Environmentalfateofchemicalsreleasedfrom consumerproducts ‐ Multimediamodellingstrategies AnnaPalmCousins DoctoralThesis DepartmentofAppliedEnvironmentalScience(ITM) StockholmUniversity 2013 1 Doctoralthesis,2013 AnnaPalmCousins DepartmentofAppliedEnvironmentalScience(ITM) StockholmUniversity S‐10691Stockholm Sweden ©AnnaPalmCousins,Stockholm2013 ISBN978-91-7447-690-3 PrintedinSwedenbyUniversitetsserviceUS‐AB,Stockholm2013 Distributor:DepartmentofAppliedEnvironmentalScience(ITM) Covergraphic:EmilyCousins 2 TillEmily,LinneaochOlivia 3 Abstract The objective of this thesis was to assess the environmental fate and transport of chemicalsemittedfromconsumerproductsthroughthedevelopmentandapplicationof modellingtools.Thefollowinghypothesesweretested:i)Multimediafatemodelscanbe applied in a multistage assessment process to emerging chemicals when limited knowledgeexiststoidentifythelikelyenvironmentalfateandtodirectfurtherresearch; ii) the indoor environment acts as a source of anthropogenic substances in consumer productstotheoutdoorenvironment;andiii)chemicalremovalpathwaysintheindoor environmentareimportantforthefateoforganicchemicalsindenselypopulatedareas. Thethesisshowsthatastructuredchemicalfateassessmentstrategycanandshouldbe appliedatearlystagesoftheevaluationofemergingchemicalstoassesstheirfateandto direct further research. Multimedia fate models play a key role in this strategy. The three‐solubility approach is a simple, rapid method that can be used to estimate physical‐chemicalpropertiesforuseinearlystageevaluation(PaperI).Emissionsinthe indoorenvironmentaffecttheurbanfateofhydrophobicorganicchemicalsbyproviding additionalremovalpathwaysandprolongingurbanchemicalresidencetimescompared tooutdooremissions(PaperIII).EmissionsofBDE209,DINPandDEHPtoStockholm indoorairwereestimatedtobe0.1,3.4and290mg/capitayear,respectively(PaperIV). The contribution of emissions indoors to outdoor air pollution varies between substances.ForBDE209,emissionsintheindoorenvironmentadded38%tothemass entering Stockholm city with inflowing air. For Sweden, the indoor environment was estimatedtoaccountfor80%ofBDE209emissionstooutdoorair(PapersIIandIV). For the phthalates, outdoor emissions and/or background inflow are the dominant sources to outdoor air pollution in Stockholm and the influence of the indoor environmentislimited(PaperIV). 4 Svensksammanfattning Målet med denna avhandling var att bedöma spridning och fördelning i miljön av kemikalier som släpps ut från konsumentprodukter genom utveckling och tillämpning av modelleringsverktyg. Följande hypoteser testades: i) Multimediamodeller kan tillämpasienstegvisbedömningavnyakemikalierdärbegränsadekunskaperfinns,för attidentifierahurämnenafördelasochtransporterasimiljön,samtgeanvisningartill ytterligare forskning, ii) inomhusmiljön fungerar som en källa till utemiljön för antropogena ämnen som används i konsumentprodukter, och iii) aktiviteter som avlägsnar kemkalier i inomhusmiljön har betydelse för hur icke‐flyktiga, svårlösliga ämnenrörsigidenurbanamiljön. Avhandlingen visar att en strukturerad strategi för kemikaliebedömning kan och bör tillämpas på ett tidigt stadium i utvärderingsprocessen av nya kemikalier för att ge kunskapomhurdessasannoliktspridsochfördelasimiljönochförattgeanvisningar förvidareforskning.Multimediamodellerharenviktigrollidennastrategi.Enenkeloch effektivmetod(”three‐solubilityapproach”)somkananvändasietttidigtskedeföratt uppskattafysikalisk‐kemiskaegenskaperförnyaämnen,illustrerasiPaperI.Utsläppi inomhusmiljön påverkar den urbana spridningen och transporten av svårlösliga, icke‐ flyktigaorganiskaämnengenomattaktiviteteriinomhusmiljönledertillbortförselav kemikalier och genom att uppehållstiden i den urbana miljön förlängs jämfört med utsläpp till utomhusluft (Paper III). Utsläppen av BDE 209, DINP och DEHP till Stockholms inomhusluft uppskattades till 0,1, 3,4 och 290 mg/capita år (Paper IV). Inomhusmiljöns betydelse för utomhusmiljön varierar mellan ämnen. För BDE 209, uppskattas inomhusmiljön tillföra 38 % till den mängd som kommer in till Stockholm med inströmmande luft. På Sverige‐nivå uppskattas inomhusmiljön stå för ca 80 % av utsläppen av BDE 209 till uteluft (Paper II och IV). För ftalater är utsläppskällor utomhusoch/ellerinflödetmedbakgrundsluftviktigareänbidragetfråninomhusmiljön förförekomsteniurbanluft(PaperIV). 5 Listofpapers Thisthesisisbasedonthefollowingpapers,referredtointhetextbytheirRoman numerals: I PalmA,CousinsI.T.,MackayD,TysklindM,MetcalfeC,AlaeeM.2002. Assessingtheenvironmentalfateofchemicalsofemergingconcern:Acase studyofthepolybrominateddiphenylethers.EnvironmentalPollution,117 (2),pp195‐213 II BjörklundJ.A.,ThuressonK.,CousinsAP.,SellströmU.,EmeniusG.,deWit, C.2012.Indoorairisasignificantsourceoftri‐decabrominateddiphenyl etherstooutdoorairviaventilationsystems.EnvironmentalScience& Technology,46(11),pp5876–5884 III CousinsA.P.2012.Theeffectoftheindoorenvironmentonthefateof organicchemicalsintheurbanlandscape.ScienceoftheTotalEnvironment, 438,pp233‐241 IV CousinsA.P.,HolmgrenT.,RembergerM.Emissionsoftwophthalateesters andBDE209toindoorairandtheirimpactonurbanairquality. (manuscript) PaperIwasreproducedwithpermissionfromElsevier,PaperIIwasreproducedwith permission from the American Chemical Society and Paper III was reproduced with permissionfromElsevier 6 Statement I,AnnaPalmCousins,madethefollowingcontributionstothepaperspresentedinthis thesis: PaperI I was responsible for data acquisition and performed the calculations of chemical properties and emissions. I did the model calculations and I took the lead role in authoringthepaper. PaperII Iwasresponsibleforcalculatingemissionstoairfromotherthanindoorsourcesusing substanceflowanalysismethodologyandIassistedinwritingthepaper. PaperIII Iplannedthestudy,developedthemodel,didallthecalculationsandwrotethepaper. PaperIV Iwasinvolvedinplanningtheprojectapartfromtheemissionexperiments.Iperformed themodelcalculations,thedataevaluationandtheuncertaintyanalysis,andItookthe leadroleinauthoringthepaper. 7 TableofContents List of abbreviations ................................................................................................................................ 9 Objectives .............................................................................................................................................. 10 1 2 3 Introduction ................................................................................................................................... 11 1.1 Human exposure to environmental pollutants – historical background .............................. 11 1.2 Urban chemical accumulation ............................................................................................... 11 1.3 Multimedia Fate Models as tools for assessing chemical fate .............................................. 12 1.4 Challenges in assessing the fate of chemicals released from consumer products ............... 13 Methods ........................................................................................................................................ 15 2.1 Study substances ................................................................................................................... 15 2.2 The six‐stage strategy ............................................................................................................ 15 2.3 Estimating physical‐chemical properties of emerging chemicals ......................................... 16 2.4 Estimating emissions of chemicals released from consumer products ................................ 17 2.5 Evaluative fate assessment ................................................................................................... 18 2.6 Regional and local – scale fate assessment ........................................................................... 18 Overall results and discussion ....................................................................................................... 20 3.1 Physical‐chemical properties ................................................................................................. 20 3.2 Emissions ............................................................................................................................... 24 3.3 Evaluative fate assessment ................................................................................................... 27 3.4 Regional and local‐scale fate assessment ............................................................................. 31 3.4.1 Influence of the indoor environment on chemical fate in urban centres ..................... 