,ik2j kEj kGj kMj

3

Where:

CBj =concentrationofcongenerjinorganism(ng/gwetweight)

k1j =rateofcongeneruptakefromrespiration(d‐1)

mO =fractionofrespiratoryventilationthatinvolvesoverlyingwater(unitless)

φ =fractionofcongenerinoverlyingwaterthatcanbeabsorbed(unitless)

CWT,O =totalconcentrationofcongenerjinoverlyingwater(ng/mL)

mp =fractionofrespiratoryventilationthatinvolvesporewater(unitless)

CWD,S,j =freelydissolvedconcentrationofcongenerjinporewater(ng/mL)

kDj =rateofcongenerjuptakebydietaryingestion(d‐1)

Pi =fractionofdietconsistingofpreyitemI(unitless)

CD,J,I =concentrationofcongenerjinpreyitemI(g/kg)

k2j =rateofcongenerjeliminationfromrespiration(d‐1)

kEj =rateofcongenerjeliminationfromegestion(d‐1)

kGj =rateofcongenerjeliminationfromgrowth(d‐1)

kMj =rateofcongenerjeliminationfrommetabolictransformation(d‐1)

ThebalanceofPCBuptakeandeliminationismodeledmoresimplyforphytoplanktonbecauseingestion,egestionandmetabolictransformationareassumedtobezero.Thissimplifiedmodelalgorithmis:

∗ CWD,S,jk2j kGj

4

Seeabovefordefinitionofterms.



Thetermsinequations3and4arecalculatedusingthesamesubmodelspresentedinCondon(2007)whichwillnotberepeatedhere.ThechangesrequiredtoadaptthemodelforuseinLakeWashingtonwere:

Salinityforfreshwaterwasassumedtobe0ppt.

Thesubmodelsforbirdsandmammalswerenotused.

LakeWashington‐specificparametervalueswereusedwhenavailable.

Therestofthissectionsummarizestheinputvaluesassumedforlake,PCBchemistry,biology,anddietaryparametersaswellastheconceptualfoodwebforLakeWashington.InputvalueswereestimatedtorepresentaverageconditionsinLakeWashington.

5.1 Lake Parameters AnumberofparametervaluesrepresentinglakeconditionswereestimatedusingpublishedandunpublishedempiricaldatafromLakeWashington(Table6).Themeanannualwatertemperatureandmeanconcentrationsofsuspendedsolids,DOC,POCanddissolvedoxygenwereestimatedasavolume‐weightedaveragevalueofmeasurementsbetween2002‐2012fromthreeKingCountylong‐termmonitoringstations.Whereapplicable,thesevaluesarethesameasthoseusedinthefatemodel(Section4.1.4).AmeanPOCconcentrationwascalculatedbysubtractingthevolume‐weightedmeanDOCconcentrationfromthevolume‐weightedmeanTOCconcentration.ThesedimentorganiccarboncontentparameterwasestimatedbasedonthemeanofavailablesedimentdatacollectedbyKingCountyandEcology(KingCounty2013d).Thisisthesamevaluethatwasusedinthefatemodel.

Theremaininglakeparameters(e.g.,densityoforganiccarboninsediment,disequilibriumfactors,proportionalityconstants)couldnotbeestimatedfromanyLakeWashington‐specificempiricaldata.Thus,theseparameterswereestimatedbasedonvaluesusedbyotherswhohavemodeledbioaccumulationofPCBsinSanFranciscoBayandPugetSound.



Table 6. Bioaccumulation model parameter values

Model parameter Symbol Mean Source

Concentration of particulate organic carbon in water (kg/L)

Xpoc 3.00E-07 Calculated from volume-weighted average TOC and DOC

Concentration of dissolved organic carbon in water (kg/L)

Xdoc 3.09E-06 Volume-weighted average from three Lake WA stations 2002-2008

Concentration of suspended solids (kg/L)

Vss 1.01E-06 Volume-weighted average from three Lake WA stations 2002-2012

Mean annual water temperature (oC)

Tw 10.9 Volume-weighted average from three Lake WA stations 2002-2012

Salinity (g/kg) PSU 0 Assumed

Density of organic carbon in sediment (kg/L)

dOCS 1 Gobas et al. (1995), Davis (2004)

Organic carbon content of sediment (unitless)

OCS 0.055 Mean sediment concentration based on available King County and Ecology sediment data (King County 2013d)

Dissolved oxygen concentration @ 90% saturation (mg O2/L)

Cox 9.29 Volume-weighted average from three Lake WA stations 2002-2012 and adjusted to 90% saturation

Absolute temperature (K) Tabs 273.16 Known constant

Molar concentration of seawater @ 35 ppt (mol/L)

MCS 0 Assumed

Disequilibrium factor for POC partitioning in water column (unitless)

Dpoc 1 Default values from Arnot and Gobas 2004 (eqn 4)

Disequilibrium factor for DOC partitioning in water column (unitless)

Ddoc 1 Default values from Arnot and Gobas 2004 (eqn 4)

Proportionality constant for phase partitioning of POC (unitless)

alphaPOC 0.35 Default values from Arnot and Gobas 2004 (eqn 4)

Proportionality constant for phase partitioning of DOC (unitless)

alphaDOC 0.08 Default values from Arnot and Gobas 2004 (eqn 4)



5.2 PCB Chemistry Parameters ThechemistryparametersnecessaryforthebioaccumulationmodelarethelogKow,molecularweight,andLeBasmolarvolume(TableTable7).ThesamevaluesusedforPCB‐118inthefatemodelwereassumedforthebioaccumulationmodelparameters.

Table 7. Chemistry parameters for PCB-118

Model Parameter Symbol PCB-118a Source

Log10KOW @ 10.9°C KOW 6.86 Schenker et al. (2005)

Molar Mass (g mol-1) MolWt 326.43 From molecular formula

Molar Volume at Boiling Point (cm3 mol-1) MVB 289.1 LeBas method (14.8*12+3.7*5+24.6*5-15-15)

5.3 Food Web Structure and Dietary Assumptions CertainfishspecieswereinitiallyidentifiedforbioaccumulationmodelingduetotheirinclusionintheWashingtonStatefishconsumptionadvisoryforPCBs(WADOH2004):northernpikeminnow(Ptychocheilusoregonensis),cutthroattrout(Oncorhynchusclarkii)andyellowperch(Percaflavescens).Otherfishspecieswereaddedbasedontheirimportanceaspreyitemsortoppredators.SomefishspeciesknowntoresideinLakeWashington,suchasbrownbullhead(Ameiurusnebulosus)andcommoncarp13(Cyprinuscarpio),werenotincludedinthefoodwebmodelbecausetheyarenotkeypreyitemsforotherfish,andlimitedinformation(e.g.,dietcomposition,wholebodyPCBtissueconcentrations)wasavailablefrompublishedLakeWashingtonstudiestoenablebioaccumulationmodeling.Adultsalmonspecieswerenotincludedinthemodelbecausethesemigratoryfishspendmostoftheirlifecycleintheocean,onlypassingthroughLakeWashingtontoreachupstreamspawningareas,andthus,theirtissueconcentrationsarenotreflectiveofPCBexposureinLakeWashington.Adultsalmonarealsotoppredatorsand,therefore,notpreyitemsnecessarytomodelotherspecies.Juvenilesockeye(Oncorhynchusnerka) wasselectedformodelingbecauseyoungsockeyeresideayearormoreinLakeWashingtonbeforemigratingtotheocean(WDFW2013);theyarealsoanimportantpreyitemforseveralpiscivorousspeciesofinterest.Invertebrateswereselectedformodelingbasedontheiridentificationaspreyitemsformodeledfishspeciesinpublisheddietstudies.ThetaxaselectedforthebioaccumulationmodelarepresentedinFigure5.Modeledfishspeciesincludejuvenilesockeyesalmon,longfinsmelt(Spirinchusthaleichthys),peamouthchub(Mylcheiluscaurinus),largeandsmallyellowperch,

13CommoncarpareincludedintheWADOHfishconsumptionadvisory;however,theadvisorywasbasedonfillet‐onlyconcentrations.PreviousstudiesoftheLakeWashingtonfoodwebhavenotincludedcommoncarp.Modelingofthethreeotherspeciesintheadvisoryisconsideredtobeadequatetoprovideinformationtoadvisefuturemanagementdecisionswhichwouldalsobenefitcarp.



Figure 5. Detailed conceptual food web for Lake Washington

threespinestickleback(Gasterosteusaculeatus),pricklysculpin(Cottusasper),smallmouthbass(Micropterusdolomieu),cutthroattroutandnorthernpikeminnow.Modeledinvertebratetaxaincludecopepods,amphipods/isopods14,mollusks,otherbenthic

14Thisgroupismodeledbasedonamphipodsbutalsorepresentssmallfractionsofisopodsidentifiedinthepricklysculpindiet.



invertebrates15,daphniaspecies,mysids(Neomysismercedis),andsmallandlargesignalcrayfish(Pacifastacusleniusculus).

ThefractionsofpreyitemsassumedforthebioaccumulationmodelweredefinedusingpublisheddietarystudiesfromLakeWashington,wherepossible,andotherwise,usingpublishedstudiesfromotherareas.Fractionsofpreyitemsfromdietstudiessamplingovermultipleseasonswereaveragedacrossseasonstocalculateanannualmeanfraction.DietsofallfishspeciesweredefinedbasedondietarystudiesofLakeWashingtonfish(Table8).Dietaryinformationfortwospeciesoffish,yellowperchandpricklysculpin,indicatedtheywerecannibalistic,eatingother,presumablysmaller,membersoftheirspecies.Toaccountforthiscannibalism,asmallercohortofthisspecieswasaddedtothefoodwebtoserveasadietaryitemforthelargecohort.Topreventthemodelfromgeneratinganegativetissueconcentration,thissmallercohortwasassumedtonotbecannibalistic.Cannibalismalsooccursinsignalcrayfish;thus,asmallandlargecohortwasalsomodeledforthisspecies.

Anadjustmentwasmadetothepublishedthreespinesticklebackdiet(McIntyre2004)basedonthestableisotopestudyresultsofMcIntyre(2004).McIntyre(2004)foundthatthehighnitrogenisotopesignatureofthreespinesticklebackindicatedtheyfedhighonthefoodweb,similartolongfinsmeltandpiscivores.However,stomachcontentdatafromthesamestudy(McIntyre2004)showedconsumptionofonlycladocera,copepodsandasmallamountofbenthicinvertebrates.Otherthreespinesticklebackstudieshaveshownthatthisspeciesfeedssubstantiallyonfisheggsandfry(BigelowandSchroeder2002,LavinandMcPhail2011).TheMcIntyre(2004)stableisotopestudyalsofoundasimilarlyhighnitrogenisotopesignatureforlongfinsmeltwhichconsumezooplanktonandbenthicinvertebrates,butwerealsofoundtohaveanaverageacrossseasonsof20%fishlarvae(bymass)intheirstomach.Consumptionoffishlarvaewouldraisethenitrogenisotopesignatureinfishthatareotherwiseplanktivores.Thesamplesizeofthreespinesticklebackwasrelativelysmallat35individuals.Also,dependingonthehabitattheseindividualswerecollectedfromwithinLakeWashington,theymayhaveconsumeddifferentdietsthanthefishsampledforPCBanalysis;threespinesticklebackphysicaljawstructureanddietcanvarybyhabitatzonewithinthesamewaterbody(LavinandMcPhail2011).Forallthereasonsmentioned,thedietforthreespinesticklebackwasadjustedtoreflectthesameproportionoffishlarvaeaslongfinsmelt(20%).

ModelingbioaccumulationofPCBsinfishlarvaepresentschallengesindefiningparametersfordiet,lipidcomposition,wetweight,etc.foranorganismthatintheeggstagedoesnoteatprey,hasanunknowntissueconcentrationofPCBstransferredfromthemother,andisanunidentifiedspeciesindietstudies.Itisexpectedthatthelipidcontentoffishlarvaeishigherthanmaturefishofthesamespeciesandthat,withmaternaltransfer,tissueconcentrationsarehigherthanprimaryconsumers(i.e.,herbivorouszooplankton).

Asanalternativetomakingestimationsforallthemodelparametersneededtopredicttissueconcentrationsinfishlarvae,thefractionsoffishlarvaeforthethreemodeledfishspeciesthatconsumethem(longfinsmelt,pricklysculpin,andthreespinestickleback)

15Ifpreyitemswereidentifiedindietstudiesas“benthos”or“benthicinvertebrates”,theywereassignedtothismodeledtaxon.Chironomidsareincludedinthistaxonomicgroup.



weremodeledassediment.ThemeanPCBconcentrationinsedimentmeasuredinLakeWashingtonandusedinthebioaccumulationmodelis55µg/kg(dryweight)andislowerthantissueconcentrationspredictedforbenthicinvertebratesandcrayfish.Thus,thisassumptionmaypotentiallyunderestimateexposurefromfishlarvae,butresultsinahigherexposurethanassumingconsumptionofphytoplankton,daphnids,andcopepods.



Table 8. Diet fraction assumptions for modeled taxa

Prey → Sedim

ent

Phytoplankton

Daphnia

Mysids

Copep

ods

Amphipods and Isopods

Mollusca

Crayfish Small

Crayfish Large

Other Ben

thic Inverteb

rates

Sockeye Salm

on (juvenile)

Longfin Smelt

Threespine Stickleb

ack

Peamouth Chub

Prickly Sculpin Small

Prickly Sculpin Large

Yellow Perch Small

Yellow Perch Large

Smallm

outh Bass

Cutthroat Trout

Northern Pikem

innow

Source

Sample Num

ber

Consumer →

Daphnia 1.00 Winder & Schindler (2004)

—

Mysids 0.05 0.35 0.24 0.36 Siegbried & Kopache (1980)a

—

Copepods 0.05 0.50 0.45 Estimated —

Amphipods and Isopods 0.05 0.50 0.45

Morrison et al. (1997)b

—

Mollusca 0.34 0.33 0.33 EPA (2009b) —

Crayfish Large 0.75 0.13 0.06 0.01 0.01 0.01 0.01 0.01 0.01 Agric (1995) 712d

Crayfish Small 0.02 0.77 0.21 Agric (1995) 712d

Other Benthic Invertebrates 0.34 0.33 0.33 EPA (2009b) —

Sockeye Salmon (juvenile) 0.53 0.04 0.33 0.10 McIntyre (2004) 55

Longfin Smelt 0.20d 0.30 0.06 0.35 0.09 McIntyre (2004) 61

Threespine Stickleback 0.20d 0.35 0.35 0.10 McIntyre (2004)a 35

Peamouth Chub 0.04 0.04 0.08 0.06 0.38 0.40 Shanbhogue (1976)c

485

Prickly Sculpin Large 0.13d 0.10 0.31 0.01 0.04 0.09 0.08 0.04 0.16 0.04

Tabor et al. (2004 and 2007)

4198

Prickly Sculpin Small 0.16 0.07 0.04 0.02 0.66 0.01 0.02 0.02

Tabor et al. (2004 and 2007)

111

Yellow Perch Large 0.04 0.04 0.29 0.09 0.09 0.10 0.25 0.10 McIntyre (2004) 32

Yellow Perch Small 0.24 0.38 0.25 0.04 0.04 0.05 McIntyre (2004) 168



Prey →

Sedim

ent / Detritus

Phytoplankton

Daphnia

Mysids

Copep

ods

Amphipods and Isopods

Mollusca

Crayfish Small

Crayfish Large

Other Ben

thic Inverteb

rates

Sockeye Salm

on (juvenile)

Longfin Smelt

Threespine Stickleback

Peamouth Chub

Prickly Sculpin Small

Prickly Sculpin Large

Yellow Perch Small

Yellow Perch Large

Smallm

outh Bass

Cutthroat Trout

Northern Pikem

innow

Source

Sample Num

ber

Consumer →

Smallmouth Bass 0.05 0.05 0.20 0.03 0.13 0.05 0.20 0.05 0.20 0.00 0.05 Fayram and Sibley (2000)

