8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
1/93
EPA/600/R-05/074
July2005
PARTITIONCOEFFICIENTSFOR
METALS
INSURFACEWATER,SOIL,ANDWASTE
by
JerryD.Allison,1,2
TerryL.Allison,.2
1HydroGeoLogic,Inc
1155HerndonParkway,Suite900
Herndon,VA20170
2AllisonGeoscienceConsultants,Inc.
3920PerryLane
FloweryBranch,GA30542
WorkAssignmentManager: RobertB.Ambrose,Jr.,P.E.
ContractNo.68-C6-0020 EcosystemsResearchDivision
NationalExposureResearchLaboratory
960CollegeStationRoad
Athens,GA30605
U.S.EnvironmentalProtectionAgency
OfficeofResearchandDevelopment
Washington,DC20460
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
2/93
NOTICE
TheinformationinthisdocumenthasbeenfundedwhollybytheUnitedStates
EnvironmentalProtectionAgencyundercontract68-C6-0020,WorkAssignment2-01to
HydroGeoLogic,Inc.IthasbeensubjecttotheAgency'speerandadministrativereview,andit
hasbeenapprovedforpublicationasanEPAdocument.Mentionoftradenamesofcommercialproductsdoesnotconstituteendorsementorrecommendationforuse.
ii
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
3/93
FOREWORD
TheNationalExposureResearchLaboratoryEcosystemsResearchDivision(ERD)in
Athens,Georgia,conductsprocess,modeling,andfieldresearchtoassesstheexposurerisksof
humansandecosystemstobothchemicalandnon-chemicalstressors.Thisresearchprovides
data,modeling,tools,andtechnicalsupporttoEPAProgramandRegionalOffices,stateandlocalgovernments,andothercustomers,enablingachievementofAgencyandORDstrategic
goalsfortheprotectionofhumanhealthandtheenvironment.
ERDresearchincludesstudiesofthebehaviorofcontaminants,nutrients,andbiotain
environmentalsystems,andthedevelopmentofmathematicalmodelstoassesstheresponseof
aquaticsystems,watersheds,andlandscapestostressesfromnaturalandanthropogenicsources.
ERDfieldandlaboratorystudiessupportprocessresearch,modeldevelopment,testingand
validation,andthecharacterizationofvariabilityandpredictionuncertainty.
Leading-edgecomputationaltechnologiesaredevelopedtointegratecorescience
researchresultsintomulti-media(air,surfacewater,groundwater,soil,sediment,biota),multi-stressor,andmulti-scale(organism,population,community,ecosystem;fieldsite,watershed,
regional,national,global)modelingsystemsthatprovidepredictivecapabilitiesforcomplex
environmentalexposurescenariosfacebytheAgency.
ExposuremodelsaredistributedandsupportedviatheEPACenterforExposure
AssessmentModeling(CEAM)(www.epa.gov/athens/ceampubl),theWatershedandWater
QualityModelTechnicalSupportCenter(www.epa.gov/athens/wwqtsc),andthroughaccessto
Internettools(www.epa.gov/athens/onsite).
ThisresearchprojectisacomponentoftheERDhazardouswasteresearchprogram,
whichseekstobetterunderstandtheenvironmentalcycling,exposure,andriskarisingfromthereleaseoforganicandinorganicpollutantsfromtreatmentfacilities.Inthisproject,metal
partitioncoefficientsweredevelopedforthewatershed,surfacewater,andsourcemodelsused
intheMultimedia,Multi-pathway,Multi-receptorExposureandRiskAssessment(3MRA)
technology.Knowledgeanddatagainedinthisevaluationwillbeusedtoimproveexposure
andriskanalysiscapabilitiesforheavymetalsevaluatedbythe3MRAandothermodelsused
byEPAinvariousregulatoryprograms.
EricJ.Weber,Ph.D.,ActingDirectorEcosystems
ResearchDivision
Athens,Georgia
iii
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
4/93
ABSTRACT
Thisreportpresentsmetalpartitioncoefficientsforthesurfacewaterpathwayandfor
thesourcemodelusedintheMultimedia,Multi-pathway,Multi-receptorExposureandRisk
Assessment(3MRA)technologyunderdevelopmentbytheU.S.EnvironmentalProtectionAgency.Partitioncoefficientsvaluesarepresentedforpartitioningbetweensoilandwater;
partitioningbetweenthesuspendedsedimentloadandthewaterinstreams,rivers,andlakes;
partitioningbetweenriverineorlacustrinesedimentanditsporewater;andpartitioningbetween
dissolvedorganiccarbon(DOC)andtheinorganicsolutionspeciesinthewaterofstreams,
rivers,andlakes. Somepartitioncoefficientsarealsopresentedtorepresentmetalpartitioning
betweenthesolidphaseofwasteanditsassociatedleachate.Partitioncoefficientsare
presentedforantimony(Sb),arsenic(As),barium(Ba),beryllium(Be),cadmium(Cd),
chromium(Cr),cobalt(Co),copper(Cu),lead(Pb),molybdenum(Mo),mercury(Hg),
methylatedmercury(CH3Hg),nickel(Ni),selenium(Se),silver(Ag),thallium(Tl),tin(Sn),
vanadium(V),andzinc(Zn).
Atwo-phaseapproachwasusedindevelopingtheneededpartitioncoefficients.Inthe
first-phase,aliteraturesurveywasperformedtodeterminetherangeandstatisticaldistribution
ofvaluesthathavebeenobservedinfieldstudies. Thisincludedthecollectionofpublished
partitioncoefficientsforanyofthemetalsinanyoftheenvironmentalmediaofinterest,orour
estimationofpartitioncoefficientsfromreportedmetalconcentrationdatawhenfeasible.In
thesecond-phaseeffort,statisticalmethods,geochemicalspeciationmodeling,andexpert
judgementwereusedtoprovidereasonableestimatesofthosepartitioncoefficientsnot
obtainedfromourliteraturesearchanddataprocessing.Thereportconcludeswithadiscussion
ofthemanysourcesofuncertaintyinthereportedmetalpartitioncoefficients.
iv
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
5/93
TABLEOFCONTENTS
page
1.0 INTRODUCTIONANDBACKGROUND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1
2.0 LITERATURESURVEYFORMETALPARTITIONCOEFFICIENTS . . . . . . . . 2-12.1 SELECTIONCRITERIAFORPARTITIONCOEFFICIENTS . . . . . . . . . . 2-3
2.2 RESULTSOFTHELITERATURESURVEY. . . . . . . . . . . . . . . . . . . . . . . 2-3
3.0 ANALYSISOFRETRIEVEDDATAANDDEVELOPMENTOF
PARTITIONCOEFFICIENTVALUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1
3.1 DEVELOPMENTOFPARTITIONCOEFFICIENTSFOR
NATURALMEDIA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1
3.1.1 EstimationfromRegressionEquationsBasedonLiteratureData. . . 3-2
3.1.2 EstimationFromGeochemicalSpeciationModeling . . . . . . . . . . . . 3-3
3.1.3 EstimationfromExpertJudgement . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6
3.2 DEVELOPMENTOFPARTITIONCOEFFICIENTSFORWASTESYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16
3.2.1 EstimationfromAnalysisofDataPresentedintheLiterature. . . . . 3-17
3.2.2 EstimationfromGeochemicalSpeciationModeling. . . . . . . . . . . . 3-19
4.0 DISCUSSIONOFRESULTSANDSOURCESOFUNCERTAINTY . . . . . . . . . . 4-1
5.0 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1
APPENDICES
APPENDIXA
APPENDIXB
APPENDIXC
APPENDIXD
APPENDIXE
METALPARTITIONCOEFFICIENTSUSEDINSOMERECENTU.S.EPARISKASSESSMENTS
SCATTERPLOTSFORLINEARREGRESSIONSUSEDTO
ESTIMATEMEANLOGKINNATURALMEDIA
EXAMPLEINPUTFILEFORTHEMINTEQA2MODEL
USEDTOESTIMATEMETALPARTITIONINGIN
SOIL/SOILWATERSYSTEMS
EXAMPLEINPUTFILEFORTHEMINTEQA2MODEL
USEDTOESTIMATEMETALPARTITIONINGTODOC
EXAMPLEINPUTFILEFORTHEMINTEQA2MODEL
USEDTOESTIMATEMETALPARTITIONINGIN
WASTEMANAGEMENTSYSTEMS
v
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
6/93
LISTOFTABLES
page
Table1 Partitioncoefficients(logKdinL/kg)fromtheliteraturesearch.. . . . . . . . . 2-5
Table2 LinearregressionequationsusedtoestimatemeanlogKdvalues(L/kg)innaturalmedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
Table3 Metalpartitioncoefficients(logKd)inkg/Lforsoil/soilwater . . . . . . . . . . 3-8
Table4 Metalpartitioncoefficients(logKd)inkg/Lforsediment/porewater . . . . . 3-10
Table5 Metalpartitioncoefficients(logKd)inkg/Lforsuspendedmatter/water . . 3-12
Table6 Metalpartitioncoefficients(logKd)inkg/Lforpartitioningbetween
DOCandinorganicsolutionspecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14
Table7 Effectivemetalpartitioncoefficientsbasedonreportedsolidphaseandsolution
phasemetalsconcentrationsfromleachtestsreportedintheliterature. . . . 3-18
Table8 ImportantparametersandconstituentconcentrationsusedinMINTEQA2
modelingoflandfillsintheacetogenicandmethanogenicstagesand
MSWIandCKDmonofills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20Table9 Estimatedrangeinlogpartitioncoefficients(L/kg)inwasteforselected
metalsdeterminedfromMINTEQA2modeling . . . . . . . . . . . . . . . . . . . . . 3-22
vi
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
7/93
LISTOFFIGURES
page
FigureB-1 DatausedtodevelopregressionequationtopredictsedimentKd
fromsoilKd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1FigureB-2 DatausedtodevelopregressionequationtopredictsedimentKd
fromsuspendedmatterKd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1
FigureB-3 DatausedtodevelopregressionequationtopredictsoilKdfromsuspendedmatterKd(andviceversa) . . . . . . . . . . . . . . . . . . . . . . . . . . B-2
FigureB-4 DatausedtodevelopregressionequationtopredictwasteKdfromsoilKd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2
vii
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
8/93
1.0 INTRODUCTIONANDBACKGROUND
Thepurposeofthisstudywastodevelopmetalpartitioncoefficientsforthesurfacewater
pathwayandforthesourcemodelusedintheMultimedia,Multi-pathway,Multi-receptor
ExposureandRiskAssessment(3MRA)technologyunderdevelopmentbytheU.S.
EnvironmentalProtectionAgency.The3MRAtechnologyprovidesforscreening-levelhumanandecologicalriskassessmentsforchronicexposuretochemicalsreleasedfromland-based
wastemanagementunits(WMUs)managedundertheHazardousWasteIdentificationRule
(HWIR). Themultimedia3MRAmodelincludesasurfacewaterpathwaymodelthatrequires
thepartitioncoefficientforeachmetaltobemodeled.
Innaturalmedia,metalcontaminantsundergoreactionswithligandsinwaterandwithsurface
sitesonthesolidmaterialswithwhichthewaterisincontact.Reactionsinwhichthemetalis
boundtothesolidmatrixarereferredtoassorptionreactionsandmetalthatisboundtothe
solidissaidtobesorbed.Themetalpartitioncoefficient(Kd;alsoknownasthesorption
distributioncoefficient)istheratioofsorbedmetalconcentration(expressedinmgmetalper
kgsorbingmaterial)tothedissolvedmetalconcentration(expressedinmgmetalperLofsolution)atequilibrium.