31 4 Conclusions .................................................................................................................................... 32 5 Future challenges .......................................................................................................................... 33 6 Acknowledgements ....................................................................................................................... 35 7 References ..................................................................................................................................... 37 8 Listofabbreviations 2,4,6‐TBP 2,4,6‐tribromophenol BDE47 2,3,4,6‐tetrabromodiphenylether BDE209 Decabromodiphenylether BFR Brominatedflameretardant CTD Characteristictraveldistance DEHP Diethylhexylphthtalate DINP Diisononylphthalate EPIWIN EstimationprogramsinterfacesuiteforWindows EQC Equilibriumcriterionmodel EURAR EUriskassessmentreport KAW Air‐waterpartitioncoefficient KOA Octanol‐airpartitioncoefficient KOW Octanol‐waterpartitioncoefficient MFM Multimediafatemodel PBDE Polybrominateddiphenylether pentaBDE pentabrominateddiphenylether,commercialmixture PSL Sub‐cooledliquidvapourpressure PSS Solidphasevapourpressure PVC Polyvinylchloride QSPR Quantitativestructurepropertyrelationship S A Solubilityinair SFA Substanceflowanalysis SMURF Stockholmmultimediaurbanfatemodel SO Solubilityinoctanol SVOC Semivolatileorganicchemical SW Solubilityinwater TaPL3 TransportandpersistencelevelIIImodel VOC Volatileorganicchemical 9 Objectives The overriding objectives of this thesis were to assess the environmental fate and transportofchemicalsemittedfromconsumerproductsthroughthedevelopmentand applicationofmodellingtools.Thiswasperformedbytestingthefollowinghypotheses: i) Multimedia fate models can be applied in a multistage assessment process to emergingchemicalswherelimitedknowledgeexists,toidentifythelikelyenvironmental fate and to direct further research; ii) the indoor environment acts as a source of anthropogenicsubstancesemployedinconsumerproductstotheoutdoorenvironment; andiii)chemicalremovalpathwaysintheindoorenvironmentareimportantforthefate oforganicchemicalsindenselypopulatedareas. ThespecificobjectivesofpapersI‐IVwere: PaperI Themainpurposeofthispaperwastodemonstrateasix‐stageassessmentprocessfor evaluation of chemicals of emerging concern. The approach was demonstrated for the polybrominated diphenyl ethers (PBDEs), a class of chemical substances extensively used in consumer products, for which the knowledge on properties, sources, fate and effectsatthetimeofwriting(2001)waslimited. PaperII The main objective of this paper was to investigate the importance of indoor air as a sourceofPBDEstourbanoutdoorair. PaperIII The main objective of this paper was to evaluate the influence of the indoor environmentonthefateoforganicchemicalsindenselypopulatedareasbydevelopinga modellingtoolwhichincorporatestheindoorenvironmentasaconstituentoftheurban environment.Theobjectivewasalsotoidentifykeytransportpathwaysandtostudythe conditionsunderwhichthesepathwaysareimportant. PaperIV ThemainobjectiveofthispaperwastoapplythemodeldevelopedinPaperIIItoassess whetherrealistic,measurementbasedemissionestimatestoindooraircanexplainthe occurrence of BDE 209 and two phthalate esters in the indoor environment and to assesshowsuchemissionscontributetourbanoutdoorairpollution. 10 1 Introduction 1.1 Humanexposuretoenvironmentalpollutants–historicalbackground Historically,organicchemicalswerereleasedfromindustrialprocesseswithoutfurther treatment of air exhausts or wastewater effluents (dioxins, PAHs, PCBs, biocides). The adverse environmental impact was sometimes immediate and obvious. This led to extensive regulatory measures, eventually resulting in the strict control programs of industrial chemical emissions that we see today. In addition, specific substances were also banned or severely regulated. For example, as a result of their persistence, bioaccumulation,andlong‐rangetransportpotential,chemicalssuchasPCBs,DDTand HCH are today listed for a global restriction/ban under the Stockholm Convention for PersistentOrganicPollutants(HagenandWalls,2005). Regulatory actions to reduce emissions may affect human exposure to industrial chemicals. Swedish biomonitoring studies using human milk show declining trends sincetheearly70’sofindustrialsemivolatileorganicchemicals(SVOCs)thathavebeen subject to strict regulations. Examples of such SVOCs are polychlorinated biphenyls (PCBs), dioxins and some pesticides. Meanwhile, levels of others, e.g. the polybrominateddiphenylethers(PBDEs),increaseduntilthelate90’s(Fängströmetal., 2008; Solomon and Weiss, 2002). Since 2000, the levels of lower brominated PBDEs appear to have declined whereas higher brominated congeners continued to rise (Fängström et al., 2008). This is possibly a result of the European ban of lower brominated BDEs. In 2005, the World Wildlife Fund published a biomonitoring study wherewomen’sbloodfromthreegenerationsof13familiesacrossEuropewasanalyzed for 107 different industrial chemicals (WWF, 2005). This study observed a changing chemical pattern from old to young, where the blood of the younger generation was contaminated with “modern” chemicals commonly applied in everyday consumer products.Thebloodoftheoldergenerationcontainedalargernumberofchemicals,but mainlyrestrictedorbannedsubstances,suchasPCBs,DDTsetc.Itwasstrikingthatthe children’sbloodwasalsocontaminatedwithPCBs,althoughtheywerebornyearsafter thebanwasintroduced.Since1999,theU.S.NationalHealthandNutritionExamination Survey(NHANES)haveregularlymonitoredenvironmentalpollutantsinhumantissues in the North American population. These surveys indicate widespread occurrence of industrial organic chemicals (e.g. PBDEs, bisphenol A and PFOA) across the North American population. Altogether, these biomonitoring studies show that despite our efforts to, sometimes effectively, minimize chemical pollution from industrial sources and regulate the use of problematic substances, human exposure to industrial organic chemicalsstilloccurs. 1.2 Urbanchemicalaccumulation Oneexplanationforthecontinuedexposuretoindustrialorganicchemicalsisthat–in line with technical development and material refinement – more synthetic functional chemicals have come into use. The increased economic well‐being in modern and developingsocietieshasledtoanincreasingdemandformodernelectronicequipment, 11 plastic polymers, clothing, and building materials. For example, the global demand for polymersforelectricalandelectronicapplicationsisexpectedtoincreaseatanannual growth rate of 6.9% from 15.6 Mtons in 2011 to over 23 Mtons in 2017 (www.plastemart.com).Thishasresultedinproductionandapplicationofthousandsof newchemicalswhichareincorporatedineverydayconsumerarticles.Ashumanstend toclusterincities,consumerarticlesandtherebychemicalsincorporatedinthemtend toalsoaccumulateintheseareas.Manyofthechemicalsoccurinthearticlesasresidues or additives, and may be released from these throughout the product lifetime. As a result,exposuretosuchchemicalsmaynotprimarilyoccurattheindustrialsitewhere theyareproduced,butratherwheretheyareused.Exposuremayalsooccurindirectly throughcontactwithoringestionofe.g.indoordustorbiologicalmatriceswherethese chemicals have ended up. The exposure to organic chemicals of anthropogenic origin maybeofparticularlyhighconcerninurbanareasbecauseofthehigherconcentration ofproductsandgoods. Ultimately,exposureofhumansandwildlifetoanthropogenicsubstanceswithpotential negative health effects should be avoided. Biomonitoring studies are helpful in measuringexposurethathasalreadytakenplace,butothertoolsareneededtoassess future exposure as well as exposure that cannot be readily measured. An integrated analysis of the implications for human exposure of chemical release pathways, properties and partitioning behaviour as well as environmental properties requires instruments that take all these aspects into consideration. This has led to the development of multimedia fate models (MFMs) (Mackay, 2001; Wania and Mackay, 1999),whichhavenowbeeninuseformorethan30years(Buseretal.,2012). 