50

Cutthroat Trout 0.13 0.05 0.24 0.13 0.23 0.17 0.03 0.02 Mazur (2004) 200

Northern Pikeminnow 0.13 0.02 0.11 0.05 0.16 0.32 0.04 0.10 0.07 Brocksmith (1999) 123

Fractions are estimates of average percent composition of diet by wet weight except where noted.a Adapted b Based on Gammarus diet c Based on percent volume dRepresents fish eggs/larvae



Forinvertebratetaxaexceptdaphnids,dietarystudiesfromLakeWashingtoncouldnotbeidentified.Dietcompositionforlargeandsmallsignalcrayfish,andmysidsweredefineddirectlyfromdietarystudiesoutsidethisregion(Agric1995,SiegbriedandKopache1980).Dietcompositionsforcopepods,amphipods/isopods,mollusks,andbenthicinvertebrateswerederivedfromassumptionsusedinotherbioaccumulationmodels(Morrisonetal.1999,EPA2009b).ThesemodelsbasedtheirassumptionsforbenthicinvertebratesonCovichetal.(1999)statingthatthesespeciesareamixofherbivores,detritivores,andcarnivoresandtherefore,canbemodeledbysplittingtheirdietevenlybetweensediment,phytoplanktonandzooplankton.Thisapproachwasalsoassumedformollusksandbenthicinvertebratesinthisbioaccumulationmodel.However,thedietsofcopepodsandamphipods/isopodsweremodifiedfromthisbasicassumptionbecausetheirfeedingstrategiesarediverse,butdonotincludedirectsedimentingestion.Harpacticoidcopepods,amphipodsandisopodsfeedonthesurfaceofthebottomsediments.Harpacticoidcopepodsarescrapers,removingfoodfromplantandsedimentparticles,andamphipodsincludeomnivores,scavengersanddetritivoresthatconsumebacteria,algae,andanimalandplantdetritus(Wetzel1975,Pennak1989)althoughtheanimaldetritusmaybecoarseparticlesorfreshlykilledorganisms(Pennak1989,Voshell2003).Theothertwotypesofcopepods,calanoidandcyclopoid,feedbyfiltrationoropportunisticraptorialbehaviorandareherbivorousorcarnivorous(Wetzel1975).Thus,somespeciesofcopepods,amphipodsandisopodsmayingestsomesediment,ifonlyincidentally,buttheirrateofsedimentingestionisexpectedtobelowerthanmollusksandbenthicinvertebrateslivingwithinthesediments.Agric(1995)measuredsedimentfractionsinsignalcrayfishgutsatabout0%forlarger(20‐45mm)and2%forsmaller(<20mm)individuals.Signalcrayfishalsofeedalongthesurfaceofthesedimentsonplantandanimalmatter.Thesedimentfractionforcopepodsandamphipods/isopodswasassumedtobe5%,slightlyhigherthancrayfish.Theremainderofthedietwassplitbetweenphytoplankton(50%)andzooplankton(45%daphnia)torepresentthealgaeandanimalmatterconsumed.

5.4 Biological Parameters ValuesforbiologicalparametersusedinthebioaccumulationmodelwerederivedfromeitherempiricaldataspecifictoLakeWashington,wherepossible,orthegeneralliteratureorwereestimated.Someofthebiologicalparametersarespecifictoeachmodeledtaxonomicgrouportaxonbutmanyaregeneral,applyingacrossmultiplemodeledtaxa.Table9presentsvaluesforgeneralbiologicalparameterswhichincludethenon‐lipidorganicmatter–octanolproportionalityconstant,theparticlescavengingefficiencyoffilter‐feeders,growthratefactors,fractionsofrespirationinvolvingporewater,dietaryassimilationefficiencies,dietarychemicaltransferefficiencies(EdAandEdB),diffusivetransferefficiency(EwA)andmetabolictransformationrate.



Table 9. General biological parameters

Organisms Model parameter Symbol Mean Source

All Non-lipid organic matter – octanol proportionality constant (unitless)

β 0.035 Gobas et al. 1999

Filter-feeding Invertebrates Particle scavenging efficiency σ 1 Default value

Fish Growth rate factor (unitless) GRFF 0.0007 Gobas and Arnot 2005

Invertebrates Growth rate factor (unitless) GRFI 0.00035 Gobas and Arnot 2005

Pelagic zooplankton Fraction of respiration that involves

sediment pore water mP 0 Estimated

Mysids, Amphipods and Isopods

Fraction of respiration that involves sediment pore water

mP 0.01 Estimated

Mollusks and Crayfish Fraction of respiration that involves

sediment pore water mP 0.05 Estimated

Benthic Invertebrates Fraction of respiration that involves

sediment pore water mP 0.10 Estimated

Fish Fraction of respiration that involves

sediment pore water mP 0 Estimated

Zooplankton Dietary absorption efficiency of lipid εL 0.72 Arnot and Gobas 2004

Zooplankton Dietary absorption efficiency of NLOM εN 0.72 Arnot and Gobas 2004

Invertebrates Dietary absorption efficiency of lipid εL 0.75 Arnot and Gobas 2004

Invertebrates Dietary absorption efficiency of NLOM εN 0.75 Arnot and Gobas 2004

Fish Dietary absorption efficiency of lipid εL 0.92 Arnot and Gobas 2004

Fish Dietary absorption efficiency of NLOM εN 0.6 Arnot and Gobas 2004

Invertebrates and Fish Dietary absorption efficiency of water εW 0.25 Arnot and Gobas 2004

Invertebrates and Fish ED constant A EdA 8.50E-

08 Gobas and Arnot 2005

Invertebrates and Fish ED constant B EdB 2.0 Gobas and Arnot 2005

Poikilotherms/Homeotherms Metabolic transformation rate (d-1) kMp 0 Arnot and Gobas 2004

Poikilotherms Ew constant A (unitless) EWA 1.85 Arnot and Gobas 2004

Notes:NLOM–non‐lipidorganicmatter



Taxon‐specificbiologicalparametervaluesusedinthebioaccumulationmodelarepresentedforeachmodeledtaxoninTables10‐29.ThebiologicalparametersspecifictophytoplanktonwerebasedonpublishedliteraturevaluesusedinotherbioaccumulationmodelssuchasEPA’sKABAMmodelforpesticidebioaccumulationinfreshwatersystems(EPA2009b)orgeneralGobas‐typemodelsforbioaccumulationofhydrophobicorganicchemicals(ArnotandGobas2004,GobasandArnot2005)(Table10).BiologicalparametervaluesformanyoftheindividualmodeledtaxawerebasedonempiricaldatafromLakeWashington.EmpiricaldatafromLakeWashingtonwereavailableforbodyweights,lipidfractions,andwaterfractionsforallthemodeledfishspeciesandforsomeofthemodeledinvertebratetaxa.ThetwostudiesfromwhichtheseempiricaldatawereobtainedwereMcIntyre(2004)andKingCounty(2013e).However,unpublishedrawdatawasalsoobtainedfromtheMcIntyre(2004)author,J.McIntyre,toenablecalculationofmeanvaluesforbiologicalparameters.MostoftheseunpublisheddataweresummarizedinKingCounty(2013e).However,thetissuesolidsdatausedtocalculatemeanvaluesforthewaterfractionparameterwerenotincludedinMcIntyre(2004)orKingCounty(2013e).ThepercentsolidsdatafromJ.McIntyreusedtocalculatemeanwaterfractionsarepresentedinAppendixA.Non‐lipidorganicmatterfractionswerederived(Labeledas“Deduced”intables)bysubtractionofthelipidandwaterfractionsfromatotalof1.0.TheYes/Noparameterforfilterfeederswassetbasedonthedominantfeedingmechanismforthespeciesinthetaxon.

Table 10. Phytoplankton biological parameters

Model parameter Symbol Units Mean Source

Lipid fraction in plant vLB Unitless 0.012 Derived from EPA 2009b

Non-lipid organic carbon fraction in plant vNB Unitless 0.088 Deduced

Water fraction in plant vWB Unitless 0.90 EPA 2009b

Growth rate constant kG d-1 0.125 Gobas and Arnot 2005

Aqueous phase resistance constant AP Unitless 6.00E-05 Arnot and Gobas 2004

Organic phase resistance constant BP Unitless 5.50E+00 Arnot and Gobas 2004

Table 11. Daphnia biological parameters

Model parameter Symbol Unit Value Source

Wet weight of the organism (kg) WB kg 1.0E-07 Peters and Downing 1984

Lipid fraction in biota (unitless) vLB Unitless 0.02 McIntyre unpublished

Filter feeders “Yes" or "No" filterFeeder Unitless Yes Estimated

Non-lipid organic matter fraction in biota vNB Unitless 0.06 Deduced

Water fraction in biota vWB Unitless 0.92 McIntyre (Appendix A)



Table 12. Mysid biological parameters


Wet weight of the organism (kg) WB kg 1.2E-05 Morrison et al. 1999

Lipid fraction in biota (unitless) vLB Unitless 0.05 Oliver and Niimi 1988; Morrison et al. 1999




Table 13. Copepod biological parameters







Table 14. Amphipod and Isopod biological parameters



Lipid fraction in biota (unitless) vLB Unitless 0.01 McIntyre 2004

Filter feeders “Yes" or "No" filterFeeder Unitless No Estimated



Table 15. Benthic Invertebrates biological parameters


Wet weight of the organism (kg) WB kg 9.3E-06 Lobo and Alves 2011

Lipid fraction in biota (unitless) vLB Unitless 0.03 EPA 2009b






Table 16. Mollusks biological parameters


Wet weight of the organism (kg) WB kg 0.002 EPA 2009b

Lipid fraction in biota (unitless) vLB Unitless 0.03 Morrison et al. 1999



Water fraction in biota vWB Unitless 0.80 Estimated

Table 17. Crayfish Large (>90 mm TL) biological parameters


Wet weight of the organism (kg) WB kg 0.0372 King County 2013e





Table 18. Crayfish Small (<90 mm TL) biological parameters


Wet weight of the organism (kg) WB kg 0.00046 McIntyre 2004

Lipid fraction in biota (unitless) vLB Unitless 0.01 Assumed same as large

crayfish



Water fraction in biota vWB Unitless 0.75 Assumed same as large

crayfish

Table 19. Juvenile Sockeye Salmon biological parameters



Lipid fraction in biota (unitless) vLB Unitless 0.05 King County 2013e





Table 20. Longfin smelt biological parameters






Table 21. Peamouth chub biological parameters





Water fraction in biota vWB Unitless 0.70 King County (Appendix A)

Table 22. Prickly sculpin large (>74 mm TL) biological parameters


Wet weight of the organism (kg) WB kg 0.0221 McIntyre unpublished




Table 23. Prickly sculpin small (<75 mm TL) biological parameters





Water fraction in biota vWB Unitless 0.80 King County and McIntyre

(Appendix A)

Table 24. Yellow perch large (>224 mm FL) biological parameters








Table 25. Yellow perch small (<225 mm FL) biological parameters






Table 26. Threespine stickleback biological parameters






Table 27. Smallmouth bass biological parameters





Water fraction in biota vWB Unitless 0.70 King County and

McIntyre (Appendix A)

Table 28. Cutthroat trout biological parameters






Table 29. Northern pikeminnow biological parameters





Water fraction in biota vWB Unitless 0.72 King County and

McIntyre (Appendix A)



6.0 SENSITIVITY AND UNCERTAINTY ANALYSIS APPROACH

Theapproachtakentoevaluatemodelsensitivityanduncertaintyofthefateandbioaccumulationmodelsdescribedpreviouslyisdescribedbelow.

6.1 Sensitivity Toevaluatemodelsensitivity,parameterswereassignedtolognormaldistributionswithastandarddeviationof5%andsampledbyMonteCarlofor1000simulations.Then,aSpearmanrankordercorrelationanalysisbetweenparametervaluesandmodeloutputwasconductedtodeterminethepercentcontributiontovariance.ThesestepswereautomatedusingaMicrosoftExcelAdd‐IncalledYASAIwprovidedasfreewarebyEcology(Pelletier2009).YASAIwisamodifiedversionofYASAI(YetAnotherSimulationAdd‐InforExcel)developedbyEcksteinetal.(2000)tofacilitatesensitivityandMonteCarloanalysisofmodels.ThisreportdescribesthedegreeofrelativeinfluenceofeachparameteronmodelresultsinSection7.0.Thisreportalsodescribesthedegreeofrelativeinfluenceofthetestedparametersanddiscussesimplicationsfortheinterpretationofresults.

Alloftheinputparametersthatwerenotconsideredtobeknownwithagreatdealaccuracy(e.g.,thevolumeofthelakewasconsideredtobeknownwithahighdegreeofaccuracy,buttheaveragewindspeedwasnot)wereincludedinthefatemodelsensitivityanalysis.Forthebioaccumulationmodel,theabioticandbiologicalparametervalues,LeBasmolarvolume,molecularweight,andsite‐specificPCBconcentrationsinwaterandsedimentwereincludedinthesensitivityanalysis.LogKow(octanol‐waterpartitioningcoefficient)wasnotincludedbecauseothershavedocumentedthatGobas‐typebioaccumulationmodelsareverysensitivetologKow(EPA2009b).LogKowhaspreviouslybeenshowntohavethegreatestinfluence(over70%overallcontributiontovariance)ofanyparameteronthemodeloutput(EPA2009b).ThisiscongruentwiththefactthatlogKowisintegraltoseveralpartitioningalgorithmsusedinthemodel(ArnotandGobas2004).Thus,exclusionoflogKowfromthesensitivityanalysisallowsforcloserexaminationofthesensitivity16oftheotherinputparameters.

Thefoodwebstructureandfeedingpreferencesinthebioaccumulationmodelinfluencepredictedtissueconcentrations,butpresentchallengestotestingbythesamesensitivityanalysistoolusedfortheotherparameters(e.g.,dietfractionsarelinkedandthespeciesofmodeledpreyitemsmayvary)andarenottypicallytested.Conductingsensitivityanalysisofthefoodwebstructureanddietaryassumptionswasconsideredbeyondthescopeofthisproject.However,EPAconductedsensitivityanalysisofdietfractions(EPA2009b)andfoundthattheseparameterscontributelessthan3%tothevarianceinpredictedtissue

16ThesensitivityanalysisusescorrelationanalysisofMonteCarlosimulationoutputtodeterminerelativecontributiontovariance.ExcludinglogKowresultsingreaterrelativevarianceattributedtootherparameters.



concentrationsforanygiventrophiclevel.Inaddition,feedingpreferencesareknowntobeinsensitiveunlesstheyincludelargechangesintrophicstatus(GobasandArnot2005).

6.2 Uncertainty Modeluncertaintyisusedtodescribeincompleteorimperfectknowledgeaboutparameters,dataandassumptions.Uncertaintycanarisefrommanysources,includingmeasurementandanalyticalerrorsformodelinputdataandimpreciseestimatesforkeyparameters.Uncertaintyanalysesinvestigatehowthemodelresultsareaffectedbythislackofknowledgeofthetruevaluesofcertaininputsandparameters.

Inthisreport,uncertaintyanalysesforthefatemodelfollowedtheproceduresemployedbyPelletierandMohamedali(2009).KeymodelinputswereselectedtoevaluatetheeffectoftheiruncertaintyonthepredictedconcentrationoftPCBinwaterandsediment.ThefatemodelinputsselectedforuncertaintyanalysisincludedtPCBloadsandlogKow.Asimilarapproachwasusedtoevaluateuncertaintyinthebioaccumulationmodel.Themostsensitiveparameters,asidentifiedinthesensitivityanalysis,wereselectedtovaryintheuncertaintyanalysis(e.g.,octanol‐waterpartitioncoefficient,thelipidassimilationefficiency,waterfractioninzooplankton).ThissameapproachwasusedinuncertaintyanalysisoftheSanFranciscoBaybioaccumulationmodel(GobasandArnot2005).