(1)
Duringtransportofmetalsinsoilsandsurfacewatersystems,metalsorptiontothesolidmatrix
resultsinareductioninthedissolvedconcentrationofmetalandthisaffectstheoverallrateof
metaltransport. Thus,transportmodelssuchasthoseusedinvariouspathwaysinthe3MRA
incorporatethemetalKdintotheoverallretardationfactor(theratiooftheaveragelinear
particlevelocitytothevelocityofthatportionoftheplumewherethecontaminantisat50percentdilution). TheuseofKdin3MRAtransportmodelingimpliestheassumptionthatlocal
equilibriumbetweenthemetalsolutesandthesorbentsisattained. Thisimpliesthattherateof
sorptionreactionsisfastrelativetoadvective-dispersivetransportofthemetal.
Foraparticularmetal,Kdvaluesinsoilaredependentuponvariousgeochemicalcharacteristics
ofthesoilanditsporewater. LikewiseforsurfacewatersystemstheKdforaparticularmetal
dependsonthenatureofsuspendedsolidsorsedimentandkeygeochemicalparametersofthe
water. GeochemicalparametersthathavethegreatestinfluenceonthemagnitudeofKdinclude
thepHofthesystemandthenatureandconcentrationofsorbentsassociatedwiththesoilor
surfacewater. Inthesubsurfacebeneathawastemanagementfacility,theconcentrationof
leachateconstituentsmayalsoinfluencethemetalKdthroughcompetitionforsorptionsites.
ThemetalsofinterestinHWIRmodelingareantimony(Sb),arsenic(As),barium(Ba),
beryllium(Be),cadmium(Cd),chromium(Cr),cobalt(Co),copper(Cu),lead(Pb),
molybdenum(Mo),mercury(Hg),nickel(Ni),selenium(Se),silver(Ag),thallium(Tl),tin
1-1
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
9/93
(Sn),vanadium(V),andzinc(Zn).Methylatedmercury(CH3Hg)andcyanide(CN)arealsoof
interest. Inthesurfacewaterpathway,the3MRAincludesseveraltransportprocessesthat
requiremetalpartitioncoefficients:(1)Theoverlandtransportofmetalcontaminantsinrunoff
waterinthewatershedandtheconsequentpartitioningbetweensoilandwater;(2)partitioning
betweenthesuspendedsedimentloadandthewaterinstreams,rivers,andlakes;(3)
partitioningbetweenriverineorlacustrinesedimentanditsporewater;and(4)partitioningbetweendissolvedorganiccarbon(DOC)andtheinorganicsolutionspeciesinthewaterof
streams,rivers,andlakes.
The3MRAmodelingscenarioalsoincludesasourcemodelforvarioustypesofwaste
managementunitsthatalsorequirespartitioncoefficients.Forthesourcemodel,thepartition
coefficientsareusedtorepresenttheratioofcontaminantmassinthesolidphasetothatinthe
leachate(water)phase. Therearefivetypesofwastemanagementunitsforwhichthesource
modelrequirespartitioncoefficients:landapplicationunits,wastepiles,landfills,treatment
lagoons(surfaceimpoundments),andaeratedtanks.
Thisreportdescribesthetwo-phaseapproachusedindevelopingtheneededpartitioncoefficients. Inthepreferred(first-phase)methodofobtainingthecoefficients,aliterature
surveywasperformedtodeterminetherangeandstatisticaldistributionofvaluesthathave
beenobservedinfieldstudies.Thisincludesthecollectionofpublishedpartitioncoefficients
foranyofthemetalsinanyoftheenvironmentalmediaofinterest,orourestimationof
partitioncoefficientsfromreportedmetalconcentrationdatawhenfeasible.Thedataretrieved
intheliteraturesearchwererecordedinaspreadsheetalongwithassociatedgeochemical
parameters(suchaspH,sorbentconcentration,etc.)whenthesewerereported.Weanticipated
thattheliteraturesearchwouldnotsupplyneededpartitioncoefficientsforallofthemetalsin
alloftheenvironmentalmediaofinterest. Therefore,inthesecond-phaseeffort,statistical
methods,geochemicalspeciationmodeling,andexpertjudgementwereusedtoprovide
reasonableestimatesofthosepartitioncoefficientsnotobtainedfromourliteraturesearchanddataprocessing.
2.0 LITERATURESURVEYFORMETALPARTITIONCOEFFICIENTS
Aliteraturesurveywasconductedtoobtainpartitioncoefficientstodescribethepartitioningof
metalsbetweensoilandsoil-water,betweensuspendedparticulatematter(SPM)andsurface
water,betweensedimentandsediment-porewater,andbetweenDOCandthedissolved
inorganicphaseinnaturalwaters. Inaddition,partitioncoefficientsweresoughtfor
equilibriumpartitioningofmetalsbetweenwastematrixmaterialandtheassociatedaqueous
phaseinlandapplicationunits,wastepiles,landfills,treatmentlagoons,andaeratedtanks.The
literaturesurveyencompassedperiodicalscientificandengineeringmaterialsaswellassome
non-periodicals,includingbooksandtechnicalreportspublishedbytheU.S.EPAandother
2-1
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
10/93
governmentagencies. Electronicsearchesofthefollowingdatabaseswereincludedaspartof
theliteraturesurvey:
AcademicPressJournals(1995-present)
AGRICOLA(1970-present)
AnalyticalAbstracts(1980-present) AppliedScienceandTechnologyAbstracts
AquaticSciencesandFisheriesAbstractSet(1981-present)
CABAbstracts(1987-present)
CurrentContents(1992-present)
DissertationAbstracts(1981-present)
EcologyAbstracts(1982-present)
EISDigestofEnvironmentalImpactStatements(1985-present)
EITechIndex(1987-present)
EnvironmentalEngineeringAbstracts(1990-present)
GeneralScienceAbstracts(1984-present)
GEOBASE(1980-present) GEOREF(1785-present)
NationalTechnicalInformationService
PapersFirst(1993-present)
PeriodicalAbstracts(1986-present)
ToxicologyAbstracts(1982-present)
WaterResourcesAbstracts(1987-present)
Twosearchstringswereusedintheelectronicsearches:distributioncoefficientand
partitioncoefficient. Useofsuchgeneralstringshastheadvantageofgeneratingmany
citations,decreasingtheprobabilitythatrelevantarticleswillbemissed,butalsocarryinga
highlaborburdenbecauseeachcitationreturnedmustbeexaminedforusefuldata.Formetalsthatarenotaswellrepresentedinthepublishedliterature,evenmoregeneralsearchstrings
wereused,sometimeswithbooleanoperators(e.g.,bariumandsoil,seleniumand
partitioning).Theworkofidentifyingarticlescontainingusefuldatafromamongallthose
retrievedwasmadeeasierbyfirstreviewingthetitlestoeliminatethoseofobviousirrelevance,
thenreviewingtheabstracts,thatwereusuallyavailableon-line.Abstractsofcitationsthat
showedpromiseforprovidingpartitioncoefficientswereprintedandgivenacodeconsistingof
thefirsttwolettersoftheleadauthorslastnameandthelasttwodigitsoftheyearof
publication. Thecode,alongwiththefirstfewwordsofthearticletitle,wasenteredinalog
bookfortracking.Loggedarticleswerequicklyreviewedatlocaluniversityresearchlibraries,
andthosecontainingrelevantdatawerecopiedforamorethoroughreviewatouroffice.Most
ofthearticleswereobtainedfromtheUniversityofGeorgiaScienceLibraryortheGeorgia
InstituteofTechnologyLibrary.Aseachcopiedarticleorreportwasreviewed,asummary
pagecontainingtheassignedcodewasstapledtothefrontwithnotesindicatingthetypeofdata
foundinthepaperandthelocation(pagenumber,tablenumber,etc.)ofusefuldata.Partition
2-2
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
11/93
coefficientsandotherdatafromthearticleswerethenenteredintoanEXCEL97spreadsheet
forcompilationandanalysis.
2.1 SELECTIONCRITERIAFORPARTITIONCOEFFICIENTS
Thefollowingcriteriaandguidelineswerefollowedintheselectionofpartitioncoefficient
valuesfromjournalarticles.Valueswereacceptedfromstudiescharacterizedby:
C Useofwholenaturalmediafordeterminationofpartitioncoefficientsinnatural
mediasystems(e.g.,rejectvaluesfromstudiesusingpuremineralphasesortreated
soils)
C Insoilsystems,useofanextractanthavinglowionicstrength(#0.1M);insurface
watersystems,lowsalinity(freshwaterpreferred,salinityupto10partsperthousand
acceptable)
C Useoflowtotalmetalconcentrations(i.e.,ifcoefficientsweredeterminedatmultiple
totalmetalconcentrations,choosethecoefficientcorrespondingtothelowestconcentrationwhereKdislesslikelytodependonmetalconcentration)
C pHvaluesinthenaturalrange(4to10)
C Noorganicchelatesintheextractant(e.g.,EDTA)
C Onepartitioncoefficientpersystemstudied
C Wheremultiplepartitioncoefficientsarepresentedforasystemduetoexperimental
variationofpHorotherparameters,selectthepartitioncoefficientcorrespondingtothe
conditionsmostcloselyapproximatingnaturalconditions.
C Batchleachingtests(preferredovercolumntestsifbothareavailableforthesamestudy
andsoil,butcolumntestsacceptable).
Thegeochemicalparametersmostlikelytoinfluencethepartitioncoefficientwereenteredinthespreadsheetalongwithreportedorcalculatedcoefficientsifsuchwerespecifiedinthe
sourcearticleorreport.ExamplesoftheseparametersarepH,totalconcentrationsofmetalin
solutionandsorbed,andconcentrationsofimportantmetalcomplexingagents(including
DOC),andweightfractionofparticulateorganicmatterandothersorbingmaterials.Physical
parametersnecessarytoconvertsorbedconcentration(mg/kg)overdissolvedconcentration
(mg/L)topartitioncoefficientsinlitersperkilogram(L/kg),i.e.,porosity,watercontent,and
bulkdensity,werealsorecordedwhenreportedinthearticles.Equationsandrelationships
presentedinjournalarticlesthatpresentKdasafunctionofpHorotherparameterswere
recordedinaremarkfieldinthespreadsheet.
2.2 RESULTSOFTHELITERATURESURVEY
Approximately245articlesandreportswerecopiedandreviewed.Atotalof1170individual
Kdvalueswereobtainedfromthesesources,eitherdirectlyorcalculatedfromreportedmedia
concentrations. ThistotaldoesnotincludemeanestimatedKdvaluesreportedinpreviously
publishedcompilationsofKdvalues(BaesandSharp,1983;Baesetal.,1984;Coughtreyetal.,
2-3
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
12/93
1985;Thibaultetal.,1990). (Thedatafromthesepreviouscompilationswererecordedinthe
spreadsheetandusedinguidingthefinalestimatesofappropriatecentraltendencyvaluesas
describedinSection3.1.3.) Approximately80%ofthe1170valuesweobtainedfromthe
literaturepertainedtothemetalsCd,Co,Cr,Cu,Hg,Ni,Pb,andZn. MoreKdswere
recoveredforCdthananyothermetal,followedcloselybyZn,Pb,andCu.Themost
frequentlyreportedtypeofKdwasthatforsuspendedmatterinstreams,riversandlakes.(Datapertainingtomarineenvironmentsweregenerallyrejected,butsomedatafromestuarieswere
includedifreportedascorrespondingtolowsalinity.)ThesecondmostfrequentlyreportedKdvaluespertainedtopartitioninginsoil.SuspendedmatterandsoilKdstogethertotaled68%of
thereporteddata. Table1showsthemedianandrangeofKdvaluesretrievedinourliterature
searchfornaturalmedia.(ValuesshownarelogKdvalues). Forsomecombinationsofmetal
andmediatype,toofewpartitioncoefficientswerefoundintheliteraturetostateamedianor
evenareasonablerange.Insomeofthesecases,meanormedianvalueswereavailablefrom
previouscompilationsofpartitioncoefficients. InTable1,blankspacesinthetablecorrespond
tonodatafound. Valuesinboldarefrompreviouscompilations.