1.3 MultimediaFateModelsastoolsforassessingchemicalfate MFMsarecomputerbasedtoolsthatutiliseknowledgeofchemicalproperties,releases, transportpathways,andenvironmentalpropertiestocalculateconcentrationsintarget environmentalmedia.Thesecanbeusedinachemicalexposureand/orriskassessment. MFMshavebeenusedfordecadestoestimatechemicaltransportandfateondifferent geographical scales to assist in decision making and risk assessment, to identify vulnerableriskgroups,andtoprioritizechemicalsforregionalmonitoringorregulatory measures. MFMs strive to mimic the nature of a defined environmental system in a simplified manner but with enough complexity to produce realistic estimates of environmental concentrations. MFMs can also be used to predict chemical persistence and response time to changes in release and/or environmental conditions. CompartmentsusuallyincludedinMFMsareair,water,soil,andsediment,butalsosub‐ compartmentssuchasparticles,interstitialwater,orairmay alsobedefined(Mackay, 2001). Depending on the purpose of the model, some MFMs include multiple compartment layers and/or vegetation (e.g. Bennett et al. (1998); Cahill and Mackay (2003);McKoneandBennett(2003)).MFMshavealsobeendevelopedspecificallyfor urban environments, one of the major differences to the generic environmental MFMs beingtheincorporationofimpervioussurfaces(Diamondetal.,2001;Prevedourosetal., 2008). More recently, indoor chemical fate models have been developed to study the 12 indoorfateofpesticides(BennettandFurtaw,2004)andPBDEs(Zhangetal.,2009)and the resulting implications for human exposure, thus providing a first step towards indoorMFMsforapplicationtochemicalsusedinconsumerproducts.However,fewif anyattemptshavebeenmadetospecificallyaddressthelinkagebetweentheindoorand theurbanoutdoorenvironment,anditsimplicationsforchemicalfateandexposure. 1.4 Challengesinassessingthefateofchemicalsreleasedfromconsumer products Assessingthefateandexposureofchemicalsinconsumerproductsposesanumberof challenges to the environmental modeller. This is especially true at early stages in the assessmentprocess,whenlimitedknowledgeexistsregardingpropertiesandemissions, and for chemicals with no clear “point sources”. Three main challenges are outlined below: 1) Physical‐chemicalproperties.Emergingchemicalsincorporatedinnewconsumer products are often poorly characterised regarding their physical‐chemical properties. In some cases where the knowledge exists, it may be restricted to producingcompaniesthatdonotdiscloseittothegeneralpublic.Thiscanbea majorhindrancetoindependentexposureassessment. 2) Quantification of emissions. Emission sources of chemicals applied in consumer productsareoftenmultipleanddiffuseandthusdifficulttoquantify.Aspointed outbyWaniaandMackay(1999),eventhebestemissionestimatesofchemicals withlimitedandspecificapplications,suchaspesticides,arehighlyuncertainand can vary by orders of magnitude. For chemicals that have multiple and varied uses,thisisnaturallyevenmoredifficult.Ifthechemicalis“new”onthemarket, the actual emission sources may also be poorly identified. Furthermore, in today’s age of global trade, chemicals are usually imported, and they are often already incorporated in finished articles. The volumes of chemicals in products enteringacertainregionareoftenunknown,limitingthepossibilitytoquantify chemicalstocksandemissions. 3) Release and transport pathways. Many modern chemicals are applied in and potentially released from articles used in the indoor environment, leading to human exposure and/or further transport to the outdoor environment. Studies addressingindoor/outdoorconcentrationratioshaveindicatedapotentialofthe indoorenvironmenttoactasasourcetothelocaloutdoorenvironment(Buttet al., 2003; Jamshidi et al., 2007; Shoeib et al., 2004; Shoeib et al., 2005). Subsequent transport from the local to the regional environment has been illustratedinstudiesmeasuringurban‐rural‐gradientsofSVOCs(Buttetal.,2003; Gouin et al., 2005; Harner et al., 2004; Wong et al., 2009). Detailed knowledge about the chemical’s uses is therefore needed to properly determine the dominantreleaseandtransportpathways. The state‐of the‐art in local and regional scale chemical risk assessment is to assign emissions to air, water or soil (ECB, 2002; ECB, 2003; ECB, 2008a; ECB, 2008b). 13 Emissionsindoorshavepreviouslybeenusedtoestimatehumanexposure,butthishas been largely limited to volatile organic chemicals (VOCs) (Destaillats et al., 2008; Mendell, 2007; Yu and Crump, 1998) and only in rare cases SVOCs (Destaillats et al., 2008). The indoor environment has not yet been studied as a primary recipient and potentialmediatoroftransportofSVOCstotheoutdoorenvironment.Instead,emissions ofSVOCsduringtheservicelifeofarticleshavetraditionallybeenregardedasemissions to“air”(i.e.outdoorair).Thismaybemisleading,sincetheindoorenvironmentcontains multiple surfaces with the potential to store chemicals for longer or shorter periods, which may alter their fate and transport and thus the potential for exposure in the indoorandoutdoorenvironments.Theindoorandoutdoorenvironmentsarenaturally linked to one another. Chemicals may be transported from the indoor to the urban outdoor environment via ventilation systems, but also through sewage systems or otherwise be released via the waste handling system. This is then followed by further transporttotheregionalenvironment(Figure1). Figure 1. Schematic illustration of the coupling between indoor, urban, and regional environments. 14 2 Methods 2.1 Studysubstances This thesis is largely process‐oriented, i.e. the models and methods presented are generalandnotchemicalspecific,butcanconceivablybeappliedtomostorganic,non‐ ionic substances. The chemicals selected as study substances are the polybrominated diphenylethers(PBDEs)(PapersI‐IV)andtwophthalateesters,diethylhexylphthalate (DEHP) and diisononylphthalate (DINP) (Papers III‐IV). PBDEs and phthalate esters areexamplesoforganic,non‐polarchemicalscommonlyappliedinconsumerproducts suchasplastics,buildingmaterials,textilesaswellaselectricalandelectronicproducts. Because of the wide and diffuse use, their emission sources are multiple and diffuse. Their use in consumer products makes them relevantfor urban‐scale fate assessment. At the time of writing Paper I, the knowledge about properties, emissions and environmentaloccurrenceofPBDEswasfairlylimitedandthereforetheyweresuitable toillustrateaninitial,evaluativefateassessment,whichprovidesasoundbaseforfuture assessments. Paper III includes two additional substances; formaldehyde and 2,4,6‐ tribromophenol, which were selected to illustrate the effect of indoor fate on outdoor environmentalpollutionforchemicalswithawiderrangeofproperties.Table1shows thechemicalstructuresofthesubstancesaddressedinthisthesis. Table 1.Chemical structures of study substances treated in this thesis. BDE209 BDE47* DEHP DINP 2,4,6‐TBP Form‐ aldehyde *otherPBDEcongenerswerealsostudied 2.2 Thesix‐stagestrategy Common difficulties facing environmental assessors of emerging chemicals are the general absence of measured physical‐chemical properties as well as quantitative informationaboutemissions.Dataonconcentrationsofthechemicalintheenvironment that are needed for evaluation of model results are often missing. Still, it is useful to 15 perform early assessments with limited information, as they can identify key weaknesses in the knowledge base and give important directions to further studies to strengthenit. Paper I illustrates a six‐stage strategy todo this, originally proposed by Mackay and co‐workers (Mackay et al., 1996a; Mackay et al., 1996b; Mackay et al., 1996c;MacLeodandMackay,1999),byapplyingittothePBDEs,which,atthetimeof publishing were fairly “new” in an environmental context. The six assessment stages include: 1) 2) 3) 4) Chemicalclassificationanddeterminationofproperties Acquisitionofdischargeoremissiondataandenvironmentalconcentrations Evaluativeassessmentofthelikelybehaviourofthechemicalintheenvironment Regionalor“far‐field”evaluationoffate,toestimateregionalfateand concentrations,withvalidationwherepossible 5) Localornear‐fieldevaluationoffate,toestablishbehaviourandconcentrations inhighlyexposedlocalities,againwithvalidationwherepossible;and 6) Comparisonoftheseestimatedandobservedconcentrationswitheffectorno‐ effectlevels. Theworkpresentedinthisthesisfocussedonthefirstfivestages,whichareaddressed tovariousextentsthroughoutthethesis. 