studies of polluted mine soils and treatment of waste waters

STUDIESOFPOLLUTEDMINESOILSANDTREATMENTOF
WASTEWATERS.APPLICATIONSOFSYNCHROTRON
BASEDTECHNIQUES,FIELDPORTABLEXRAY
FLUORESCENCEANDADVANCEDOXIDATIONPROCESSES
MartaÁvilaPérez
Tesidoctoral
ProgramadedoctoratenQuímica
Directors:ManuelValienteMalmagro,GustavoPérez
González
DepartamentdeQuímica
FacultatdeCiències
Any2011
MemòriapresentadaperaspiraralGraudeDoctorper
MartaÁvilaPérez
Vistiplau,elsdirectors
Dr.ManuelValienteMalmagro
Bellaterra,10dejunyde2011
Dr.GustavoPérezGonzález
Inthebeginningtherewasnothing.Godsaid,'Lettherebelight!'.Andtherewaslight.
Therewasstillnothing,butyoucouldseeitawholelotbetter.
EllenDegeneres
AGRAÏMENTS
L’altrediaparlantambalgunscompanysdelgrupsobrequealatesihisurtenelnomde
l’Elenaoelde’nGuscomaautorsd’algunesfotos,laPilivaveureunafiguraenlaqueellaem
vaajudaracollocarleslíniesrectesienFranundibuixonellemvaajudaratriarelscolors
(sent daltònic) i em vaig adonar que és totalment veritat pensar que en una tesi tothom hi
collabora una miqueta, al menys en la meva. Pot ser en una figura, una ratlla vertical, una
paraulaounaidea,isobretotdonantmemoltsuportentotmoment.Moltesgràciesatots!!!
Evidentmentlapartd’aquestatesiquecorresponaenManoloienGusésmoltmésgran
queladelarestadelespersones.GràciesManoloperhavermedonatlaoportunitatdeferla
tesialgrup!Novulldesmerèixerelsaltresgrups,peròdesdelprimerdiasemprehecregutque
no podia haver anat a un grup millor i en gran part això és gràcies a tu i al teu esforç. Gus,
muchasgraciasporestarsiempredispuestoaayudarmeyaguantarmemisneuras,séqueno
esfácil,muchasgraciasdeverdad.Eresuntíogenial,yunpapáaunmejor,jeje!
Iaratocaatotalarestadelagent,elsmeusamicsquenosóndeveritatalsquinoestimo
gensisoupoquetsamés!Montsetanteshoresviscudes,tanteshistòriesjuntes,etdesitjotota
lafelicitatdelmón!AGTShihaqualitatitun’etsunamostra!Fran,elmeureietonet,etsel
mésmaco,trobaréafaltarlestevesabraçadesdeles10:22itoteslesaltres.Pili,jaséquiets!
Ets una bona amiga i una bona companya, i a més molt divertida, però no ho diré a ningú
perquènoperdislatevafama.AngélicaAngélica,micompañeradelahoradecomer,mevasa
hacermuchafalta,ylosabes.DejoVIBRAenmemoriadelzentinelparaquedevezencuando
Diego la ponga en el laboratorio. Dieguito todo (o casi todo) lo que dijimos en “El día de
halagaraDiego”eraverdad,eresunsol!Muchasgraciasportuayudaconlainformáticayen
todoloquenoesinformática!Bea,companyadetantesestadesdelsincrotró,m’hohepassat
moltbéambtuitambéalesgransfestesqueorganitzes!Olgucha,elnuevogranfichajedel
grupo,graciasporvendermea“Daviduño”apreciodesaldoynodejesqueseolvidenuncala
canción de Alfi “venga anímate vamos a aprender, qué divertido será genial y no podrás
parar!”.Agus,hemanatseguintaquestcaminetjuntstotal’estonagairebédesdelprincipifins
aragairebéelfinal,hassigutungrancompany,semprefentgaladelteu“señorío”,jeje!Oriol
Beisbol,portarésempreamborgulllasamarretadeElsPétitPÛt!Laproperavegadaquesiguis
alumnemeurestarépuntsperfaltad’assistència!ElenaPeralta(miotraamigaElena,jeje!)ya
sabes lo mucho que te aprecio, muchas gracias siempre por tu ayuda! DJ Lluís Soler, tens el
meu vot per a presidir Catalunya i proclamarne la independència! Ara, no la facis explotar
amb hidrogen. Amanda, que no pots evitar trencar cors allà on vagis (léase vendedor de
refrescosdelCirqueduSoleil)maideixisdepensarque“todoesmuybonito!”perquètensraó.
Patri, sempre et recordaré amb un somriure. Berta que vens a robarnos però amb un
somriuresempreidientnos“guapos”,aixídónagustqueetrobin,jeje!Julio,tico,siempretan
amableytandispuestoaayudaryaescuchar(yatraerpasteles!),MartayKike(Kiki,jijijiji!).
GràciesalaMariaDolorspersersempretandiligent,eh,reina;alaCristina,alaMariaMuñozi
alaMontseLópez.
Tampocnoemvulloblidard’agraïrl’ajudadelarestadelagentambquihecompartittants
momentsiquejanovoltenperaquí:Aleixtinctantsitanbonsrecordsdetuqueompliriala
tesi sencera! Moltes gràcies per tot el que em vas ajudar. Així com també ho van fer l’Anna
Torradoaquiencaraenyoro,l’Àngelsambquihemcompartitunaamistatdemoltsanysifinsi
totcursosd’altacuina,l’AnnaBernausqueemvaintroduirenelfabulósmóndelsincrotró,el
Johannessiempredispuestoaayudarme,elJordyMacanás(Mac)lawikipediaambpotesque
sempresempret’ajudai trobasolucions,laNadiailaRajaa(I missyouuuuu!!!),elSachin,el
Mouhssine, en Xavi Gaona, la Tània Gumí, el José A. Muñoz, en Franki, l’Amàlia, el Marc
Renom, el Jordi Nualart... Segurament em deixi algú, potser fins i tot algú molt important i
anticipadamentdemanoperdóperlamevamemòriadepeix...ups!Delfín!LamascotadeGTS!
IallàalceldelspeixetstambéunrecordperalPezón.
Elmeugabinetdepsicologiaesmereixunraconetenaquestsagraïments.GràciesAnabel
perlatevaamistatitotal’ajudaiconsellsquesemprem’hasdonat;ygraciasSusana,Tamaray
Sandraporvuestraayudayvuestrasonrisasiempreyporestarsiempredispuestasaapuntarse
a todo! Y también quiero agradecer al resto de vuestro laboratorio aguantarme cuando
aparezco:AnnaySole.Recordadmesiemprecomo“Esachicaqué?”!
GraciasatiDavidZamora,porserelmisterdelaseleccióndefutbolfemeninodelatorre
dequímicayportodalaayudaextradeportiva,jeje!NosotrasAhí!Eldreamteamlideratperel
míster David amb qui quasi vam aconseguir arribar a la final però el que segur que vam
aconseguir va ser passarho molt bé! Montse, Susana, Tamara, Pili, Sandra, Silvia, Amanda,
Amàlia,Sole,Cata,Núria.
Gracias también a ti Miguel, compañero de la planta durante algunos años por estar
siempre a mi lado y ayudarme y ser mi mejor amigo. I gràcies a l’Elena, l’Adela, la Salut, en
Pep,enDavidielGuga,persertambéelsmeusamics!
No puc oblidarme de les meves companyes de pis, la Pilar de València amb qui vaig
compartir tants anys i tantes experiències, Tufaria y Yannich, Viri la mexicanita y mis
colombianitas queridas: Julix, Pekas y Denise. Julix muchas gracias por haberme aguantado
estos últimos años, fuiste una muy buena amiga, te dejo en arriendo una parcelita de mi
corazón contrato indefinido y a Pekas también pero una parcelita más pequeña porque me
aguantó mucho menos tiempo, jeje! Mis pichurrias! Denise te mereces un apartado para ti
solita.
Sandrita y Denise gracias por vuestra colaboración en el diseño de esta tesis, espero que
seadevuestroagrado.YespecialmentegraciasaTu,miangelito,portodotuapoyosiempre,
por tus palabras, por tus risas y tu sonrisa! Por este trocito de camino en el que me has
acompañado siempre tan pendiente. Algún día sabrás lo grande que es la parcela que te
correspondeati!
Peròaquivullenganyar?Usestimomoltatots!
AlsmeusparesiaenDavid
SUMMARY
Summary
Metals have been used since prehistoric times and they play a key role in civilization
development.Despiteminingindustrystillrepresentsanimportanteconomicactivityinmany
countries,largeamountsofsolidandliquidwastesremainstoredincontrolledtailingsduring
miningoperationsandsometimesinanuncontrolledway,afterthemineclosure.Solidwastes
areproducedfromoreprocessingsuchascrushing,grindingandmillingandaredisposedoffin
surroundingland.Highamountsofwaterarespenttowashtheoreandtoreducethemineral
to its metallic form. These mining wastewaters are generally dumped into ponds secured by
dams.Inthissense,elevatedlevelsofheavymetalsfrommetalliferousminesarefoundinand
aroundthemines.Suchmetalsrepresentagreathazardthatcanrestrictsoiluse,whiletailing
damfailurescanproducehugehumanandenvironmentaldamages.
Thus, this PhD thesis is aimed at the characterization of the heavy metal pollution
around abandoned mines as well as to develop process for waste water treatment including
either inorganic or organic pollutants from industrial activities, i.e., mining or textile related
industries.
Inthisconcern,thestudiescarriedoutaresummarizedasfollows:
ThecharacterizationoffourabandonedminesofMarrakechregion(Morocco)bymeans
of heavy metal spatial concentration and the identification of hazardous sites by means of
mobility tests. In this concern, the studies carried out represent a first insight into four
abandonedminesfromMarrakechregion(Morocco):DraaLasfar,Kettara,SidiBouOthmane
and Bir Nehass. The characterization of the heavy metal pollution was performed by Field
Portable Xray Fluorescence (FPXRF) while the spatial variability was determined by
Geographic Information Systems (GIS). A prediction of the risk of each sampling point was
completedbydeterminingthemobilityofanthropogenicenhancedheavymetalsusingsingle
leachingtests.ThecalculationoftheConcentrationEnrichmentRatios(CER)revealedarsenic,
copper,leadandzincasthemainpollutantsinallmineareas.DraaLasfarGIScontourmapsof
these pollutants depict the most polluted areas at the vicinity of the mine, especially at the
northwest area, probably linked to weathering effects and topography of the area. The
mobilityassaysindicategreatermobilityofAsandZnduetotheirloweradsorptionprocessin
the soil, independently of their respective concentration. GIS contour maps generally reveal
higher concentration around sampling points localized at deposits of mining residues. The
distribution of pollutants at Kettara is similar for arsenic, copper and lead, whilst zinc
distributionismorehomogeneousalongtheminearea.Inaddition,leadcanbeconsideredthe
main pollutant considering its high CER values. Regarding SBOthmane mine area, GIS maps
observeareaswithhighcontamination,assomesampleshaveCERvaluesabove200.Leadand
Summary
zinc canbe consideredthemainpollutantsinSBOthmanemine area.BirNehassminearea,
likewise SBOthmane, is less contaminated with arsenic and copper being lead and zinc the
mainpollutants.Inthissense,auniquehotspotcanbeobservedforarsenicandleadaround
anareacorrespondingtoaresiduedepositwhileseveralhotspotswithCER=200canbeseen
forleadandzinc,alsorelatedtoresiduedeposits.Themobilityresultspointoutthegreatest
partofsamplestohaveverylowmobility.Ontheotherhand,samplesofSBOthmaneandBir
NehassarehighlyconcentratedonPbandZnandpresentanextremelyhighcontentonPband
Zninthemobilephase,especiallyhighforthesamplestakenatthedepositsofresidues.Given
the high content of lead and zinc it is likely that the concentration of metals exceed the
capacity of the soil to retain them and the migration to a mobile phase may take place, so
remediation treatments should be applied to these areas if the soil is intended for further
purposes.