ToquantifytheuncertaintyinfatemodelpredictionsbasedonlogKowandPCBload,themodelwasrunwiththreecombinationsoftheseinputs:

1. ArangeoflowandhighlogKowsalongwiththebestloadingestimateof0.672kgyr‐1.ThelowandhighlogKows(6.01and6.86)werebasedonselectionofPCB‐66andPCB‐153asrepresentativeoftherangeinpossiblerepresentativePCBcongeners.

2. ArangeoflowandhightPCBloadingestimatesderivedfromKingCounty(2013b)–0.333and0.889kgyr‐1.

3. AcombinationoflowandhighlogKow/tPCBloading.

Toevaluatethecumulativeuncertaintyofselectparametersandinitialconditions,thebioaccumulationmodelwasexecutedonceusinginputvaluesthatgeneratealowestimateandagainusinginputvaluesthatgenerateahighestimate.Meanwhile,alloftheothermodelinputswereheldattheir“bestestimate”values.

Aspreviouslydiscussed,PCBswereproducedasamixtureofcongenerswiththetradenameAroclor®,eachAroclor®differedinitscongenercomposition.WhileAroclors®havebeenoutofproductionsincethe1970sandthosethatenteredtheenvironmenthavesubstantiallyweathered,sedimentandfishtissueresultsfromLakeWashingtonstillhavemeasurablequantitiesofrecognizableAroclors®.ThemostcommonlydetectedAroclor®inLakeWashingtonis1254(Moshenberg2004,Era‐Milleretal.2010,KingCountyunpublisheddata).ThisAroclor®isdominatedbypentachlorinatedPCBcongenerswhichcomprisedjustover55%ofitsmass(Frameetal.1996).PCB‐118isapentachlorinatedcongenerwhichcomprisedabout10.5%ofAroclor®1254andhas,thus,servedasagoodestimateofthosePCBcongenersbioaccumulatingintheLakeWashingtonfoodweb.BecauseothermorehighlychlorinatedandlesschlorinatedcongenersarealsopresentinAroclor®1254andinLakeWashingtonwaters,thereissomeuncertaintyoverthe



representativenessofPCB‐118formodelingpurposes.UsingthephysicalattributesofallpossiblecongenerstoboundtheuncertaintyofPCB‐118’sphysicalparametersisnotreasonablebecausethecommercialAroclors®foundinLakeWashingtonfishandsedimentsnevercontainedsomePCBcongeners.Forinstance,Aroclor®1254containslessthan0.02%monochlorinatedcongenersandlessthan0.04%nonachlorinatedcongeners(Frameetal.1996).Thus,forthepurposesofboundingtheuncertaintyofthephysicalpropertiesofPCB‐118,representativetetrachlorinatedandhexachlorinatedcongenerswerechosen.Aroclor®1254iscomprisedof17.3%tetrachlorinatedand24.4%hexachlorinatedcongeners.Thesumoftetra‐,penta‐,andhepta‐chlorinatedcongenerscompriseover97%ofAroclor®1254.

PCB‐66wasselectedasarepresentativetetrachlorinatedPCBcongenerforthelowendestimateoflogKow,whilePCB‐153wasselectedasarepresentativehexachlorinatedcongenerforthehighendestimateofLogKow.TogetherwithPCB‐118,thesecongenersrepresentthreeofthefivecongenersmodeledforSanFranciscoBay(Davis2004).Theothertwocongenerswereamonochlorinated(PCB‐18)andnonachlorinated(PCB‐194)congeners.ThelogKowsofPCB‐66andPCB‐153atthemeanlakewatertemperatureare6.18and7.09,respectively(Table30).TheselogKowsplausiblyboundtheuncertaintyofthemodelswithrespecttotherepresentativenessofPCB‐118’slogKowof6.86atthemeanlakewatertemperature.

Inputparametersforthebioaccumulationmodelarebasedonsite‐specificempiricaldata,generalexperimentaldata,andassumptionsbasedonprofessionaljudgment.Allofthesesourceshaveassociateduncertaintiesofvariablemagnitude.Forexample,themeanestimatedLakeWashingtonwatertemperatureisbasedonthevolume‐weightedaverageofmeasurementsatmultipledepthsover10yearsand,therefore,islikelytohaverelativelylowuncertaintycomparedtothefractionofporewateringestedbymysidswhichhasnotbeenmeasured.

Themostsensitiveparametersidentifiedinthesensitivityanalysis(thosecontributinggreaterthan20%tovariance)wereselectedtoestimateuncertaintyinthebioaccumulationmodel:LogKow,lipidassimilationefficiencyinfishandcrayfish;tPCBconcentrationinwater;waterfractionincopepods,amphipods/isopods,anddaphnids.Inaddition,thetPCBconcentrationinsedimentwasaddedtotheuncertaintyanalysisbecauseofthesuspectedbiasintheempiricalestimateofthemean(Section4.1.3.3).Becausethenon‐lipidorganicmatter(NLOM)fractioninbiotaisaderivedvaluefromthewaterandlipidfractions,itwasnotincludedintheuncertaintyanalysis.Itsvaluewillvarywiththatofthewaterfraction.ThemostimportantsourceofuncertaintyistheLogKowduetothesubstantialcontributionofthisparametertovarianceinthemodeloutput.

Lipidabsorptionefficiencyisafractionlimitedtovaluesbetween0and1.Thestudyprovidingthebestestimateoflipidabsorptionefficiencyinfishreportedaverysmall(~1%)standarderror(Gobasetal.1999).Thus,alowandhighestimateforlipidassimilationefficiencyinfishwassetto0.91and0.93(Table30).

Lipidabsorptionefficiencyinaquaticinvertebratesishighlyvariable(15‐96%)anddependentonfoodqualityandspeciesdigestivephysiology(ArnotandGobas2004).Forexample,invertebratetaxalikewormshavelowassimilationefficienciesandfeedonhigh



quantitiesofpoorqualitysubstrate(i.e.,sediment).Thebestestimateofinvertebratelipidassimilationefficiencyusedinthemodelwasageneralvaluerepresentingallinvertebrates.Comparedtootherinvertebrates,crayfishfeedonhighqualityfood(e.g.,othercrayfish,phytoplankton).Thus,itislikelythattheirlipidassimilationefficiencyisonthehighendoftherange.Fortheuncertaintyanalysis,thelowestimateforcrayfishwassetto0.55andthehighestimateat0.95(Table30).

Theestimatedwaterfractionsforcopepods,amphipods/isopodsanddaphnidswerecalculatedfrommeasuredsolidsfractionsinsixzooplanktonandsixdaphnidcompositesamplescollectedfromLakeWashington(AppendixA).Thus,thedaphnidspecificsolidsdatawereusedtoestimatethewaterfractionfordaphnidsandthesolidsdatafromthezooplanktonsampleswereusedtoestimatethewaterfractionforcopepodsandamphipods/isopods.Theminimumandmaximumvaluesfromthesedatawereusedashighandlowvalues,respectively,duetotheinverserelationshipbetweenwaterfractioninbiotaandpredictedtissueconcentrations(Table30).NLOMfractionsforcopepods,amphipods/isopods,anddaphnidsandallothertaxawerederivedfromthefractionsassumedforlipidsandwater.Thus,ifthewaterfractionischanged,thecorrespondingNLOMfractionwillchange.

Table 30. Input values for parameters varied in uncertainty analysis

Parameter Value for Low

Estimate Value for High

Estimate

Log Kow @ 10.9˚C (PCB Congener) 6.18 (PCB - 66) 7.09 (PCB - 153)

Lipid Absorption Efficiency in Fish 0.91 0.93

Lipid Absorption Efficiency in Crayfish 0.55 0.95

Water tPCB Concentration (pg/L) 47 126

Sediment tPCB Concentration (ug/Kg dw) 9.1 24

Water Fraction in Copepods and Amphipods/Isopods

0.90 0.88

Water Fraction in Daphnids 0.93 0.91

Modelpredictionsusingthelowandhighestimatesyieldedarangeofpossibleoutcomesandrevealedwhetheruncertaintyinthetruevalueoftheparameter,loadorinitialconditionhadasignificanteffectonpredictionsoftPCBconcentrationsinwater,sedimentorbiota.Thisreportdocumentstheparametersthatweretestedandidentifiesanyparametersthathavegreatuncertainty.



7.0 RESULTS AND DISCUSSION Adiscussionofthemodelresults,includingmodelperformance,sensitivity,anduncertaintyarepresentedforthefateandbioaccumulationmodelsbelow.Theresultsofthefatemodelarediscussedfirstassomeoftheresultsfromthefatemodelareusedintheevaluationofthebioaccumulationmodel.

7.1 Fate Model Thesectionsbelowdescribetheevaluationoftheperformanceofthefatemodel(comparisonsofmodeloutputtoestimatedtPCBconcentrationsandmassbasedonobservedvalues),thesensitivityofthemodelpredictionstochangesinmodelinputparametersandanevaluationofmodeluncertainty.

7.1.1 Performance Theperformanceofthefatemodelwasevaluatedbyusingthemodelinhindcastmode(seeOrametal.2008).Inhindcastmode,themodelwassetupwithnotPCBinlakewaterorsediment.Themodelwasrunfor100yearsundercontinuousloadingwhichprovidedapredictionofthesteady‐stateconcentration(andmass)oftPCBinwaterandsedimentattheendofthesimulation.

UsingthebestestimateoftPCBloadingtoLakeWashington(0.672kgyr‐1)andthemodelinputsdescribeaboveinSection4.0,includingtPCBchemicalpropertiesrepresentedbyPCB‐118,themodelpredictswaterandsedimentconcentrationsthataresimilartotheestimatedaveragetPCBconcentrationsinwaterandsediment(Figure6).Averagewaterconcentrationswerepredictedtobeslightlyhigher,butwithin3percentoftheestimatedmeanwaterconcentration(95vs92pg/L)andlower,butwithin70percentoftheestimatedmeansedimentconcentration(18vs55µg/kgdw).

Itwasnotexpectedthatthemodelpredictionswouldsocloselymatchthebestavailableestimatesofmeanwaterandsedimentconcentrationsconsideringthesimpleassumptionsusedbythemodel(singlewellmixedbox,treatingmanyPCBcongenerswitharangeofchemicalpropertiesasasumwiththechemicalpropertiesofoneparticularPCBcongener,etc.)anduncertaintyinexternalloading.Therefore,therelativelylargedifferencebetweenmodeledsedimenttPCBconcentrationswasnotunexpected.However,itwaspreviouslynotedthattheobservedmeansedimenttPCBconcentrationmaybebiasedhighduetothenon‐randomnatureoftheavailabledata–asubsetofsamplelocationswereselectedtorepresenttheinfluenceofsuspectedcontaminationsourcesandmanyofthesamplespenetratedtothe10‐cmsedimentdepth,whichwouldcapturehigherlevelsofcontaminationburiedbelowlesscontaminatedsurfacesediments.



Figure 6. Comparison of fate model-predicted water and sediment concentrations to estimated

average concentrations based on observed data.

Inordertoincludeanestimateoftheuncertaintyintheobserveddataincomparisontothemodelpredictions,figuresshowingthe100‐yrhindcastsimulationwerecreatedthatincludedverticallinesrepresentingthemeanwaterandsedimentconcentrationsaswellasthemedian(50th‐percentile)and25thand75thpercentileconcentrations(Figure7andFigure8).TheclosematchbetweenthepredictedmeanlaketPCBconcentrationandthevolume‐weightedmeanconcentrationestimatedfromtheobserveddataisillustratedinFigure7.AlsoshowninFigure7isthepredicteddissolvedtPCBconcentration,whichwas75pg/Lorabout80percentofthetotaltPCBconcentrationatsteady‐state(i.e.,attheendofthe100‐yearsimulation).ThepercentoftPCBinthedissolvedphasepredictedbythemodelatsteady‐stateiswithintherangeofestimatesobservedinthefielddatacollectedaspartofthisstudy(KingCounty2013a).

Figure8showsthatalthoughthemodeledactivesedimentlayertPCBconcentrationsdonotcloselymatchthemeantPCBconcentrationsestimatedfromobservedconcentrations,thepredictionisbetweenthe25th‐and75th‐percentileconcentrationsbasedon69samplescollectedfromthelake(seeTable5aboveinSection4.0)andwasslightlylowerbutwithin20percentofthemedian(50th‐percentile).Notethattheobservedmeanconcentrationisgreaterthantheobserved75th‐percentileconcentration,indicatingthestrongskewintheavailableobservedsedimenttPCBconcentrationdata.AsnotedaboveandinKingCounty(2013c),thisislikelyduetoacombinationofbiastowardsamplingsuspectedhotspotsandsamplesthatpenetratebelowtheactivesedimentlayertothedepthwherehistoricaltPCBsedimentcontaminantlevelsarehighest.

Notethatalthoughthepredictedsteady‐stateconcentrationoftPCBdissolvedinsedimentporewaternormalizedtotheconcentrationofdrysedimentisverysmall(1.3x10‐4µg/kgdw)(Figure8),themodelpredictsadissolvedtPCBconcentrationof130pg/Lbasedonthevolumeofporewaterintheactivesedimentlayer.Noestimateofsedimentporewater



Figure 7. Hindcast model predictions of total and dissolved tPCB concentration in lake water

from initial sediment and water concentration of zero and a steady loading rate of 0.672 kg yr-1. Also included are lines representing a statistical summary of the available observed concentration data (mean, 25th, 50th, and 75th percentiles).

Figure 8. Hindcast model predictions of total and dissolved tPCB concentration in lake

sediment from initial sediment and water concentration of zero and a steady loading rate of 0.672 kg yr-1. Also included are lines representing a statistical summary of the available observed concentration data (mean, 25th, 50th, and 75th percentiles).

tPCBisavailablefromthisstudy.Availableporewaterdatafromothersystemsgenerallyrepresentmoreheavilycontaminatedsediments(Booijetal.2003,Hawthorneetal.2011,Luetal.2011,Martinezetal.2013)makingevengeneralcomparisonsdifficult.However,

0

20

40

60

80

100

120

140

0 20 40 60 80 100

tPCB

(pg/L)

Time (years)

Modeled Total

Modeled Dissolved

25th‐percentile

50th‐percentile

75th‐percentile

Mean

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

tPCB

(µg/kg)

Time (years)

Modeled Total

Modeled Dissolved

25th‐percentile

50th‐percentile

75th‐percentile

Mean



datapresentedinBooijetal.(2003)andHawthorneetal.(2011)suggestthatthemodel‐predictedconcentrationiswithinafactoroftwoorless,whichisconsistentwiththepredictionsofwatercolumnandsedimentconcentrations.Booijetal.(2003)alsocomparedporewatertooverlyingwaterconcentrationsandfoundporewatertooverlyingwaterconcentrationsnearone,whichisalsorelativelyconsistentwiththemodelpredictions.Asnotedabove,andasevidencedbytheliteraturecited,sedimentwaterpartitioningmodelsareanactiveareaofresearch.Testingofthecurrentmodel(andalternativemodels)wouldrequirethecollectionofrelevantdataspecifictoLakeWashingtonsediments.

Figure9illustratestheresultsofthe100‐yrhindcastsimulationpredictingthemassoftPCBinwaterandsedimentovertheperiodofsimulation.Thepredictedsteady‐statetotalmassoftPCBinLakeWashingtonwas4.9kg,whichwaswithinthe25thand75th‐percentileestimatesofthetotalmassinthewatercolumnandactivesedimentlayerandsomewhatlower,butwithin20percentofthemedian(50th‐percentile)estimatedmassoftPCBinthelake(Figure9).

NotethatobservedtPCBdataindicatethatthemajorityofthemassoftPCBiscontainedinthesediments–about98percent.Thisisrelativelyconsistentwiththesteady‐statemodelresults,whichpredictedthat95percentofthetPCBmasswascontainedintheactivesedimentlayer.TheseresultsareconsistentwithotherstudiesthathaveshownthathydrophobiccompoundsliketPCBstendtoaccumulateinbottomsediments(e.g.,Gobasetal.1995,Davis2004).