2.3 Estimatingphysical‐chemicalpropertiesofemergingchemicals Physical‐chemical properties of emerging pollutants are seldom reported in the literature, and lack of pure compounds may hinder property determination. For chemicals belonging to a homologue series, such as the PBDEs and phthalate esters, property data reported for some of the substances within the series can be used as a basis to estimate properties for other substances within the same group. This is achieved through the application of a quantitative structure‐property relationship (QSPR)method.Theso‐called“threesolubilityapproach”providesasystematicscheme where QSPRs are used to relate physical‐chemical properties to environmental partitioning and to generate internally consistent estimates of key partitioning parameters. It was originally developed and applied to the chlorobenzenes (Cole and Mackay,2000)andlatertothephthalateesters(CousinsandMackay,2000).InPaperI, itwasappliedtothePBDEs. In brief, the three solubilities (mol/m3): SA in air, SW in water and SO in octanol are calculatedasfollows: SA=PSL/RT; SW=WS/MF; SO=KOWSW (1) (2) (3) HereRisthegasconstant(8.314Pam3/Kmol),Tisthetemperature(K),WSisthesolid solubility in water (g/m3), M is molar mass (g/mol) and KOW is the octanol‐water partitioncoefficient.Finequation2isthefugacityratio,whichisestimatedfrom 16 LogF=‐6.79(TM‐T)/(2.303T) (4) whereTMisthemeltingpointandTisthetemperatureofthesystem(Mackay,2001).PSL in equation 1 is the sub‐cooled liquid vapor pressure (Pa), which is derived from the solidvaporpressurePSS(Pa)andthefugacityratioFthrough PSL=PSs/F (5) By calculating solubilities based on all available reported data and correlating them againstamoleculardescriptor,inthiscasetheLeBasmolarvolume(Reidetal.,1987), best‐estimates of the three solubilities can be derived for the entire chemical group. Fromthese,thekeypartitioncoefficientsKOW,KAWandKOAwerederived. 2.4 Estimatingemissionsofchemicalsreleasedfromconsumerproducts Emission estimates have been a major question throughout this thesis. Four different methodswereapplied: a) Population‐basedextrapolationofanationalsubstance‐flowanalysis(SFA)study combined with industrial and technical information for homologue‐specific releaseestimates(PBDEs,PaperI). b) Region‐specific SFA calculations (PBDEs, Paper II). This usually considers emissionsfromproductsaswellaspotentialindustrialsources. c) Inverse multimedia fate modelling, i.e. tuning emissions to fit the model’s predictions of concentrations in environmental media to environmental monitoringdata(PBDEs+phthalateesters,PaperIV) d) Experimentalmeasurementsofsource‐specificreleaserates(PBDEs+phthalate estersPaperIIandIV).Thismethodcanbeusedonitsownorasacomponentin methodb. In Paper I, emissions of PBDEs were estimated based on a Danish substance flow analysisofbrominatedflameretardantsasagroup(Lassenetal.,1999).Noemissionsof technicalproductsorspecificcongenerswerepresentedintheDanishstudy.Technical andindustryinformationonmarketsharesofdifferentBFRproductsweresoughtand the general congener composition of these products was used to estimate per capita emissions to air, water and soil. In Paper II, a region‐specific SFA was employed to calculate emissions of PBDEs to outdoor air. This was combined with new, empirical measurementsofreleasesfromtheindoorenvironment.Theliteraturewassearchedfor additionalpotentialemissionsourcesandmeasuredemissionratesandtheoccurrence of such sources in Sweden was investigated. One chemical for which emissions from products(i.e.PVC)hasbeeninvestigatedinanumberofstudiesisDEHP(Afsharietal., 2004;Clausenetal.,2004;Clausenetal.,2011;Clausenetal.,2010;Clausenetal.,2007). InPaperIV,similarmeasurementswereconductedforDINP. 17 2.5 Evaluativefateassessment Whenachemicalisfirstdiscoveredinenvironmentalsamples,itisbeneficialtoassess its general transport and fate. By conducting such an evaluative fate assessment, a “benchmark”environmentalprofilecanbegeneratedandcomparedtothefateofother chemicals in the same evaluative environment. In Paper I, the EQuilibrium Criterion (EQC) model (Mackay et al., 1996a) was applied to assess the likely fate of PBDEs, correspondingtoStage3underthesix‐stagestrategypresentedabove.TheEQCmodel is an evaluative fate model with generic environmental properties. The long‐range transport potential of the PBDEs was assessed using the TaPL3 model (Beyer et al., 2000), which is based on the EQC model, but without advective losses. It is used to calculatethecharacteristictraveldistance(CTD),mainlyforcomparativepurposes. 2.6 Regionalandlocal–scalefateassessment Theaimofstage4inthemulti‐stagestrategyistoreconcileestimatedemissionswith observedmonitoringdataontheregionalscale.Thisistoascertainthatallemissionsare fully accounted for and to determine if the key fate processes are in accordance with modelpredictions.InPaperI,preliminaryemissionestimateswereusedasinputtothe regionalassessmenttoolSimpleBox2.0(Brandesetal.,1996).SimpleBoxwasselected since it is a recognized regulatory tool and the preferred modelling tool for risk assessment within the European Union. The results were compared to available monitoringdata. Urban areas are believed to be of special interest for chemicals in consumer products such as PBDEs and phthalate esters, for reasons outlined in section 1.2. Urban areas were also recommended as study areas for local‐scale fate assessment (stage 5 in the multi‐stage strategy) of PBDEs in Paper I. The Stockholm Multimedia URban Fate (SMURF) model (Figure 2) is a local‐scale MFM parameterized to the municipality of Stockholm,developedinPaperIII.Itwasappliedtofourorganicchemicalstoillustrate the impact of emissions indoors on the overall urban fate of chemicals with different properties. 18 Figure 2. Illustration of the compartments in the SMURF model and the processes considered (Paper III). An important part of the local‐scale assessment is to evaluate how well emissions fed into the model generate concentrations that can be reconciled with measured concentrations. Site‐specific emissions and monitoring data are thus crucial for conducting a thorough local‐scale assessment. In Paper II, sampling and analysis of PBDEs in air and dust in Stockholm indoor environments were performed, which generated a substantial amount of data used for comparison with model predicted concentrationsofBDE209inPaperIV. 19 3 Overallresultsanddiscussion 3.1 Physical‐chemicalproperties One of the innovative elements of Paper I was the application of the three‐solubility approach to estimate the physical‐chemical properties of the PBDEs. Using the information available in the literature, correlations between the solubilitiesinair (SA), water(SW),andoctanol(SO)(mol/m3)ontheonehandandtheLeBasmolarvolumes(V, cm3/mol)ontheotherwereobtained: log(SA)=3.5737–0.035V,r2=0.84,SE=0.64 (6) log(SW)=7.9418–0.044V,r2=0.95,SE=0.45 (7) log(SO)=5.4075–0.012V,r2=0.36,SE=0.67 (8) Fromequations(6)to(8)theequationsforthepartitioncoefficientswerederived: logKOW=log(SO)–log(SW)=‐2.5343+0.032V (9) logKAW=log(SA)–log(SW)=‐4.3681+0.009V (10) logKOA=log(SO)–log(SA)=1.8338+0.023V (11) where KOW, KAW and KOA are the octanol‐water, air‐water and octanol‐air partition coefficients,respectively. These preliminary property correlations in Paper I have stood up well to the test of time, as the later discussion will show. Some of the uncertainties were due to the necessity of using property estimates derived from commercially available software tools, because of the scarcity of experimental data. Estimated properties sometimes differed considerably from experimental values. This was particularly the case for melting points used to calculate fugacity ratios. In addition, the few experimental data available were often in conflict. Uncertainties associated with solubilities in air and octanolpropagatedthroughtotheestimatesofpartitioningcoefficients. In the 10 years that followed after the publication of Paper I, more data and more refined methods have become available. A comparison between the estimated properties derived from the equations in Paper I and three more recent studies is showninFigure3anddiscussedbelow. More measured data became available already at the time of or shortly after the publicationofPaperI(e.g.Braekeveltetal.