The speciation of mercury on three European important mercury mines to determine
toxicity in soils. Besides mobility assays, another technique has been applied in the present
worktodeterminetoxicityofsoils.Suchtechniqueinvolvesthedeterminationofthechemical
speciesinwhicheachmetalispresentinsoils.Asoneofthemosttoxicheavymetals,mercury
(Hg)andtheirrelatedcompounds,canbeabsorbedbylivingtissuesinlargedoses,becominga
greathazardduetoitsabilitytobeconcentratedandstoredoverlongperiodsoftime.Inthis
work, synchrotronbased Xray Absorption Near Edge Structure (XANES) has been used to
determinethespeciationofmercuryingeologicalsamplesfromthreeofthelargestEuropean
mercury mining districts: Almadén (Spain), Idria (Slovenia) and Asturias (Spain). XANES has
been complemented with a single extraction protocol for the determination of Hg mobility.
Ore,calcines,dumpmaterial,soil,sedimentandsuspendedparticlesfromthethreesiteshave
beenconsideredinthestudy.Inthethreesites,ratherinsolublesulfidecompounds(cinnabar
and metacinnabar) were found to predominate. Minor amounts of more soluble mercury
compounds (chlorides and sulfates) were also identified in some samples. Single extraction
proceduresindicateastrongdependenceofthemobilitywiththeconcentrationofchlorides
and sulfates. The mercury species found in each mine are related to the efficiency of its
roastingfurnaces.
Therecoveryofzincfromaminetailingpondatlaboratoryandpilotplantscaletosolve
an environmental problem while providing an economic output. Other activities performed
through the framework of this thesis deal with the reduction of the amount of wastewater
contained in mine tailing ponds and avoid tailing dam breaches. In this thesis a process to
recover zinc from a real mine tailing pond is proposed. This mine tailing pond stores huge
amountsofwastewatercontainingabout1g/LofZnandsignificantamountsofferrous,ferric,
Summary
calcium,copper,aluminumandmanganeseions.Inthissense,therecoveryofzinccanprovide
economicvaluetotheprocesswhilesolvinganenvironmentalproblem.Asolventextraction
processwasconsideredasthebestmethodologyandinthepresentPhDthesisarereported
theresultsfortheselectionofthebestextractantamongstDEHPA,Cyanex272andIonquest
290.AsnoneoftheextractantswereabletoextractZnselectivelyfromasolutioncontaining
Fe,abiooxidationprocessfollowedbyanalkalineprecipitationstepwasperformedpriorto
theSXtreatmentinordertoobtainasolutionwithoutiron.TheFeremovalaswellastheSX
processhavebeendevelopedsuccessfullyatlaboratoryscaleandverifiedinapilotplanton
site,usingtwoBatemanPulsedColumnsfortheextractionandstrippingofZn.Giventhatthe
recyclingoftheorganicphaseleadtoarelativeimportanceoftheextractantcosts,Ionquest
290 was selected as the most suitable extractant for the target stream due to its higher
selectivity and loading capacity towards Zn extraction. Ketrul D100 is the solvent
recommendedowingitslowervolatilityandflammability.Thepilotplantprovedthefeasibility
oftheprocess,obtainingazincrecoveryof95%andleavinglessthan50mg/Lintheraffinate.
ThestrippingwasefficientandonlyasinglestageatO:A=20wasrequiredtoachieveatransfer
of40g/L.ForaZnpriceaboveUS$2/kgtheoperatingcostsarecoveredwhile,additionally,a
seriousenvironmentalproblemissolved.
TheremovaloforganiccompoundsfromwastewaterbytheFentonreactionusingFe3+
loaded materials. As an example of another remediation technique, this thesis presents the
removaloforganicwastewatersbythreedifferentmaterialswhichhavebeenexchangedwith
FefortheirevaluationasheterogeneousFentoncatalysts.TheFentonreaction,consistingon
the generation of the highly oxidant hydroxyl radical is employed to degrade the organic
pollutants. The hydroxyl radical is formed by hydrogen peroxide and iron salts acting as a
catalyst. Several drawbacks arise from the use of iron salts mainly related to its removal by
precipitation of iron salts to generate a red mud that should be treated. The employed
catalysts involve a synthetic commercial zeolite (USY zeolite), a natural zeolite (clinoptilolite)
andaclay(montmorillonite)loadedwithFe.TheresultsindicatedthathighFecontentcould
be introduced into such materials with minimum time and reagents consumption and, in
addition, these Feloaded materials can be successfully employed for the decolorisation of
AR14 solutions and the mineralization of acetic acid and phenol. In this sense, Fe3+USY
decolorisation kinetics was equal to the homogeneous catalysis (less than 15 min to achieve
total decolorisation) whereas Fe3+MMT and Fe3+clinoptilolite showed slower kinetics lasting
30 and 60 min, respectively. Moreover, tests performed to acetic acid and phenol solutions
demonstrated 30% and 95% of COD removal, respectively, whereas homogeneous catalysis
Summary
onlyprovideda25%and85%CODremovalrespectively.Columnexperimentsusingthemore
economical material, clinoptilolite, were performed obtaining also successfully results hence
indicatingthefeasibilityoftheselowcostFeloadedmaterialsasheterogeneouscatalystsfor
theFentonreaction.TheminimallossesofFefromthematerialsavoidedthenecessityofred
mudremoval.
The removal of arsenic from inorganic wastewater by using Fe3+loaded materials.
Finally, taking profit of the affinity of Fe(III) compounds towards arsenic inorganic species,
severalFe3+loadedmaterialswithhighexchangecapabilitysuchaszeoliteUSY(USY),zeoliteY
(ZY)andasponge(Sp)havebeenappliedfortheremovalofarsenicfrominorganicpolluted
wastewaters. Arsenic contamination in groundwater generates widespread human health
disasters around the world (especially in Southeast Asia). In this sense, besides their
applicationascatalystsinFentonprocesses,Feloadedmaterialscanbealsoemployedforthe
removal of arsenic. These materials were characterized by FPXRF and Extended Xray
AbsorptionFineStructure(EXAFS)techniquesinordertoshedlightontothedifferentsorption
mechanisms of arsenic into such materials. The sorption mechanism reveals a strong
dependenceonthespecificsurfaceareaandtheavailablesites,thusaszeoliteYhasspecific
surface area higher than zeolite USY and Forager sponge, its Fe loading becomes greater.
Forager sponge, has an As:Fe absorption ratio higher than the one expressed by zeolites
mainly owed to tertiary amine salt groups contained in the sponge that can bind anionic
contaminants,suchasarsenic,chromateoruraniumoxidespecies.Thecharacterizationofthe
adsorption of arsenate onto these Fe3+loaded materials revealed arsenate bidentate corner
sharingbondasthemainadsorptionprocess.
CONTENTS
1.INTRODUCTION ...........................................................................................................................3 MINESITESCHARACTERIZATION ......................................................................................................3
1.1.MININGOVERVIEW ..........................................................................................................................3 1.2.SOILIMPACTSFROMMINING ..........................................................................................................4 1.3.CHARACTERIZATIONOFMINESITES ................................................................................................7 1.3.1.Sampling .....................................................................................................................................7
1.3.2.SoilPhysicochemicalCharacterization ......................................................................................8 1.3.3.MetalAnalysis ..........................................................................................................................10
1.4.SOILQUALITYREGULATIONS..........................................................................................................11 1.5.CHEMICALSPECIATIONANDFRACTIONATIONINSOILS ...............................................................12
1.5.1.SequentialExtractionSchemes ................................................................................................14 1.5.2.SingleLeachingTests................................................................................................................15 1.6.SOILRISKASSESSMENTTOOLS.......................................................................................................16 1.6.2.GeographicInformationSystems .............................................................................................16 1.6.3.PrincipalComponentAnalysisinGeosciences .........................................................................17
1.7.WEAKNESSESANDNEEDSOFMININGSITESCHARACTERIZATION...............................................19
REMEDIATIONTECHNIQUESOFINDUSTRIALCONTAMINATEDWATER ................................20
1.8.SOLVENTEXTRACTIONFORTHERECOVERYOFZnFROMACIDICMINEWATERS........................20
1.8.1.ZincOverview ...........................................................................................................................21
1.8.2.TheSolventExtractionProcess ................................................................................................22
1.8.3.ScalingSolventExtractionToaPilotPlant ...............................................................................23
1.9.FELOADEDMATERIALSFORTHEREMOVALOFORGANICANDINORGANICCONTAMINANTS ..24
1.9.1.Zeolites .....................................................................................................................................24
1.9.2.Clays..........................................................................................................................................27
1.9.3.Sponges ....................................................................................................................................28
1.10.THEFENTONREACTION ................................................................................................................29
1.11.ARSENICSORPTIONUSINGFELOADEDMATERIALS ...................................................................31
1.11.1.Arsenictoxicity ....................................................................................................................... 31
1.11.2.Arsenicsorbents .....................................................................................................................32
1.12.WEAKNESSESANDNEEDSOFINDUSTRIALLYCONTAMINATEDWATERS...................................33
ANALYTICALTECHNIQUES .........................................................................................................35
1.13.XRAYFLUORESCENCE ..................................................................................................................35
1.13.1.XRayinteractionwithmatter ................................................................................................35
1.13.2.XRayFluorescence.................................................................................................................37
1.13.4.FieldPortableXRFinstrumentation .......................................................................................37
1.14.SYNCHROTRONBASEDTECHNIQUES...........................................................................................39
1.14.1.SynchrotronLightSources......................................................................................................39
1.14.2.DesignandOperationofaSynchrotronLightSource ............................................................39
1.14.3.XRayAbsorptionSpectrometry .............................................................................................41
1.15.OBJECTIVES ...................................................................................................................................45
1.16.REFERENCES ..................................................................................................................................46
2.METHODOLOGY...................................................................................................................57
MINESITESCHARACTERIZATION..........................................................................................59
2.1.STUDIEDMINESDESCRIPTION .......................................................................................................59
2.1.1.MarrakechMines:DraaLasfar,Kettara,SidiBouOthmaneandBirNehass(Morocco)..........59
2.1.2.EuropeanMercuryMiningDistricts:Almadén,MieresandIdrija ............................................61
2.1.3.AznalcóllarTailingPond............................................................................................................63
2.2.SAMPLING .......................................................................................................................................64
2.2.1. Marrakech Mining Districts: Draa Lasfar, Kettara, SidiBou Othmane and Bir Nehass
(Morocco) ...........................................................................................................................................64
2.2.2.EuropeanMercuryMiningDistricts:Almadén,Asturias(Spain),Idrija(Slovenia) ...................64
2.3.CHARACTERIZATION .......................................................................................................................65
2.3.1.PhysicochemicalParameters...................................................................................................65
2.3.2.Totalmetalconcentration ........................................................................................................65
2.3.3.TotalMercuryContent .............................................................................................................67