Nodirectlyreportedpartitioncoefficientsforthewastesystemsofinterestwerediscoveredintheliteraturesurvey,andnoneareincludedinTable1.Therearemanyreasonsforwishingto
understandthebehaviorofmetalsinnaturalsystems.Therichliteratureofsoilscience,plant
nutrition,aquaticchemistry,geology,andtoxicologyareallexamplesofinvestigativeareasof
longstandingwheremetalpartitioncoefficientsarefrequentlyencountered.Theimpetusfor
researchwithregardtowastesystemsissignificantlydifferentfromthatofnaturalsystems.
Moreover,thebehaviorofmetalsinwastematerialsaretypicallystudiedandreportedpriorto
theirdisposalandconsequentmixingwithahostofothersubstancesfewstudieshave
focusedonthebehaviorofmetalswithinactualdisposalunitscontaininga(usuallyunknown)
mixtureofmaterials.Moststudiesinvolvingmetalconcentrationsinwasteareconcernedwith
predictingthemetalconcentrationinleachatebymeansofaphysicaltest(i.e.,aleachate
extractiontest).Section3.2presentsfurtherfindingswithregardtoleachtestsandappropriatemetalpartitioncoefficientsforwastesystems.
2-4
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
13/93
Table1
Partitioncoefficients(log KdinL/kg)fromtheliteraturesearch.
Medianvalueslistedinboldfacearefromapreviouscompilation.
Blankspacesrepresentinstancesforwhichnodatawasfoundortoo
fewvalueswerefoundtoprovidemeaningfulstatistics.
Metal Soil/Water
Suspended
Matter
/Water
Sediment/
Water DOC/Water
Ag
median
2.6 4.9 3.6
range 1.0-4.5 4.4-6.3 2.1-5.8
N 21 15
As
median
3.4 4.0 2.5
range 0.3-4.3 2.0-6.0 1.6-4.3
N 22 25 18
Ba
median
4.0
range 0.7-3.4 2.9-4.5
N 14
Be
median3.1 4.1
range 1.7-4.1 2.8-6.8
N 2 17
Cd
median
2.9 4.7 3.6 5.2
range 0.1-5.0 2.8-6.3 0.5-7.3 3.4-5.5
N 41 67 21 4
Co
median
2.1 4.7 3.3 4.5
range (-1.2)-4.1 3.2-6.3 2.9-3.6 2.9-4.8
N 11 29 3 2
2-5
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
14/93
Table1
Partitioncoefficients(log KdinL/kg)fromtheliteraturesearch.
Medianvalueslistedinboldfacearefromapreviouscompilation.
Blankspacesrepresentinstancesforwhichnodatawasfoundortoo
fewvalueswerefoundtoprovidemeaningfulstatistics.
Metal Soil/Water
Suspended
Matter
/Water
Sediment/
Water DOC/Water
Cr(III)
median
3.9 5.1 4.5
range 1.0-4.7 3.9-6.0
N 43 25
Cr(VI)
median
1.1
range (-0.7)-3.3
N 24
Cu
median
2.7 4.7 4.2 5.5
range 0.1-3.6 3.1-6.1 0.7-6.2 2.5-7.0
N 20 70 12 17
Hg
median3.8 5.3 4.9 5.3
range 2.2-5.8 4.2-6.9 3.8-6.0 5.3-5.6
N 17 35 2 3
CH3Hg
median
2.8 5.4 3.6
range 1.3-4.8 4.2-6.2 2.8-5.0
N 11 2 4
Mo
median
1.1 2.5
range (-0.2)-2.7
N 8
2-6
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
15/93
Table1
Partitioncoefficients(log KdinL/kg)fromtheliteraturesearch.
Medianvalueslistedinboldfacearefromapreviouscompilation.
Blankspacesrepresentinstancesforwhichnodatawasfoundortoo
fewvalueswerefoundtoprovidemeaningfulstatistics.
Metal Soil/Water
Suspended
Matter
/Water
Sediment/
Water DOC/Water
Ni
median
3.1 4.6 4.0 5.1
range 1.0-3.8 3.5-5.7 4.7-5.4
N 18 30 4
Pb
median
4.2 5.6 5.1 5.0
range 0.7-5.0 3.4-6.5 2.0-7.0 3.8-5.6
N 33 48 24 9
Sb
median
2.4 4.0
range 0.1-2.7 2.5-4.8 2.7-4.3
N 3
Se
median 1.0 3.6
range -0.3-2.4 3.1-4.7
N 23
Sn
median
2.9 5.6 4.7
range 2.1-4.0 4.9-6.3
N 3
Tl
median3.2
range 3.0-3.5
N 6
2-7
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
16/93
Table1
Partitioncoefficients(log KdinL/kg)fromtheliteraturesearch.
Medianvalueslistedinboldfacearefromapreviouscompilation.
Blankspacesrepresentinstancesforwhichnodatawasfoundortoo
fewvalueswerefoundtoprovidemeaningfulstatistics.
Metal Soil/Water
Suspended
Matter
/Water
Sediment/
Water DOC/Water
V
median
range 1.1-2.7
N
Zn
median
3.1 5.1 3.7 4.9
range (-1.0)-5.0 3.5-6.9 1.5-6.2 4.6-6.4
N 21 75 18 9
CN
median
3.0
range 0.7-3.6
N 3
PartitioncoefficientsusedinseveralrecentU.S.EPAriskassessmentsarepresentedinAppendixA.Becausetheoriginofthesedataisgenerallyunknown,theywerenotincludedin
thecollectionofKdvaluesappearingelsewhereinourspreadsheet,norweretheyincludedin
thestatistical summaryofKdvaluesobtainedfromtheliteratureasreportedherein.
2-8
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
17/93
3.0 ANALYSISOFRETRIEVEDDATAANDDEVELOPMENTOFPARTITION
COEFFICIENTVALUES
Thedatagatheredfrompublishedsourceswereinsufficienttoestablishareasonablerange
and/ormedianvalueforthepartitioncoefficientforallmetalsinallmedia-types.Therefore,
thesecondpartoftheeffortwasdirectedataugmentingthevaluesobtainedfromtheliteraturesoastoprovideareasonablerangeandcentraltendencyofKdforeachmetalineachmedia-
type. Statisticalanalysisofretrieveddata,geochemicalmodeling,andexpertjudgementwere
allusedtodevelopthesepartitioncoefficientvalues. Thenatureoftheavailabledatafor
naturalmediaandwastesystemswasdifferenttotheextentthatitseemedbesttoconsider
thesetwocategoriesseparately.
3.1 DEVELOPMENTOFPARTITIONCOEFFICIENTSINNATURALMEDIA
Inanalyzingthepartitioningdatacollectedfromtheliteratureforsoilandsurfacewater
systems,weattemptedtoidentifytheshapeoftheprobabilitydistributionforeachmetalin
eachmedium. Foraparticularmetalinaparticularmedium,thedegreetowhichtheliteraturesampleistrulyrepresentativeofthepopulationofmetalpartitioncoefficientsisdependenton
thenumberofsamplepoints,theactualvariabilityofimportantmediumpropertiesthat
influencepartitioning(pH,concentrationofsorbingphases,etc.),andhowwellthisvariability
isrepresentedinthesample. Insomecases,itwasnecessarytoeliminatedatapointsfromthe
literaturesampletoavoidobviousbias. Forexample,thesampleofliteratureKdvaluesfor
Cr(III)insoilincludedvaluesobtainedinapHtitrationofthreesoilssuchthateachofthethree
wasrepresentedbyeightdifferentKdvalues. Althoughtheyprovideinterestingdataonthe
dependenceofKdonpHinthesesoils,multiplemeasurementsfromthesamesoilandvalues
determinedatotherthantheambientsoilpHintroducebiasinthenaturalprobability
distributionofKd. Therefore,incaseswhereKdassociatedwithmultiplepHvalueswere
presented,theKdassociatedwiththepHvalueclosesttotheambientsoilpHwaschosen.IftheambientsoilpHwasnotspecified,thenasingleKdvaluewaspickedrandomlyfromamong
thosepresentedandtheotherKdvaluesforthesoilwerediscarded.Inthisfashion,thesample
ofliteraturedataforeachmetalandmedia-typewaseditedbeforeattemptingtoidentifythe
underlyingprobabilitydistributionforKd.
StatisticaltestswereperformedtodeterminetheshapeofthefrequencydistributionofKdfor
eachmetalandmedia-type.Thesetestsemployedwidelyrecognizedtechniquesavailablein
thestatisticalpackageAnalyze-It(version1.32),anadd-onmoduleforMicrosoftEXCEL97.
TheShapiro-WilktestandtheKolmogorov-Smirnovtestwereusedtotestthesamplesfor
normality.ApositivetestinShapiro-Wilkdoesnotensureanormaldistribution.Rather,it
providesameasureofconfidencethatthesampledataarenotinconsistentwithanormal
distribution. TheShapiro-Wilktestisageneraltestfornormality;itisnotnecessarytoknow
thepopulationmeanorstandarddeviation. TheKolmogorov-Smirnovtestwasusedwhen
resultsfromtheShapiro-Wilktestwerenegative. Inonlyafewcaseswerethedatasufficient
toidentifytheunderlyingdistributionwithanydegreeofcertainty.Manyofthesamplesets
(includingthemostcomplete(largest)samplesets),gaveapositivetestfornormalityafter
3-1
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
18/93
transformingtheavailabledatatologspace,suggestingthatthefrequencydistributionofthe
underlyingpopulationofKdvaluesforaparticularmetalinaparticularmediumismostlikely
log-normal.
Insomecases,thereweretoofewrepresentativedatapointsinthesampletohaveconfidencein
thedescriptivestatisticsofthedata. Inthesecases,threemethodswereusedtoaugmenttheavailabledatainestimatingthemean,standarddeviation,andminimumandmaximumKdvalues. Thethreemethodswere:estimationfromlinearregressionequationsdevelopedfrom
theliteraturesamples,estimationfromtheresultsofgeochemicalspeciationmodeling,and
estimationbyexpertjudgement. Eachmethodisdiscussedbelow.