(2003);TittlemierandTomy(2001)).These were used to update the initial estimates, whereby the majority of the software generateddatawereexcluded(CousinsandPalm,2003).Theupdatedcalculationsofthe sub‐cooledliquidvapourpressurewerefairlysimilartotheoriginalestimatesinPaper I, (see Figure 3a), in particular for the lower brominated congeners. The sub‐cooled liquidsolubilityinwater(Figure3b)showedamarkedlyreduceddecreaseperbromine 20 added relative to the original estimates (0.49 compared to 1.03 log units in Paper I), althoughthedecreaseswerecomparableuptoabrominesubstitutionlevelofabout4. TheKOWwaspredictedtoincreaseby0.46logunitsperaddedbromine,thusshowinga flatter slope than in Paper I (0.75 log units per bromine added). The observed discrepancies between the two assessments, in particular for solubility in water, also resultedinareverseintheslopeoftheKAW;fromanestimatedincreasewithincreasing substitution(Figure3ainPaperI)toadecreaseof0.35logunitsperbromineaddedin Cousins and Palm (2003). The updated properties thus imply a decreasing preference forpartitioningtoairrelativetowaterwithincreasingsubstitution,whichhasalsobeen observed for other homologue series such as the chlorobenzenes (Cole and Mackay, 2000)andthePAHs(deMaagdetal.,1998).Thestandarderrorsfortheregressionlines were improved by about 0.1 log units for the solubilities in air andoctanol when only experimental data were used, but they were similar to the original regression for the solubilityinwater. Wania and Dugani (2003) estimated the properties of the PBDEs using a similar approach as in Paper I, with molecular weight as the descriptor. In addition, they accounted for the apparent uncertainty in the reported values by considering the standarddeviationofcalculatedaveragesaswellasmeasurementuncertainty.Theyalso added an adjustment procedure (Beyer et al., 2002) to generate internally consistent properties for use in the derivation of QSPR equations. The resulting properties were verysimilartotheupdatedvaluesderivedwiththethreesolubilityapproach(Cousins and Palm, 2003). Consequently, the discrepancies between the Wania and Dugani estimates and the original estimates (Paper I) are similar to those described in the previousparagraph. Braekevelt et al. (2003) derived a QSPR for estimating log KOW for PBDEs, based on directmeasurementsofthisproperty(Figure3c).Theseestimatesshowedanincreasing discrepancy from the estimates in Paper I with increasing level of substitution. The maximaldifferenceof0.8logunitswasobservedforthedecabrominatedcongener.For thedi‐tetrasubstitutedcongeners,thedifferenceswerelessthan0.4logunits. An even more sophisticated approach was taken by Papa et al. (2009). Theyused 602 different molecular descriptors to derive optimized QSPR models for Henry’s Law constant,meltingpoint,sub‐cooledliquidvapourpressure,watersolubility,logKOAand log KOW of PBDEs. The trend of increasing vapour pressure with molar volume was similartothatinPaperI,andinmostcasesthecalculatedrangeofvapourpressuresper homologuecoveredthesinglevalueinPaperI(Figure3a).Papaetal.(2009)foundan increaseinlogKOWwithincreasingsubstitution,theslopebeingsimilartothatinPaper I, with a maximum discrepancy of 1.3 log units for BDE 209. For the water solubility, however,theaveragedecreaseperbromineaddedwasonly0.22logunits,comparedto 1.03 log units as estimated in Paper I. In addition, Papa et al. showed a considerable variability in properties within a given homologue (e.g. up to nearly four orders of magnitudedifferenceinthewatersolubilityfordifferentpentabrominatedisomers).As 21 pointed out by the authors, this suggests that single descriptors such as molecular volume or molar mass may not always be sufficient, since such relationships are insensitivetoresponsesrelatedtootherstructuralproperties. Altogether, these comparisons suggest that simple QSPR‐methods based on single molecular descriptors should be calculated from experimentally derived properties rather than in silico estimated properties, since substantial discrepancies sometimes exist in the latter. Using experimental values, the three‐solubility approach and the method employed by Wania & Dugani give comparable results. The QSPR models developedbyPapaetal.(2009)showedmuchhighercorrelationcoefficients(r2=0.92‐ 0.99) compared to the three‐solubility approach, but the general pattern of change in keypropertieswithsubstitutionlevelwassimilartothatfortheothermethods.Papaet al. (2009) put substantial effort into internal and external validation, as well as definition of applicability domain, and showed that properties can vary by several ordersofmagnitudebetweendifferentisomers.Asshowninsection3.3,however,these refined values have limited impact on evaluative fate predictions. Hydrophobic and involatile organic chemicals such as the majority of the PBDE congeners are mainly associatedwithparticlesintheenvironment.Therefore,evenfairlylargeadjustmentsin physical‐chemicalpropertieswillnotaltertheirpredictedpartitioningbehaviouraslong as these properties still result in them being primarily associated with particles. The main advantage with the three‐solubility approach is that it is a simple method that workswellifbasedonmeasuredpropertiesofchemicalsundergoingsimilarmolecular interactions,e.g.differenthomologuesofahomologueseries.Itcanthenbesuccessfully usedtoevaluatetheavailabledataforconsistencyandforgettingreasonableestimates of properties across the full set of chemicals. However, it also has its limitations, as shownbytheisomerspecificestimatesofwatersolubility(Papaetal.,2009),whichis importanttokeepinmind. 22 Figure 3.Comparison of estimated physical‐chemical properties (Log KOW, Log SW,L and Log PL, the subcooled liquid vapour pressure) of PBDEs in Paper I, to the later updated values (Cousins and Palm, 2003; Palm et al., 2004) applied in Paper IV and estimated values by Wania and Dugani (2003), Braekevelt et al. (2003) and Papa et al. (2009). 23 3.2 Emissions The emissions estimated in Papers I, II and IV were converted to units of mg/capita yearandsummarizedinFigure4,togetherwithreportedemissionsfromotherstudies. Onlyemissionstoindoorandoutdoorairarepresented.Twoofthestudiesfoundinthe literature presented emission figures that were based on total estimated consumption and the vapour pressure or the log KOA of the substance. These are denoted “property based”inFigure4.Theresultsarediscussedbelowinrelationtothedifferentestimation methodsused. Figure 4. Estimated annual per capita emissions to outdoor air (“Out”) and indoor air (“In”) of pentaBDE, BDE 209, DINP and DEHP using different emission estimation methods (these are outlined in the text below). Method a – combining SFA results from another country with industry information. The annual atmospheric emissions estimated using method (a) (Paper I) were 0.8‐6.3 and 5.8–46.1 mg/capita for pentaBDE and BDE 209, respectively. The advantage with this method is that it allows for rapid assessment of approximate levels of emissions. It is crucial that the country of the original SFA has a similar chemical use pattern as the country of interest. As exemplified in Paper I, industry information on market shares andthechemicalcompositionoftechnicalproductscanimprovesuchassessmentswhen theoriginalstudydoesnotprovidesubstancespecificemissionestimates.Inthiscase, emissionswerereportedfor“totalBFRs”andindustryinformationwasusedtocalculate emissions on a technical product basis and down to the congener level for BDE 209. 24 These first emissionestimates liesomewhere in the middle or in the lower end of the estimatespresentedinlaterstudies(whitebarsinFigure4). Methodb–region‐specificSFAstudies.InPaperII,aregion‐specificSFAwasconducted to quantify atmospheric releases of PBDEs in Sweden. The estimates for annual emissions to air were 0.35‐11 and 0.1‐3.8 mg/capita for pentaBDE and BDE 209, respectively. For pentaBDE this range was of a similar order of magnitude as the estimatefromPaperI,butforBDE209itwasafactorof10‐60lower.The10yearsthat passed between the two studies should not have affected the BDE 209 emissions. The emissionsofBDE209arelikelytohavepeakedaround2004,andshouldbeaboutthe same order of magnitude in 2012 as in 2000 (Earnshaw et al., 2013). PentaBDE emissions,however,areexpectedtohavedeclinedsincethemid‐90s(Prevedourosetal., 2004),whichisnotreflectedinthetwoestimatesofPapersIandII. Turningtotheliterature,foreachchemicalstudiedherethereareordersofmagnitude differencesbetweentheemissionestimatesindifferentstudiesthatappliedmethod(b) (Figure 4). There are several possible explanations for this. First, the European risk assessments (labelled EU RAR in Figure 4) were conducted so that the result is “conservative”, i.e. that emissions are rather over‐ than underestimated. This may explain why the EU RAR figures are among the highest. A second explanation is that emissions can vary between different geographical regions. Therefore, normalising to populationmaynotalwaysbeappropriate.Evenintheabsenceofproductionfacilities, variationsinactivitiessuchasrecycling,wastetreatment,combustionandaccumulation of consumer products may also give rise to different emission patterns. Emissions generatedforSweden(PaperII)maythereforenotbedirectlycomparabletoemissions generatedfortheEU(Earnshawetal.(2013);Anderssonetal.(2012)),Japan(Sakaiet al. (2006)) or Stockholm (Jonsson et al. (2008)). A third explanation is that emissions maydifferbetweenyears.Earnshawetal.