2.3.4.Mobilityoftheminesamples...................................................................................................68
2.3.5.XASmeasurements................................................................................................................... 69
2.4.DATATREATMENT ..........................................................................................................................70
2.4.1.ConcentrationEnrichmentRatios ............................................................................................70
2.4.2.GeographicInformationSystems .............................................................................................71
2.4.3.StatisticalTools.........................................................................................................................71
2.4.4.XASDataTreatment .................................................................................................................72
REMEDIATIONTECHNIQUESOFINDUSTRIALCONTAMINATEDWATER .........73
2.5.ZINCSOLVENTEXTRACTION ...........................................................................................................73
2.5.1.LaboratoryExperiments ...........................................................................................................73
2.5.2.ScalingtheSXtoaPilotPlant ...................................................................................................74
2.6. FeEXCHANGE MATERIALS FOR THE REMEDIATION OF ORGANIC AND INORGANIC POLLUTED
WATERS..................................................................................................................................................76
2.6.1.FentonReaction ....................................................................................................................... 77
2.6.2.Arsenicremoval........................................................................................................................ 78
2.7.REFERENCES ....................................................................................................................................79
3.RESULTSANDDISCUSSION .......................................................................................85
MINESITESCHARACTERIZATION..........................................................................................85
3.1.HEAVYMETALCONTAMINATIONANDMOBILITYATTHEDRAALASFARMINEAREA .................85
3.1.1.Physicochemicalparameters...................................................................................................85
3.1.2.Heavymetalconcentrationintheminearea...........................................................................86
3.1.3.GIScontourmapsofthemainpollutants.................................................................................85
3.1.4.Effectofparticlesizeandmobility ...........................................................................................86
3.2.CHARACTERIZATIONOFKETTARA,SIDIBOUOTHMANEANDBIRNEHASSMINEAREAS............92
3.2.1.Physicochemicalcharacterization ...........................................................................................92
3.2.2.Heavymetalconcentrationintheminearea...........................................................................93
3.2.3.Applicationofchemometrics ...................................................................................................95
3.2.4.GIScontourmapsofthepollutants.........................................................................................98
3.3. XANESSPECIATION OFMERCURY IN THREE MINING DISTRICTS:ALMADEN (SPAIN), ASTURIAS
(SPAIN)ANDIDRIJA(SLOVENIA) .........................................................................................................104
3.3.1.Chemicalanalysisofthesamples ...........................................................................................104
3.3.2.XANESspeciationandmobilityresults ...................................................................................106
REMEDIATIONTECHNOLOGIES .............................................................................................111
3.4.EXTRACTANTANDSOLVENTSELECTIONTORECOVERZINCFROMAMININGEFFLUENT:FROM
LABORATORYSCALETOPILOTPLANT.................................................................................................111
3.4.1.SXlaboratoryresults ..............................................................................................................111
3.4.2.SXpilotplantprocess .............................................................................................................114
3.5.FELOADEDMATERIALSFORTHEREMEDIATIONOFORGANICANDINORGANICCONTAMINATED
WASTEWATERS ...................................................................................................................................119
3.5.1.FeloadedmaterialsappliedasFentoncatalysts ...................................................................120
3.5.2.Feloadedmaterialsappliedtoarsenicremoval ....................................................................124
3.6.REFERENCES ..................................................................................................................................128
4.CONCLUSIONS .....................................................................................................................131 ANNEXES
ANNEX I. HEAVY METAL CONTAMINATION AND MOBILITY AT THE MINE AREA OF DRAA LASFAR
(MOROCCO). Marta Avila, Gustavo Perez, Mouhsine Esshaimi, Laila Mandi, Naaila Ouazzani, Jose L.
Brianso and Manuel Valiente. The Open Environmental Pollution & Toxicology Journal. Accepted
Manuscript.
ANNEX II. XANES SPECIATION OF MERCURY IN THREE MINING DISTRICTS – ALMADEN, ASTURIAS
(SPAIN), IDRIA (SLOVENIA). Jose Maria Esbri, Anna Bernaus, Marta Avila, David Kocman, Eva M.
GarciaNoguero, Beatriz Guerrero, Xavier Gaona, Rodrigo Alvarez, Gustavo PerezGonzalez, Manuel
Valiente,PabloHigueras,MilenaHorvatandJorgeLoredo.JournalofSynchrotronRadiation.(2010).
Volume:17,Issue:2,Pages:179186.
ANNEXIII.EXTRACTANTANDSOLVENTSELECTIONTORECOVERZINC.MartaAvila,GustavoPerezand
ManuelValiente.SolventExtractionandIonExchange(2011),29:384–397.
ANNEXIV.ZINCRECOVERYFROMANEFFLUENTUSINGIONQUEST290:FROMLABORATORYSCALETO
PILOTPLANT.M.Avila,B.Grinbaum,F.Carranza,A.Mazuelos,R.Romero,N.Iglesias,J.L.Lozano,G.
Perez,M.Valiente.Hydrometallurgy(2011),107:6367.
1
INTRODUCTION
MINESITESCHARACTERIZATION ........................................................................................................3 1.1.MININGOVERVIEW ...........................................................................................................................3
1.2.SOILIMPACTSFROMMINING ...........................................................................................................4 1.3.CHARACTERIZATIONOFMINESITES..................................................................................................7 1.4.SOILQUALITYREGULATIONS...........................................................................................................10 1.5.CHEMICALSPECIATIONANDFRACTIONATIONINSOILS .................................................................12
1.6.SOILRISKASSESSMENTTOOLS ........................................................................................................16 1.7.WEAKNESSANDNEEDSOFMININGSITESCHARACTERIZATION.....................................................19
REMEDIATIONTECHNIQUESOFINDUSTRIALCONTAMINATEDWATER ...................................20
1.8.SOLVENTEXTRACTIONFORTHERECOVERYOFZnFROMACIDICMINEWATERS...........................20
1.9.FELOADEDMATERIALSFORTHEREMOVALOFORGANICANDINORGANICCONTAMINANTS......24
1.10.THEFENTONREACTION.................................................................................................................29
1.11.ARSENICSORPTIONUSINGFELOADEDMATERIALS .....................................................................31
1.12.WEAKNESSESANDNEEDSOFINDUSTRIALLYCONTAMINATEDWATERS......................................33
ANALYTICALTECHNIQUES...................................................................................................................35 1.13.XRAYFLUORESCENCE ................................................................................................................... 35
1.14.SYNCHROTRONBASEDTECHNIQUES ............................................................................................39
1.15.OBJECTIVES....................................................................................................................................44
1.16.REFERENCES...................................................................................................................................45
1
2
1.Introduction
This chapter is addressed to provide general information related to the work that has
beenperformedinthisthesis.Inthissense,generalaspectsofmines,itscharacterizationby
meansofdifferenttechniquesandparameters,aswellastechniquestoremediateorganicand
inorganicindustrialwastewaterseithersyntheticorfromspecificminewaterareintroduced.
Thus, three main sections have been distinguished: Mine sites characterization, Remediation
techniquesandAnalyticaltechniques.
MINESITESCHARACTERIZATION
In this section is focused on the role of mining as a key element for human progress
together with the related environmental problems such as heavy metal contamination or
mining water impoundments (tailings ponds) breachings. The description of several
parameterstocharacterizethecontaminationaroundmineareasisalsoincluded.
1.1.MININGOVERVIEW
Sinceprehistory,mininghasbeenkeytothedevelopmentofcivilizations.Inthissense,
theculturalagesofmanareassociatedwithmineralsortheirderivatessuchastheStoneAge
(priorto4000BC),theBronzeAge(4000to5000BC)ortheIronAge(1500BCto1780BC)[1].
Inthisregard,flintimplementsforagriculturalorconstructionpurposesfoundwiththebones
of the Paleolithic man (300,000 years ago) revealed mining activities since prehistoric times.
However, the oldest known underground mine, located at Bomvu Ridge (Swaziland), is
believedtobe40,000yearsold.Nonetheless,itwasuntilEgyptiantimesthatminesattained
depthsof250m.DuringtheBronzeandIronAgeshumansdiscoveredsmeltingandlearnedto
reduce ores into pure metals or alloys, which greatly improved their ability to use these
metals.Lateron,theRomansdevelopedlargescaleminingmethodssuchashydraulicmining
methods to prospect the ore deposits and the use of large volumes of water brought by
numerous aqueducts to the mine where it was stored in large reservoirs and tanks used to
remove rock debris. All the main mine areas exploited nowadays, were already exploited in
roman times, or even previously to roman times. Iberian peninsula was the most important
mining region, of special relevance were the mines of Río Tinto, Cartagena district, Linares,
3
1.Introduction
Sierra Morena and Almadén in Spain; and Aljustrel, Sâo Domingos, Valongo, Jales and Três
MinasinPortugalalthoughalltheregionsoftheRomanEmpirewerealsoexploited(Table1.1)
[2].
Table1.1.PrincipalminesexploitedduringRomanEmpireandmineralsextracted
Mine
Mineralsextracted
UsesduringtheRomanempire
Ríotinto(Spain)
Almadén(Spain)
LasMédulas(Spain),
Dolaucothi(Wales)
Aljustrel(Portugal)
Lead
Piping(aqueductsplumbing,guttersforvillas)
Silver
Coins,weapons
Mercury
Pigment
Gold
Tools,weapons,jewellery,coins
Zinc,lead
Alloycopperintobrassforweapons
Nowadays,miningactivitiesstillrepresentanimportantroleintheworlddevelopment
and an important economic activity in many countries. In this sense, in 2001 the mining
industryproducedover6billiontonsofrawproductvaluedatseveraltrilliondollars.Mineral
processing of these raw materials adds further value as raw materials and products are
createdtoserveallaspectsofindustryandcommerceworldwide[3].
1.2.SOILIMPACTSFROMMINING
Miningconsistsintheextractionofvaluablemineralsorothergeologicalmaterialsfrom
theearth,usuallyfromanorebody,veinor(coal)seam,thatimpliestheremovalofsoil.Ore
bodiesarenaturallyoccurringconcentrationsofmineralswithsufficientlyhighconcentrations
of metals as to make them economically worthwhile exploited. However, it has been
estimatedthatmorethan70%ofallthematerialexcavatedinminingoperationsisdiscarded,
andhighamountsofwaterarespentonmineralprocessing(i.e.washingtheoretoenablethe
separationofvaluablemetalsormineralsfromtheirgangueorwastematerial,toreducethe
mineraltoitsmetallicformsincemostmetalsarepresentinoresasoxidesorsulfides,etc.).
These wastes (called tailings) are commonly spread throughout the mine area or, when
consisting in mining wastewaters, dumped into ponds secured by dams [4]. Hence, elevated
levelsofheavymetalsfrommetalliferousminesarefoundinandaroundtheminesduetothe
dischargeanddispersionofminewastematerialsintotheecosystemresultinginlargeareasof
agriculturallandcontaminatedposinganenvironmentalriskforhumansandecosystemsand
thusrestrictingsoiluse[5].Thus,thenatureofminingprocessescreatesapotentialnegative
impactontheenvironmentbothduringtheminingoperationsandforyears,afterthemineis
closed and many regions have been contaminated causing huge impact in the soils
surroundingmineareas.
4
1.Introduction
Therearearound560,000abandonedminesonpublicandprivatelyownedlandsinthe
UnitedStatesaloneanditwasestimatedthatin2000existedmorethan3,500tailingsponds
with water containing high amounts of metals [6]. Every year, 2 to 5 major failures and 35
minorfailuresoccurred;hencereleasinghighamountsofhighlycontaminatedwatersintothe
environment[7].Todate250casesoftailingsdamfailuresintheworldhavebeencompiled
producinghugehumanandenvironmentaldamages(Table1.2)[8,9].