3.1.1 EstimationfromRegressionEquationsBasedonLiteratureData
Ofthe13metalsforwhichliteraturedatawereretrievedcharacterizingKdinsoil,sediment,
andsuspendedmatter,12ofthemexhibitedaprogressionofdecreasingaffinityforsorption
materialintheordersuspendedmatter>sediment>soil. Inotherwords,comparisonofmean
KdvaluesforparticularmetalsshowedtheresultthatKd,SPM>Kd,Sediment>Kd,Soil. IntwoothercaseswhereatleasttwooftheKdtypescouldbecharacterizedfromtheliteraturedata,both
conformedtothissamepattern. Inaddition,asomewhatconsistentprogressioninKdmagnitudeformetalswithinthethreenaturalmediawasnoted.Forthebestrepresented
metals,thefollowingpatternsofdecreasingKdwereobserved(basedonorderingthemeanKdvaluesfromhighesttolowestmagnitudeforeachmedium):
Soils: Pb>Cr III>Hg>As>Zn=Ni>Cd>Cu>Ag>Co
Sediment: Pb>Hg>CrIII>Cu>Ni>Zn>Cd>Ag>Co>As
SPM: Pb>Hg>Cr III=Zn>Ag>Cu=Cd=Co>Ni>As
TherewassomeshufflingaboutoftheKdmagnitudeorderingamongthesemedia-types,asmightbeexpectedforadatasetthatisundoubtedlyincomplete. Themostobvious
inconsistencyintheprogressionofKdmagnitudeisforAs. Nevertheless,thesimilaritiesare
worthyofnote.Someaspectsoftheoveralltrendareinagreementwiththehard-softacid-base
(HSAB)conceptsofPearson(1963),however,PbandHghavegreateraffinitiesthanHSAB
predicts.Certainly,therearemultipleadsorptionsurfacespresentinallofthesematerials.The
consistencyofaffinityrelationshipsamongthesemetalssuggeststhatthedistributionofKdis
partlyduetocharacteristicsuniquetothemetalsthemselvesandpartlyduetocharacteristics
associatedwiththesorbingsurfaces.Regardlessofthereason,itappearsfeasibletoexploit
thesetrendstoprovideanestimateofKdforagivenmetalinonemediumifitsvalueinanother
mediumisavailable. Forexample,theliteraturedataprovidedareasonablenumberofKdvaluesinsoilsandsuspendedmatterfortheninemetalsAg,Cd,Co,Cr(III),Cu,Hg,Ni,Pb,
andZn. Foreachofthesemetals,themeanvalueofKdinsoilwasintheneighborhoodoftwo
ordersofmagnitudelessthanthemeanvalueinsuspendedmatter.Thistrendwas
characterizedmoreexactlybydevelopingalinearregressionequation.Theregressionequation
wasthenusedtoestimatemeanKdvaluesformetalsforwhichtheliteratureprovidedan
estimateofmeanKdinsoil,butnotinsuspendedmatter. Inasimilarmanner,linearregression
3-2
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
19/93
equationsweredevelopedtoestimatethemeanKdinsedimentfromtheliteratureestimateof
meanKdinsoilorsuspendedmatter,orthemeansoilKdfromthatinsedimentorsuspended
matter. Theregressionequationsweredevelopedfromcaseswheretheliteraturesurveydata
providedreasonableestimatesofthemeanKdforatleasttwoofthethreemedia.Themetals
usedindevelopingtheregressionequationsincludedcadmium,copper,zinc,andothermetals
thatwerebetterrepresentedintheliterature.ThedistributionofKdvaluesforaparticularmetalwasassumedtobelog-normalsothattheregressionequationswereactuallybasedon
meanlogKdandwereusedtopredictmeanlogKd. Thestandarddeviationwasestimatedfrom
themeanandminimumvaluesassumingtheminimumvaluerepresentstwostandard
deviationsfromthemean. Thestandarddeviationwasalsoestimatedusingthemeanand
maximumvaluesratherthanmeanandminimum. Thelargerofthetwoestimatesofstandard
deviationwasretainedasthefinalestimate.TheregressionequationsusedareshowninTable
2alongwiththenumberofobservationsuponwhicheachequationisbased,thecorrelation
coefficient(r2),andthe95%confidenceintervalfortheslopeandintercept.Scatterplots
showingtheregresseddatapointsandstraightlineregressionsareshowninAppendixB.
Table2LinearregressionequationsusedtoestimatemeanlogKdvalues(L/kg)innaturalmedia.
Usedto Independent slope intercept
Estimate Variable (+/-95%CI) (+/-95%CI) r2 N
meanlogKd meanlogKd 1.080 0.796 0.7 5
sediment soil (1.035) (3.190) 9
meanlogKd meanlogKd 1.418 -3.179 0.6 5
sediment suspended (1.923) (9.868) 5
matter
meanlogKd meanlogKd 0.380 3.889 0.3 9
suspended soil (0.444) (1.338) 7
matter
meanlogKd meanlogKd 0.969 -1.903 0.3 9
soil suspended (1.136) (5.703) 7
matter
TheregressionequationswerealsousedtoestimatemeanKdvaluesforsuspendedmatterand
sedimentsfromanestimateofthemeanKd
insoilobtainedfromgeochemicalspeciation
modelingasdiscussedinthenextsection.
3.1.2 EstimationFromGeochemicalSpeciationModeling
Geochemicalspeciationmodelingwasusedtoestimatesoil/waterpartitioningifdata-based
regressionequationscouldnotbeused. ThepartitioningofmetalcationsbetweenDOCand
3-3
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
20/93
theinorganicportionofthesolutionphasewasalsoestimatedbyspeciationmodeling.Inboth
cases,theU.S.EPAgeochemicalspeciationmodelMINTEQA2,version4.0(Allisonetal.,
1990),wasusedtoestimatetheKdvalues. TheinputdataforMINTEQA2weredeveloped
fromvarioussourcesaspresentedinthefollowingsections.
MODELINGDETAILSANDINPUTDATAFORSOILPARTITIONCOEFFICIENTS
Theconcentrationsofmajorionsusedingeochemicalspeciationmodelingforsoilswerethe
averageconcentrationsinriverwaterasreportedbyStummandMorgan(1996).Thesoil-
waterphosphateconcentrationwasobtainedfromBohnetal.(1979).Theionicstrengthwas
heldconstantat0.005Mafterasensitivitytestintherange0.01to0.001Mrevealedthatthe
impactonresultsofdoingsowassignificantlylessthantheeffectofvariabilityinother
importantparameters. Modelinputvaluesforseveralofthemostsignificantmaster
variablesaffectingKdwerevariedoverreasonablerangesinordertocapturetheexpectedrange
ofKdvalues. ThesemastervariablesincludepH,concentrationofdissolvedorganiccarbon
(DOC),concentrationofparticulateorganiccarbon(POC),andconcentrationofmetaloxide
bindingsites.Therangeforeachofthesemastervariableswascharacterizedbylow,medium,andhighassignedvalues,andthemodelwasexecutedatallpossiblecombinationsofthese
settings. ThepHrangecorrespondedtothatreportedfromtheSTORETdatabase(U.S.EPA,
1996a)withaslightdownwardadjustment(6.5forthemediumvalueinsteadof6.8,and4.5for
thelowvalueinsteadof4.9)toaccountforthemoreacidicenvironmentofsurfacewatershed
soils. TheconcentrationsusedforDOCwere0.5,5.0,and50.0mg/L,takenasareasonable
rangeinsoil-water. TheassignedPOCconcentrationvalueswereobtainedfromanalysisof
datainadatabaseforshallow,silt-loamsoils(Carseletal.,1988andR.Parrish,personal
communication). Thelow,medium,andhighvaluescorrespondedtothe10th,50th,and90th
percentiles,respectively,forparticulateorganicmatterconcentration(0.41,1.07,and2.12
wt%).
Thedominantmetaloxidesorbingsurfacewasassumedtobehydrousferricoxide(HFO).
Becausewehadlittlereliableinformationastotheappropriateconcentrationrange,andalsoin
considerationoftheimportanceofthisvariableindeterminingKd,theHFOconcentrationwas
usedasacalibratingvariable.Thelow,medium,andhighvalueswereinitiallysetto
correspondtothevaluesusedinU.S.EPA(1996a). Thosevalueswerebasedonaspecialized
extractionofreactiveFefromasetof12samplesfromvariousaquifersandsoils.Themean
KdforCd,Cu,Ni,Pb,andZnwerecomputedusingthesevaluesinMINTEQA2. These
computedKdvalueswerecomparedwithmeanKdvaluesforthesesamemetalsinsoilobtained
fromourliteraturesurvey.Thelow,medium,andhighHFOconcentrationswerescaledin
subsequentmodelingsuchthatthemeanKdvaluefromMINTEQA2waswithinthe95%
confidenceintervalofthemeanliteratureKdvalueforeachofthesemetals. (EachMINTEQA2
executionresultedin81differentKdvaluesduetoutilizingalldifferentcombinationsoflow,
medium,andhighassignedvaluesforthefourdifferentmastervariables.Themeanvaluefrom
MINTEQA2wastakenastheaverageofthethreeKdvaluescorrespondingtothemedium
settingofpH,DOC,andHFOandthelowsettingofPOC;themediumsettingsofpH,DOC,
andHFO,andthemediumsettingofPOC;andthemediumsettingsofpH,DOC,andHFO,
3-4
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
21/93
andthehighsettingofPOC.)AppendixCshowsatypicalMINTEQA2inputfileusedin
estimatingKdforsoil/water.
TheminimumandmaximumKdvalueswereestablishedbycombiningtheavailableliterature
dataandMINTEQA2results.Again,thedistributionwasassumedtobelog-normal. Oncethe
meanlogKd
valueforametalwasestablishedforsoilfromthemodelingexercise,the
previouslydescribedregressionequationsbasedonourliteratureanalysisprocesswereusedto
estimatethemeanKdvaluesforsedimentandsuspendedmatterifthesewerelackingfromthe
literaturedata. Thestandarddeviationwasestimatedasdescribedpreviouslyforthelinear
regressionestimates.
MODELINGDETAILSANDINPUTDATAFOR DOCPARTITIONCOEFFICIENTS
ThepartitioningofmetalsbetweenDOCandotherinorganicformsinwaterisnotusually
reported intermsofapartitioningcoefficient.Infact,specializedalgorithmswithinspeciation
modelsarefrequentlyemployedtoestimatethefractionofmetalboundwithDOCbasedonthe
pH,majorioncompositionofthesolution,andionicstrength. Thedevelopmentofsuch
specializedmethodsforestimatingmetalbindingwithDOCisanongoingresearcharea.MINTEQA2includesaspecializedsub-modelforestimatingDOCinteractionstheGaussian
distributionmodel(Dobbsetal.,1989;AllisonandPerdue,1994). ThismodelrepresentsDOC
asamixtureofmanytypesofmetalbindingsites.Theprobabilityofoccurrenceofabinding
sitewithaparticularlogKisgivenbyanormalprobabilityfunctiondefinedbyameanlogK
andstandarddeviationinlogK. AlimitationoftheDOCbindingcalculationsinMINTEQA2
andsimilarmodelsisthatthemetal-DOCreactionsnecessarytoobtainresultsareknownonly
foralimitednumberofmetalcations,andfornoneoftheanionicmetals.MINTEQA2
includesmeanlogKvaluesforthemetalcationsCd,Cu,Ba,Be,Cr(III),Ni,Pb,andZn.For
othermetalcationsofinterest(Ag,Co,Hg(II),Sn(II),andTl(I)),itwasnecessarytoestimate
themeanlogKforDOCbindingforusewiththeGaussianmodel.ForHg(II),theestimateof
themeanlogKwasdeterminedfromaregressionofknownmeanlogKvaluesagainstthebindingconstantsforhumic-andfulvicacid(HAandFA,respectively)reportedbyTipping
(1994). ThemetalsCd,Cu,Ni,Pb,andZnwerealsorepresentedinthedatabaseofHAandFA
bindingconstants,sothesedatawereusedtodeveloptheregressionrelationshipshownin
Equation(2).
(2)
Asderived,Equation(2)hasacorrelationcoefficient(r2)of0.95,andproducesanestimateof
9.0forthemeanlogKforHg2+bindingwithDOC(meanlogKDOC,Hg). (NOTE: Thisequation
givesthemeanlogKDOC,aformationconstantforuseintheMINTEQA2speciationmodelforthechemicalreactionbetweenDOCandHg.ItisnotthesameasthemeanKdforHgbinding
withDOC. ThelatterisalwaysdesignatedKd;theobjectiveoftheMINTEQA2modelingisto
estimatetheKdforthosemetalsforwhichliteraturedataislacking.)
ThemeanlogKvaluesforDOCbindingtotheothercations(Ag+,Co2+,Sn2+,andTl+)were
derivedfromalinearfreeenergyrelationshipusingthefirsthydrolysisconstants(logKOH)and
3-5
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
22/93
thebindingconstantforacetate(logKAcet). TheknownvaluesoflogKOHandlogKAcetforthe
metalsCd,Cu,Fe,Ni,Pb,andZnwereusedtoderivethefollowingrelationship:
(3)
Thecorrelationcoefficient(r2)forEquation(3)is0.98. Equation(3)wasusedtoestimatethe
meanlogKDOCvaluesforAg+,Co2+,Sn2+,andTl+foruseinMINTEQA2modeling.Themean
logKDOCvaluesestimatedforthesemetalswere2.0,3.3,6.6,and1.0,respectively.