(2013)addressedthisbyapplyingadynamic SFAmodeltoenableassessmentofchangingemissionswithtime.Itwaspointedoutby the authors that experimentally derived emission factors for release from consumer products and waste were still lacking, rendering the estimates uncertain. Fourth and foremost, SFA studies are subjective by nature. They include multiple decisions and expertjudgementsregardingthevalidityoftheunderlyingdataused.Apotentialsource maybesuspectedtoberelevant,butduetolackofinformationregardingemissionrates and activity, that source may be excluded or extrapolated from data sources of high uncertainty. Hence different scientists provided with the same information for an SFA canobtaindifferentresults. Method c – Inverse modelling. This method relates to the regional and local‐scale assessmentinthesix‐stagestrategy.Theideaistouseanappropriatemultimediafate model parameterized to the region of interest, and back‐calculate the emissions by fittingmodelpredictedconcentrationstoobservedconcentrationsinrelevantmatrices. In Paper IV, we used the SMURF model developed in Paper III to back‐calculate emissionstoindoorairinthecityofStockholmfrommeasuredconcentrationsinindoor 25 airanddust.Onapercapitabasis,annualemissionsof0.1mgforBDE209,290mgfor DEHP and 3.4 mg for DINP were proven to explain the observed concentrations in indoor air and dust. Most other studies for these chemicals concern emissions to outdoor air and often include emissions from products or indirect releases from the indoorenvironment.Thismakesitdifficulttocomparetheindooremissionsestimated in Paper IV to outdoor emissions calculated using other methods without detailed informationaboutthesourcesincludedintheseotherstudies. Amoregeneralpictureoftheurbanemissionsignalofchemicalsinconsumerproducts can be obtained by fitting an urban model to outdoor air concentrations, as done in Moeckel et al. (2010). They used a modelling approach to back‐calculate atmospheric PBDE emissions for the city of Zürich, Switzerland, based on the diel pattern of PBDE concentrationsinambientair.Intheirmodeltheyassumedavariable“PBDEpoolarea” as the pure volatilization source in the city and varied the area until reconciliation of modelpredictedPBDEconcentrationsinambientairwithobservedconcentrationswas obtained.TheresultingpercapitaemissionsforthesumoffourPBDEcongenersinthe pentamixturearewithinaboutafactorof10oftheotherestimatesandonlyafactorof 6 different than the original estimate in Paper I. Furthermore, the Moeckel et al. estimatesagreewiththeestimatesofemissionstoindoorairbyAnderssonetal.(2012) fortheEU(Figure4).IfoneassumesthatemissionsofPBDEstoindoorairaresimilarto theoverallemissionstourbanair(whichissupportedbyPaperII,wherereleasesfrom theindoorenvironmentwereestimatedtocontribute80%oftotalemissionstooutdoor air of pentaBDE), then these two studies are very consistent with each other. Both Paper IV and Moeckel et al. illustrate the necessity of having a relevant, site‐specific modellingtoolandhigh‐qualitymonitoringdatatoobtainagoodquantitativeestimate ofemissionsusingmethod(c). Methodd–Source‐specificemissionmeasurements.InPapersIIandIV,source–specific emissionratesofDINPandBDE209weremeasuredandusedtoderiveemissionrates. The results from Paper II were used as a component in the regional SFA and are discussed above. In Paper IV, previously measured emission rates of DEHP (see references in Paper IV) were re‐calculated for Swedish conditions and used to derive emissionratesfromPVC.CombinedwithindustryinformationontotalPVCsurfacearea soldperyear,annualemissionswerecalculatedtobe3and58mg/capitaforDINPand DEHP, respectively. The emissions obtained for DINP were in good agreement with emissionscalculatedusingmethodcaspresentedabove,buttheestimatedemissionsof DEHPdifferedbyafactorof5comparedtothoseobtainedusingmethod(c).Pettersson etal.(2012)estimatedBDE209indooremissionstobe0.004mg/capitayearbasedon measured release rates from personal computers and statistical information on PCs in use.Thesewereafactorof30lowerthanthefiguresobtainedwithmethod(c)(Paper IV), suggesting that number of PCs were underestimated by Pettersson et al., or that emissionrateswerenotrepresentative.Itcouldalsobethatadditionalsourcesexistin the indoor environment, for which there is some evidence (de Wit et al., 2012). One limitation of method (d) is the difficulty in obtaining representative conditions in the 26 experimental set‐up. For example, volatilization of chemicals is dependent on factors suchastemperatureandwindspeedorventilationrateanditmaythusbedifficultto generateemissionfactorsthataregenerallyapplicable. Basedonthediscrepancyintheemissionestimatesobtainedwiththedifferentmethods, it is difficult to recommend one method as better than another. Each method has its strengths and weaknesses as discussed above. They can rather be seen as being complementarytoeachother.Noneofthemethodsabovecanbyitselfprovidereliable emissions estimates across large spatial and temporal scales. To gain confidence in an emissionestimate,itisimportanttocross‐checkitwithalternativemethods,ratherthan torepeatthesamemethod.ItisencouragingthatthefirstemissionestimatesofPBDEs (PaperI)areinthemiddleoftherangeoflaterestimates.Thisillustratestheusefulness oftheearlyassessmentmethod(a)toprovidefirstestimatesthatcanserveasabasisfor laterandmorerefinedestimates.Theregion‐specificSFAmethod(b)islimitedbythe fact that it usually relies on existing data (sometimes complemented with new measurements,asinPaperII).Thecompletenessoftheemissioninventorydependson theavailabilityofsuchdata.Therefore,method(d)isanessentialelementofmethod(b). The advantage with the SFA methodology is that it can generate an understanding for thelikelihoodthatacertainemissionsourceisimportantandalsopointoutwheremore data are needed. Estimating emissions from consumer products is particularly tricky sinceitrequirescarefulmappingofalltheproductswherethechemicalisapplied,and ideally a large database of measured releases from these products (d), including informationontheenvironmentalconditionsunderwhichtheseemissionsoccur.Such databasesarerarelyavailable.Inthesecases,method(c)maybethemostappropriate toachieveanunderstandingoftheemissionstrength.Method(c)canbeconductedon the urban scale as in Moeckel et al. (2010) if a general picture of the regional to local scale emission strength is desired, or closer to the suspected sources as done for the indoorenvironmentinPaperIV. 3.3 Evaluativefateassessment AnevaluativefateassessmentwasconductedforthePBDEsinPaperIusingthegeneric environmental fate model EQC. Long‐range transport potential was assessed using the TaPL3model.Theassessmentindicatedapreferencetopartitiontoorganiccarbonrich matrices for all of the modelled congeners, and high bioaccumulation potential. These initial conclusions have been verified in many studies since then, and PBDEs are commonlyfoundinhighconcentrationsinaquaticbiotaandsediment,andonlyatlow concentrationsinwater(deWitetal.,2010). Paper I also suggested that long‐range transport of the highly brominated congeners would be limited, a conclusion that was also drawn by Wania and Dugani (2003). However,thereisnowstrongevidenceshowingthateventhedecabrominatedcongener istransportedtoremoteregionsandthatatmosphericlevelsintheArcticareincreasing (deWitetal.,2010;Hungetal.,2010;Suetal.,2007).Thesecontradictoryobservations have led to new insights into how hydrophobic chemicals are transported in the 27 atmosphere.ThehighlybrominatedBDEsaretransportedprimarilyinassociationwith particles(e.g.Gouinetal.(2006)).Chemicalfatemodelsthatassumethatparticlesare continuallybeingremovedfromtheatmosphereviawetdeposition(astheTaPL3)may underestimatethelongrangetransportpotentialofchemicalsintheparticulatephase (Breivik et al. (2006)). In reality, wet deposition is intermittent, not continuous, and during periods with no deposition these chemicals can be transported over long distances. Asshowninsection3.1,thephysical‐chemicalpropertiesofPBDEsderivedinPaperI were later refined with the help of updated and/or more sophisticated QSPR models. Thegreatestdiscrepanciesbetweentheearlyestimatesandthelaterandmorerefined estimateswereobservedforBDE209,butdifferenceswerealsoobservedforthelower brominated congeners. It is interesting to evaluate if these differences would have influenced the general conclusions about the environmental fate of PBDEs made in Paper I, had they been known at the time of that first assessment. Therefore, BDE 47 andBDE209wererunthroughtheEQCmodel(i.e.asdoneinPaperI)usingtherefined properties (Table 2). For simplicity, uniform values of the fugacity ratio F (F=0.28 for BDE47and0.0019forBDE209)wereusedtoconvertliquidphasepropertiestosolid phaseproperties(equations2and5insection2.3).Theassumeddegradationhalf‐lives were the same as in Paper I. Emissions were assigned to air, water and soil simultaneouslywiththeillustrativeemissionrateof1000kg/h. TheEQCmodeloutputsaresimilarforthefourdifferentpropertycombinations(Table 3).Infact,forBDE209,thepredictedpercentagepartitioningisidenticalforallsetsof properties.Thereasonforthisisthattheextremelyhydrophobicandinvolatilenatureof