These huge amounts of heavy metals deposited in waste dumps and tailings ponds
require management and monitoring once the activity has deceased [10] as several metals
(e.g.mercury,cadmium,lead,nickel,arsenic,zinc,copper)arehazardoustohumanhealthand
terrestrialecosystems.Sothedeterminationofmetalsincontaminatedsoilsshouldbecarried
out to obtain information about the nature, quantity, distribution and behavior of
contaminantsand,ifnecessary,toselectthemostappropriateuseofthesite[11].Thus,itisa
foremosttasktocharacterizeheavymetalconcentrationaroundmineareasoncetheactivity
has deceased to detect the degree of contamination in order to apply proper management
tools.
Inthissense,inseveraldevelopingcountries,miningactivitiesrepresentahighareaof
activitythusconstitutingagreathazardduetothepresenceofhighamountsofheavymetals
related to functioning or abandoned mines. Despite mining is an important part of the
industrial development in many developing countries (Philippines, Morocco, Peru, etc.),
relativelyfewenvironmentalstudiesonminingsiteshavebeenundertakentodeterminethe
heavymetalconcentrationaroundmineareasandtheirimpactonsurroundingsoilandwater
resources, where commonly no national program for the rehabilitation of existing polluted
sitesisimplemented[12,13,14,15].
5
1.Introduction
Date
Table1.2.Majortailingdamfailuresinthelast25years
Location
Release
Impacts
2010, Oct
4th
2010,Jun.
25
2009,
Aug.29
2009,
May14
2008,
Sep.8
Kolontár,
Hungary
Huancavelica,
Peru
Karamken,
Russia
Huayuan
County,China
Taoshi,China
2006,
Nov.6
Nchanga,
Zambia
2006,
April30
2004, Sep
5
2003,
Oct.3
2002,
Aug. 27 /
Sep.11
near Miliang,
China
Riverview,
Florida,USA
Cerro Negro,
Chile
San Marcelino,
Philippines
3
700,000m ofcausticred several towns flooded, 10 people killed, approx.
mud
120peopleinjured
21,420m3oftailings
contaminationofEscalerariverandOpamayoriver
110kmdownstream
?
Elevenhomeswerecarriedawaybythemudflow;
atleastonepersonwaskilled
3
50,000m oftailings
A home destroyed, three people killed and four
peopleinjured.
?
A mudslide several meters high buried a market,
severalhomesandathreestoreybuilding.Atleast
254peopleweredeadand35injured
?
Release of highly acidic tailings into Kafue river;
drinkingwatersupplyofdownstreamcommunities
shutdown
?
Fiveinjuredpeople,17residentsmissingandmore
than130localresidentsevacuated.
227,000 m3 of acidic liquid spilled into Archie Creek that leads to
liquid
HillsboroughBay
50,000tonnesoftailings
tailings flowed 20 kilometers downstream the La
Liguariver
?
Aug.27:sometailingsspilledintoMapanuepeLake
and eventually into the Sto. Tomas River
Sep. 11: villages flooded with mine waste; 250
familiesevacuated
?
tailingswavetraveledatleast6km,killingatleast
twomineworkers,threemoreworkersaremissing
2001,Jun. Sebastião das
22
Águas Claras,
Brazil
2000,
Nandan county, ?
Oct.18
China
2000,Jan. Baia
Mare, 100,000 m3 of cyanide
30
Romania
contaminatedliquid
1999,
Apr.26
1998,
Apr.25
1997,
Oct.22
1996,
Aug.29
1996,
Mar.24
1994,
Oct.2
1994,
Feb.22
at least 15 people killed, 100 missing; more than
100housesdestroyed
contamination of the Somes/Szamos stream,
tributary of the Tisza River, killing tonnes of fish
and poisoning the drinking water of more than 2
millionpeopleinHungary
of 17homesburied,51hectaresofricelandswamped
Placer,
Philippines
Aznalcóllar,
Spain
Pinto
Valley,
USA
ElPorco,Bolivia
700,000
tonnes
cyanidetailings
3
45 million m of toxic
waterandslurry
3
230,000m oftailingsand
minerock
400,000tonnes
Marcopper,
Philippines
Payne
Creek
Mine,USA
Harmony,
Merriespruit,
SouthAfrica
Roxby Downs,
SouthAustralia
1.6millionm 3
6.8millionm3
3
600,000m thousands of hectares of farmland covered with
toxicslurry
tailingsflowcovers16hectares
300kmofPilcomayorivercontaminated
Evacuation of 1200 residents, 18 km of river
channelfilledwithtailings,US$80milliondamage
500,000 m3 released into Hickey Branch, a
tributaryofPayneCreek
tailings traveled 4 km downstream, 17 people
killed,extensivedamagetoresidentialtownship
5
million
m3 of ?
contaminated water into
subsoil
1993
Marsa,Peru
?
6peoplekilled
1985, July Stava, Trento, 200,000m3
tailings flow 4.2 km downstream at 90 km/h; 268
19
Italy
peoplekilled,62buildingsdestroyed
1994,
Feb.14
6
1.Introduction
1.3.CHARACTERIZATIONOFMINESITES
Inthelastyearsthesystematiccontrolofcontaminatedareashasbecomeakeyissueto
definehealthcarepolicies,costeffectiveenvironmentalplanningandriskassessmenttools.In
this sense, sampling of potentially contaminated soil from polluted areas is intended to
provide data of several physicochemical parameters or metal content of the soil for the
assessmentofwhetherthepollutionhascausedormaycauseenvironmentalproblems.
1.3.1.SAMPLING
As a previous step to characterize a mining area, a sampling strategy is needed. The
selectionandlocationofthesamplingpointsdependontheobjectivesoftheinvestigation,the
preliminary information available and the onsite conditions. Experiences (and theoretical
considerations)showthatinmanycasessystematicsamplingonaregulargridisbothpractical
and sufficiently productive to allow the creation of a detailed picture of variations in soil
properties.
Aregulargridisusuallyemployedinenvironmentalstudies,inwhichthesamplingareais
large(forexample,soilswithdifferentapplications).Whenthepurposeofthesamplingis,for
instance,settingthevaluesofcertainpropertiesinanhomogeneousareaorafirstprospection
inanareawherecontaminationissuspected,irregulargridsinformofX,W,S,etc.areusually
carriedout,inwhichthesamplingpointsarealsopredefined(Figure1.1).Thesesamplesare
usuallymixedtoformcompositesamples.
Figure1.1.GridsinformofWandXforsystematicsampling
Othersimilarapproachesincludesimpleregulargrids,circularorclustered(Figure2)in
ordertoestimatetheimpactofasourceofpollutionintheareaofstudy(withthepossibility
ofconcentrationgradients)ortoestimateconcentrationlevels.Thegridsshouldbedesigned
tostudyareaswhereallpointscanhaveasimilarconcentrationofthetargetanalytes,asisthe
caseofsimpleirregularsgrids(Figure1.2A)andalternativesthatallowthesubdivisionofthe
areasinquintets(Figure1.2B)orcancoveranareaassumingalocalizedsourceofthetarget
analytes,asisthecaseofcirculargrids(Figure1.2C).
7
1.Introduction
Sampling using clusters is actually a combination of random and systematic strategies,
with or without composite samples. It consists of the random or systematical selection of a
certainnumberofblocksinaregulargrid,andtakeanumberofindividualsamplesatrandom
(Figure 1.2D). The samples are analyzed individually or as composite samples, allowing an
estimationofvariabilityatthelocallevel(withineachgroup)orglobal(betweengroups).
Figure1.2.Systematicsamplinggrids
The number of sampling points can be easily increased (e.g., in areas meriting more
detailedinvestigation),thegridiseasymarkedbymeansofGPSsystemsandsamplingpoints
canbeeasilyrelocated. However,sometimesotherpatternsarefollowedbasedonaknown
local distribution or hot spot distributions along a line towards specific receptors allowing a
reductiononcostsandresourceconsumption.
1.3.2.SOILPHYSICOCHEMICALCHARACTERIZATION
Usually it is necessary to determine the nature, concentrations, and distribution of
naturally occurring substances and contaminants (extraneous substances), the physical
properties and the presence and distribution of chemical species of interest to identify
immediate hazards to human and to the environment. This information will also help to
determine the suitability of a soil for an intended use (agricultural production or residential
development amongst others) or to assess the transfer of substances from soils to plants
(bioavailability).
Several inorganic parameters should be taken into account when characterizing and
assessingrisksfromcontaminatedsitessuchaselectricalconductivity,pH,lossonignitionor
thecarbonatecontentamongstmanyothers[16].
Acidity.SoilpHreflectstheintensityofaciditythatinturninfluencessoilconditionsand
plant uptake of metal contaminants. pH influences the solubility and activity of various
8
1.Introduction
biologically important elements and processes. Depending on the soil:water ratio and the
compositionandtemperatureoftheequilibrationsolution,theresultingpHwillvary.ThepH
of surface soils (0100mm) commonly range from 6.0 to 8.0, and it is useful to note that pH
valuesaround4.0orlesssuggestthepresenceofsulfides,whilelevelsabove8.5areindicative
ofthepresenceofsignificantquantitiesofexchangeableNa+.
Soil salinity. Soil salinity is estimated from the electrical conductivity (EC) of a soil
saturated paste. The electrical conductivity (EC) of a soil suspension provides an estimate of
the concentration of soluble salts in the soil, mostly due to predominantly cations Na+, Mg2+
and Ca2+ and anions Cl, SO42 and HCO3. Typical soil:water ratios (deionized or distilled)
employedtodeterminesalinityare1:1,1:2,1:2.5and1:5althoughthe1:5ratioispreferredas
it gives an approximation of soil ionic strength [17]. Salinity measurements provide
information about the ability of a site to support plant growth as well as some information
regardingpotentialleachinganddrainageproblems.Electricalconductivityisagrossmeasure
ofdissolvedsaltsinsoilsolution,butprovidesnoinformationastowhichsaltsarepresentand
inwhatproportion.Fornonsensitiveplants,ECmeasurements<4dSm1aresatisfactory.Soils
with EC > 4dS m1 are considered saline and plant growth may be inhibited. Electrical
conductivity values increase with increasing temperature and must be corrected if not
measuredat25ºC[18].
Organicmatter.Thelossonignition(LOI)methodisasimpleandrelativelyinexpensive
method for determining organic matter [19]. The method is based on differential thermal
analysisofthesampleweightafterheatingat500550ºCtooxidizetheorganicmattertoCO2
and SO2. However, the ignition temperature and the heating time influence the results as
organicmattermaynotbecompletelyconvertedintoCO2andSO2iftemperatureistoolowor
if burning time is too short. If temperature is too high or heating too long, inorganic
compounds such as carbonates and sulfate may be also converted to CO2 and SO2 [20]. Soil
organicmatteraffectsthechemicalandphysicalpropertiesofthesoilincreasingthesoilbuffer
capacity, so the presence of organic matter tends to lower pH variations. Furthermore, the
retentivecapacityoforganicmatterisgreaterthanmostreactiveclays.
Carbonate content. Carbonate plays an important role in soil chemistry influencing the
pHofsoilsgivenitscarbonatebicarbonatebufferingequilibrium[21].Inaddition,carbonates
cancomplexseveralcations,thusaffectingtheamountofexchangeablecations,thepresence
ofeasilysolublesalts,theredoxpotentialandthepartialpressureofCO2inthesoilair[22].