Theestimationproceduresoutlinedpreviouslyinthissectioncannotreliablybeextendedto
anions. However,anionsaretypicallynotasstronglyboundtoorganicmatter.Therefore,we
usedMINTEQA2toestimateKdvaluesforbindingtoDOCforcationicmetalsonly,and
includedconservativeestimatesofKdvaluesfortheanionsbasedonjudgementalone.
Theconcentrationsofmajorionsusedinestimatingmetal-DOCbindingwithMINTEQA2weretheaverageconcentrationsinriverwaterasreportedbyStummandMorgan(1996).The
concentrationofDOCandthepHweretreatedasmastervariables,witheachassignedthree
levelscorrespondingtolow,medium,andhigh. Theassignedmediumvaluewasthemeanof
thereportedriverandstreamsamplesfromtheliteraturesurvey,andthelowandhighvalues
wereselectedtoencompasstherangeobservedintheliteraturesurveydata.Specifically,the
low,medium,andhighconcentrationsofDOCwere0.89,8.9,and89mg/L,respectively,and
thelow,medium,andhighpHwere4.9,7.3,and8.1,respectively.Thebindingofeachofthe
metalcationswascomputedinninesimulationsthatrepresentedallpossiblecombinationsof
pHandDOCconcentrationlevel. ThemeanKdvalueforeachcationwasspecifiedasthat
valuecomputedbyMINTEQA2whenthepHandDOCconcentrationweresettotheirreported
meanvaluesinsurfacewater. AtypicalMINTEQA2inputfileusedtoestimatemetal
partitioningtoDOCisshowninAppendixD.
TheresultscomputedusingMINTEQA2forbothsoilsandDOCwereusedtoaugmentthe
partitioningdatacollectedintheliteraturesurvey.Althoughitwasconsideredreasonableto
useMINTEQA2toestimatemeanpartitioncoefficients,itwasnotpossibletoestablishthe
shapeoftheKdfrequencydistributioncurvefromtheMINTEQA2results.However,thereis
nocompellingreasontosupposeotherthanthelog-normaldistributionsuggestedbythe
literaturesurveydata.
3.1.3 EstimationfromExpertJudgement
WhenneithertheregressionequationsnorMINTEQA2couldreasonablybeusedtoestimatea
neededmeanlogKd,themeanvaluewasestimatedsubjectivelyusingexpertjudgement.
Factorsconsideredinthisprocessincludedanyvaluesobtainedfromourliteraturesurvey,
reportedmeanvaluesorrangesfrompreviouscompilations,similaritiesofbehavioramong
metals,andqualitativestatementsfromarticlesandreports.TheminimumandmaximumKd
3-6
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
23/93
valuesfromtheliteraturewerealsousedifreasonablevalueswereavailable.Otherwise,the
extremesinKdwerealsoestimatedbyexpertjudgement.Ineithercase,thestandarddeviation
wasestimatedasdescribedpreviouslyaboveforlinearregressioninSection3.1.1.
Finally,arelativeconfidencelevel(CL)wassubjectivelyassignedtoeachofthefinalvalues
presented. TheCLvaluesrangefrom1to4,withthehighestconfidencecorrespondingtoa
valueof1andthelowesttoavalueof4. Ingeneral,estimatesbasedonourliteraturesurvey
forawell-studiedmetalwithalargeliteraturesamplewasdeemedtomeritaCLof1. Datafor
ametalnotrepresentedintheliteratureforwhichthefinalvalueswerepurelyestimatesfrom
MINTEQA2orothermeanswithanotabledegreeofexpertjudgementinvolvedwereassigned
aCLof4. ManyvaluesweredeterminedincircumstancesthatwarrantedaCLbetweenthese
extremes(e.g.,arangewasgivenintheliterature,avaluewasavailablefromaprevious
compilation,estimatesfromcombinationsoftheselattercircumstancescouldbecombinedwith
estimatesfrommodeling,etc.). Inthesecases,aCLof2or3wasassignedasseemed
appropriate.
Thefinalvaluesweassignedtothemetalpartitioncoefficientsforsoil,sediment,suspendedmatter,andDOCarepresentedinTables3,4,5,and6,respectively.Themethodusedtoarrive
ateachassignedvalue(useofallorasubsetofthecollectedliteratureKdvalues,useof
regressionequations,modelingresults,orexpertjudgement)isindicatedforeachmetaland
media-type,asisthesubjectivelyassignedconfidencelevel.
3-7
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
24/93
---
---
Table3
Metalpartitioncoefficients(logKd)inL/kgforsoil/soilwater. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Ag(I) 2.6 2.6 0.8 1.0 4.5 Fromliteraturedata(raw,
n=21);log-normal;CL=1
Asa 3.4 3.2 0.7 0.3 4.3 Fromliteraturedata(raw,n=21);(log-normal
assumed);oxidationstate
usuallynotspecifiedin
literature;CL=2
Ba(II) 2.0 0.7 0.7 3.4 SuspendedmatterK dregressionequationfor
mean;(log-normal
assumed); CL=2
Be(II) 2.2 1.0 1.7 4.1 SuspendedmatterK dregressionequationfor
mean;(log-normal
assumed); CL=3
Cd(II) 2.9 2.7 0.8 0.1 5.0 Fromliteraturedata(edited,
n=37);log-normal;CL=1
Co(II) 2.1 2.1 1.2 -1.2 4.1 Fromliteraturedata(raw,
n=11);log-normal;CL=1
Cr(III) 3.9 3.8 0.4 1.0 4.7 Fromliteraturedata(raw,
n=22);log-normal;CL=2
Cr(VI) 1.1 0.8 0.8 -0.7 3.3 Fromliteraturedata(raw,
n=24);(log-normal
assumed); CL=2
Cu(II) 2.7 2.5 0.6 0.1 3.6 Fromliteraturedata(raw,
n=20);log-normal;CL=1
3-8
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
25/93
---
Table3
Metalpartitioncoefficients(logKd)inL/kgforsoil/soilwater. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Hg(II) 3.8 3.6 0.7 2.2 5.8 Fromliteraturedata(raw,
n=17);log-normal;CL=1
MeHg 2.8 2.7 0.6 1.3 4.8 Fromliteraturedata(raw,n=11);log-normal;CL=2
Mo(VI) 1.1 1.3 0.6 -0.4 2.7 Fromliteraturedata(raw,
n=5);(log-normal
assumed);oxidationstate
notalwaysspecifiedin
literaturedata;CL=3
Ni(II) 3.1 2.9 0.5 1.0 3.8 Fromliteraturedata(raw,
n=19);log-normal;CL=1
Pb(II) 4.1 3.7 1.2 0.7 5.0 Fromliteraturedata(edited,n=31);(log-normal
assumed); CL=2
Sbb 2.3 1.1 0.1 2.7 Fromliteraturedata(mean
istheaverageofseveral
reportedmeanvalues,n=5);
(log-normalassumed);
CL=4
Se(IV)c 1.4 1.3 0.4 -0.3 2.4 Fromliteraturedata(edited,
n=11);(log-normal
assumed); CL=2
3-9
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
26/93
---
---
---
---
---
Table3
Metalpartitioncoefficients(logKd)inL/kgforsoil/soilwater. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Se(VI) -0.2 1.1 -2.0 2.0 Meanestimatedfrom
MINTEQA2result;(log
normalassumed);min,max
fromexpertjudgement;
CL=4
Sn(II) 2.7 0.7 2.1 4.0 Fromliteraturedata;(log
normalassumed);CL=3
Tl(I) 0.5 0.9 -1.2 1.5 Estimatedfrom
MINTEQA2result;(log
normalassumed);CL=4
V(V) 1.7 1.5 0.5 2.5 Mean,min,maxfrom
suspendedmatterKdregressionequation;(log
normalassumed);CL=4
Zn(II) 3.1 2.7 1.0 -1.0 5.0 Fromliteraturedata(raw,
n=21);(log-normal
assumed); CL=1
CN- 0.7 1.6 -2.4 1.3 Estimatedfrom
MINTEQA2result;(log
normalassumed);CL=4
a PublishedpartitioningdataforAsdoesnotallowdifferentiationofAs(III)andAs(V).Itisprobablethatpublishedvaluesrepresentresultsinvolvingbothoxidationstates.
b PublishedpartitioningdataforSbisrareanddoesnotallowdifferentiationofSb(III)
andSb(V).
PositiveresultinShapiro-Wilktestfornormalityofdatanotlog-transformed.But
samplesizeissmallanddatamaynotbeveryrepresentative.
3-10
c
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
27/93
---
---
---
---
---
Table4
Metalpartitioncoefficients(logKd)inL/kgforsediment/porewater. Valuesinitalics
wereestimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Ag(I) 3.6 1.1 2.1 5.8 MeanfromsoilK dregressionequation;(log
normalassumed); min,max
fromliteraturedata;CL=3
Asa 2.2 2.4 0.7 1.6 4.3 Fromliteraturedata;log
normal;oxidationstatenot
specifiedinliteraturedata;
CL=2
Ba(II) 2.5 0.8 0.9 3.2 Mean,min,maxfrom
suspendedmatterKdregressionequation;(log
normalassumed);CL=3
Be(II) 2.8 1.9 0.8 6.5 Mean,min,maxfrom
suspendedmatterKdregressionequation;(log
normalassumed);CL=3
Cd(II) 3.7 3.3 1.8 0.5 7.3 Fromliteraturedata(n=14,
edited);log-normal;CL=1
Co(II) 3.1 1.0 2.9 3.6 MeanfromsoilK dregressionequation;(log
normalassumed); min,max
fromliteraturedata;CL=3
Cr(III) 4.9 1.5 1.9 5.9 Mean,min,maxfromsoil
Kdregressionequation;
(log-normalassumed);
CL=4
3-11
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
28/93
---
---
---
---
---
Table4
Metalpartitioncoefficients(logKd)inL/kgforsediment/porewater. Valuesinitalics
wereestimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Cr(VI) 1.7 1.4 0.0 4.4 Mean,min,maxfromsoil
Kdregressionequation;
(log-normalassumed);
CL=4
Cu(II) 4.1 3.5 1.7 0.7 6.2 Fromliteraturedata(raw,n
=12);log-normal; CL=1
Hg(II) 4.9 0.6 3.8 6.0 Fromliteraturedata(raw,
n=2);(log-normal
assumed); CL=2
MeHg 3.9 0.5 2.8 5.0 Fromliteraturedata(edited,
n=2);(log-normal
assumed); CL=2
Mo(VI) 2.5 0.8 0.4 3.7 Meanfromliteraturedata
(reportedmeanvaluewith
oxidationstatenot
specified);(log-normal
assumed);min,maxfrom
soilKdregressionequation;
CL=4
Ni(II) 3.9 1.8 0.3 4.0 MeanfromsoilK dregressionequation;(log
normalassumed); min,maxfromliteraturedata;CL=3
Pb(II) 5.1 4.6 1.9 2.0 7.0 Fromliteraturedata(edited,
n=14);log-normal;CL=1
3-12
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
29/93
---
---
---
---
---
---
Table4
Metalpartitioncoefficients(logKd)inL/kgforsediment/porewater. Valuesinitalics
wereestimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Sbb 3.6 1.8 0.6 4.8 Fromliteraturedata
(reportedmeanvalue);(log
normalassumed);CL=4
Se(IV) 3.6 1.2 1.0 4.0 Meanfromliteraturedata
(reportedmeanvalue);(log
normalassumed); min,max
fromexpertjudgement;
CL=4
Se(VI) 0.6 1.2 -1.4 3.0 Mean,min,maxfromsoil
Kdregressionequation;
(log-normalassumed);
CL=4
Sn(II) 3.7 0.7 3.1 5.1 Mean,min,maxfromsoil
Kdregressionequation;
(log-normalassumed);
CL=3
Tl(I) 1.3 1.1 -0.5 3.5 Mean,minfromsoilKdregressionequation;(log
normalassumed); max
fromliteraturedata;CL=4
V(V) 2.1 0.9 0.4 3.2 Mean,min,maxfrom
suspendedmatterKdregressionequation;(log
normalassumed);CL=4
Zn(II) 4.8 4.1 1.6 1.5 6.2 Fromliteraturedata(edited,
n=13);(log-normal
assumed); CL=1
3-13
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
30/93
---
Table4
Metalpartitioncoefficients(logKd)inL/kgforsediment/porewater. Valuesinitalics
wereestimatedbyregressionorfromMINTEQA2results.Anentryoflog-normal
indicatesthatthesampledatagaveapositiveresultintheShapiro-Wilktestfor
normalityofthelog-transformeddata.Anentryoflog-normalassumedin
parenthesesmeansthatdatawerenotsufficienttoestablishthedistribution,butlognormalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalue
of1to4(1=highest,4=lowest). Anentryof---forthemedianoccurswhere
regressionequationswereusedtoestimatethemean,minimum,andmaximumvalues
andnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
CN- 1.6 1.7 -1.8 2.2 Mean,min,maxfromsoil
Kdregressionequation;
(log-normalassumed);
CL=4
a PublishedmetalpartitioningdatadoesnotallowdifferentiationofAs(III)andAs(V).Itis
probablethatthedatapresentedincluderesultsforbothoxidationstates.b PublishedpartitioningdataforSbisrareanddoesnotallowdifferentiationofSb(III)and
Sb(V).