BecausesedimenttPCBdominatesthetotalmassoftPCBinthelake,theskewnotedinthesedimenttPCBdatacarriesthroughtotheestimatedtotalmassoftPCBinthelakesothatthemeanestimatedtPCBmassisslightlygreaterthanthe75th‐percentilemass(Figure9).Themodel‐predictedtotaltPCBmassismuchlowerthan,butwithin70percentoftheestimatedmeantotalmass(4.9vs15kg).



Figure 9. Hindcast model predictions of the total mass of tPCB in water and sediment h an wit

initial sediment and water concentration of zero and a steady loading rate of 0.672 kg yr-1. Also included are lines representing a statistical summary of the available estimates of observed tPCB mass (mean, 25th, 50th, and 75th percentiles).

Anothertestofthemodelperformancethatcanbemadeisacomparisonofthemodel‐predictedandobservedexportoftPCBthroughthelakeoutletattheMontlakeCut(basedonmeasuredconcentrationsandestimatedoutletflowrate).AlthoughthereisanapparentskewintheobservedtPCBconcentrationsattheMontlakeCutlocation(themeanand75th‐percentiletPCBexportratesarethesame),themodel‐predictedexportoftPCBiswithinthe25thand75thpercentileestimates(Table31).

Table 31. Comparison of modeled and observed tPCB export via the Lake Washington outlet at Montlake Cut (kg yr-1).

Modeled Estimated Export based on observations a

Mean Percentiles

25th 50th 75th 0.118 0.140 0.073 0.110 0.140

afromKingCounty(2013b)

Figure10illustratestherelativeimportanceofthevariouslosspathwayspredictedbythemodeloverthe100‐yrhindcastsimulationinitializedwiththesteady‐statepredictedwaterandsedimentconcentrations.Attheendofthe100‐yearsimulationwhenthelakelosspathwayshavereachedsteady‐state,themodelpredictsthatthedominantlosspathwayisdeepburialinsediment(44percent).Volatilizationandexportviathelakeoutletare



predictedtobethesecondtwomostsignificantlosspathwaysafterburial(24and16percent,respectively).Degradationinwaterandsedimentistheleastsignificantlosspathway(about9percent).

Figure 10. Hindcast model predictions of losses through different pathways based on an initial

sediment and water concentration of zero and a steady loading rate of 0.672 kg yr-1.

7.1.2 Sensitivity Thesensitivityofthefatemodelto21oftheparametervalues,specifiedinTables3and4,wasevaluatedusingYASAIw.Parametersthatweregenerallyknownandinvariant(e.g.,lakevolumeandsurfacearea)wereexcludedfromthesensitivityanalysis.

Thesensitivityofthemodelwasevaluatedwithrespecttotworesponses:

1. Predictedsteady‐statetPCBmassattheendofthe100yearsimulation(basedontheinitialtPCBloadof0.672kgyr‐1andnoinitialsedimentorwatertPCBcontamination)

2. PredictedpercentoftPCBmassremainingin20yearsbasedontheinitialpredictedsteady‐statemassoftPCBinwaterandsedimentanda50percentreductionintheinitialtPCBloadof0.672kgyr‐1

Eachofthesesensitivityanalyseswasevaluatedwithrespecttothetotalmassinthelakeaswellasthemassinwaterandsediment.ThisapproachissimilartothatusedbyDavis(2004)toexplorethesensitivityofthePCBboxmodelofSanFranciscoBay.

7.1.2.1 Sensitivity to Predicted Steady-State Mass

Theresultsofthesensitivityanalysisofthemodeltothepredictedsteady‐statemassattheendofthe100yearsimulationareprovidedinTable32.Thepredictedsteady‐statetotaltPCBmassinthelakeisoverwhelminglymostsensitivetotheselectedlogKow(octanol‐waterpartitioningcoefficient)at25°C,whichcontributesover90percentofthetotal



varianceinthepredictedtotaltPCBmass.ThesecondmostsensitiveparameterwastheselectedtPCBload,whichcontributedabout3percenttothetotalvarianceinpredictedtotaltPCBmassinthelake.Theremainingparametersindividuallycontributedlessthan1percenttothesensitivityofthepredictedtotaltPCBmass.BecausethemajorityofthetPCBiscontainedinthesediment,thesensitivityofthemodeltothepredictedtPCBmassinsedimentwasverysimilartothesensitivityofthemodeltothepredictedtotallakemass–about92and3percentofthevarianceinmodeledsedimenttPCBmasswasattributedtologKowandtPCBload.

Thesensitivityofthemodel‐predictedmassoftPCBinthewatercolumnwassomewhatdifferentthanfortheothertwomodelendpointsevaluated.ThepredictedwatercolumntPCBmasswasmuchlesssensitivetologKow(onlyabout5percentcontributiontothevarianceinthepredictions)andmostsensitivetothetPCBload(~37percentcontributiontothevarianceinpredictions).Themodel‐predictedwatermasswasalsomoderatelysensitive(greaterthan5percentcontributiontovariance)toalargernumberofparameters,includingtheconcentrationofsuspendedsolidsandthesedimentsolidsconcentration(~10percenteach),thesedimentburialcoefficient(~9percent),theproportionalityconstantforphasepartitioningtoPOC(~8percent),theDOCconcentration(~7percent)andtheaveragewindspeed(~6percent).

Themodelwasrelativelyinsensitivetoanumberofinputs(lessthan1percentcontributiontooutputvariance),includingdepthoftheactivesedimentlayer,watertemperature,organiccarbonfractionofbottomsediment,solidssettlingrateandothers.TherelativeinsensitivityofthemodeltowatertemperaturesuggeststhatdevelopmentofaseasonallystratifiedtwolayerwatercompartmentmodelwithdifferenttemperatureswouldnotsubstantiallychangetheoveralltPCBmassbalanceorconcentrationspredictedbythemodel.Atestusingdifferentmeantemperaturesforvolatilization(14oC),adjustmentofwatercolumnKow(10.9oC)andadjustmentofsedimentKow(8oC),whicharereasonableapproximationsoftheannualmeanepilimnetic,wholelakeandhypolimneticlaketemperatureshadanegligibleeffectonthemodelresults.

Therewerealsosomeinterestingdifferencesinthesignofthecorrelationsbetweenspecificparametersandpredictedsteady‐statetotal,sedimentandwatermass.Forexample,eventhoughthepredictedwatermasswasrelativelysensitivetoandpositivelycorrelatedwiththesuspendedsolidsconcentration(i.e.,highersuspendedsolidsconcentrationmeanthigherwatermassoftPCB),thepredictedsedimentmasswasfairlyinsensitive,butnegativelycorrelatedwiththesuspendedsolidsconcentration.Regardless,thetotalmassoftPCBinthelakewasdrivenbythepredictedsedimenttPCBmass.

7.1.2.2 Sensitivity of Predicted Mass in 20 years

Resultsofthesensitivityanalysisofthemodeltothepredictedresponseofa50percentreductionintPCBloadingin20yearsareprovidedinTable33.ThesensitivityofthemodeltothepredictedreductionintotalandsedimenttPCBmasswasgenerallyverysimilartotheresultsofthesensitivityofthemodeltopredictedsteady‐statetPCBmass(seeTable32).BothoftheseendpointsweremostsensitivetologKow–withgreaterthan90percentcontributiontothevarianceinpredictedmass.Thesecondlargestcontributionto



thevarianceinpredictedpercentoftotalandsedimentmassremainingafter20yearswasthetPCBloadingrate.Thepredictedpercentoftheinitialwatermassin20yearswasmostsensitivetothetPCBload(~37percentcontributiontothevarianceinpredictedpercentofinitialtPCBmassinwater).Themodelwassecondarilysensitivetothesuspendedsolidsconcentration(~14percentcontributiontovarianceinmodelpredictions).Alsoasinthefirstsensitivityanalysis,thesensitivityofthemodeltothepredictedtotalmasswasdrivenprimarilybythesensitivityofthepredictionsofsedimentmass.Forexample,noteoppositesignsofthecorrelationandcontributionstovarianceoftheinputsuspendedsolidsconcentration(Table33).



Table 32. Comparison Summary of sensitivity analysis results of the fate model to predicted steady-state mass of tPCB in Lake Washington.

Parameter Predicted Total Mass Predicted Water Mass Predicted Sediment Mass

Spearman's rho

Contribution to Variance

Spearman's rho


Spearman's rho


Log Kow @ 25 oC 0.9313 91.48% 0.2082 4.95% 0.9326 91.65%

PCB load (kg/yr) 0.1752 3.24% 0.5673 36.74% 0.1655 2.89%

Sediment burial coefficient (m/d) ‐0.0932 0.92% ‐0.2779 8.82% ‐0.0881 0.82%

Depth of active sediment layer (m) 0.0908 0.87% ‐0.0349 0.14% 0.0924 0.90%

Concentration of solids in sediment (kg/L) 0.0898 0.85% ‐0.2929 9.80% 0.0965 0.98%

Average wind speed at 10 m height (m/s) ‐0.0782 0.64% ‐0.2199 5.52% ‐0.075 0.59%

Proportionality constant for phase partitioning to POC 0.0592 0.37% ‐0.257 7.54% 0.0646 0.44%

Particulate organic carbon in water column (kg/L) 0.0540 0.31% ‐0.1718 3.37% 0.058 0.35%

Organic carbon fraction of bottom sediment 0.0538 0.31% 0.0331 0.12% 0.0528 0.29%

Temperature of water (oC) ‐0.0486 0.25% ‐0.0579 0.38% ‐0.0481 0.24%

Suspended solids concentration (kg/L) ‐0.0391 0.16% 0.302 10.41% ‐0.0451 0.21%

Degradation in water (1/d) 0.0336 0.12% 0.0469 0.25% 0.0329 0.11%

Dissolved organic carbon in water column (kg/L) ‐0.0329 0.11% 0.2388 6.51% ‐0.038 0.15%

Water outflow (L/d) ‐0.0329 0.11% ‐0.0764 0.67% ‐0.0319 0.11%

Solids settling rate (m/d) ‐0.0283 0.08% ‐0.0485 0.27% ‐0.027 0.08%

Density of sediment organic carbon (kg/L) ‐0.0230 0.06% 0.0011 0.00% ‐0.0228 0.05%

Degradation in sediment (1/d) ‐0.0202 0.04% 0.0217 0.05% ‐0.0208 0.05%

Sediment porosity (fraction) 0.0176 0.03% 0.0232 0.06% 0.0172 0.03%

Henry' Law constant (Pa m3/mol) ‐0.0149 0.02% ‐0.0383 0.17% ‐0.0144 0.02%

Water‐to‐sediment diffusion coefficient (m/d) 0.0117 0.01% ‐0.0181 0.04% 0.0125 0.02%

Proportionality constant for phase partitioning to DOC 0.0110 0.01% 0.1917 4.19% 0.0071 0.01%



Table 33. Summary of sensitivity analysis results of the fate model to predicted percent of initial mass of tPCB in Lake Washington remaining in 20 years following a 50 percent reduction from the current best estimate loading rate.

Parameter Predicted % of Initial Total

Mass in 20 yrs Predicted % of Initial Water Mass in 20 yrs

Predicted % of Initial Sediment Mass in 20 yrs

Spearman's rho


Spearman's rho


Spearman's rho


Log Kow @ 25 oC 0.9382 90.95% 0.1476 2.49% 0.9402 91.24%

PCB load (kg/yr) 0.1763 3.21% 0.5694 37.12% 0.1653 2.82%

Sediment burial coefficient (m/d) ‐0.1180 1.44% ‐0.2445 6.84% ‐0.1131 1.32%

Proportionality constant for phase partitioning to POC 0.1029 1.09% ‐0.247 6.98% 0.1089 1.22%

Temperature of water (oC) ‐0.1015 1.06% ‐0.0735 0.62% ‐0.0998 1.03%

Concentration of solids in sediment (kg/L) ‐0.0662 0.45% ‐0.2758 8.71% ‐0.062 0.40%

Suspended solids concentration (kg/L) ‐0.0630 0.41% 0.3534 14.30% ‐0.072 0.53%

Particulate organic carbon in water column (kg/L) 0.0625 0.40% ‐0.2246 5.78% 0.0684 0.48%

Average wind speed at 10 m height (m/s) ‐0.0513 0.27% ‐0.1907 4.17% ‐0.0485 0.24%

Degradation in water (1/d) 0.0395 0.16% 0.0538 0.33% 0.0388 0.16%

Water outflow (L/d) ‐0.0371 0.14% ‐0.0779 0.69% ‐0.0355 0.13%

Water‐to‐sediment diffusion coefficient (m/d) 0.0311 0.10% ‐0.053 0.32% 0.0325 0.11%

Degradation in sediment (1/d) ‐0.0285 0.08% ‐0.0207 0.05% ‐0.0281 0.08%

Sediment porosity (fraction) ‐0.0281 0.08% 0.0209 0.05% ‐0.0284 0.08%

Organic carbon fraction of bottom sediment 0.0262 0.07% 0.0167 0.03% 0.0259 0.07%

Henry' Law constant (Pa m3/mol) ‐0.0164 0.03% 0.0115 0.02% ‐0.0167 0.03%

Depth of active sediment layer (m) ‐0.0119 0.01% 0.0271 0.08% ‐0.0136 0.02%

Dissolved organic carbon in water column (kg/L) ‐0.0081 0.01% 0.2243 5.76% ‐0.013 0.02%

Solids settling rate (m/d) 0.0065 0.00% ‐0.0057 0.00% 0.0065 0.00%

Density of sediment organic carbon (kg/L) ‐0.0051 0.00% 0.0022 0.00% ‐0.0055 0.00%

Proportionality constant for phase partitioning to DOC ‐0.0031 0.00% 0.222 5.64% ‐0.0085 0.01%



7.1.3 Uncertainty Basedontheprecedingsensitivityanalysis,thequantifiableuncertaintyinthefatemodelisdueprimarilytotheselectedlogKowandtheestimatedtPCBloadingrate.Toquantifyuncertaintyfromtheseinputs,themodelwasrunwiththreecombinationsoftheseinputs:

1. RangeoflowandhighlogKowsandbestloadingestimateof0.672kgyr‐1.LowandhighlogKow(6.01and6.86at25oC/6.18and7.09at10.9oC)werebasedonselectionofPCB‐66andPCB‐153asrepresentativeoftherangeinpossiblerepresentativePCBcongeners.

2. RangeoflowandhightPCBloadingestimatesderivedfromKingCounty(2013b)–0.333and0.889kgyr‐1.

3. CombinationoflowandhighlogKow/tPCBloading.

TheresultsofthesemodeluncertaintytestsaresummarizedinTable34formodel‐predictedsteady‐stateconcentrationsinwaterandsediment.Ingeneral,itappearsthatalthoughthefatemodelismostsensitiveinarelativesensetologKow,therelativelylargeuncertaintyintotaltPCBloadingtothelakeisthelargestdriverinmodelpredictionuncertainty.Thecombinationoflow/hightPCBloading/logKowresultedinonlyasmallrelativeincreaseintherangeofmodel‐predictedwaterandsedimentconcentrationsrelativetotheeffectoflow/hightPCBloadsalone.Formodel‐predictedtotalwaterconcentrations,thelow/hightPCBloadrangewas47to126pg/Lcomparedtoalow/highload/logKowrangeof44to128pg/L.ThecontributionoftheselectionoflogKowontheuncertaintyinmodel‐predictedsedimenttPCBconcentrationswasabithigher,withthepredictionuncertaintyrangegoingfrom9to24µg/kgdwforlow/hightPCBloadto4to28µg/kgdwforthecombinationofloadandlogKow.Ingeneral,therangeinmodelpredictionuncertaintywaswithinorjustoutsidetherangeintheestimateduncertaintyoftheobservedconcentrations(seeTable34).

Table 34. Summary of fate model uncertainty analysis results.