9
1.Introduction
1.3.3.METALANALYSIS
One of the most critical properties of metals, which differentiate them from organic
pollutants,isthattheyarenotbiodegradableintheenvironment[23].Asaresultmetalstend
to persist in the various reservoirs of natural systems such as water, soils and sediments, or
accumulateinbiologicalsystems,leadingtoanimportanthazardtoenvironmentandhuman
health.Inanycase,atypicalfeatureoftheweatheringofminingwaste,apartfrompossible
acidicwaterformation,isthereleaseofmetalsfromthemineralmatrixintotheenvironment.
To determine metal concentration on solid samples from polluted sites, normally,
analytical methodologies based on recommended methods, are applied for water,
wastewater, sludge, and agricultural soils. Chemical analysis of polluted soil samples can be
difficult because of interferences due to the complex soil matrix (e.g. mixture of
elements/pollutants at high concentrations such as Al, Fe or Ca and mixtures of organic
compoundssuchasPAHs,PCBsorhydrocarbons),sousuallytheanalysisofmetalsonsoilsis
performedafterdigestionwithastrongacidsolutioninconjunctionwithahotplate,aboiling
device or microwave heating system. After digestion, the samples are analyzed by means of
atomic absorption spectrometry (AAS), inductively coupled plasma optical emission
spectroscopy (ICPOES) or inductively coupled plasma mass spectroscopy (ICPMS). In this
sense, wet chemistry instrument techniques for elemental analysis require destructive and
timeconsuming sample preparation, often using concentrated acids or other hazardous
materials. Moreover, the sample is destroyed and hazardous waste streams are generated
duringtheanalyticalprocessrequiringdisposal.Allthesefactorsleadtoarelativelyhighcost
persample.However,wetchemistryinstrumentalanalysistechniquesarestillnecessarywhen
lower elemental concentrations are the primary measurement need. Thus, during the lasts
yearsXrayfluorescence(XRF)hasemergedasavaluabletoolforthemeasurementofheavy
metals in the environment given their reliable and rapid measurement [24, 25, 26, 27]. XRF
analyticalmethodologyisoftenchosenasthemostappropriatewhenthereisnohistoricalsite
informationasinitialsamplingcostsarereducedandanalysesareconductedquicklyandwith
lessrigoroussamplepreparation.
1.4.SOILQUALITYREGULATIONS
Soilprovidesuswithfood,biomassandrawmaterials.Itservesasaplatformforhuman
activitiesandlandscapeandasanarchiveofheritageandplaysacentralroleasahabitatand
gene pool. It stores, filters and transform many substances, including water, nutrients and
carbon. Thus, soil contamination may have important consequences affecting ecological
10
1.Introduction
systems and biological cycling of nutrients or being unable to act as filter and buffer. In this
sense,hydrosphere,groundwaterresourcesandaquaticecosystemscanbethreatened[28].In
cases of severe contamination and in places where risks to human health and/or the
environmentareobserved,soilremediationisnecessary.
Duringthelastyears,soilprotectionpolicieshavebeendevelopedandimplementedin
several countries focused on different contaminants, diverse land uses and on varied
contaminationsources(asforexampleminingandindustrialactivities,agriculturalpracticesor
oilspills)suchastheNetherlandsGuide[29]andtheFrenchGuidelinesvalues[30](Table1.3)
andatinternationallevel inthe Europeanstrategy forsoilprotectionframework[31].These
guidelinesdefinedifferentqualitystandardvaluesbasedonthetotalconcentrationofseveral
trace metals in soils and sediments to facilitate decisions on intervention in soils after
determining the existence or not of contamination, considering the actual or future soil use
(naturalpark,agricultural,residential,recreationalorindustrial).
Table1.3.GuidelinesnationalvaluesforheavymetalsinsoilsforNetherlands,Franceand
Catalonia(Spain)
Metals
Netherlands[29]
France[30]
Spain(Catalonia)[32]
Target
Intervention
Sensitive Nonsensitive Industrial
Urban
Other
Value
value
use
Use
use
use
uses
As
29
55
37
120
30
30
30
Pb
85
530
400
2,000
550
60
60
Cd
0.8
12
20
60
55
5.5
2.5
Cu
36
190
190
950
Cr(total)
100
380
130
7,000
Cr(III)
1,000
1,000
50
Cr(VI)
25
10
1
Hg
0.3
10
7
600
30
3
2
Ni
35
210
140
900
1,000
470
45
Zn
140
720
9,000
1,000
650
170
The target value is the baseline concentration value below which compounds and/or
elements are known or assumed not to affect the natural properties of the soil while the
intervention value is the maximum tolerable concentration above which remediation is
requiredandbecomesmandatory.
In this sense, it is intended that guideline values could represent an indication to an
assessorthatsoilconcentrationsabovethislevelcouldposeanunacceptablerisktothehealth
of site users and that further investigation and/or remediation is required. As the
concentration of metals on plants does not necessary correlate with the total content of
metalsinrelatedsoils,thesevaluesrepresentanestimationofthepotentialhazard,although
it can be considered as the most pessimistic interpretation as it is considered that the total
11
1.Introduction
amountofmetalinsoilisavailabletobeabsorbedbyplantsorcanbemobilized.So,notonly
thetotalcontentofheavymetalsshouldbeconsidered,butalsoitsmobilityandbioavailability
todeterminetherealtoxicity.
1.5.CHEMICALSPECIATIONANDFRACTIONATIONINSOILS
Mobility and bioavailability of metals in the environment depends strongly on their
specificchemicalformsortypesofbindingratherthanthetotalelementcontent[33,34].Soit
canbegenerallyconsideredasanindicationoftoxicityandconsequentlythechemicalspecies
presentinasoilshouldbedeterminedinordertoassessthetoxiceffects.However,nowadays
this distinction is not reflected in the legislation, which account for the total content of the
pollutantsratherthanfortheavailablecontentofthepollutants.
In this regard, the characteristics of just one species of an element may have such a
radicalimpactonlivingsystems(evenatextremelylowconcentrations)thatthetotalelement
concentration becomes of little value in determining the impact of the trace element. Good
examplesaremercuryandtin.Theinorganicformsoftheseelementsaremuchlesstoxic(or
even do not show toxic properties) than the alkylated forms which are highly toxic. In these
sense,itisnecessarytoevaluateandcharacterizethechemicalformsoftheelementsinorder
to understand their properties, their evolution possibilities, as well as the prediction of the
related environmental consequences. Such characterization is carried out the methodologies
knownbyspeciationanalysis.
At this point it is required to define the term speciation. The IUPAC has defined the
terminologyonelementalchemicalspeciationasfollows[35]:
Chemical species: Specific form of an element defined as to isotopic composition,
electronicoroxidationstate,and/orcomplexormolecularstructure.
Speciationanalysis:Analyticalactivitiestoidentifyingand/ormeasuringthequantitiesof
oneormoreindividualchemicalspeciesinasample.
Speciationofanelement:Distributionofanelementamongstdefinedchemicalspecies
inasystem.Whenelementalspeciationisnotfeasible,fractionationisemployed.
Fractionation:Processofclassificationofananalyteoragroupofanalytesfromacertain
sample according to physical (e.g., size, solubility) or chemical (e.g. bonding, reactivity)
properties.
Althoughnogenerallyaccepteddefinitionofthetermexists,speciationcanbroadlybe
defined as the identification and quantification of the different, defined species, forms or
phasesinwhichanelementoccurs[36].Theterm"fractionation"(alsoreferredtoasindirect
12
1.Introduction
speciation)isfrequentlyusedinterchangeablywithspeciationbutemphasizestheconceptof
subdividinga"totalcontent".Also,theanalyticalpreparationsforseparatingmetalspeciesare
referredtoas"fractionation".Anoverviewoftechniquesusedinchemicalspeciationanalysis
isgiveninTable1.4[37].
Table1.4.Analyticalmethodsappliedforchemicalspeciationofmetals
Method
Electroanalysis
Ionselectiveelectrodes
Voltammetry
Spectroscopy
Spectrophotometry
Hydridegeneration
LIQUID
PHASE
SynchrotronXrayspectroscopy
Chromatography
HPLC
GCorLC
Physicochemicalfractionation
Ionexchangeresin
UVirradiation
Solventextraction
Sizefractionation
Filtration
Centrifugation
Dialysis
Ultrafiltration
Gelfiltrationchromatography
SOLID
PHASE
Singlereagentleaching
Sequentialextractions
Ionexchangeresins
SynchrotronXrayspectroscopy
Metalspeciesdetermined
Freeionicconcentrations
Freeionsandlabilecomplexes
Specificforms
Inorganic and organometallic species; different
oxidationstates(Sn,As,Sb,Bi,Se,Te)
Specificforms
Cations,anions,metalcomplexes,inorganicspecies
Organometalliccompoundsofmercury,tinandlead
Freeionsandlabilecomplexes
Organiccomplexes
Organiccomplexes
Dissolvedandsuspendedmatterassociated
Dissolvedandsuspendedmatterassociated
Differentcharge,differentmolecularsize
Molecularsize
Free forms and complexes of different molecular
size
Reagentsolublefractions
Geochemicalfractions
Labilefractions
Specificforms
Whilst different elemental speciation methods are available for aqueous systems [38],
implementations of methodologies for speciation studies in solids have been less well
developed.Thespeciationstudiesinvolvingsoilandsedimentanalysisareoftenbasedonthe
useofextractionprocedures(singleorsequential).
The determination of specific chemical species or binding forms is difficult and often
hardly possible. Therefore, in practice, determinations of broader “operationally or
functionally defined” forms or phases can be a reasonable compromise to arrive at a sound
environmentalpolicy.Inthisregard,singleandsequentialextractionschemesweredesigned
inthe1980sinordertoassessthedifferentretention/releaseofmetalsinsoilandsediment
samplesasaresultofnaturalprocessesoranthropogenicactivitiesandcanbeemployedasa
valuabletooltoassessthepotentialimpactintheenvironmentofminingdistricts[39,40].
13
1.Introduction
1.5.1.SEQUENTIALEXTRACTIONSCHEMES
Despitebeingquitelaborious,sequentialextractionschemes(SES)havebeenthemain
tools employed to estimate the availability of contaminants in polluted soils, sediments and
sludge [41, 42, 43]. SES procedures try to mimic the various natural conditions under which
soils may release metals into the environment using sequentially leaching reagents of
increasing strength. The determination of these metal fractions allows certain predictions
regardingthepossiblereleaseofagivenanalyte(metal)fromasoilorsedimentphaseunder
certainconditionsofgraduallixiviationpower.
Theappliedstrategyconsistsontheuseofreagentsabletoselectivelydissolveametal
fractionbondedtocertainsoilmaterials,i.e.watersolublecompounds,exchangeablecations,
carbonates, easily reducible, oxidizable phase and residual. These fractions may vary among
different extraction schemes. Most common reagents used include: no hydrolysable salts,
weakacids,reducingagents,oxidantagentsandstrongacids[44].
Several SES schemes have been developed to evaluate metal fractionation in soils and
sedimentsnormallyvaryinginthenumberofextractionstepsbetween3and8andthosemost
widelyusedareTessier[35,42]andBCRSES[45,46](Table5).Comparatively,bothmethods
provide a similar fractionation, although the exchangeable fraction of BCR resumes
“exchangeable”and“carbonate”fractionsfromTessier.