3-14
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
31/93
---
Table5
Metalpartitioncoefficients(logKd)inL/kgforsuspendedmatter/water. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryof log-normalindicatesthat
thesampledatagaveapositiveresultintheShapiro-Wilktestfornormalityofthelog-
transformeddata.Anentryoflog-normalinparenthesesmeansthatdatawerenotsufficient
toestablishthedistribution,butlog-normalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalueof1to4(1=highest,4=lowest). Anentryof---forthemedian
occurswhereregressionequationswereusedtoestimatethemean,minimum,andmaximum
valuesandnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Ag(I) 5.2 5.2 0.6 4.4 6.3 Fromliteraturedata(edited,n=9);
log-normal;CL=2
Asa 4.0 3.9 0.5 2.0 6.0 Fromliteraturedata(raw,n=25);
(log-normalassumed);oxidationstatenotspecifiedintheliterature
data;CL=2
Ba(II) 4.0 4.0 0.4 2.9 4.5 Fromliteraturedata(raw,n=14);
log-normal;CL=2
Be(II) 4.1 4.2 0.7 2.8 6.8 Fromliteraturedata(raw,n=17);
log-normal;CL=2
Cd(II) 5.0 4.9 0.6 2.8 6.3 Fromliteraturedata(edited,
n=38);log-normal;CL=1
Co(II) 4.7 4.8 0.8 3.2 6.3 Fromliteraturedata(edited,
n=20);log-normal;CL=1
Cr(III) 5.1 5.1 0.4 3.9 6.0 Fromliteraturedata(raw,n=25);
log-normal;assumesunspecified
oxidationstateis(III);CL=2
Cr(VI) 4.2 0.5 3.6 5.1 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
Cu(II) 4.7 4.7 0.4 3.1 6.1 Fromliteraturedata(edited,n=42);log-normal;CL=1
Hg(II)b 5.3 5.3 0.4 4.2 6.9 Fromliteraturedata(edited,
n=26);log-normal;CL=1
3-15
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
32/93
---
---
---
---
---
---
Table5
Metalpartitioncoefficients(logKd)inL/kgforsuspendedmatter/water. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryof log-normalindicatesthat
thesampledatagaveapositiveresultintheShapiro-Wilktestfornormalityofthelog-
transformeddata.Anentryoflog-normalinparenthesesmeansthatdatawerenotsufficient
toestablishthedistribution,butlog-normalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalueof1to4(1=highest,4=lowest). Anentryof---forthemedian
occurswhereregressionequationswereusedtoestimatethemean,minimum,andmaximum
valuesandnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
MeHg 4.9 0.7 4.2 6.2 MeanfromsoilK dregression
equation;(log-normalassumed);
min,maxfromliteraturedata;
CL=3
Ni(II)b 4.3 4.4 0.4 3.5 5.7 Fromliteraturedata(edited,
n=25);log-normal;CL=1
Mo(VI) 4.4 1.0 3.7 4.9 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
Pb(II)c 5.7 5.7 0.4 3.4 6.5 Fromliteraturedata(edited,
n=38);(log-normalassumed);
CL=1
Sbd 4.8 0.5 3.9 4.9 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
Se(IV) 4.4 0.4 3.8 4.8 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
Se(VI) 3.8 1.0 3.1 4.6 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
Sn(II) 4.9 0.8 4.7 6.3 Mean,minfromsoilKdregressionequation;(log-normalassumed);
maxfromliteraturedata;CL=4
3-16
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
33/93
---
---
---
c
Table5
Metalpartitioncoefficients(logKd)inL/kgforsuspendedmatter/water. Valuesinitalicswere
estimatedbyregressionorfromMINTEQA2results.Anentryof log-normalindicatesthat
thesampledatagaveapositiveresultintheShapiro-Wilktestfornormalityofthelog-
transformeddata.Anentryoflog-normalinparenthesesmeansthatdatawerenotsufficient
toestablishthedistribution,butlog-normalhasbeenassumed. RelativeconfidenceinthedataisindicatedbytheCLvalueof1to4(1=highest,4=lowest). Anentryof---forthemedian
occurswhereregressionequationswereusedtoestimatethemean,minimum,andmaximum
valuesandnoestimatewasmadeforthemedian.
Metal Median Mean
Std.
Dev. Min Max Comments
Tl(I) 4.1 1.0 3.0 4.5 MeanfromsoilKdregression
equation;(log-normalassumed);
otherparametersfromexpert
judgement;CL=4
V(V) 3.7 0.6 2.5 4.5 Meanfromliteraturedata(raw,
n=5);(log-normalassumed);min,
maxfromexpertjudgement;
oxidationstatenotalways
specifiedinliterature;CL=3
Zn(II) 5.1 5.0 0.5 3.5 6.9 Fromliteraturedata(edited,
n=47);log-normal;CL=1
CN- 4.2 0.6 3.0 4.4 Mean,min,maxfromsoilKdregressionequation;(log-normal
assumed); CL=4
a PositiveresultforShapiro-Wilktestfornormalityofdatanotlog-transformed.Published
metalpartitioningdatadoesnotallowdifferentiationofAs(III)andAs(V).Itisprobable
thatthedatarepresentedincluderesultsforbothoxidationstates.b FailedShapiro-Wilktestfornormalityoflog-transformeddata,butpassedthe
Kolmogorov-Smirnovtestandhistogramexhibitslog-normalcharacter.
FailedShapiro-WilkandtheKolmogorov-Smirnovtestfornormalityoflog-transformed
data,buthistogramexhibitslog-normalcharacterd PublishedpartitioningdataforSbisrareanddoesnotallowdifferentiationofSb(III)and
Sb(V).
3-17
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
34/93
Table6
Metalpartitioncoefficients(logKd)inL/kgforpartitioningbetweenDOCand
inorganicsolutionspecies. Valuesinitalicswereestimatedbyregressionorfrom
MINTEQA2results.Log-normaldistributionsareassumed.Relativeconfidenceinthe
dataisindicatedbytheCLvalueof1to4(1=highest,4=lowest).
Metal Mean
Std.
Dev. Min Max Comment
Ag(I) 2.5 1.0 1.5 4.5 MeanestimatedfromMINTEQA2
results;otherparametersfromexpert
judgement;(log-normalassumed);
CL=3
As 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
(log-normalassumed);CL=4
Ba(II) 3.6 1.0 2.5 4.0 MeanestimatedfromMINTEQA2
results,valuesforotherparametersfrom
expertjudgement;(log-normal
assumed); CL=3
Be(II) 2.1 1.0 1.1 3.8 Allparametersestimatedfrom
MINTEQA2results;CL=3
Cd(II) 3.8 0.9 2.0 5.5 MeanestimatedfromMINTEQA2
results;min,maxfromexpert
judgement;CL=3
Co(II) 3.8 0.9 2.0 5.5 MeanestimatedfromMINTEQA2
results;min,maxfromexpert
judgement;CL=3
Cr(III) 1.1 1.6 -0.6 4.3 MeanestimatedfromMINTEQA2
results;min,maxfromexpert
judgement;CL=4
Cr(VI) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Cu(II) 5.4 1.1 2.5 7.0 Fromliteraturedata(raw,n=17);(log
normalassumed);CL=2
Hg(II) 5.4 1.2 3.0 6.0 Meanfromliteraturedata(raw,n=3);
(log-normalassumed); min,maxfrom
expertjudgement;CL=4
3-18
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
35/93
Table6
Metalpartitioncoefficients(logKd)inL/kgforpartitioningbetweenDOCand
inorganicsolutionspecies. Valuesinitalicswereestimatedbyregressionorfrom
MINTEQA2results.Log-normaldistributionsareassumed.Relativeconfidenceinthe
dataisindicatedbytheCLvalueof1to4(1=highest,4=lowest).
Metal Mean
Std.
Dev. Min Max Comment
MeHg 5.0 1.1 2.8 5.5 Mean,min,maxestimatedbasedon
relativeKdsofHg(II)andMeHgfor
suspendedmatterandHg(II)Kdwith
DOC; (log-normalassumed);CL=4
Ni(II) 3.7 0.9 1.9 5.4 MeanestimatedfromMINTEQA2
results;min,maxfromexpert
judgement;(log-normalassumed);
CL=3
Mo(VI) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Pb(II) 4.9 0.5 3.8 5.6 Fromliteraturedata(raw,n=9);(log
normalassumed);CL=2
Sb 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Se(IV) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Se(VI) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Sn(II) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Tl(I) 1.6 1.0 0.0 3.0 MeanestimatedfromMINTEQA2,
valuesforotherparametersfromexpert
judgement;(log-normalassumed);
CL=4
3-19
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
36/93
Table6
Metalpartitioncoefficients(logKd)inL/kgforpartitioningbetweenDOCand
inorganicsolutionspecies. Valuesinitalicswereestimatedbyregressionorfrom
MINTEQA2results.Log-normaldistributionsareassumed.Relativeconfidenceinthe
dataisindicatedbytheCLvalueof1to4(1=highest,4=lowest).
Metal Mean
Std.
Dev. Min Max Comment
V(V) 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);
CL=4
Zn(II) 5.1 0.7 4.6 6.4 Fromliteraturedata(raw,n=9);(log
normalassumed);CL=3
CN 2.0 1.0 0.0 3.0 Nodata,valuesfromexpertjudgement
(conservative);(log-normalassumed);CL=4
3-20
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
37/93
3.2DEVELOPMENTOFPARTITIONINGCOEFFICIENTSFORWASTESYSTEMS
Themultimedia,multi-pathwayriskassessmentfor3MRAutilizesasourcemodelthatassumes
equilibriumpartitioninginlandapplicationunits(LAUs),wastepiles,landfills,treatmentlagoons
(surfaceimpoundments),andaeratedtanks. Theavailabledataforcharacterizingthepartitioningof
metalsinwasteconsistsalmostexclusivelyofleachateextractiontestresultsforspecificwastes.