Water Sediment (pg/L) (µg/kg dw)

Base case a 95 18 Observed 25th and 75th-percentile concentrations 51 - 118 11 - 53

Uncertainty Scenario

Low tPCB Load (0.333 kg yr-1) 47 9

High tPCB Load (0.889 kg yr-1) 126 24

Low log Kow (6.01 @ 25 oC) 89 8

High log Kow (6.86 @ 25 oC) 96 21

Low load/low log Kow 44 4

High load/high log Kow 128 28

atPCB load = 0.672 kg yr-1 and log Kow = 6.65 @ 25 oC.



Figure11andFigure12provideavisualillustrationoftherangeinmodelpredictionuncertainty,includingacomparisontotherangeinuncertaintyoftheobserveddata.Therangeinestimatedmodeluncertaintywithrespecttothepredictedtotalwaterconcentrationreflectscloselytherangeinuncertaintyintheobservedmeanwatercolumnconcentration(Figure11).However,therangeinestimateduncertaintyofpredictedsedimenttPCBconcentrationclustersmorecloselytotherangeinuncertaintyoftheobservedsedimentconcentrations–nearthe50thpercentileandlower(Figure12).Again,thisisconsistentwiththehypothesisthattheobservedsedimentconcentrationsarebiasedhighduetopreferentialselectionofsamplinglocationsandthedeeperpenetrationdepthofmanyofthesamples.

Figure 11. Uncertainty in model-predicted tPCB concentrations in water compared to uncertainty

in observed concentrations based on range of possible inputs for total tPCB loading and log Kow.



Figure 12. Uncertainty in model-predicted tPCB concentrations in the active sediment layer

compared to uncertainty in observed concentrations based on range of possible inputs for total tPCB loading and log Kow.

Itshouldbenotedthatthereareothersourcesofuncertaintythataremoredifficulttoquantify.Thesesourcesofuncertaintypotentiallyincludemissingormis‐specifiedprocessesthatresultprimarilyfromnecessarymodelsimplificationsofnaturalsystems.Oneparticularsourceofpotentialuncertaintythatcannotbeeasilyquantifiedisthespecificationofthetypeofsedimentandwaterpartitioningapproachusedinthemodel.Currentresearchhasfocusedonmulti‐phasepartitioningmodels,withthegoalofimprovingthecertaintyinmodelpredictions(e.g.,Greeneetal.2013).However,developmentandevaluationofmorecomplexwaterandsedimentpartitioningmodelsrequiresmoredetailedsite‐specificdatathanarecurrentlyunavailableforLakeWashington.

AnothersourceofuncertaintyistherepresentationofLakeWashingtonasasinglecompletelymixedlayerforcedbyaverageinputs,includingtPCBloading,windspeedandwatertemperature,whenthelakehasobviouslyexperiencedahistoryofPCBinputsandtime‐varyingwindsandtemperatures.However,relativelysimplecontaminantfatemodelsofotherdynamicsystems(e.g.,LakeOntario,SanFranciscoBay)havebeendevelopedanddescribedasusefulmanagementtools(Mackayetal.1994,Gobasetal.1995,Davisetal.2007).Anothersimplificationincorporatedintothefatemodelistheomissionofacompartmentforbiota.BasedoninformationavailableinKingCounty(2013e),thetotalmassoftPCBinbiotaisontheorderof0.2kg,whichisabout4percentofthetotalpredictedmassoftPCBinthelake(assuminganactivesedimentlayerof2.5cm).OfthetPCBmassinbiota,about60percentappearstobeassociatedwithobligatebenthicorganisms,whichiswherethemajorityofthemassoftPCBresides.Asafirst‐approximation,itdoesnotappearthatthesesimplificationscompromisethegeneralestimatesofthepartitioningandfateoftPCBinLakeWashington.



Additionalmodelcomplexitydoesnotnecessarilyequatewithbettermodelfittothedata(ArhonditsisandBrett2005)orbettermanagementdecisions(Bartholow2003).Ingeneral,thissimplemassbudgetmodelsupportsthetotaltPCBloadingestimateandprovidesaninitialsteptowardsunderstandingthelongtermfateofPCBsinthelake.

7.2 Bioaccumulation Model Thissectiondescribestheresultsofmodeltesting,andsensitivityanduncertaintyanalysesforthebioaccumulationmodel.

7.2.1 Performance PerformanceofthebioaccumulationmodelwasgaugedbycomparisonofpredictedtPCBconcentrationsintissuetomeanobservedtotalPCBconcentrationsavailableforthesamespecies.ThisapproachwasusedtoevaluatethePugetSoundbioaccumulationmodel(PelletierandMohamedali2009)andavariationofthisapproachwasusedtoevaluatetheSanFranciscoBaybioaccumulationmodel(GobasandArnot2005).Theratioofpredictedtoobservedtissueconcentrationswascalculatedforeachmodeledtaxonwithratiosbelow1.0indicatingunder‐predictionandabove1.0indicatingover‐prediction.Ratioswerecalculatedformodelpredictionsusingempirically‐derivedmeansediment(55µg/kgdryweight)andwater(92pg/L)tPCBconcentrationsandusingthefatemodel–predictedmeansediment(18µg/kgdryweight)andwater(95pg/L)PCBconcentrationsasinput(Figure13).

Aspreviouslydiscussed,theempirically‐derivedmeansedimenttPCBconcentrationispotentiallybiasedhighbecausealargeportionofthesamplesinthedatasetwerecollectedfromthe0‐10cmdepthwhichisdeeperthanthebiologicallyactivezone(Section4.1.3.1)andlikelyincorporateshigherconcentrationsnearthe10cmdepthhorizonthatresultedfromthepeakofPCBinputtothelake.ThesedeepersampleshadhigherPCBconcentrationsthanthosecollectedfrom0‐2cmdepth(KingCounty2013d).Thus,thefatemodel‐predictedsedimentconcentrationsprovidealower,potentiallymorerealisticestimateofthemeansedimentPCBconcentration.Theempirically‐derivedandfatemodel‐predictedwaterconcentrationsarecomparable.

Modelbiasiscalculatedasthegeometricmeanofindividualpredicted/observedratios.Modelbiasbetween0.5and2.0isconsideredgoodperformanceandcomparabletootherapplicationsofthisbioaccumulationmodel(PelletierandMohamedali2009,Condon2007,GobasandArnot2005).Modelbiasunderempirically‐derivedwaterandsedimentinputconcentrationswasjustoutsidethisrangeat2.3(Table35).Individualtaxonratioswerewithinthisrangeforalltaxaexceptlargecrayfish,smallpricklysculpin,smallandlargeyellowperch,cutthroattrout,andnorthernpikeminnowwhichwereallover‐predicted.However,theratiosforcutthroattroutandnorthernpikeminnowwerelessthan3.0andthoseforyellowperchwerelessthan4.0.Thelargecrayfishratiowasnotablyhighatnearly23,butwasbasedonanobservedsamplesizeoffourindividualsandadetectionlimit‐derivedtPCBconcentrationbecauseallobservedresultswerenondetect.

Applicationofthefatemodel‐predictedsedimentandwaterinputconcentrationsresultedinamodelbiasof1.2(Table35).Thelongfinsmeltratiowasjustbelow0.5andtheratio



forlargeyellowperchwas3.4.Thelargecrayfishratioremainednotablyhighatapproximately13.Ratiosforallothertaxaindicatedpredictedtissueconcentrationswerewithinafactoroftwoofobserved.

Over‐predictionofthelargeyellowperchtissueconcentrationsmaybeduetouncertaintyinspeciesandsizeoffishconsumed.ThedietaryassumptionsinthemodelaretakenfromMcIntyre(2004)whichspecifiesfractionsofsculpinconsumedbutdoesnotidentifyfractionsofotherfishspecies.Largeyellowperchareestimatedtoconsumeanannualaverageof38%otherfish.Forthisbioaccumulationmodel,thisfractionwasdividedbetweensmallyellowperch,adultthreespinestickleback,adultlongfinsmelt,andjuvenilesockeyesalmon.However,bioenergeticsmodelingbyMazur(2004)indicatedthatthesizesofthemodeledpreyfishmayoverestimatePCBexposure.Mazur(2004)describedlargeyellowperchaseatingjuvenilesockeyesalmonbutthatmostwereinthefrystage.Also,fisheggscomprisedaportionoftheotherfishconsumed(Mazur2004).TheconsumptionofsmeltandsculpiniscongruentwithMazur’sfindings.Iffryandfisheggsaresubstantialfractionsofthelargeyellowperchdiet,thesefishwouldpresentlowerexposurethanthejuvenileandadultlifestagesoffishconsumedinthemodel.Hence,thiscouldaccountfortheover‐predictionofPCBtissueconcentrations.

Thehighover‐predictionoftissueconcentrationsinlargecrayfishmayreflectmultipleuncertainties,namelythelackofspecificdietinformationforLakeWashingtoncrayfish,thelimitednumberofsamplescollectedtoprovidelipidcontent,bodyweight,andPCBconcentrations,andthesmallrangeofsizescollected(KingCounty2013e).Theuncertaintiesapparenthereidentifyadatagapthatcouldbefulfilledinfuturefieldsamplingefforts.

Table 35. Predicted/observed ratios for PCB tissue concentrations modeled using empirically-derived or fate model-predicted mean sediment and water input concentrations

Modeled Taxon Ratios - Empirical Ratios - Modeled

Daphnia 1.0* 1.1*

Mysids 1.2* 1.1*

Crayfish Large 26.0 * 13.2*

Sockeye Salmon (juv.) 1.4 0.9

Longfin Smelt 0.9 0.4

Peamouth 1.2 0.6

Prickly Sculpin Large 1.8 0.9

Prickly Sculpin Small 4.2 1.9

Yellow Perch Large 6.9 3.4

Yellow Perch Small 3.6 1.8

Threespine Stickleback 1.1 0.5

Smallmouth Bass 1.9 0.9

Cutthroat Trout 2.8 1.3

Northern Pikeminnow 2.1 1.0

Model Bias 2.3 1.2

*Observed mean tissue concentrations are maximum detection limits because no samples had PCB detections.



Meantissueconcentrationsmodeledusingempirically‐derivedwaterandsedimentdatawerepredictedwithinthestandarddeviationoftheobservedconcentrationsforthreespinestickleback,longfinsmelt,largepricklysculpin,peamouth,smallmouthbass,andnorthernpikeminnow(Figure13).Themeanpredictedconcentrationofjuvenilesockeyesalmonisjustabove(90µg/kg)theupperstandarddeviationofobservedconcentrations(82µg/kg).Predictedtissueconcentrationsincutthroattrout,smallpricklysculpinandsmallandlargeyellowperchwereapproximately1.5to5timeshigherthantheupperstandarddeviationofobservedconcentrations.

Phytopla

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Observed Mean (SD)Observed Maximum Detection Limit Predicted Using Empirical Water and Sedt Concentrations

Figure 13. Model-predicted tissue concentrations using empirically-derived water and sediment

PCB concentrations compared to observed tPCB tissue concentrations

Predictedtissueconcentrationsusingfatemodel‐predictedwaterandsedimentconcentrationswerelowerthanobservedandwithinonestandarddeviationofthemeanformostfishexceptsmallpricklysculpinandsmallandlargeyellowperchwhichwerehigher,andthreespinesticklebackwhichwasslightlylower(Figure14).Themaindifferencebetweenmodeledtissueconcentrationsusingempirically‐derivedversusfatemodel‐predictedwaterandsedimentconcentrationsareduetothelowermeansedimentconcentrationofthelattersource.



Phytopla

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Observed Tissue Concentrations (SD)Observed Maximum Detection LimitPredicted Using Fate Model Water and Sedt Concentrations

Figure 14. Model-predicted tissue concentrations using water and sediment concentrations

predicted by fate model compared to observed Total PCB tissue concentrations.

Becausemodelbiaswas1.2usingthefatemodel‐predictedwaterandsedimentconcentrationscomparedto2.3usingtheobservedwaterandsedimentconcentrations,valuesoftheformerwereusedasinputtothemodelforthesensitivityanduncertaintyanalyses.

7.2.2 Sensitivity Variationofallbioticandabioticinputvalues,exceptlogKow(octanol‐waterpartitioningcoefficient),byastandarddeviationof5%indicatedthatthedietaryabsorptionefficiencyoflipidinfishcontributedtothegreatestvariance(42‐69%)inpredictedtissueconcentrations(Table36).Condon(2007)andGobasandArnot(2005)alsofounddietaryabsorptionefficiencyoflipidstobeoneofthemostinfluentialbiologicalparametersinthesameorsimilarbioaccumulationmodels.Otherrelativelysensitiveparametersincludethelipidassimilationefficiencyincrayfish,waterconcentrationoftPCBs(phytoplankton,mysidsanddaphnids17),thewater,lipidandnon‐lipidorganicmatterfractionsof

17Taxainparenthesesarethosewhosepredictedtissueconcentrationsaresensitivetothenamedparameter.



invertebrates(invertebrates),sedimentconcentrationoftPCBs(benthicinvertebrates),andtheaqueousphaseresistanceconstantandgrowthrateconstantofphytoplankton.Allotherparametervariationsresultedinlessthana10%contributiontoanysinglemodeledtaxon’stissuePCBconcentration(AppendixC).

Totestthesensitivityofpredictedtissueconcentrationstochangesinwaterversussedimentconcentrations,thesedimentconcentrationwasheldconstantwhilethewaterconcentrationwasreducedby20%.Then,theoppositewasdonetovarysedimentinputwhileholdingthewaterconcentrationconstant.Comparingthedifferenceinpredictedtissueconcentrationsbetweenthesetwomodelrunsandthebase(i.e.nochange)modelrunshowedthattheoveralleffectonfishtissueconcentrationsofchangingthewaterwascomparabletochangingsediment(Table37).ThisisindicativeoftherelativelyimportantrolethatpelagicpreyplayintheLakeWashingtonfoodweb.AlthoughthemajorityofPCBmassinthelakeisstoredinsediment,andsedimentisakeydriverofbioaccumulation,PCBconcentrationsinwaterarealsoimportantasanexposurerouteintobiota.