BDE 209 means that the chemical will be more or less exclusively associated with the solidphaseinthedifferentcompartments,andthiswillnotchangeasaresultofaltered physical‐chemical properties within the range given in Table 2. The evaluative fate is instead entirely controlled by the assumed degradation rates. For BDE 47, slight differences are observed in the percentage partitioning, but the general pattern is similar for all property combinations; limited partitioning to air and water, approximately equal distribution between soil and sediment, and an environmental residencetimeofabout7‐8months. This exercise shows that the three‐solubility approach, even at a very early stage, producedpropertyestimatesthatwerefullyadequatetoassesstheevaluativefateofthe PBDEs (Paper I). Improvement in QSPR‐predictions regarding basic physical‐chemical properties does not appear to have a significant impact on the environmental fate assessment for very hydrophobic and involatile chemicals such as BDE 209. Instead, focus should be put on understanding degradation rates. For the lower halogenated BDEs, the original property estimates were similar to the refined properties, and thus the conclusion regarding the environmental fate of these chemicals did not change. Assessingtheimpactonmodeloutputofdifferentmodelinputparameterssuchasthe physical‐chemicalpropertiescanandshouldalsobeaddressedineachmodellingstudy 28 byconductingasensitivityand/oruncertaintyanalysis,aswasdoneinPapersIIIand IV. In conclusion, Paper I suggests that evaluative fate modelling can and should be performedatearlystagesinthechemicalassessmentprocess,evenifonlyfewdataare availableandemissionsarepoorlyquantified.Itillustrateshowearlystageassessment can identify the likely fate of a chemical or a group of chemicals and identify key data gaps. In Paper I, the PBDEs were confirmed to be of environmental concern, and especiallythetetra‐andpentabrominatedcompoundswerehighlightedasproblematic. Later on, the European risk assessment came to the same conclusion, leading to a European ban of pentaBDE and octaBDE in 2004 (EU, 2003) and of decaBDE in electronical products in 2008 (EU, 2008). PBDEs were also included in the Stockholm ConventiononPersistentOrganicPollutantsin2009(UNEP,2009). 29 Table 2. Refined properties of BDE 47 and BDE 209 used in evaluative fate modelling with the EQC model. MW WSOL (g/m3)* PS (Pa)* LogKOW TM (C) Half‐lives (h) Air Water Soil Sediment BDE 47 BDE 209 Cousins & Wania & Papa et al. Cousins & Wania & Dugani, Papa et al. Palm, 2003 Dugani, 2003 2009 Palm, 2003 2003 2009 485.8 485.8 485.8 959.2 959.2 959.2 5.6×10‐2 9.7×10‐5 6.23 80.5 256 3600 3600 14400 6.9×10‐2 6.8×10‐5 6.29 80.5 6.6×10‐4 8.5×10‐5 6.27 87.85 4.0×10‐7 6.0×10‐12 9.0 300 5.4×10‐7 9.6×10‐12 8.7 300 5.9×10‐4 1.2×10‐11 9.8 261.8** 256 3600 3600 14400 256 3600 3600 14400 7620 3600 3600 14400 7620 3600 3600 14400 7620 3600 3600 14400 *Derivedfromthesub‐cooledpropertiesthroughequations2and5usingafugacityratioof0.28(BDE47)and0.0019(BDE209). **TheestimatedmeltingpointwasreportedtobeoutsideoftheQSPRmodelapplicabilitydomain Table 3. Percentage distribution and residence times of BDE 47 and BDE 209 from Level III calculations in the EQC model region as predicted in Paper I, and as a result of applying refined physical‐chemical properties from Cousins and Palm (2003), Wania and Dugani (2003) and Papa et al. (2009). Paper I Air (%) Water (%) Soil (%) Sediment (%) Residence time (h) 30 0.26 1.48 39.7 58.6 5986 BDE 47 Cousins & Wania & Papa et al., Palm, 2003 Dugani, 2003 2009 0.29 0.25 0.37 2.01 1.89 1.72 41.7 41.9 48.1 56 55.9 49.8 5593 5786 5166 Paper I 0.12 1.09 41.8 57 6926 BDE 209 Cousins & Wania & Palm, 2003 Dugani, 2003 0.12 0.12 1.09 1.09 41.8 41.8 57 57 6925 6924 Papa et al. 2009 0.12 1.09 41.8 57 6930 3.4 Regionalandlocal‐scalefateassessment The regional scale assessment conducted with the SimpleBox 2.0 regional scenario in PaperIresultedinpredictedairconcentrationsthatwerewithintheobservedranges. This implies that the assigned emissions to air had the right order of magnitude. The conclusionisstrengthenedbyFigure4,whichshowsthattheearlyemissionestimates areinthemiddleoftherangeofthelaterestimates,asdiscussedpreviously.Theneed forrepresentativemonitoringdatainsoilsandsedimentstostrengthentheassessment wasalsohighlighted. A local‐scale assessment is an important part of a human risk assessment. Once adequate certainty has been gained in emission estimates by cross‐checking using alternative methods, the local‐scale fate model is an appropriate tool to predict environmental concentrations to use for estimating exposure. As discussed in the previous section, local‐scale assessment in itself can be used to estimate emissions by fittingobservedenvironmentalconcentrationstomodelpredictions,anditcanbeused to better characterize suspected sources and to assess their relative contributions. An increasingamountofliteraturehaspointedtowardsurbanenvironmentsasimportant source areas of PBDEs and other consumer chemicals (Butt et al., 2003; Harrad and Hunter, 2006). This led to the hypothesis that the indoor environment may play an importantroleasasourcetotheoutdoorenvironment. 3.4.1 Influenceoftheindoorenvironmentonchemicalfateinurbancentres Theimpactofchemicalemissionsfromconsumerproductstoindooraironthelevelsof the chemicals in the outdoor environment was addressed in Papers II‐IV. In Paper II chemical concentrations in air and dust were monitored in different Swedish microenvironments.Median10PBDEconcentrationsinindoorair(93‐3700pg/m3)for different building types corresponded well to the concentrations in ventilation outlets (92‐4700pg/m3).BDE209wasthemostabundantPBDEcongenerinindoorairaswell asinventilationoutlets.ThisshowsthatdespitethehighlogKOAthischemicalmaystill bereleasedfromconsumerproductsintheindoorenvironmentandbetransportedout ofthebuildingsinsubstantialamountsattachedtoveryfineparticles.Onceoutdoors,it may be subject to atmospheric dilution and long‐range transport. Observed concentrations in Stockholm ventilation outlets were used to estimate the annual outflowratesofPBDEsfromprivateandpublicindoorenvironmentstooutdoorairin Sweden. The annual outflow of pentaBDE (sum of congeners 28, 47, 99 and 153) was estimatedtobe0.28‐9.6kg/yearandtheoutflowofBDE209wasestimatedtobe0.84‐ 32 kg/year. These flows were compared to estimated emissions to outdoor air from othersources(PaperII)anditwasconcludedthatindoorairmaycontributeasmuchas 81‐82 % and 84‐87 % of total emissions to outdoor air of pentaBDE and BDE 209 in Sweden. ThemodelstudyinPaperIIIshowedthattheindoorenvironmentcontainsadditional chemicalremovalmechanismsthatarenotaccountedforintraditionalMFMmodelling, andthatthisaffectstheurbanfateofhydrophobicsubstances.Hydrophobicchemicals 31 with a high tendency to partition to indoor surfaces and dust (log KOA > 9) are particularlysusceptibletoremovalintheindoorenvironment.Thenestedcharacterof theindoorenvironmentwithintheoutdoorenvironmentalsoleadstolongerchemical residencetimes,notjustforhydrophobicchemicalsbutalsoformorevolatileandwater solublespecies,suchasformaldehydeand2,4,6‐tribromophenol(PaperI). InPaperIV,theSMURFmodelwasusedtoreconcileemissionsofDINP,DEHPandBDE 209withmeasuredindoorconcentrationsandtoassesstheimpactofemissionsindoors on urban air quality. For urban Stockholm, inflowing air was shown to dominate the chemicalmassbalanceoftheatmosphereforthestudiedsubstances.ForBDE209,the indooremissionsadded38%tothemassenteringthecitywithinflowingair.Whilethis appears to be in contradiction of the finding in Paper II that the majority of BDE 209 emissions to outdoor air come from indoor air, this may simply be evidence that advective transport into Stockholm/Sweden is relatively more important than the internal emissions.The BDE 209 advectedinto Stockholm may also have originated in indoor environments in other regions. For the phthalates, Paper IV showed that the assumed uncertaintyin background concentrations ininflowing air contributed nearly 100%tothemodelledvarianceinoutdoorairconcentrations,whereasthecontribution oftheassumeduncertaintyinindooremissionshadnegligibleimpactontheoutdoorair concentration. The model predicted much lower concentrations of phthalates in Stockholmairthanobservations,indicatingthattheimpactofemissionsfromindoorair couldbeevenlesssignificant. The finding that indoor air may be an important source to outdoor air pollution for PBDEs raised questions about the validity of current exposure assessment methods, wheretheenvironmentalfateisusuallyassessedbyassumingemissionstooutdoorair, waterandsoil.Becauseofthedifferentcharacteroftheindoorenvironment,chemicals released indoors may follow different transport pathways that should be taken into account to achieve a sound description of chemical fate, especially in urban areas. An integrated indoor – outdoor model such as the SMURF model allows for a combined indoor‐outdoor exposure assessment. Also, to achieve a wider understanding of urban chemicalflowssuchasreleasestothewastewatersystem,itmaybenecessarytotake theindoorenvironmentintoaccount. 