Ourliteraturesearchdidnotproduceanystudythatspecificallyprovidesmeasuredpartitioning
coefficientsformetalsinthemixedmaterialspresentinwastemanagementunits.
Severalstudieshaveaddressedtheissueoftheapplicabilityofleachateextractiontestdatato
predicttheleachatecompositionexitinglandfills(U.S.EPA,1991).TheU.S.EPAToxicity
CharacteristicLeachingProcedure(TCLP)wasspecificallydesignedtocharacterizeleachate
compositionsproducedbyspecificwastesco-disposedwithmunicipalsolidwaste.Recentpapers
suggestthattheconcentrationobservedinanyleachtestdependsagreatdealonleachingtimeand
thecumulativesolid-liquidratio(vanderSlootetal.,1996). Threeregimesarerecognizedinthe
leachingprocess(deGrootandvanderSloot,1992).Inthefirstregime,theleachatecompositionis
controlledbyinitialwash-offoflooselyadheredcontaminant;inthesecond,theleachatecompositioniscontrolledbydissolutionofprimarymaterialsandperhapsre-precipitationofmore
stablephases;andinthethird,theleachatecompositioniscontrolledbythediffusionofwaste
constituentsfromtheinteriorofwasteparticlestotheparticlesurface.Thetimeofonsetand
durationoftheseregimesarehighlyvariable,anddependonthelife-cycleofthespecificwaste
system(acetogenesis,methanogenesis,etc.).Theoverallchemicalcomposition(majorion
concentrationandconcentrationofmetal-complexingorganicligands)isalsoimportantin
determiningthemetalleachateconcentrationthatwillbeobservedinanyparticularcase.In
general,itwouldseemthatthehighestmetalleachateconcentrationswouldbeexpectedduringthe
initialwash-offperiod,withconcentrationsdecliningthereafter.Animmediatelyobviousquestion
is:Whatperiodisofconcerninthemodelingforthe3MRAasusedforHWIRrulemaking?Since
the3MRAmodeldoesnotallowatime-variablepartitioncoefficient,itwouldseemthatanaggregatepartitioncoefficientthatrepresentsanaverageoveranappropriateexposureorwaste
managementunitlifetimewouldbedesired. Unfortunately,thereiscurrentlynowaytoknow
whetherthepartitioningobservedinaTCLPtestcorrespondstosuchanaveragevalue. Most
authorsseemtoregardtheTCLPasanaggressivetestthatmayoverestimatemetalleachate
concentrations. However,thereisnoconsensusonthispoint.
Inviewofthelackofdatadescribingpartitioningofmetalsindifferenttypesofwasteunits,the
followingsimplificationsareproposed:
1) Forlandapplicationunits,thepartitioncoefficientsforsoilspresentedinTable3shouldbe
used. ThissimplificationassumesthatthepartitioningbehaviorofmetalsinanLAUislikelytobedominatedbythesorptivecharacteristicsofthesoilunderlyingtheunit.
2) Forsurfaceimpoundmentsandaeratedtanks,thepartitioncoefficientsforsuspendedmatter
presentedinTable5shouldbeused. Thisseemsareasonablestepinthatpartitioninginsuch
systemsmustinvolvesorptiontosuspendedparticlesandsediments. Thecompositionand
3-21
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
38/93
quantityofsuspendedandsedimentedsorbingparticlesmustbequitevariable,butthereisno
sourceofdataonwhichtobasemorespecificmodelingorotherestimatingtechniques.
3) Wastepilesandlandfillsshouldbetreatedthesameasregardsmetalpartitioning.
Adoptingthesesimplifications, itisonlynecessarytoderiveseparateestimatesofmetalpartition
coefficientsspecificallyforwastepilesandlandfills.Thefollowingsectionsdetailhowtheselatter
twosetsofwastemanagementunitcoefficientshavebeenestimatedfromavailableTCLPand
similarleachateextractionteststhatcharacterizeboththesolidphaseandthecorresponding
leachatemetalconcentrations. Wehavealsousedstatisticalmethodsandgeochemicalspeciation
modelingtoextendresultstometalsnotrepresentedinreportedTCLPorotherleachtestresults,
andtoexaminethesimilaritybetweenexpectedwastepartitioningandpartitioninginnaturalmedia.
3.2.1 EstimationfromAnalysisofDataPresentedintheLiterature
TherearenumerouspapersandjournalarticlesdescribingresultsfromaTCLPorsimilarleachtest
foraparticularwaste. Thesepublishedstudiesoftenfocusonwasteconstituentleachabilitybeforeandafterawastestabilizationortreatmentprocess. Therearemanypublishedstudiesofthe
leachabilityofmetalsfromincineratorash,withtheaimofinvestigatingthesuitabilityoftheash
materialsfordisposalorforuseinconstruction.Unfortunately,leachateextractiontestresults
(metalleachateconcentrations)oftenarereportedwithoutthecorrespondingconcentrationinthe
solidphase. Thisomissionmakesthosedatauselessinestimatingexpectedmetalspartitioning.
Ourliteraturesurveyproduced203leachtestresultsforwhichbothleachateandsolidphasedata
werepresented.Table7showstherangeandmeanvaluesofeffectivepartitioncoefficients
calculatedforeachmetalforwhichsufficientdatawasfound.Werefertotheseaseffective
partitioncoefficientsbecausetheyaresimplytheratioofmetalconcentrationinthesolidphaseto
thatinthesolutionphaseasrepresentedintheleachtestresults. Thesecoefficientsmayormaynot
representequilibriumpartitioning.
Severalauthorsdiscussedthesimilaritiesinmetalleachabilityoverarangeofdifferentmaterials.A
studybyvanderSlootetal.(1996)examinedtheleachingbehaviorofCdandZnfromvariousash
materials,shreddedmunicipalsolidwaste,sewagesludge-amendedsoil,andsoil.Similar
characteristicswerenotedinpHdependentleachingofbothCdandZnfromtheninedifferent
materialsstudied.Differencesamongthedifferentmaterialswereattributedtowaste-specific
chemicalparametersthatcausedadifferentchemicalspeciation.Forexample,theauthorscite
possibleCdcomplexationwithchloridethattheyinvestigatedusingMINTEQA2.Theyfoundthat
anincreasedleachabilityofCdinsomeoftheashmaterialswascorrelatedwithincreasedchloride
concentrationinthewaste.
Flyhammar(1997)concludedthattherearesimilaritiesinthemetalbindingpropertiesofmunicipal
solidwaste(MSW)andsediments. Hefoundthatthefractionationofmetalsamongvarious
availableandreactiveforms(asdeterminedbysequentialchemicalextractions)wassimilarbetween
freshMSWandanoxicsediment. Similaritieswerealsofoundinthefractionationpatternsofaged
MSWandanoxicsediments.
3-22
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
39/93
Table7
Effectivemetalspartitioncoefficientsbasedonreportedsolidphaseandsolutionphasemetals
concentrationsfromleachtestsreportedintheliterature.Nisnumberofsamples;meanand
rangeareexpressedinlogunits(L/kg).
Metal N Mean Range
As 11 2.8 1.0-5.1
Ba 7 3.0 1.8-3.7
Be 2 2.8 2.7-6.8
Cd 31 1.3 0-3.9
Co 6 2.8 1.6-3.8
Cr(III) 27 3.0 0.6-6.2
Cr(VI) 6 4.1 2.2-6.2
Cu 16 3.3 2.0-5.1
Hg 8 3.1 1.7-4.4
Ni 12 2.3 1.3-4.7
Pb 31 2.7 0.0-4.9
Sb 4 2.7 1.7-3.2
V 4 2.9 2.7-3.1
Zn 23 2.6 1.2-4.7
TheconsistencyinthemetalspartitioningaffinityrelationshipsnotedinSection3.1.1andthe
similaritiesnotedbytheselatterauthorsinthefractionationandbehaviorofmetalsinwasteversus
thatinsoilsandsedimentsleadstothesuppositionthatthepartitioningbehaviorofmetalsinmixed
wastesystemsmightnotbealtogetherdifferentfromthatinanaturalmedium.Itwouldperhapsbe
surprisingiftherelativeaffinitiesfordifferentmetalsinwasteweremarkedlydifferentfromtheir
relativeaffinitiesinnaturalmaterials. Theremaycertainlybesomedeviationsduetothepresence
ofoneormorecomplexingagentsinwastesystemsthathaveapreferenceforcombiningwith
certainofthemetals;however,intheabsenceofdatatoquantifythiseffect,andalsoin
considerationofthepaucityofactualpartitioningdataforwastesystems,wehavedevelopeda
regressionequationthatpredictswasteKdfromsoilKdforusewithmetalsforwhichlittleorno
datawasfound.WechosetousesoilKdasthepredictorbecauseacomparisonofKdvaluesfor
soils,sedimentsandsuspendedmattersuggestedthatthesolidtoliquidconcentrationratiois
importantindeterminingthemagnitudeofKd. (ThisapparentdependenceofKdonsolidtoliquid
ratiohasbeennotedinotherstudiesandissometimesreferredtoastheparticleconcentration
3-23
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
40/93
effect.) Thissolidtoliquidconcentrationratioforlandfillsandwastepilesisprobablymore
similartothatofsoilsthantoanyothermedium.Also,wenotethatlandfilledwasteistypically
coveredwithsoiltoformsoil/wastelayerswithinalandfillcell.Indevelopingourregression
relationship,weusedtheeffectivepartitioncoefficientsforthemetalsforwhichwehadthemost
complete(largest)sample.Theregressionequationthusdeterminedis:
(4)
Thisrelationshiphasaratherlowcorrelationcoefficient(r2)of0.4implyingthatonly40%ofthe
variationinlogKd,wastefromtheleachtestdataisaccountedfor.TheimplicationisthatlogKdvaluespredictedbymeansofthisequationmustberegardedashighlyuncertain.AppendixB
showsthescatterplotofdatafromwhichthisrelationshipwasdeveloped.
3.2.2 EstimationfromGeochemicalSpeciationModeling
TheMINTEQA2geochemicalspeciationmodelwasusedtoinvestigatethepossiblerangeofmetal
partitioncoefficientsforlandfills. Theinputrequirementsofthemodelforestimatingmetalpartitioningincludetheconcentrationsofmajorsoluteions,thepH,theconcentrationsofsorbing
phases,andtheDOCconcentration. Fourlandfillmodelingscenariosweredeveloped,
distinguishedprimarilybytheconcentrationsofmajorsoluteions,theDOCconcentration,thePOC
concentration,andthepH.Thesescenariosincludedlandfillscontainingmunicipalsolidwastein
theacetogenicstageandinthemethanogenicstage,amonofillcontainingashfromincinerationof
municipalsolidwaste(MSWIash),andamonofillcontainingcementkilndust(CKD).
ForeachoftheMINTEQA2modelingscenarios,ahydrousferricoxidesorbingphasewasassumed.
AparticulateorganiccarbonsorbentwasalsoassumedfortheacetogenicandmethanogenicMSW
landfills.Particulateorganiccarbonwasassumedtohavebeenconsumedintheincinerationprocess
fortheMSWIandCKDscenarios. Theconcentrationofthesorbentiscrucialindeterminingthe
numberofsitesavailableformetalsorption. Unfortunately,theconcentrationofsorbent
appropriateinthevariouswastemanagementsystemsissubjecttoaveryhighdegreeofuncertainty.