Table1.5.SequentialextractionproceduresdefinedbyTessierandBCRSESappliedto1gofsample
Method
Fraction
Extractionconditions
T1:Exchangeable
T2:Linktocarbonates
T3:Linktoironand
manganeseoxides
Tessier
T4:Linktoorganicmatter
T5:Residual
BCRSES
Watersoluble,exchangeable
andlinktocarbonates
Linktoironandmanganese
oxides
Linktoorganicmatterand
sulfides
8mL1MMgCl2pH7,25ºC,1h
8mL1MCH3COONa+CH3COOH,pH5,25ºC,5h
20mL0.04MNH2OHHCl(25%v/vCH3COOH),96ºC,6h
3mL0.02MHNO3+2ml30%H2O2(pH2),85ºC,2h;3
mL30%H2O2(pH2),85ºC,2h;5mL3.2MCH3COONH4
in20%HNO3+7mLH2O25ºC,30min
7.5mL37%HCl+2.5mL65%HNO3,25ºCduring1
night,reflux2h
20mL0.1MCH3COOH,25ºC,16h
20mL0.5MNH2OHHCl,pH2,25ºC,16h
5mL30%H2O2,25ºC,1h+5mL30%H2O2,85ºC,1h+
25mL1MCH3COONH4,pH2,25ºC,16h
Nevertheless,SEShaveseveraldrawbacksmainlyrelatedtotheexcessivetimerequired
(atraditionalsequentialextractionrequiresatleast50hours)andthepossiblemodificationof
the metal species during extraction procedure [47]. It is worth mentioning that not all the
fractionsobtainedfromapplyingSESareequallyimportantfromtheenvironmentalriskpoint
ofview.Themetalsrelatedtotheresidualfraction(obtainedthroughextractionordigestion
14
1.Introduction
with mixtures of strong acids) are unlikely to be released under weathering conditions;
whereasmetalslinkedto thesolubleandexchangeablefractions,andthoserelatedtomore
labilemetalspeciesaremoremobileandhencemoreavailable.Therefore,inordertoassess
the environmental hazard, efforts should be applied only on the measurement of these
fractionsopeningthepossibilityofusinglesslaboriousmethodsbasedontheextractionofthe
metalfractionofinterestusingauniqueextractingreagent(singleleachingtests).
1.5.2.SINGLELEACHINGTESTS
Singleleachingtestsarenonselectiveextractionsthattargetgroupsoflabileormobile
phases.Thisapproachcanprovideausefulassessmentforscreeningpurposestoidentifytrace
metal pollution with minimum time consumption [48]. Single extractants differ by their
dissolutionpower,including:i)mildunbufferedextractantsthatextractthefractionofeasily
exchangeable elements; ii) acidic extractants that release the fraction remobilized by
acidificationprocesses;andiii)complexingreagents(Table1.6).[49].
Group
Table1.6.Leachingtestsusedinsoilanalysis[50]
Typeandsolutionstrength
Acidextraction
Chelatingagents
Bufferedsaltsolution
Unbufferedsaltsolution
HNO30.432.0M
Aquaregia
HCl0.11M
CH3COOH0.1M
HCl0.05M+H2SO40.0125M
EDTA0.010.05MatdifferentpH
DTPA0.005M+TEA0.1M+CaCl20.01M
CH3COOH 0.02 M+NH4F 0.015 M + HNO3
0.013M+EDTA0.001M
NH4acetate,acetateacidbuffer1MpH=7
NH4acetate,acetateacidbuffer1MpH=4.8
CaCl20.010.1M
NaNO30.1M
NH4NO31M
AlCl30.3M
BaCl20.1M
References
[51]
[52]
[53]
[54]
[55]
[53]
[56]
[57]
[58]
[53]
[53]
[58]
[53]
[59]
[60]
Leaching tests are focused on providing information about the release of specific
componentsundergivenconditions,orunderconditionsthatmayapproximatemoreclosely
or simulate the actual field situation under consideration. Such conditions try to reproduce
those chemical reactions that can take place in soils and sediments on a particular
environment (i.e, adsorption–desorption, dissolutionprecipitation, reduction–oxidation, and
complexationdecomplexationprocesses),andcanmodifytheconcentrationofmetalsinsoil
solution [61,56]. The application of these procedures to polluted or naturally contaminated
soilsismainlyfocusedtoascertainthepotentialavailabilityandmobilityofmetals,instudies
15
1.Introduction
on the soilplant transference and metal migration in a soil profile due to groundwater
transport. Subsequently, this information is usually used for the risk assessment of wastes
whentheyaredepositedinalandfillortocharacterizeandclassifythemintermsofrisk[62].
At present, several single extraction procedures (leaching tests) based on aqueous or acidic
extractions are widely approved analytical tools in national and international legislation
organisms such as Germany [63], France [64], Italy [65] and The Netherlands [66] amongst
others.
Hence,leachingtestssuchas(NH4)2SO4orHClsinglenonselectiveextractionsmethods,
can provide a useful assessment for screening purposes to identify labile or mobile phases
[45]. In addition, it is demonstrated a correlation between the mobility observed by some
leachingtestsandthemobilityprovidedbythesumofdifferentstagesofSES[67].Themain
advantagesofthesesingleleachingtestsagainstSESaremainlyrelatedtotheircostefficiency,
easy to use and a reduction on bias induced by sequential translation and accumulation of
proceduralerrors.
However, despite being very useful tools for environmental assessment of chemical
species, SES and single leaching procedures cannot provide direct speciation of soils. In
addition,SESaredestructivetechniques.Fordirectspeciation,synchrotronbasedtechniques
havearoseasavaluabletoolbymeansoftechniquessuchasXrayAbsorptionSpectroscopy
(XAS)usingsynchrotronfacilitiesasXraysradiationsources.
1.6.SOILRISKASSESSMENTTOOLS
Varioustoolscanbeemployedtodetermine the degreeofcontaminationof aspecific
site, like an abandoned mine site, such as concentration enrichment factors, geographic
information systems or the use of statistical tools. These tools can be used for a better
determinationoftheriskofacontaminatedsiteandthushelpthedecisionmaking.
1.6.2.GEOGRAPHICINFORMATIONSYSTEMS
Further characterization in environmental studies of polluted soils is achieved through
the determination of the pollutants spatial variability in a polluted area through Geographic
InformationSystems(GIS)[68,69,70].Fromapracticalpointofview,theyprovidedthefirst
referencevaluesforassessingsoilcontaminationatagivenpotentiallypollutedsite.Assessing
the spatial extent of soil metal concentration is also a powerful tool in understanding and
monitoringtheadverseeffectsof contamination.Soilmapsare usedinsoildescription,land
appraisal (taxation), and for soil monitoring sites to establish the basic information on the
16
1.Introduction
genesis and distribution of naturally occurring or manmade soils, their chemical,
mineralogical,biologicalcomposition,andtheirphysicalpropertiesatselectedpositions.
Spatial variability of soil properties and pollutants concentration can be done with
different interpolation methods such as inverse distance weighting (IDW), Kriging and spline
functions[71].Whilesplinemethodsinvolveaconsiderableinterpolationerrorwhenthereare
largechangesinthesurfacevalueswithinashorthorizontaldistance,Krigingmethodmaynot
bemetinpracticeunlessemploying100samplesinordertoobtainareliablevariogramthat
correctly describes spatial structure. In contrast, IDW interpolator assumes that each input
pointhasalocalinfluencethatdiminisheswithdistance[72],andnoassumptionsarerequired
forthedata,beingthismethodsuitableforirregularsamplings[73].
CombiningGeographicInformationSystems(GIS)withsomeanalyticaltoolsthespatial
variabilityinaminearea,canbedetermined.Suchcombinationlettoproducemapswhichare
helpfulforacosteffectiveidentificationofthesourcesandthespatialpatternsofpollutants
[71,72,74].
1.6.3.PRINCIPALCOMPONENTANALYSISINGEOSCIENCES
Withintheenvironmentalinvestigationsassociatedwiththeimpactofmetalsinsoils,it
is often necessary the determination of multiple parameters, obtaining multivariate data. A
first comparison between samples across individual parameters can be performed, although
whenthesimultaneousconsiderationofalltheparametersdeterminediscarriedout(known
as multivariate methods of analysis), a characterization of the combined effect of different
variablesandespeciallyofthevariousrelationshipsbetweenthemcanalsobeobtained.
Principal Component Analysis (PCA) was invented in 1901 by Karl Pearson [75] and is
nowadays an extremely useful technique to "summarize" all the information in a more
understandable form. Typically, PCA is used to reduce the dimensionality of a dataset, while
retainingasmuchoftheoriginalinformationaspossible.
PCAworksbydecomposingtheXmatrixthatcontainsallthedataastheproductoftwo
smallermatrices,whicharecalledtheloadingandscorematrices:
X=TPT+E
(Equation1.1)
Theloadingmatrix(P)containsinformationaboutthevariables.Itiscomposedofafew
vectors(PrincipalComponents,PCs)whichare(obtainedas)linearcombinationsoftheoriginal
Xvariables. The score matrix (T) contains information about the objects. Each object is
described in terms of its projections onto the PCs, (instead of the original variables). The
17
1.Introduction
informationnotcontainedinthesematricesremainsas"unexplainedXvariance"inaresidual
matrix(E)whichhasexactlythesamedimensionalityastheoriginalXmatrix.
ThePCs,amongmanyothers,havetwointerestingproperties:
x
They are extracted in decreasing order of importance. The first PC always contains
moreinformationthanthesecond,thesecondmorethanthethirdandsoon...
x They are orthogonal to each other. There is absolutely no correlation between the
informationcontainedindifferentPCs[76].
PCAissensitivetotherelativescalingoftheoriginalvariablessocenteringofthedata
foreachattributeispreviouslyrequired.TheresultsofaPCAareusuallydiscussedintermsof
componentscores(thetransformedvariablevaluescorrespondingtoaparticularcaseinthe
data) and loadings (the weight by which each standardized original variable should be
multipliedtogetthecomponentscore)[77].
Often, PCA can be thought of as revealing the internal structure of the data in a way
whichbestexplainsthevarianceofthedata.Ifamultivariatedatasetisvisualizedasasetof
coordinates in a highdimensional data space (1 axis per variable), PCA can supply the user
with a lowerdimensional picture, when viewed from its most informative viewpoint. This is
done by using only the first few principal components so that the dimensionality of the
transformeddataisreduced.
So,byusingPCAalargenumberofvariablessuchasconcentrationofelementscanbe
transformed into linearly independent sources of “information” (referred to as components)
thatcanbeinterpretedtoprovideinsightintotheprocessesorinterrelationshipsthatunderlie
the data. Principal components analysis is commonly used in a variety of geosciences
disciplines,suchasaeolianapplicationstohelpdeterminesourceregionsofparticulatematter
pollution[78,79],tocharacterizeparticlesizedata,includingstudiesofsoilfertility[80]and
pollution [81] while textural and other sedimentological data has been used to differentiate
between marine and terrestrial sediments in Florida [82]. Thus, interpretation of PCA
componentscanhelpidentifysourceofcontaminationaswellascontaminationpatterns.
PCA[83]wasalsoappliedtoexplaintheunderlingstructuresoftheobtaineddata,i.e.to
identify pollutant sources, certain distribution patterns and their contributions on soils
affectedbyminingpollution.