Table 36. Sensitivity of Predicted Tissue Concentrations to Individual Parameters Contributing Over 10% to Variance

Output Parameter Spearman'

s Rho Contribution to variance

Yellow Perch Small Yellow perch small: Dietary absorption efficiency of lipid 0.9108 68.60%

Cutthroat Trout Cutthroat trout: Dietary absorption efficiency of lipid 0.8770 65.00%

Peamouth Peamouth chub: Dietary absorption efficiency of lipid 0.7786 55.63%

Sockeye Salmon (juv.) Sockeye juvenile: Dietary absorption efficiency of lipid 0.8400 55.41%

Prickly Sculpin Large Sculpin: Dietary absorption efficiency of lipid 0.7988 51.40%

Prickly Sculpin Small Prickly sculpin small: Dietary absorption efficiency of lipid 0.8089 51.28%

Longfin Smelt Longfin smelt: Dietary absorption efficiency of lipid 0.7721 50.14%

Smallmouth Bass Smallmouth bass: Dietary absorption efficiency of lipid 0.7718 49.73%

Northern Pikeminnow Northern pikeminnow: Dietary absorption efficiency of lipid 0.7668 49.04%

Yellow Perch Large Yellow perch large: Dietary absorption efficiency of lipid 0.7690 47.33%

Threespine Stickleback Stickleback: Dietary absorption efficiency of lipid 0.7053 42.29%

Mysids tPCBs water concentration 0.6137 32.96%

Copepods Copepods: Water fraction in biota -0.7360 32.25%

Copepods Copepods: NLOM fraction in biota 0.7295 31.68%

Amphipods/Isopods Amphipods/Isopods: Water fraction in biota -0.7002 30.33%

Amphipods/Isopods Amphipods/Isopods: NLOM fraction in biota 0.6949 29.86%

Phytoplankton tPCBs water concentration 0.5878 29.81%

Daphnia Daphnia: Water fraction in biota -0.6114 25.02%

Daphnia Daphnia: NLOM fraction in biota 0.5963 23.80%

Crayfish Large Crayfish Large: Dietary absorption efficiency of lipid 0.5428 22.86%

Crayfish Small Crayfish small: Dietary absorption efficiency of lipid 0.5236 21.54%

Crayfish Small Crayfish small: Water fraction in biota -0.4497 15.89%

Crayfish Small Crayfish small: NLOM fraction in biota 0.4455 15.59%

Phytoplankton Phytoplankton: Aqueous phase resistance constant -0.4222 15.38%

Phytoplankton Phytoplankton: Growth rate constant -0.4202 15.24%

Daphnia tPCBs water concentration 0.4730 14.97%

Mysids Mysids: Lipid fraction in biota 0.4108 14.77%

Mollusks Mollusks: Water fraction in biota -0.4162 12.78%

Benthic Invertebrates Benthic invertebrates: Lipid fraction in biota 0.3966 12.78%

Benthic Invertebrates tPCBs sediment concentration 0.3934 12.57%

Benthic Invertebrates Benthic invertebrates: Dietary absorption efficiency of lipid 0.3911 12.43%

Mollusks Mollusks: NLOM fraction in biota 0.4033 11.99%

Yellow Perch Large Sculpin: Dietary absorption efficiency of lipid 0.3828 11.73%

Crayfish Large Crayfish Large: Water fraction in biota -0.3851 11.51%

Mollusks Mollusks: Lipid fraction in biota 0.3927 11.38%

Crayfish Large Crayfish Large: NLOM fraction in biota 0.3821 11.33%

Benthic Invertebrates Benthic invertebrates: Water fraction in biota -0.3696 11.10%

Benthic Invertebrates Benthic invertebrates: NLOM fraction in biota 0.3529 10.12%

NLOM = non-lipid organic matter fraction



Table 37. Sensitivity of Bioaccumulation Model to Change in Water and Sediment Concentrations

Organism Base Sediment <20% Water <20%

Phytoplankton 2.5 2.47 1.98

Daphnia 4.2 4.21 3.37

Mysids 8.9 8.83 7.27

Generic Copepods 3.5 3.47 2.80

Amphipods and Isopods 7.4 7.12 6.17

Mollusca 6.1 5.75 5.23

Crayfish 52.8 47.76 47.34

Benthic Invertebrates 34.4 29.83 32.08

Crayfish Small 25.5 22.83 23.02

Sockeye Salmon (juv.) 56.0 52.47 48.24

Threespine Stickleback 81.3 71.79 74.47

Longfin Smelt 81.5 72.25 74.40

Peamouth 183.5 162.87 167.35

Prickly Sculpin Large 132.4 118.63 119.63

Yellow Perch Large 454.0 406.22 411.05

Smallmouth Bass 619.4 555.38 559.47

Cutthroat Trout 323.0 288.48 292.87

Northern Pikeminnow 632.8 567.86 571.11

Prickly Sculpin Small 73.1 64.60 67.00

Yellow Perch Small 82.1 74.30 73.47

7.2.3 Uncertainty Basedonresultsofthesensitivityanalysis,thefollowinginputvalueswerevariedtogivealowandhighestimateforthemodeluncertaintyanalysis:LogKow,lipidassimilationefficiencyinfishandcrayfish,tPCBsconcentrationinwaterandsediment,andwaterfractionincopepods,amphipods/isopods,anddaphnids.Lowandhighestimatesofpredictedtissueconcentrationscomparedtothepredictedbestestimateandobservedmean±SDarepresentedinTable38andFigure15.Therangeoftheseestimatesreflectsacumulativecontributionofuncertaintyfromthemostimportantsources.Other,moreminorsourcesofuncertaintymaynominallyimpacttherangeofpredictedtissueconcentrations,butmayalsocanceleachotherout.

Thepredictedlowandhighestimatesarewithinafactoroffiveforphytoplankton,daphnia,copepods,mollusks,amphipods/isopods,andmysids.Therangewidensforbenthicinvertebrates,crayfish,andfish.Comparingthepredictedlowandhighestimatestotherangeofobservedtissueconcentrationssuggeststhatuncertaintyintheobserveddataisgreaterthanthatassociatedwithkeymodelinputsforlongfinsmelt,largepricklysculpin,peamouth,smallmouthbass,cutthroattrout,andnorthernpikeminnow.Overall,thepredictedbestestimatesarewithinafactoroftwoofthehighestimateofuncertaintyandwithinafactorofapproximately5‐10ofthelowestimateofuncertainty.Thus,reducinguncertaintyismorelikelytolowerthanraisethebestestimate.

Thedatausedtoestimatetheobservedtissueconcentrationshavetheirownsourcesofuncertainty.ObservedtissueconcentrationsandsamplesizesweresummarizedinKing



County(2013e).Samplingmethodsusedbythedifferentstudieswereadequatelydiverseandtheanalyticallaboratorieswerestate‐certifiedresultinginminimalbiasfromthesesources.However,therearenotableweaknessesintheobservedtissueconcentrationdatasetwhichmaypresentbias.Theseweaknessesinclude:

PCBdataareverylimitedforinvertebratesandformanyinvertebratetaxa,dataarenotavailable.

tPCBswerenotdetectedindaphnid,signalcrayfish,ormysidsamples.Observedconcentrationsarerepresentedbythehighestmaximumdetectionlimit(MDL).

OnlyfourindividualsignalcrayfishandeightwholeindividualsmallmouthbasssampleswereanalyzedfortPCBsresultinginsmallsamplesizesandhighuncertaintyintheobservedestimateoftissueconcentrationsforthesespecies.Nocompositesampleswereanalyzedforthesespecies.

McIntyre(2004)analyzedthreeandKingCounty(2013e)analyzedfivesmallmouthbass.tPCBconcentrationsinthethreesamplesfromMcIntyre(2004)weresimilar(303‐425ug/kgwetweight).However,tPCBconcentrationsinthefiveKingCountysamplesrangedgreatly(63‐1,755ug/kgwetweight)presentinghighuncertaintyintheobservedtPCBestimateforsmallmouthbass.

AsdiscussedinSection5.3,themostnotablesourcesofuncertaintyfordietarypreferencesarethelackofLakeWashingtonspecificinformationforcrayfish,theincongruencebetweenthethreespinesticklebackdietandthestablenitrogenisotopesignature,thelackofbiologicalinformationregardingfisheggandlarvaeconsumedbyfish,andthehighvariabilityoftPCBconcentrationsobservedinsmallmouthbass.



Table 38. Range of uncertainty in predicted compared to observed (mean ±SD) tissue concentrations (µg/kg wet)

Organism Predicted

Low Estimate

Predicted Best

Estimate

Predicted High

Estimate

Observed Mean ±SD

Phytoplankton 1.31 2.47 2.50 N/A

Daphnia 1.03 4.21 6.11 N/A

Copepods 0.81 3.48 5.24 N/A

Mollusks 2.09 6.10 7.84 N/A

Amphipods/Isopods 1.51 7.38 10.99 N/A

Mysids 2.63 8.94 11.75 N/A

Benthic Invertebrates 7.87 34.40 51.81 N/A

Crayfish Small 3.87 25.47 63.32 N/A

Crayfish Large 5.97 52.84 178.94 N/A

Sockeye Salmon (juv.) 9.60 55.95 82.68 48-82

Threespine Stickleback 17.74 81.25 116.17 82-247

Longfin Smelt 18.31 81.48 115.72 15-373*

Prickly Sculpin Large 15.97 132.37 235.70 0-382*

Prickly Sculpin Small 9.12 73.11 125.98 6.7-70

Peamouth 40.14 183.45 260.18 149-502*

Yellow Perch Large 60.80 454.04 746.91 56-214

Yellow Perch Small 13.25 82.09 126.65 27-63

Smallmouth Bass 80.96 619.36 1129.20 35-1271*

Cutthroat Trout 51.82 322.97 500.05 47-441*

Northern Pikeminnow 86.79 632.76 1080.93 0-1294*

SD–standarddeviation*observedmean±SDrangeisgreaterthanpredictedlow/highestimates



Phytopla

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Figure 15. Low and high uncertainty estimates for the Lake Washington bioaccumulation model



8.0 CONCLUSIONS ThisreportdescribedthedevelopmentandperformanceofafatemodelandbioaccumulationmodelofPCBsinLakeWashington.Althoughthefatemodelisasimple,two‐compartmentboxmodel(waterandsediment),itperformedwellinhindcasttestingagainstobservedwaterandsedimentconcentrations.Thistestsupportstheassumptionsmadetodevelopthemodel,butmostimportantlytheseresultsalsosupportthetPCBloadingestimatederivedbythisstudyasareliablefirst‐approximationofthecurrenttPCBloadingratetothelake.ThepredictedequilibriumwaterandsedimenttPCBconcentrationcloselymatchthebestestimatesofcurrentconditionsinLakeWashington.ThepredictedequilibriumsedimenttPCBconcentrationwasapproximatelythreetimeslowerthanthebestestimatebasedonempiricaldata.However,aspreviouslydiscussed,theempiricallybasedsedimenttPCBconcentrationestimateissuspectedtobebiasedhighduetothenon‐randomstudydesignsandsampledepthsgreaterthan3cmreflectingdeeper,morecontaminatedsedimentsuponwhichthisestimateisbased(KingCounty2013d).Therefore,thehindcastmodelbasedestimatemaybemoreaccuratethanthecomparisontotheobservedsedimentdatasuggest.

Accordingtohindcasttestingofthefatemodel,theresponsetimeforLakeWashingtonsedimentandwaterconcentrationstoreachequilibriumwithaconstantloadisapproximately40years.Themostrapidchangeinconcentrationsoccursinthefirst20years.ThissuggestsanychangesreducingPCBloadtoLakeWashingtonwillrequireseveraldecadestobefullyreflectedinfishtissues.

ThefatemodelwasfoundtobeverysensitivetologKow(octanol‐waterpartitioningcoefficient)andtPCBloads.Whentheseinputswerevariedintheuncertaintyanalysisbothindependentlyandtogether,itwasdeterminedthattPCBloadingestimatescontributedmoretomodelpredictionuncertaintythanlogKow.

TissueconcentrationspredictedusingwaterandsedimenttPCBconcentrationsfromfielddatacomparedtothoseusingthefatemodeloutputdemonstratedgoodperformanceofthebioaccumulationmodelwithboth,butbetterperformanceusingthesedimentandwaterconcentrationspredictedbythefatemodel.18.ThiswasexpectedbasedonthesuspectedhighbiasinobservedsedimenttPCBconcentrations.ModelbiasusingthefatemodeloutputindicatedthebioaccumulationmodelperformedwellandsimilartoapplicationsofthismodelinSanFranciscoBay,GeorgiaBasinandPugetSound(GobasandArnot2005,Condon2007,PelletierandMohamedali2009).

18Fielddatawerelikelybiasedhighbecausesamplingoftenfocusedonareassuspectedofcontamination(e.g.,nearcombinedseweroverflowsorstormdrains)andincludedsamplesthatrepresentedupto10cmofsurfacesediment,whichpotentiallyincludeshighertPCBconcentrationsthatoccurredinsedimentsdepositedinthe1970s.Becauseofthisbiasandbettermodelfitofthefatemodelpredictedsedimentandwaterconcentrations,thelattersourcewasselectedforuseinmodelapplicationoccurringlaterintheproject.



LogKow,waterandsedimenttPCBconcentrations,lipidassimilationefficiencyinfishandcrayfish,andwaterfractionsincopepods,amphipods/isopods,anddaphnidswereidentifiedasthemostsensitiveinputstothebioaccumulationmodel.Theuncertaintyanalysisdeterminedtheseparameterscollectivelycontributedmoreuncertaintybelowthanabovethebestestimate.Therangeinobservedtissueconcentrationsisgreaterthantherangeinpredictedconcentrationsassociatedwithkeymodelinputsforlongfinsmelt,largepricklysculpin,peamouth,smallmouthbass,cutthroattroutandnorthernpikeminnow.

Whilemodelperformancetestingindicatedthefateandbioaccumulationmodelsperformwellforthepurposesofthisproject,additionaleffortscouldfurtherimprovemodelperformance.Thefollowingeffortsarerecommendedtoreduceuncertaintyinmodelpredictionsandobservationdatausedinmodeltestingiffutureeffortsaimtoimprovemodelperformance.

ImprovetheempiricallybasedestimateofmeansedimenttPCBconcentrationbyimplementingasedimentsamplingstudywitharandomsamplingdesignthatfocusesontheactivesedimentlayer.

ReduceuncertaintyinlogKowbyanalyzingarelativelysmallnumberofsedimentandfishtissuesamplesforPCBcongeners,compositedforefficiency.

Reduceuncertaintyinobservedfishtissueconcentrationsforsmallmouthbass,pricklysculpinandnorthernpikeminnowbycollectingmorewhole‐bodysamplesforPCBAroclor®analysis.

Fisheggsandlarvaearekeypreyitemsforlongfinsmelt,threespinestickleback,andyellowperch.Theaccuracyofthemodeledfoodwebcouldbeimprovedwithfurtherdietarystudiesandcharacterizationofthesepreyitems(i.e.,meanlipidcontent,bodymass,waterfraction).

Ifseasonalityormorespatialresolutionisfoundtoberelevant/necessaryformanagementdecisions,atwo‐layer,seasonallyvaryingfateandbioaccumulationmodelcouldbedevelopedpredicatedonthecollectionofhigherresolutionPCBdataintime(e.g.,monthly)andspace(e.g.,epilimnion\hypolimnionornearshore\offshore).Also,withtheadditionofaverticalonedimensionalsedimentcompartment,atimehistoryofPCBinputstothelakecouldbedevelopedandusedtocalibratethemodeltoreproducethehistoryofsedimentPCBaccumulation.

Inconclusion,thetaskoffateandbioaccumulationmodeldevelopmentforapplicationinsubsequentstepsofthisprojectwascompleted.Inthenextphaseofthisproject,thefateandbioaccumulationmodelswillbecoupledtotesttotaltPCBloadingreductionscenariostoinformwaterqualitymanagersandstakeholdersonthemagnitudeofchangerequiredforLakeWashingtonfishtoreachsafelevelsoftPCBsandeliminatetheconsumptionadvisory.Theresultsofthesemodelsimulations,alongwithareviewofprojectfindingsandoverallrecommendationsforfuturework,willbepresentedinaseparateandfinalreportforthisproject.



9.0 REFERENCES Agric,1995.EcologicalstudiesonthecrayfishPacifastacusleniusculus(Dana).Ph.D.thesis.

SchoolofSciences,TheUniversityofBuckingham,UnitedKingdom. Anderson,G.C.1954.Alimnologicalstudyoftheseasonalvariationofphytoplankton

populations.Ph.D.thesis.UniversityofWashington,Seattle,WA.

Arhonditsis,G.B.andM.T.Brett.2005.Evaluationofthecurrentstateofmechanistic

aquaticbiogeochemicalmodeling.Mar.Ecol.Prog.Ser.271:13‐26.

Arnot,J.A.andF.A.P.C.Gobas.2004.Afoodwebbioaccumulationmodelfororganic

chemicalsinaquaticecosystems.Environ.Toxicol.Chem.23:2343‐2355.

Bartholow,J.2003.Modelinguncertainty:Quicksandforwatertemperaturemodeling.HydrologicalScienceandTechnology19(1‐4):221‐232.

Barnes,R.S.1976.Atraceelementsurveyofselectedwaters,sediments,andbiotaoftheLakeWashingtondrainage.MSthesis.UniversityofWashington,Seattle,WA.169pp.

Beck,M.B.,1987.WaterQualityModeling:AReviewoftheAnalysisofUncertainty.WaterResourcesResearch23(8),1393.

Bhavsar,S.P.,D.A.Jackson,A.Hayton,E.J.Reiner,T.Chen,andJ.Bodnar.2007.ArePCBlevelsinfishfromtheCanadianGreatLakesstilldeclining?J.Gt.LakesRes.33:592‐605.