4 Conclusions Themostimportantcontributionsfromtheworkpresentedinthisthesisare: 32 A structured chemical fate assessment strategy can and should be applied at early stagesoftheevaluationofemergingchemicalstoassesstheirlikelyfateandtodirect further research, even if data availability is limited. Evaluative multimedia fate modelsplayacentralroleinthisstrategy(PaperI). Thethree‐solubilityapproachisasimpleandrapidQSPR‐methodthatcanbeusedat early stages of the evaluation of emerging chemicals to estimate physical‐chemical properties,evenifonlylimitedexperimentaldataareavailable(Paper I;summary section3.3) EmissionsofBDE209,DINPandDEHPtoStockholmindoorairareestimatedtobe 0.1,3.4and290mg/capitaandyear,respectively(PaperIV). TheindoorenvironmentisasignificantsourceofPBDEstooutdoorair.ForBDE209, itadded38%totheBDE209massenteringStockholmcitywithinflowingair.For Swedenasawhole,theindoorenvironmentwasestimatedtoaccountforca80%of allBDE209emissionstooutdoorair(PapersIIandIV). For the phthalates, outdoor emissions and/or background inflow are the dominant sources of outdoor air pollution in Stockholm; the influence of the indoor environmentislimited(PaperIV). Compared to emissions to outdoor air, emissions in the indoor environment affect the urban fate of hydrophobic organic chemicals by providing additional removal pathwaysandbyprolongingurbanchemicalresidencetimes(PaperIII). Emissions of chemicals from consumer products are best assessed by combining different estimation methods and should be cross‐checked through fitting multimedia fate models against empirical monitoring data. As a first approach, substanceflowanalysismethodologymaybeappliedandscaledusingpopulation,as longastheinformationusedisvalidforthecontextoftheSFA(PapersI,IIandIV). 5 Futurechallenges This thesis provides valuable insights into the use of modelling tools to assess the environmentalfateandbehaviourofchemicalsreleasedfromconsumerproducts.Itwas shownthatcurrentemissionestimatesofphthalateesterstoindooraircannotexplain theobservedconcentrationsinStockholmoutdoorair.Itwouldthereforebevaluableto conduct an urban upwind‐downwind monitoring study of phthalate esters in the area, whereconcentrationsaremeasuredoveralongerperiodoftime.Thiscouldbeusedto estimate the atmospheric emissions of the phthalates on the urban scale by fitting concentrations predicted with an environmental fate model to the observed concentrations. This would give important information about the relative contribution from other local sources relative to the influence of regional background inflow and generateimprovedunderstandingoftheemissionsofphthalateestersinurbanareas. The thesis also highlights the high potential of indoor surfaces to store hydrophobic chemicals, which affects their urban residence time and has implications for their 33 environmental fate as well as for human exposure. An improved characterization of indoorsurfaces,aswellaschemicalremovalprocessesintheindoorenvironmentwould helpimprovingfuturemodellingstudiesontheindoorfateofhydrophobicsubstances. For example, it would be valuable to investigate the occurrence and thickness of the organicfilmondifferenttypesofindoorsurfaces,aswellastoperformamulti‐building inventory on the occurrence of hard and soft surfaces, since these have presumably differentpartitioningproperties.Acontrolledstudyespeciallytargetingindoorremoval processes (e.g. dust removal through vacuuming and wet mopping, sorption to indoor materials etc.) could provide valuable contribution to the understanding of the overall massbalanceoforganiccontaminantsindoors. This thesis has mainly focussed on the contribution of indoor chemical releases to outdoorairpollutionthroughtransportviatheventilationsystem.Thereare,however, alternativereleasepathwaysfromtheindoortotheoutdoorenvironment.Forexample, variouscleaningandwashingactivitiesmaygeneratesubstantialchemicaltransportto the wastewater treatment system that may be of relevance for outflow to the aquatic environment.Thisisatopicthatwouldbeofinteresttoaddressinfuturestudies. Finally, the assessment strategy illustrated in Paper I should be applied to emerging chemicals for which current knowledge is limited, such as for example the siloxanes, novel BFRs, or other chemical groups listed in the recent review by Howard and Muir (2010). 34 6 Acknowledgements Phew!Såvardetdåäntligendagsattavrundadettaomfattandearbete,ochdetgenom attriktanågravarmatacktillallaersompåolikasättbidragittillattjagsentomsider lyckatsuppnåmittmålatterhållaendoktorsexamen. First,IwouldliketothankmysupervisorMichaelMcLachlan,fortakingmeunderyour wings and guiding me through the most intensive parts of writing, always giving me constructiveandcriticalcommentsthatmademethinkdeeper,harderanddifferently.I havelearnedalot! Ialsowishtothankallmyco‐authorstoPapersI,II,andIV.Ettsärskilttackvilljagrikta tillJustinaBjörklund,TomasHolmgren,MikaelRembergerochCindydeWitförert ovärderligabidrag. Stort tack också till mina tidigare lärare i Umeå som fick mig att våga ta språnget ut i stora världen, och även för intressanta senare samarbeten: Karin Wiberg, Mats Tysklind,PeterHaglundochPatrikAndersson. ThankstoMattMacLeodandIanCousinsforrecruitingmewhenIwasstillayoungand eager student up in Umeå. How coincidental that you both eventually ended up in Sweden! I will always be grateful to Don Mackay, who introduced me into the world of environmental fate models. You gave me the greatest start of my professional career I couldhavedreamedof,anunforgettableexperienceand...well,youknowtherest.Thank youeversomuch!IamalsogratefultothebunchofpeoplewhoworkedatCEMCinthe summer 2000 and gave me a very pleasant first encounter with the field; Matt, Ian, Todd,Alison,Angela,Steve,Lynne,Tom,Eva,DavidandRajeshaswellastheplayers intheinfamousPeterborough“yellowsoccerteam”andallthefellowsattheThursday pubnights. EttstorttackvilljagocksåriktatillallaminanuvarandeochtidigarekollegorvidIVL: Eva Brorström‐Lundén som anställde mig, tack för allt stöd och uppmuntran genom åren.JohnSternbeck,somlotsademiginiyrkeslivetpåettföredömligtsätt.Tackockså till Tomas Rydberg, Martin Erlandsson, Anna Jöborn, Björne Olsson, Micke Olshammar och Helene Ejhed, ert stöd och er förståelse har varit helt avgörande! Övrigakollegorvidenhet6000samtövrigaIVLsomgörattdetärroligtattgåtilljobbet varjedag.Eranamnskulletauppytterligarefemsidoridennaavhandling. ThankstoallITMstaffforalwaysmakingmefeelwelcomeduringmyshortsessionsat thedepartment. Tack,mammaochpappaförattnialltidtrottpåmigochföralluppmuntranvadjagän tagitförmig,varesigdethandlatomtyska,skidåkning,fotbollellernaturvetenskap.Det är tryggt att veta att ni alltid finns där! Dessutom för hjälp med barnpassning och all 35 annanpraktisksupport(ochokejdåpappa,tackförattduskjutsadesåofta).Tackockså till min käre bror Anders, för teknisk support och för din förmåga att lugna ner en yrhättatilllillasyster.OchförståsKarinochWilmaförattnifinnsochberikarmittliv. Och sist men inte minst, min underbara familj, mitt andningshål och min energikälla i tillvaron. Emily, Linnea och Olivia, ni är mitt livs största glädje, och världens bästa tjejer!Tackförerförståelsenärmammasuttituppeochjobbatsenanätter,ochförera fina teckningar till avhandlingen. Ian, jag nyper mig fortfarande i armen med jämna mellanrumförattförsäkramigomattalltintebaraärenvackerdröm.Detärenynnest att få ha dig som livskamrat och från tid till annan även som kollega. Du är bäst! Jag älskardig. Forskningen har finansierats genom en kombination av medel från Trent University i Canada,frånEUgenomprojektetCOHIBA,Naturvårdsverketochforskningsprogrammet ChEmiTecssamtdetnationellascreeningprogrammet,samtStockholmsUniversitet. 36 7 References Afshari A, Gunnarsen L, Clausen PA, Hansen V. Emission of phthalates from PVC and other materials. Indoor Air 2004; 14: 120‐128. Andersson H, Cousins AP, Munthe J, Brorström‐Lundén E, Wickman T, Pettersson M, et al. WP4 Background paper ‐ Identification of sources and estimation of inputs to the Baltic Sea ‐ Final Version 2012. Bennett DH, Furtaw EJ. Fugacity‐based indoor residential pesticide fate model. Environmental Science & Technology 2004; 38: 2142‐2152. Bennett DH, Kastenberg WE, McKone TE. 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