18
1.Introduction
1.7.WEAKNESSANDNEEDSOFMININGSITESCHARACTERIZATION
x Weaknesses:
x
Soil risk assessments covered by the current legislations consider only heavy metal
concentration and do not account for the real risk of contaminated sites such as
abandoned mines better explained by heavy metals mobility determined by its
chemical species. In addition the origin of the contamination (either natural or
anthropogenic)isnotconsidered.
x
Agreatdealofminesisabandonedeveryyearwithoutconcernfortheenvironment.
This is especially dramatic in developing countries were no legislation concerning
contaminatedsoilsareimplemented.
x Needs:
x
The characterization of several parameters including not only heavy metal
concentrationbutalsoitsdistributionaroundthepollutedarea,thephysicochemical
parameters,theoriginofthecontaminationandthemobilityoftheheavyelementsof
soilsshouldbeperformedtoassessenvironmentalhealthhazards.
19
1.Introduction
REMEDIATIONTECHNIQUESOFINDUSTRIAL
CONTAMINATEDWATER
Thedevelopmentofviablewaysofrecyclingindustrialwatersandtheirderivatesludge
suchas miningeffluents ratherthanitsdisposalasahazardouswasteinspeciallycontrolled
landfills can be a benefit from both environmental and economical point of view. In this
section, the application of various remediation techniques for the treatment of organic and
inorganiccontaminatedwastewatersisresumed.
1.8. SOLVENT EXTRACTION FOR THE RECOVERY OF ZN FROM ACIDIC
MINEWATERS
Refused mining tailings and water containing rejected materials are generally pumped
intotailingpondstoavoidtheirtransportationbywindintopopulatedareaswherethetoxic
chemicalscouldbedangeroustohumanhealthaswellastoallowthesedimentationofsolid
particles[84]. However, mine tailing ponds are potentially hazardous as they can represent a
sourceofaciddrainagebutespeciallyduetodamfailuresoftailingponds.Whenatailingline
breaksoradambreaches,highamountsofcontaminatedwaterandclayswithdissolvedmetal
ionsarereleasedtotheenvironmentcausingseriousdamagesandhavingtoxiceffectsonthe
biota in the downstream water[85]. Some mining operations are able to recycle relatively
smallamountsofwaterfordrillinganddustsuppressionbyusingsimplesumpstoclarifythe
water,butinmostcases,thetotalvolumeispumpedtothesurfacefortheirtreatmentwith
the other aqueous drainage components so as to minimize longterm environmental effects
onceactivemininghasceased[86].
The volume and characteristics of materials contained in tailing ponds can vary widely
dependingonminingmethodsandthehydrogeologicalcharacteristicsoftheregion.WaterpH
canvaryfrombasictoveryaciddependingonthenatureoftheoreanditshostrockandit
maycontainhighlevelsofdissolvedmetals,suspendedsolids,someoilsandammonia.
Asanexampleofeconomicalfeasibleandvaluablerecoveryofaheavymetalbyproduct,
the recovery of Zn from mine waters can diminish the volume of hazardous materials
containedintheminetailingwhileprovidingeconomicalprofit.
20
1.Introduction
1.8.1.ZINCOVERVIEW
Zincisthe23rdmostabundantelementintheearth'scrust.Zincisnecessarytomodern
living, and, in tonnage produced, stands fourth among all metals in world production being
exceeded only by iron, aluminum, and copper. Over 11 million tonnes of zinc are produced
annuallyworldwide.Nearly50%oftheamountisusedasacoatingtoprotectironandsteel
from corrosion (galvanized metal). Approximately 19% is used to produce brass and 16% go
intotheproductionofzincbasealloystosupplythediecastingindustry.Significantamounts
arealsoemployedforcompoundssuchaszincoxideandzincsulfateandsemimanufactures
including roofing, gutters and downpipes [5] (Figure 1.3a). Main application areas are:
construction(45%)followedbytransport(25%),consumergoods&electricalappliances(23%)
andgeneralengineering(7%)(Figure1.3b).Zincisalsoanecessaryelementforpropergrowth
anddevelopmentofhumans,animals,andplants;itisthesecondmostcommontracemetal,
afteriron,naturallyfoundinthehumanbody.
Enduse
Firstuse
4%
7% 7%
16%
7%
23%
45%
47%
25%
19%
Zinccoatedsteels
Brass
Zincbasealloys
Semimanufactures
Compounds
Others
Construction
Transport
Consumer&ElectricalGoods
GeneralEngineering
Figure1.3.Zincdemand:FirstuseandEndusein2003estimate(Source:ILZSG/BrookHunt/
Outokumpu/CRU[87])
The level of zinc recycling is increasing each year, as a consequence of the progress in
thetechnologyofzincproductionandzincrecycling.Today,over80%ofthezincavailablefor
recycling is indeed recycled. Although, at present, approximately 70% of the zinc produced
worldwideisstilloriginatedfromminedores[86].
In this context, it is clear the need for the recovery of Zn. Several technologies are
currently employed for separation of zinc from waters including precipitation, ion exchange,
adsorption, electrochemical recovery, membrane separation and solvent extraction (SX) [88]
beingthelatterthemosteconomicalandpracticalprocesstoextractZnfromindustrialwaters
[89,90,91].InrecentyearsSXhasbecomeessentialtothehydrometallurgicalindustrydueto
agrowingdemandforhighpuritymetals,rigidenvironmentalregulations,theneedforlower
21
1.Introduction
productioncosts,aswellasduetothediminishingproductioninhighgradeorereserves[92,
93].
1.8.2.THESOLVENTEXTRACTIONPROCESS
Solventextractioninvolvestheextractionofthetargetelementfromtheinitialaqueous
solutionbyanextractantusuallydilutedinanorganicsolvent(organicphase),leavingallthe
other constituents in the aqueous raffinate. A subsequent reextraction/stripping of the
extractedelementpresentintheorganicphaseisusuallycarriedoutwithsomeacidicsolution
(stripping solution) with higher affinity for the target element than the organic phase.
However,whenundesirablemetalsareextractedtogetherwiththetargetelement,scrubbing
of the solvent previous to the stripping step should be performed. An additionally step of
regeneration of the organic phase after the stripping step may be also performed when the
strippingofthetargetelementorthescrubbingstepisnotcompleteforfurtherreuseofthe
organicsolvent(Figure1.4).
Figure1.4.Typicalsolventextractionsteps
Scrub and regeneration steps generally increase the cost of the process due to the
expenditureinbothreactantsandtimesoitispreferabletouseaselectiveextractantanda
properstripsolutionsoastothesestepscanbeavoided.
Nowadays,awidenumberofextractantsareavailableforuseinSXfortherecoveryof
metals, some of them, suitable for a specific metal, while others must be used at a certain
conditions to avoid extraction of impurities [94, 95]. In this sense, the most widely used
extractants for Zn recovery are those corresponding to the organophosphorous acids group,
such as Di(2ethylhexyl) phosphoric acid (DEHPA) and bis(2,4,4trimethylpentyl) phosphinic
acid(Cyanex272)(Figure1.5).
22
1.Introduction
CYANEX 272
DEHPA
(Di-(2-ethylhexyl) phosphoric acid
Bis(2,4,4-trimethylpentyl) phosphinic
acid
CAS No. 298-07-7
HO
O
HO
Cas No. 83411-71-6
P
P
O
O
O
CYANEX 301
CYANEX 302
Bis(2,4,4-trimethylpentyl) dithiophosphinic
acid
Bis(2,4,4-trimethylpentyl)
monothiophosphinic acid
CAS No. 107667-02-7
CAS No. 132767-86-3
HS
HO
P
P
S
S
Figure1.5.ChemicalstructuresofDEHPA,Cyanex272,Cyanex301andCyanex302
DEHPAhasbeensuccessfullyusedasanextractantformanymetalionsincludingZndue
toitsgreatextractioncapacityandlowcost[96,97,98].IthasbeenusedtoextractZnmore
efficiently than other bivalent metal ions such as Cu, Ni, Co and Cd [99]. The order of
extraction of eight metal ions from a sulfate solution using DEHPA has been reported as a
functionofpHtobeFe3+>Zn2+>Cu2+>Co2+>Ni2+>Mn2+>Mg2+>Ca2+[100].Inamorerecentstudy
oftheseparationofdivalentmetalionsfromasyntheticsolution,theextractionofmetalions
was in the order Zn2+>Ca2+>Mn2+>Cu2+>Co2+>Ni2+>Mg2+ [101]. The target metal (or even
different metals) can be separated from the bulk solution by varying in successive steps the
acidicconditionsandthetemperatureasmainparameterstogetpuresolutionsofthetarget
metals.
Cyanex 272 has been used as well as its thiosubstituted derivatives (Cyanex 302 and
Cyanex301)intheextractionofseveralmetalions[102].Variousstudiesreporttheadequacy
ofCyanex272toextractFe,Zn,Cr,CuandNifromsulfuricand/orsulfatesolutions[103,104,
105].
1.8.3.SCALINGSOLVENTEXTRACTIONTOAPILOTPLANT
The development of new solvent extraction solutions at the laboratory level, require
fromapilotplantstepinordertovalidatetheconcept.Thedesignofapilotplantisbasedon
thedataobtainedbothinthelaboratoryandbyprocessmodeling.
Laboratoryexperimentsareperformedtocheckthefeasibilityofthesolventsthatseem
to be suitable by testing their chemical (liquidliquid equilibrium) and hydrodynamic (phase
23
1.Introduction
separation) properties.At laboratory,thedistributioncoefficient (D) can be knownforevery
solute so an approximation of the number of steps of the process can be estimated. For a
multicomponent process, the feasibility of separation between various species may be
determinedtoo.Inaddition,bymeansofkineticexperimentstheresidencetimeasafunction
of temperature and intensity of mixing required for completion of the process can also be
determined.
Computer simulation programs allows to determine the optimal configuration at the
pilotplant:temperature,pH,phaseratio,thenumberofstagesandoptimalflowsheetofthe
process[106].
Thus,thepilotplanthastotestmainlytheparametersthatcannotbepredictedbythe
simulation: preferred dispersion, flux, entrainment, accumulation phenomena, precipitation,
deteriorationofthesolventand theequipmenttobeused. In thissense,therecommended
equipmentistestedinadedicatedpilotplant,toestimatethemasstransfer,entrainmentand
phase separation, and optionally for quick accumulation phenomena, e.g., precipitation,
foaming,etc.(thislastpointcanbecheckedonlybyrunningthepilotplantwithrealprocess
solutions)andtohaveanexperimentalproofoftherecommendedflowsheet.Itcanbeused
todiscoverproblemsthatcouldnothavebeenotherwisedetected.
Theconfigurationandtypeofequipmentofthepilotplantshouldbesimilartothefull
scaleplant,anditshouldberuninthedesignatedsite,usingtherealrawmaterials.Itsmain
purpose is the verification of the results that were obtained in the benchscale regarding
productionrate,recovery,productqualityandanalysisofaccumulationphenomena.
All these parameters are sufficient for a rough economical estimate of the industrial
plant.Everydollarinvestedinthepilotplantpaysitselftenfoldintheindustrialplant[107].
1.9. FELOADED MATERIALS FOR THE REMOVAL OF ORGANIC AND
INORGANICCONTAMINANTS
Another technology to be employed on the recovery of inorganic contaminants in
industrialpollutedeffluentsdealswiththeionexchangeprocesses.Specifically,theFeloaded
materialsthatcanbeemployednotonlytoselectivelyremovethepollutantfromthetarget
effluentbutalsototreatorganicwaste.Throughthenextsection,theuseofseveralmaterials
asasupportforirontobeusedeitherasheterogeneousFentoncatalystorasarsenicsorbent
willbedescribed.