Theuncertaintyarisesfromthevariablecompositionofwastesthataredisposedinlandfillsandthe
possiblechangesincompositionovertimeasleachatepercolatesthroughthematerials.Itislikely
thatsolidsurfacesexposedtolandfillgasandleachateundergochangeswithrespecttotheir
sorptivecharacterovertime. Possiblechangesincludedissolutionorprecipitationofoxideor
organicsurfacecoatings.Theseprocesseshavenotbeenstudiedinactuallandfillsamplesin
sufficientdetailtoallowquantitativerepresentation. Kerstenetal.(1997)citedevidenceofsorption
controlofPbleachinginMSWIleachtests.TheyattemptedtomodeltheobservedPb
concentrationsbyutilizingaspeciationmodelwithsurfacecomplexationsorptionreactionsparameterizedfortheconstantcapacitancemodelassuminghydrousferricoxide(HFO)asthe
sorbent.Theyobtainedreasonableresultsassuming0.7g/LfortheHFOconcentrationandusinga
sitedensityof1.35x10-4molsites/gHFO. TheMINTEQA2modelingpresentedhereutilizeda
similarsurfacecomplexationmodel(thediffuse-layermodel).Kerstenetal.(1997)hadnotedthat
theirsorbentconcentrationwasperhapstoolow,soourmodelingwasconductedbothwiththeir
3-24
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
41/93
valueof0.7g/L,andusing7g/Lasareasonableupper-rangevalue.Inbothcases,asitedensity
1.35x10-4molsites/gHFOwasused.
Thevaluesofotherparametersandconstituentconcentrationsusedinourmodelingforthefour
landfillscenariosareshowninTable8. Afterconcentrationofsorbingsites,themostcriticalmodel
parameterispH,sothemodelingwasconductedatthreedifferentpHvaluesforeachscenario.The
threepHvaluesusedfortheacetogenicandmethanogenicscenarios(4.5,6.1,7.5and7.5,8.0,9.0,
respectively)wereinkeepingwiththeminimum,maximumandmeanpHcitedfortheselandfill
stagesinastudyof15landfillsbyEhrig(1992).Themajorionconcentrationsfortheacetogenic
andmethanogenicscenarioswerealsoasspecifiedinEhrig(1992).ThethreepHvaluesforthe
MSWIscenario(8.0,9.0,10.0)wereselectedtodefineareasonablerangeandcentraltendency
valueforthisscenario. Thesevalueswerebasedondatacollectedintheliteraturereviewportionof
thisstudy,aswerethemajorionconcentrationsfortheMSWIscenario.ThepHvaluesassociated
withtheCKDscenario(9.0,10.0,11.0)wereselectedwithdueconsiderationtothehighlyalkaline
conditionsassociatedwiththismaterial,buttheylackstatisticalsignificance.Anexample
MINTEQA2inputfileforeachofthescenariosispresentedinAppendixE.
Itshouldbenotedthattheconfidencelevelassociatedwithallofthemodelingparametersforwaste
systemsislow.Thereisnotanextensivedatabaseofobservationsfromwhichtoextractreasonable
modelvaluesformostoftheseparameters,especiallytheconcentrationofsorbentsandsorbing
sites. Withoutreliableinformationforcharacterizingthesorbents,itisnotpossibletoaccurately
establishthetotalsystemconcentrationsofcompetingions(Ca,Mg,etc.)thatshouldbeusedinthe
model. Theresultsmustbeinterpretedinlightofthisshortcoming.
Table8
ImportantparametersandconstituentconcentrationsusedinMINTEQA2
modelingoflandfillsintheacetogenicandmethanogenicstagesandMSWIand
CKDmonofills.
Model
Parameter
Scenario
MSW
Acetogenic
MSW
Methanogenic
MSWI
Ash
Monofill
CKD
Monofill
pH 4.5,6.1,7.5a 7.5,8.0,9.0a 8.0,9.0,
10.0b9.0,10.0,
11.0c
IonicStrength(M)
0.1c 0.1c 0.1c 0.1c
Ca (mg/L) 6000d 975d 1,700b 2850f
Mg
(mg/L)
625d 500d 10b 10f
3-25
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
42/93
c
Table8
ImportantparametersandconstituentconcentrationsusedinMINTEQA2
modelingoflandfillsintheacetogenicandmethanogenicstagesandMSWIand
CKDmonofills.
Model
Parameter
Scenario
MSW
Acetogenic
MSW
Methanogenic
MSWI
Ash
Monofill
CKD
Monofill
Na (mg/L) 1350e 1350e 300b 300f
K (mg/L) 1100e 1100e 380b 400f
CO3 (mg/L) 500c 250c 50c 50f
Cl (mg/L) 2100e 2100e 1,200b 380f
Fe (mg/L) 780e 0 0 0
SO4 (mg/L) 500d 80d 1,400b 630f
DOC (mg/L) 100c 50c 15c 15c
POC (mg/L) 100,000c 50,000c 0 0a Minimum,average,andmaximumvaluesreportedinEhrig(1992).
b ObtainedfromanalysisofMSWIdataobtainedinourliteraturesurvey.
Reasonableguesses.d ComputedfromtypicaldissolvedvaluesreportedinEhrig(1992),assumingequilibrium
withthemodelsorbentsatthemedianpHforacetogenicandmethanogeniccases.e ReportedastypicalvaluesinEhrig(1992).f GeneratedfromsimulationofTCLPonCKDusingMINTEQA2(U.S.EPA,1998b).
ThepartitioningcoefficientsforselectedwastesestimatedfromtheMINTEQA2modelingexercise
forseveralmetalsareshowninTable9. Thepartitioncoefficientswerecalculatedastheratioof
thesimulatedsorbedanddissolvedconcentrationsasexpressedinEquation(1). TheunitsofKdwereconvertedtoL/kgbyassumingthatoneliterofleachatesolutionisassociatedwith5kgof
wastematerial. Therangeinestimatedpartitioncoefficientsisshownforeachlandfillmodeling
scenario. Ininterpretingtheseresults,itmustberememberedthatnostatisticalsignificancecanbe
assignedbecausenonecanbeassociatedwithmostofthemodelinputparameters.Atbest,these
resultsshouldberegardedasindicatingapossiblerangeofcentraltendencyvalues,andeventhismustbequalifiedbecausetheresultsaresosensitivetoseveralpoorlycharacterizedparameters,
mostnotably,theconcentrationofsorbents.Theresultsalsoreflectonlyasinglesetof
concentrationvaluesforthemajorambientionsvariabilityintheseconcentrationswillinfluence
metalpartitioning. Someionsexertgreaterinfluenceonthepartitioningofparticularmetals.For
example,thelowpartitioncoefficientsassociatedwithCdinTable9appeartoberelatedto
complexationwithchloridethatisenteredatrelativelyhighambientconcentrationinallscenarios.
3-26
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
43/93
Thiseffectisinkeepingwithobservationsbyothers(vanderSlootetal.,1996).Anothermajor
ambientionwhoseconcentrationlevelcaninfluencemetalpartitioningiscalcium.Atthehigh
concentrationsofcalciumcationsfoundinwastesystems,especiallyMSWIashandCKD,the
competitionforbindingsitescanbecomeveryimportantwithregardtotracemetalbinding.For
thosetracemetalswhosepartitioningissignificantlyinfluencedbytheconcentrationlevelofa
majorambientionsuchaschlorideorcalcium,itisexpectedthatthisfactalonewouldcontributeto
abroaderrangeofobservedpartitioncoefficientsinrealsystemsthanthatcalculatedinthis
modelingexercise.
Table9
Estimatedrangeinlogpartitioncoefficients(L/kg)inwasteforselectedmetalsdetermined
fromMINTEQA2modeling.
Metal
EstimatedlogKd(L/kg)
MSWAcetogenesis
MSWMethanogenesis
MSWI
AshMonofill
CKDMonofill
Be 0.8-3.9 3.3-4.4 (-0.4)-4.0 (-2.7)-2.4
Cd (-0.3)-0.0 0.6-1.7 (-1.0)-1.1 (-0.4)-1.2
Co 0.2-0.3 0.9-1.8 (-0.9)-0.4 (-2.0)-0.2
Cr(III) 1.1-3.5 3.8-4.8 (-0.2)-3.2 (-2.5)-2.3
Cu 1.1-1.9 2.0-2.5 0.0-2.9 (-2.0)-2.1
Ni 0.2-0.4 1.1-1.9 (-0.04)-1.1 (-1.5)-0.9
Pb 1.7-2.7 3.3-4.2 2.4-3.6 0.7-3.4
Zn 0.4-0.7 1.5-2.1 (-0.6)-1.3 (-2.7)-1.1
WecomparedthepartitioncoefficientsestimatedforwastesusingMINTEQA2withvalues
predictedbythepreviouslydiscussedregressionequation(logKd,waste=0.7logKd,soil+0.3;see
Section3.2.1). Thedegreeofagreementvariedamongmetals.(Wedefinedthemeasureof
agreementforametaltobewhetherthevaluepredictedbytheregressionequationusingthemean
soilKdvalueofTable3fallswithintherangeofMINTEQA2estimatesforthatmetal.Usingthis
ratherlaxrequirementforagreement,theMINTEQA2-modeledKdvaluesforBe,Cr(III),Cu,and
Pbagree,thoseofCdandNidonotagree,andthoseofCoandZnaremarginal.)In
agreementwiththeliterature-reportedKdvaluesfornaturalmedia,PbandCr(III)tendtohavehigh
KdestimatesfromtheMINTEQA2wastesimulations.Ingeneral,theMINTEQA2resultsforthe
acetogenicandmethanogeniclandfillscenariosagreedmorecloselywithvaluesestimatedbythe
regressionrelationshipbasedonsoilKdvaluesthanforthemorealkalineashandCKDlandfill
3-27
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
44/93
scenarios. ItisprobablethatthelowerKdvaluesinthelatterscenariosareduetothecombination
ofhighermajorambientionconcentrationsthatcompetewiththetracemetalsforsorbingsitesand
solubilizethemetalsbycomplexation,plustheassumedabsenceofparticulateorganiccarboninthe
modellandfillsystems.
3-28
8/10/2019 Partition Coefficients for Metals in Surface Water, Soil and Waste
45/93
4.0DISCUSSIONOFRESULTSANDSOURCESOFUNCERTAINTY
Partitioncoefficientsobtainedfromliteraturedataaresubjecttonumeroussourcesofuncertainty.
Manypreviousstudieshavedemonstratedthatinavarietyofsoilsandforavarietyofmetals,
partitioncoefficientsvarywithpHandwiththeconcentrationofsorbingphasesinthesoilmatrix
(e.g.,weightpercentorganicmattercontentandweightpercenthydrousferricoxidesand
correspondingoxidesofaluminumandmanganese)(Janssenetal.,1997;HassanandGarrison,
1996;BangashandHanif,1992;AndersonandChristensen,1988).Itiswellknownthatdissolved
ligandspresentinsoilporewater(e.g.,dissolvedorganicmatter,anthropogenicorganicacids)can
complexwithmetals,reducingtheirpropensityforsorptioninproportiontotheconcentrationofthe
ligands(Christensenetal.,1996). Inmulti-metalsystems,competitionamongmetalsforsorption
sitesandtheattendantreductioninthepartitioncoefficientincomparisonwithsingle-metalsystems
hasalsobeenreported(Jinetal.,1996).Withinthepopulationofsoils,thenaturalvariabilityin
soilcompositionandcompositionofassociatedsoilporewateraresuchastoresultinvariationinKdoverordersofmagnitude,evenforasinglemetal.Forthisreason,anycomprehensivecompilation
ofKdvaluesselectedfromtheliteratureshouldbeexpectedtopresentvaluesthatdefinea
distribution. Infact,foranyparticularmetal,Kddependsontheseandothercharacteristicsofthenaturalmediasystem(soil,sediment,surfacewater),andinanationwideriskassessmentitis
desirabletosamplethenationalpopulationofsuchnaturalmediasystemstoobtainafrequency
distributionofKd.
Unfortunately,thecollectionofnaturalmediasystemschosenforstudybyvariousresea
Top Related