Bigelow,H.B.andW.C.Schroeder.2002.FishesoftheGulfofMaine.FisheryBulletinoftheFishandWildlifeService.Bulleint74,Volume53.Onlineedition.

http://www.gma.org/fogm/Default.htm

Birch,P.B.1976.TherelationshipofsedimentationandnutrientcyclingtothetrophicstatusoffourlakesintheLakeWashingtondrainagebasin.PhDthesis.Universityof

Washington,Seattle,WA.

Booij,K.,J.B.HoedemakerandJ.F.Bakker.2003.DissolvedPCBs,PAHs,andHCBinpore

watersandoverlyingwatersofcontaminatedharborsediments.Environ.Sci.

Technol.37:4213‐4220.

Brocksmith,R.1999.Abundance,feedingecology,andbehaviorofanativepiscivore

northernpikeminnow.Athesissubmittedinpartialfulfillmentoftherequirements

forthedegreeofMasterofScience,UniversityofWashington.



Cerco,C.F.,M.R.Noel,S.‐C.Kim.2004.Three‐dimensionaleutrophicationmodelofLake

Washington,WashingtonState.PreparedforKingCountyDepartmentofNatural

ResourcesandParks,Seattle,WA.U.S.ArmyEngineerResearchandDevelopment

Center,Vicksburg,MS.ERDC/ELRR‐04‐12.

http://el.erdc.usace.army.mil/elpubs/pdf/trel04‐12.pdf

Chapra,S.1997.SurfaceWater‐QualityModeling.McGraw‐HillCompanies,Inc.Boston,MA.

Chrzastowki,M.1983.HistoricalchangestoLakeWashingtonandrouteoftheLake

WashingtonShipCanal,KingCounty,Washington.DepartmentoftheInterior,

UnitedStatesGeologicalSurvey.Open‐fileReport81‐1182.

http://gis.ess.washington.edu/grg/courses/ess320/readings/chrzastowski_historical_changes_lake_wa.pdf

Condon,C.D.2007.Development,evaluation,andapplicationofafoodwebbioaccumulationmodelforPCBsintheStraitofGeorgia,BritishColumbia.Thesis:MasterofResourceManagement.SchoolofResourceandEnvironmentalManagement.SimonFraserUniversity,Barnaby,B.C.,Canada.

Covich,A.P.,Palmer,M.A.andCrowl,T.A.1999.Theroleofbenthicinvertebratespeciesinfreshwaterecosystems:zoobenthicspeciesinfluenceenergyflowsandnutrient

cycling.BiologicalSciences.49(2):119‐127.

Cullon,D.L.,S.J.Jeffries,P.S.Ross.2005.Persistentorganicpollutantsinthedietofharborseals(Phocavitulina)inhabitingPugetSound,Washington(USA),andthestraitofGeorgia,BritishColumbia(Canada):AfoodbasketapproachEnvironmentalToxicologyandChemistry.24(10):2562‐2572.

Davis,J.A.2003.TheLong‐TermFateofPCBsinSanFranciscoBay.RMPTechnicalReport66.SanFranciscoEstuaryInstitute,Oakland,CA,USA.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.192.1707&rep=rep1&ty

pe=pdf

Davis,J.A.2004.Thelong‐termfateofpolychlorinatedbiphenylsinSanFranciscoBay

(USA).Environ.Toxicol.Chem.23:2396‐2409.

Davis,J.A.,F.Hetzel,J.J.Oram,andL.J.McKee.2007.Polychlorinatedbiphenyls(PCBs)in

SanFranciscoBay.EnvironmentalResearch105:67‐86.

DeVoogt,P.andU.A.Brinkman.1989.Production,propertiesandusageofpolychlorinated

biphenyls.In:Halogenatedbiphenyls,terphenyls,naphthalenes,dibenzodioxinsand



relatedproducts.2ndEd.Kimbrough,R.,A.A.Jensen,eds.ElsevierScience,New

York,NY.

Diamond,M.L.,L.Melymuk,S.A.Csiszar,andM.Robson.2010.EstimationofPCBstocks,

emissions,andurbanfate:Willourpoliciesreduceconcentrationsandexposure?

Environ.Sci.Technol.44:2777‐2783.

Eckstein,J.,S.T.Riedmueller,andS.Reed.2000.YASAI:YetAnotherSimulationAdd‐Infor

Excel.Version2.0UserGuide.http://www.yasai.rutgers.edu/yasai‐guide‐20.html

Edmondson,W.T.1975.MicrostratificationofLakeWashingtonsediments.Verh.Internat.

Verein.Limnol.19:770‐775.

Edmondson,W.T.1977.LakeWashington.In:NorthAmericanProject.AStudyofU.S.WaterBodies.AReportfortheOrganizationofEconomicCooperationandDevelopment.EPA‐600/3‐77‐086.U.S.EnvironmentalProtectionAgency,Corvallis,OR.

Edmondson,W.T.1991.SedimentaryrecordofchangesintheconditionofLakeWashington.Limnol.Oceanogr.36:1031‐1044.

Edmondson,W.T.andD.E.Allison.1970.Recordingdensiometryofx‐radiographsforthestudyofcrypticlaminationsinthesedimentofLakeWashington.Limnol.Oceangr.15:138‐144.

Edmondson,W.T.andJ.T.Lehman.1981.Theeffectofchangesinthenutrientincomeon

theconditionofLakeWashington.Limnol.Oceanogr.26:1‐29.

EPA.2009a.GuidanceontheDevelopment,Evaluation,andApplicationofEnvironmentalModels,CouncilforRegulatoryEnvironmentalModeling.EPA/100/K‐09.U.S.EnvironmentalProtectionAgency,Washington,D.C.

http://www.epa.gov/crem/library/cred_guidance_0309.pdf

EPA.2009b.KABAMversion1.0User’sGuideandTechnicalDocumentation.Environmental

FateandEffectsDivision,OfficeofPesticidePrograms,U.S.Environmental

ProtectionAgency,Washington,D.C.April7,2009.

http://www.epa.gov/oppefed1/models/water/kabam/kabam_user_guide.html

EPA.2013.AroclorandOtherPCBMixtureswebsite.UnitedStatesEnvironmental

ProtectionAgency.http://www.epa.gov/osw/hazard/tsd/pcbs/pubs/aroclor.htm

Era‐Miller,B.,R.JackandJ.Colton.2010.GeneralCharacterizationofPCBsinSouthLake

WashingtonSediments.WashingtonStateDepartmentofEcology,Olympia,WA.



PublicationNo.09‐03‐106.

https://fortress.wa.gov/ecy/publications/summarypages/1003014.html

Farley,K.J.,R.V.Thomann,T.F.Cooney,III,D.R.DamianiandJ.R.Wands.1999.An

IntegratedModelofOrganicChemicalFateandBioaccumulationintheHudson

RiverEstuary.PreparedfortheHudsonRiverFoundation.Environmental

EngineeringDepartment,ManhattanCollege,Riverdale,NY.

Fayram,A.H,andT.H.Sibley.2000.Impactofpredationbysmallmouthbassonsockeye

salmoninLakeWashington,Washington.N.Am.J.Fish.Mgmt.20(1):81‐89.

Fletcher,D.2009.ConcentrationsofPBDEsandPCBsinWaterintheCedarRiverandFish

fromtheLakeWashington/Cedar/SammamishWatershed.MasterofSciencethesis,

UniversityofWashingtonSchoolofForestResources.

Frame,G.M.,J.W.Cochran,andS.S.Bowadt.1996.CompletePCBcongenerdistributionsfor17Aroclormixturesdeterminedby3HRGCsystemsoptimizedforcomprehensive,quantitative,congener‐specificanalysis.J.HighResol.Chromatogr.19:657‐668.

Furl,C.,C.Meredith,andM.Friese.2009.DeterminationofPBTChemicalTrendsinSelectedWashingtonLakesUsingAge‐DatedSedimentCores:2008SamplingResults.WashingtonStateDepartmentofEcology,Olympia,WA.PublicationNo.09‐

03‐030.https://fortress.wa.gov/ecy/publications/summarypages/0903030.html

Furlong,E.T.1986.Sedimentgeochemistryofphotosyntheticpigmentsinoxicandanoxicmarineandlacustrinesediments:DabobBay,SaanichInlet,andLakeWashington.Ph.D.thesis.UniversityofWashington,Seattle,WA.

Gobas,F.A.P.CandJ.A.Arnot.2005.SanFranciscoBayPCBFoodWebBioaccumulationModel:FinalTechnicalReport.PreparedfortheCleanEstuaryPartnershipbyFrank

Gobas,PhDandJonArnot,MET,SimonFraserUniversity.

Gobas,F.A.P.CandJ.A.Arnot.2010.FoodwebbioaccumulationmodelforpolychlorinatedbiphenylsinSanFranciscoBay,California,USA.Environ.Toxicol.Chem.29:1385‐

1395.

Gobas,F.A.P.C,M.N.Z’GraggenandX.Zhang.1995.TimeresponseoftheLakeOntario

EcosystemtovirtualeliminationofPCBs.Environ.Sci.Technol.29:2038‐2046.

Gobas,F.A.P.C.,J.B.Wilcockson,R.W.Russell,andG.D.Haffner.1999.Mechanismsof

biomagnificationsinfishunderlaboratoryandfieldconditions.Environ.Sci.

Technol.33:133‐141.



Greene,R.W.,D.M.DiToro,K.J.Farley,K.L.PhillipsandC.Tomey.2013.Modelingwater

columnpartitioningofpolychlorinatedbiphenylstonaturalorganicmatterand

blackcarbon.Environ.Sci.Technol.47:6408‐6414.

Griffiths,M.andW.T.Edmondson.1975.BurialofoscillaxanthininthesedimentofLake

Washington.Limnol.Oceanogr.20:945‐952.

Häkanson,L.andM.Jansson.2002.PrinciplesofLakeSedimentology.BlackburnPress,

Caldwell,NJ.316pp.Hawthorne,S.B.,C.B.Grabanski,D.J.MillerandH.P.H.Arp.2011.

Improvingpredictabilityofsediment‐porewaterpartitioningmodelsusingtrends

observedwithPCB‐contaminatedfieldsediments.Environ.Sci.Technol.45:7365‐

7371.

Hawthorne,S.B.,C.B.Grabinski,D.J.MillerandH.P.H.Arp.2011.Improvingpredictabilityofsediment‐porewaterpartitioningmodelsusingtrendsobservedwithPCB‐contaminatedfieldsediments.Environ.Sci.Technol.45:7365‐7371.

Hickey,J.P.,S.A.Batterman,andS.M.Chernyak.2006.TrendsofchlorinatedorganiccontaminantsinGreatLakestroutandwalleyefrom1970to1998.Arch.Environm.Con.Tox.50:97‐110.

Johnson,A.,K.Seiders,C.Deligeannis,K.Kinney,P.Sandvik,B.Era‐Miller,andD.Alkire.

2006.PBDEFlameRetardantsinWashingtonRiversandLakes:ConcentrationsinFishandWater,2005‐06.WashingtonStateDepartmentofEcology,Olympia,WA.PublicationNo.06‐03‐027.https://fortress.wa.gov/ecy/publications/summarypages/0603027.html

Johnson,M.T.2010.Anumericalschemetocalculatetemperatureandsalinitydependentair‐watertransfervelocitiesforanygas.OceanSci.6:913–932.

KingCounty.2003.Sammamish/WashingtonAnalysisandModelingProgram.Lake

WashingtonExistingConditionsReport.PreparedbyTetraTechISG,Incand

Parametrix,Inc.forKingCountyDepartmentofNaturalResourcesandParks,WaterandLandResourcesDivision,Seattle,WA.

KingCounty.2008.10‐YearMajorLakesSedimentMonitoringProgramPlan.Preparedby

DeanWilson,WaterandLandResourcesDivision.Seattle,Washington.

KingCounty.2013a.EstimatingPCBandPBDELoadingReductionstotheLakeWashingtonWatershed:DataReport.PreparedbyRichardJackandJenéeColton,

WaterandLandResourcesDivision.Seattle,Washington.



KingCounty.2013b.PCB/PBDELoadingEstimatesfortheGreaterLakeWashington

Watershed.PreparedbyCurtisDeGasperi,WaterandLandResourcesDivision.

Seattle,Washington.

KingCounty.2013c.LakeWashingtonPCBModelingFinalQualityAssuranceProjectPlan.

PreparedbyKingCountyDepartmentofNaturalResourcesandParks,Waterand

LandResourcesDivision.Seattle,Washington.

KingCounty.2013d.LakeWashingtonSedimentAddendumtoEstimatingPCBandPBDE

LoadingReductionstotheLakeWashingtonWatershed:DataReport.Preparedby

CarlyGreyell,RichardJack,andJenéeColton,WaterandLandResourcesDivision.

Seattle,Washington.

KingCounty.2013e.LakeWashingtonTissueDataAddendumtoEstimatingPCBandPBDELoadingReductionstotheLakeWashingtonWatershed:DataReport.PreparedbyCarlyGreyell,RichardJack,andJenéeColton,WaterandLandResourcesDivision.Seattle,Washington.

Krahn,M.M.,Hanson,M.B.,Baird,R.W.,Boyer,R.H.,Burrows,D.G.,Emmons,C.K.,Ford,J.K.B.,Jones,L.L.,Noren,D.P.,Ross,P.S.,Schorr,G.S.,Collier,T.K.,2007.Persistentorganicpollutantsandstableisotopesinbiopsysamples(2004/2006)from

SouthernResidentkillerwhales.MarinePollutionBulletin54:1903–1911.

Lavin,P.A.andJ.D.McPhail.2011.Adaptivedivergenceoftrophicphenotypeamongfreshwaterpopulationsofthreespinestickleback(Gasterosteusaculeatus).Can.J.FisheriesandAq.Sci.43(12):2455‐2463.

Lobo,HandAlves,R.G.2011.InfluenceofbodyweightandsubstrategranulometryonthereproductionofLimnodrilushoffmeisteri(Oligochaeta:Naididae:Tubificinae).Zoologia.28(5):558‐564.

Lu,X.A.Skwarski,B.DrakeandD.D.Reible.2011.PredictingthebioavailabilityofPAHsandPCBswithporewaterconcentrationsmeasuredbysolid‐phasemicroextraction

fibers.Environ.Toxicol.Chem.30:1109‐1116.

Mackay,D.,S.Sang,P.Vlahos,M.Diamond,F.GobasandD.Dolan.1994.Arateconstant

modelofchemicaldynamicsinalakeecosystem:PCBsinLakeOntario.J.GreatLakesRes.20:625‐642.

Martinez,A.,C.O’Sullivan,D.ReibleandK.C.Hornbuckle.2013.Sedimentporewater

distributioncoefficientsofPCBcongenersinenrichedblackcarbonsediment.

EnvironmentalPollution182:357‐363.



Mazur,M.M.2004.Linkingvisualforagingwithtemporalpreydistributionstomodel

trophicinteractionsinLakeWashington.(DissertationforDoctorofPhilosophy)

UniversityofWashington,Seattle,WA.

McIntyre,J.K.2004.Bioaccumulationofmercuryandorganochlorinesinthefoodwebof

LakeWashington.M.S.thesis.UniversityofWashington,Seattle,WA.

Morrison,H.A.,F.A.Gobas,R.Lazar,D.M.Whittle,andG.D.Haffner.1997.Developmentand

verificationofabenthic/pelagicfoodwebbioaccumulationmodelforPCB

congenersinWesternLakeErie.Env.Sci.Techn.31(11):3267‐3273.

Morrison,H.A.,Whittle,D.M.,Metcalfe,C.D.andNiimi,A.J.1999.Applicationofafoodweb

bioaccumulationmodelforthepredictionofpolychlorinatedbiphenyl,dioxin,and

furancongenerconcentrationsinLakeOntarioaquaticbiota.CanadianJournalofFisheriesandAquaticSciences.56:1389‐1400.

Moshenberg,K.L.2004.ASedimentTriadAnalysisofLakesSammamish,Washington,andUnion.KingCountyDepartmentofNaturalResourcesandParks,WaterandLandResourcesDivision.Seattle,Washington.

Oliver,B.G.andNiimi,A.J.1988.TrophodynamicanalysisofpolychlorinatedbiphenylcongenersandotherchlorinatedhydrocarbonsintheLakeOntarioecosystem.

EnvironmentalScienceandTechnology.22:388‐397.

Oram,J.J.,L.J.McKee,C.E.Werme,M.S.Connor,D.R.Oros,R.GraceandF.Rodigari.2008.AmassbudgetofpolybrominateddiphenylethersinSanFranciscoBay,CA.EnvironmentInternational34:1137‐1147.

Osterberg,D.andG.Pelletier.2012.QualityAssuranceProjectPlan.PugetSoundRegionalToxicsModel:Enhancements,DataIntegration,andReductionTargetEvaluation.

EnvironmentalAssessmentProgram,WashingtonDepartmentofEcology.Olympia,

WA.Pub.No.12‐03‐108.http://www.ecy.wa.gov/biblio/1203108.html

Pelletier,G.2009.YASAIw.xla–AmodifiedversionofanopensourceaddinforExcelto

provideadditionalfunctionsforMonteCarlosimulation.WashingtonDepartmentof

Ecology,Olympia,WA.

Pelletier,G.andT.Mohamedali.2009.ControlofToxicChemicalsinPugetSound:Phase2,

Developmentofsimplenumericalmodels:Thelong‐termfateandbioaccumulation

ofpolychlorinatedbiphenylsinPugetSound.WashingtonDepartmentofEcology.

Olympia,WA.Pub.No.09‐03‐015.http://www.ecy.wa.gov/biblio/0903015.html



Pennak,R.W.1989.FreshwaterInvertebratesoftheUnitedStates.Thirdedition.Published

byJohnWileyandSons,NewYork,NY.628pp.

Peterman,P.H.,J.L.Zajicek,andC.J.Schmidtt.1990.Nationalcontaminantbiomonitoring

program:ResiduesoforganochlorinechemicalsinU.S.freshwaterfish,1976‐1984.

Arch.Environ.Contam.Toxicol.19:748‐781.

Peters,R.H.andDowning,J.A.1984.Empiricalanalysisofzooplanktonfilteringandfeeding

rates.LimnologyandOceanography.29(4):763‐784.

Robson,M.L.Melymuk,S.A.Csiszar,A.Giang,M.L.Diamond,andP.A.Helm.2010.

ContinuingsourcesofPCBs:Thesignificanceofbuildingsealants.Environ.Internat.

36:506‐513

RossP.S.,EllisG.M.,IkonomouM.G.,BarrettLennardL.G.,AddisonR.2000.HighPCBconcentrationsinfree‐rangingpacifickillerwhales,Orcinusorca:effectsofage,sexanddietarypreference.MarPollBull40:504‐515.

Ross,P.S.,S.J.Jeffries,M.B.Yunker,R.F.Addison,M.G.Ikonomou,andJ.C.Calambokidis.2004.HarborSeals(Phocavitulina)inBritishColumbia,Canada,andWashingtonState,USA,RevealaCombinationofLocalandGlobalPolychlorinatedBiphenyl,Dioxin,andFuranSignals.Env.Tox.Chem.23:157‐165.

Rowe,A.A.,L.A.Totten,M.Xie,T.J.FikslinandS.J.Eisenreich.2007.Air‐waterexchangeofpolychlorinatedbiphenylsintheDelawareRiver.Environ.Sci.Technol.41:1152‐1158.

Schenkeretal.2005.Improvingdataqualityforenvironmentalfatemodels:Aleast‐squaresadjustmentprocedureforharmonizingphysicochemicalpropertiesof

organiccompounds.Environ.Sci.Technol.39:8434‐8441.

Schwarzenbach,R.P.,P.M.GschwendandD.M.Imboden.1993.EnvironmentalOrganic

Chemistry.JohnWileyandSons,NewYork,NY.

Seiders,K.,C.DeligeannisandP.Sandvik.2007.WashingtonStateToxicsMonitoringProgram:ContaminantsinFishTissuefromFreshwaterEnvironmentsin2004and

2005.EnvironmentalAssessmentProgram,WashingtonDepartmentofEcology.

Olympia,WA.Pub.No.07‐030‐024.

https://fortress.wa.gov/ecy/publications/summarypages/0703024.html

Shanbhogue,S.L.1976.Studiesonfoodandfeedinghabitsofpeamouth(Mylocheiluscaurinus)inLakeWashington.Adissertationsubmittedinpartialfulfillmentofthe

requirementsforthedegreeofDoctorofPhilosophy.UniversityofWashington.



Siegbried,C.A.andM.E.Kopach.1980.FeedingNeomysismercedis(Holmes).Biol.Bull.

159:193‐205.

Sloan,C.A.,B.F.Anulacion,J.L.Bolton,D.Boyd,O.P.Olson,S.Y.Sol,G.M.Ylitalo,L.L.Johnson.

2010.PolybrominatedDiphenylEthersInOutmigrantJuvenileChinookSalmon

FromTheLowerColumbiaRiverAndEstuaryAndPugetSound,WA.Archivesof

EnvironmentalContaminationandToxicology,58(2):403‐414.

Tabor,R.A.,Warner,E.J.,Fresh,K.L.,Footen,B.A.andChan,J.R.2007.Ontogeneticdiet

shiftsofpricklysculpinintheLakeWashingtonbasin,Washington.Transactionsof

theAmericanFisheriesSociety.136:1801‐1813.

Tabor,R.A.,M.T.Celedonia,F.Mejia,R.M.Piaskowski,D.L.Low,B.Footen,andL.Park.

2004.PredationofjuvenileChinooksalmonbypredatoryfishesinthreeareasoftheLakeWashingtonBasin.ByU.S.FishandWildlifeService,WesternWashingtonFishandWildlifeOffice,FisheriesDivisioninLacey,WAandMuckleshootIndianTribeinAuburn,WAandNOAAFisheries,NorthwestFisheriesScienceCenterinSeattle,WA.

Taylor,J.R.,1997.AnIntroductiontoErrorAnalysis:TheStudyofUncertaintiesinPhysicalMeasurements,SecondEdition.UniversityScienceBooks,Sausalito,CA.

Totten,L.A.,P.A.Brunciak,C.L.Gigliotti,J.Dachs,T.R.Glenn,IV,E.D.NelsonandS.J.

Eisenreich.2001.Dynamicair‐waterexchangeofpolychlorinatedbiphenylsintheNewYork‐NewJerseyHarborEstuary.Environ.Sci.Technol.35:3834‐3840.

VanMetre,P.C.,J.T.Wilson,E.Callender,andC.C.Fuller.1998.Similarratesofdecreaseofpersistent,hydrophobiccontaminantsinriverinesystems.Environ.Sci.Technol.32:3312‐3317.

VanMetre,P.C.,Wilson,J.T.,Fuller,C.C.,Callender,Edward,andMahler,B.J.,2004,

Collection,analysis,andagedatingofsedimentcoresfrom56U.S.lakesand

reservoirssampledbytheU.S.GeologicalSurvey,1992–2001:U.S.GeologicalSurveyScientificInvestigationsReport2004–5184,180p.

VanMetre,P.C.andB.J.Mahler.2005.Trendsinhydrophobicorganiccontaminantsin

urbanandreferencelakesedimentsacrosstheUnitedStates,1970‐2001.Environ.

Sci.Technol.39:5567‐5574.

Voshell,J.R.2003.AGuidetoCommonFreshwaterInvertebratesofNorthAmerica.

PublishedbytheMcDonaldandWoodwardPublishingCompany,Blacksburg,VA.

442pp.



WADOH.2004.FinalReport:EvaluationofContaminantsinFishfromLakeWashington,

KingCounty,Washington.WashingtonStateDepartmentofHealth,Olympia,WA.

Wakeham,S.G.andR.Carpenter.1976.AliphatichydrocarbonsinsedimentsofLake

Washington.Limnol.Oceanogr.21:711‐723.

Wakeham,S.G.,J.Forrest,C.A.Masiello,Y.Gélinas,C.R.Alexander,andP.R.Leavitt.2004.

HydrocarbonsinLakeWashington.A25‐yearretrospectiveinanurbanlake.

Environ.Sci.Technol.38:431‐439.

WDFW.2013.LakeWashingtonSockeyeSalmonBackground.WashingtonDepartmentof

FishandWildlife.

http://wdfw.wa.gov/fishing/salmon/sockeye/lkwash_background.html

West,J.E,S.M.O'Neill,andG.M.Ylitalo.2008.Spatialextent,magnitude,andpatternsofpersistentorganochlorinepollutantsinPacificherring(Clupeapallasi)populationsinthePugetScienceofTheTotalEnvironmentVolume.394(Issues2‐3):369‐378.

Wetzel,R.G.1975.Limnology.PublishedbyW.B.SaundersCompany,Philadelphia,PA.743pp.

Winder,M.andD.E.Schindler.2004.Climatechangeuncouplestrophicinteractionsinanaquaticsystem.Ecology85:2100‐2106.

Windward.2010.FinalLowerDuwamishWaterwayRemedialInvestigation.PreparedbyWindwardEnvironmental,LLCforsubmittaltotheU.S.EnvironmentalProtectionAgency,Region10,SeattleWAandWashingtonStateDepartmentofEcology,NorthwestRegionalOffice,Bellevue,WA.

WindwardandAnchorQEA.2012.DraftSupplementalRemedialInvestigationReportfor

EastWaterwayOperableUnitSupplementalRemedialInvestigation/FeasibilityStudy.PreparedbyWindwardEnvironmental,LLCandAnchorQEAforsubmittalto

TheUSEnvironmentalProtectionAgency,Region10,SeattleWA.

Yake,B.2001.TheUseofsedimentcorestotrackpersistentpollutantsinWashingtonState.AReview.WashingtonStateDepartmentofEcology,Olympia,WA.PublicationNo.

01‐03‐001.

https://fortress.wa.gov/ecy/publications/SummaryPages/0103001.html



AppendixA.TissueSolidsandDerivedMoistureData

Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids

% Moisture (derived)

Source

Crayfish 1 58.4 119.5 45.92 24.8 75.2 McIntyre (unpublished)

Cutthroat Trout 1 182.0 220.2 60.00 19.7 80.3 McIntyre (unpublished)

















Daphnia C N/A 1 7.18 7.21 92.8 McIntyre (unpublished)






Longfin Smelt C N/A 52.8 0.49 15.9 84.1 McIntyre (unpublished)

Longfin Smelt C 96.4 104.0 6.68 21.5 78.5 McIntyre (unpublished)


Longfin Smelt C N/A 79.5 1.88 24.2 75.8 McIntyre (unpublished)

Longfin Smelt 1 125.6 135.0 17.70 29.1 70.9 McIntyre (unpublished)


Mysids C N/A 10 2.81 17.2 82.8 McIntyre (unpublished)



Mysids C N/A 10 N/A 14.1 85.9 McIntyre (unpublished)



Mysids C N/A N/A N/A 14.3 85.7 McIntyre (unpublished)



Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids


Source

Northern Pikeminnow

1 215.0 239.8 208.00 21.5 78.5 McIntyre (unpublished)

Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow

1 185.0 N/A 62.00 23.2 76.8 King County (unpublished)

Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow




Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids


Source

Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Northern Pikeminnow


Peamouth Chub 3 241.7 N/A 183.33 27.9 72.1 King County (unpublished)









Prickly Sculpin C N/A 30.7 0.29 18.8 81.2 McIntyre (unpublished)

Prickly Sculpin 1 N/A 122.0 20.62 19.4 80.6 McIntyre (unpublished)










Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids


Source








Prickly Sculpin 75 N/A < 20 N/A 18.5 81.5 King County (unpublished)

Prickly Sculpin 75 N/A < 20 N/A 18.7 81.3 King County (unpublished)

Prickly Sculpin 20 N/A > 20 N/A 23.8 76.2 King County (unpublished)

Smallmouth Bass 1 265.0 N/A 336.00 27.7 72.3 King County (unpublished)





Smallmouth Bass 1 428.0 465.7 1540.00 31.3 68.7 McIntyre (unpublished)



Sockeye Salmon (juv.)

C 103.4 114.1 N/A 21.2 78.8 McIntyre (unpublished)






C 111.6 124.5 13.70 29.7 70.3 McIntyre (unpublished)






C N/A N/A N/A 28.7 71.3 McIntyre (unpublished)


C N/A 71.5 3.74 29.4 70.6 McIntyre (unpublished)



Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids


Source





Trichoptera Larvae C N/A 23 0.36 31.3 68.7 McIntyre (unpublished)







Yellow Perch 1 126.4 130.0 24.67 19.6 80.4 McIntyre (unpublished)


















Yellow Perch 1 262.8 273.0 N/A 30.5 69.5 McIntyre (unpublished)












Species # Fish

Fork Length (mm)

Total Length (mm)

Weight (g)

% Solids


Source

Zooplankton C N/A 1 26.00 10.7 89.3 McIntyre (unpublished)

Zooplankton C N/A 1 N/A 11.1 88.9 McIntyre (unpublished)

Zooplankton C N/A 1 N/A 12.3 87.7 McIntyre (unpublished)

Zooplankton C N/A N/A 46.00 10.1 89.9 McIntyre (unpublished)



N/A – not measured C ‐ Composite sample

juv ‐ Juvenile

Trichoptera Larvae used for benthic macroinvertebrate estimates

Zooplankton used for copepod, isopod and amphipod estimates



AppendixB.Volume‐weightedAverageParameterCalculations Average Measurements Volume‐Weighted Measurements Depth (m)

Volumea (m3) DOC (mg/L)

DO (mg/L)

Temp (°C)

TOC (mg/L)

TSS (mg/L)

DOC (mg) DO (mg)

Temp (°C)

TOC (mg) TSS (mg)

< 5 4.21E+08 3.18 10.11 14.3 3.53 1.22 1.34E+09 4.25E+09 6.0E+09 1.48E+09 5.13E+08

5 to 10 3.84E+08 9.94 14.4 1.22 3.82E+09 5.5E+09 4.70E+08

10 to 15 3.51E+08 9.20 13.1 0.97 3.23E+09 4.6E+09 3.39E+08

15 to 20 3.23E+08 8.95 10.6 0.83 2.89E+09 3.4E+09 2.69E+08

20 to 25 2.95E+08 9.24 9.3 0.84 2.72E+09 2.7E+09 2.49E+08

25 to 30 2.67E+08 9.18 8.7 2.46E+09 2.3E+09

30 to 35 2.30E+08 9.09 8.4 2.09E+09 1.9E+09

35 to 40 1.91E+08 8.95 8.1 0.81 1.71E+09 1.6E+09 1.54E+08

40 to 45 1.57E+08 3.07 8.87 8.0 3.46 0.89 4.81E+08 1.39E+09 1.3E+09 5.42E+08 1.39E+08

45 to 50 1.21E+08 2.94 8.43 7.8 3.21 0.89 3.57E+08 1.02E+09 9.4E+08 3.89E+08 1.08E+08

50 to 55 9.15E+07 3.02 8.59 7.8 3.08 1.16 2.76E+08 7.86E+08 7.1E+08 2.82E+08 1.06E+08

55 to 60 4.69E+07 2.91 8.06 7.6 3.10 1.20 1.37E+08 3.78E+08 3.6E+08 1.45E+08 5.62E+07

60 to 65 7.28E+06 3.06 7.67 7.7 3.21 1.76 2.23E+07 5.59E+07 5.6E+07 2.34E+07 1.28E+07

Sum of Volume‐Weighted Measurements 2.61E+09 2.68E+10 3.1E+10 2.87E+09 2.42E+09

Sum of Volume (representative of measurement depths) 8.45E+08 2.89E+09 2.9E+09 8.45E+08 2.39E+09

Volume‐Weighted Average Measurements 3.09 9.29 10.9 3.39 1.01 a Volumes from Barnes (1976) p. 40



AppendixC.BioaccumulationModelSensitivityAnalysisResults(seeseparate PDF file)

development of lake washington pcb fate and ... · figure 6. comparison of fate model‐predicted...

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