24
1.Introduction
1.9.1.ZEOLITES
Theword"zeolite"comesfromtheGreek“zeo”and“lithos”thatmeans"boilingstone"
becauseoftheobservationthatzeolitesreleasewaterwhenheated.Zeolitesarealargegroup
of natural and synthetic hydrated aluminum silicates characterized by complex three
dimensionalstructureswithlarge,cagelikecavitiesthatcanaccommodatesodium,calciumor
othercations(positivelychargedatomsoratomicclusters);watermolecules;andevensmall
organicmolecules.Theseencagedionsandmoleculescanberemovedorexchangedwithout
destroyingthealuminosilicateframework[108].
The atomic structures of zeolites are based on threedimensional frameworks of silica
andaluminatetrahedrainatetrahedralconfiguration,whereeachoxygenatomisbondedto
two adjacent silicon or aluminum atom, linking them together. Clusters of tetrahedra form
boxlike polyhedral units that are further linked to build up the entire framework. The wide
varietyofpossiblezeolitestructuresisduetothelargenumberofwaysinwhichtheseunits
canbelinkedtoformvariousstructures(Figure1.6).Eachtypeofzeolitehasspecificuniform
poresize,forinstance,3.54.5ÅforzeoliteLTA,4.56.0ÅforZSM5and6.08.0ÅforzeoliteX,
Ytype.
Figure1.6.Frameworktopologiesof:a)Sodalite;b)ZeoliteA/ZK4;c)ZeolitesX/Y
Zeolites occur naturally as minerals, although, only 6 of the 63 natural zeolites
commonly occur in large beds: analcime, chabazite, clinoptilolite, erionite, mordenite and
phillipsite.AnotherzeolitesuchasFerrieriteoccursinafewlargebeds,thusofferingalimited
rangeofatomicstructuresandproperties.Eachofthesevenalsohasbeensynthesized,and
those synthetic zeolites have a wider range of properties and larger cavities than natural
zeolites.Theprincipalsynthetic(aluminosilicate)zeolitesincommercialuseareLindeTypeA
(LTA),LindeTypesXandY(AlrichandSirich),Silicalite1andZSM5,andLindeTypeB(zeolite
P).Allarealuminosilicatesorpuresilicaanalogues.
Thealuminosilicateframeworkofazeolitehasanegativecharge,whichisbalancedby
thecationslocatedinthecagelikecavitiesthatcanparticipateinionexchangeprocesses.This
characteristicyieldssomeimportantpropertiesforzeolitessuchaslessdensestructuresthan
25
1.Introduction
othersilicates.Inthissense,between20and50percentofthevolumeofazeolitesstructure
arevoids.
Synthetic zeolites were first produced in the 1950s and nowadays more than 100
different zeoliteshavebeenmadewithanannualproductionof syntheticzeolitesexceeding
12,000 tons. The International Zeolite Association (IZA) database shows that the number of
structuraltypesofuniquemicroporousframeworkshasbeengrowingrapidly,from27in1970
to 133 in 2001, whereas currently this number has reached 180 [109]. In table 1.9 are
presentedsometypicaloxideformulaofsyntheticzeolites.
Table1.9.Typicaloxideformulaofsomesyntheticzeolites
Zeolites
ZeolitesA
ZeolitesNA
ZeolitesH
ZeolitesL
ZeolitesX
ZeolitesY
ZeolitesP
ZeolitesO
Zeolites
ZeolitesZK4
ZeolitesZK5
Typical oxide formula
Na2O.Al2O3.2SiO2.4,5H2O
(Na,(CH3)4N+)2O.Al2O3.4,8SiO2.7H2O
K2O.Al2O3.2SiO2.4H2O
(K2Na2)O.Al2O3.6SiO2.5H2O
Na2O.Al2O3.2,5SiO2.6H2O
Na2O.Al2O3.4.8SiO2.8,9H2O
Na2O.Al2O3.25SiO2.5H2O
(Na2,K2,(CH3)4N+2)O.Al2O3.7SiO2.3,5H2O
(Na,(CH3)4N+)2O.Al2O3.7SiO2.5H2O
0,85Na2O.0,15((CH3)4N+)2O.Al2O3.3,3SiO2.6H2O
(R,Na2)O.Al2O3.46SiO2.6H2O
Theusesofzeolitesderivefromtheirspecialproperties:
i)Ionexchange:Zeolitescaninteractwithwatertoabsorborreleaseions.Inthissense,
theyareusedaswatersofteners,toremovecalciumions,whichreactwithsoaptoformscum.
Zeolites have also been used to clean radioactive wastes, in this sense, radioactive Sr90 and
Cs137 have been removed from radioactive waste solutions by passing them through tanks
packed with the natural zeolite clinoptilolite. In addition, clinoptilolite is used to clean
ammoniumions(NH4+)fromsewageandagriculturalwastewater.Naturalzeolitesarealsothe
most effective filters yet found for absorbing sulfur dioxide from waste gases. As efforts to
improvethecontinuousairquality,zeolitescanbeusedtohelppurifythegasesfrompower
plantsthatburnhighsulfurcoal.
ii) Molecular sieves: Zeolites can selectively absorb ions that fit the cavities in their
structures. Industrial applications make use of synthetic zeolites of high purity, which have
larger cavities than the natural zeolites. These larger cavities enable synthetic zeolites to
absorborholdmoleculesthatthenaturalzeolitesdonot.Somezeolitesareusedasmolecular
sievestoremovewaterandnitrogenimpuritiesfromnaturalgas.
26
1.Introduction
iii)Catalyticcracking:Zeolitescanholdlargemoleculesandhelpthembreakintosmaller
pieces. Because of their ability to interact with organic molecules, zeolites are important in
refining and purifying natural gas and petroleum chemicals. The zeolites are not affected by
these processes, so they are acting as catalysts. Zeolites are used to help break down large
organic molecules found in petroleum into the smaller molecules that make up gasoline
(cracking).Zeolitesarealsousedinhydrogenatingvegetableoilsandinmanyotherindustrial
processesinvolvingorganiccompounds.
Althoughmostzeolitesusedascatalystsaresyntheticandmadeforspecificapplications,
afewnaturalzeoliteshavealsobeenemployed.Amongstthenaturalzeolites,themostusedis
clinoptilolite for being the most common zeolite occurring in large quantities. Moreover,
takingprofitoftheirhighexchangecapacity,severalFeloadedzeoliteshavebeenalsowidely
employed. In this regard, Febearing zeolites have been applied to N2O decomposition [110,
111],selectivecatalyticreductionofNOwithhydrocarbons[112,113,114]orNH3[115,116,
117],oxidationofbenzenetophenol[118,119],epoxidationofpropene[120,121],oxidation
ofvolatileorganiccarbons[122],decolorisationbymeansofFentontypereaction[123],etc.
1.9.2.CLAYS
Clays form almost 70% of the earth's crust and are defined as a sedimentary rock
containing mixtures of different minerals, mainly hydrated aluminum silicate, iron or
magnesium, along with various impurities, particulate extremely small crystal in varying
proportions.
Thecrystalstructureofclaysconsistsmainlyintetrahedralsilicaandoctahedralalumina
linkedtogethertoformlayersoftetrahedraandoctahedra.Theselayerswillsharetheapical
oxygenfromthetetrahedrallayerwiththefreeoxygenoftheoctahedrallayer.Alayerpacking
type1:1 containsonetetrahedraland oneoctahedrallayer,alayer2:1type twotetrahedral
andoneoctahedralandalayer2:2type,twolayersofeach(Figure1.7).
(Al,Si)O4
2:1silicate
layer
interlayer
2:1silicate
layer
Exchangeable
cation
(Al,Mg,Fe)O6
Figure1.7.Schematicstructureofa2:1layerexpandableclay
27
1.Introduction
TheoctahedralsitesareusuallyoccupiedbyAl3+orMg2+.WhentheionisMg2+,allthe
holes are occupied and the configuration is trioctahedral, but if the ion is Al3+, due to their
highercharge,only2/3ofthesitesareoccupied,resultinginadioctahedralstructure.
TheSi4+andAl3+inthetetrahedralandoctahedrallayerrespectively,maybesubstituted
by other elements with an ionic radius suitable to fit into the structure (called isomorphic
substitution).Thus,Si4+canbereplacedbyAl3+,andAl3+byMg2+,Mn2+,Ca2+orNi2+causinga
negativechargedensitythatshouldbecompensatedbycationsintheinterlaminarspacethat
canbeexchangeable(cationexchange).
The swelling properties are reversible unless the collapse occurs by elimination of all
polar molecules interspersed. The principal advantage of these materials, apart from its
availability,isthatduetolaminarstructure,forceachemicalreactionoccursinaplaneandno
threedimensionalspace,makingitmuchfaster.
Commercialclaysaremainlydedicatedtothemanufactureofrawmaterialsforbuilding
materialsaccountingfor90%ofproductionandonly10%isallocatedtootherindustriessuch
as manufacture of paper, rubber, paints, absorbent, bleach, molding sand, chemicals and
pharmaceuticals,agriculture,etc.[124].
Compositionally, clay minerals are similar to zeolites. Both are aluminosilicates and
hence, they possess high cation exchange capacity. However, they differ in their crystalline
structure:zeoliteshavearigidthreedimensionalcrystallinestructureconsistingofanetwork
ofinterconnectedtunnelsandcages whilstclayshavealayeredcrystalline structureandare
subjecttoshrinkingandswellingaswaterisabsorbedandremovedbetweenthelayers.
1.9.3.SPONGES
In the same way, Forager™ sponge is a high porosity and economic ionexchange
materialwithselectiveaffinityfordissolvedheavymetalsinbothcationicandanionicstates.
Such material is able to promote high rates of adsorption and flexibility which enables their
compressibilityintoanextremelysmallvolumetofacilitatedisposaloncethecapacityofthe
materialhasbeenexhausted[125].Foragerisanopencelledcellulosespongewhichcontains
a waterinsoluble polyamide chelating polymer formed by the reaction of polyethyleneimine
andnitrilotriaceticacid.Thismaterialisclaimedto containfree availableethyleneamineand
iminodiacetategroupstointeractwithheavymetalsionsbychelationandionexchange.Inthis
sense, it has selective affinity for dissolved heavy metals in both cationic and anionic states.
Foragerspongeandotheradsorbentspongeshavebeensuccessfullyusedinthetreatmentof
heavymetalssolutions[126,127].
28
1.Introduction
Severaladvantagesofthespongematerialwereidentified.Thefirstwasitsopencelled
naturethatallowsrelativelyhighflowrates;thesecondwascosteffectiveness;andthethird
was the material's low affinity for sodium, potassium, and calcium, three common naturally
occurring groundwater ions that can interfere with the effectiveness of typical ion exchange
systems for treating specific priority pollutant metals. The selective affinity of the polymer
enablestheForager™Spongetobindtoxicheavymetalsoverbenignmonovalentanddivalent
cations such as calcium, magnesium, potassium and sodium. In addition, prior studies have
shownthatthespongematerialiseffectiveoverawiderangeofpH.ThepHatthesitewas
determined to range from 4 to 5 standard units. Another advantage was that a simple
treatment system could be designed and installed similar to a typical carbon adsorption
system. It is an opencelled cellulose housing iminodiacetic acid groups which chelate
transition metal cations by cation exchange processes in the following affinity sequence:
Cd2+>Cu2+>Hg2+>Pb2+>Au3+>Zn2+>Fe3+>Ni2+>Co2+>Al3+.Thespongepolymeralsocontainstertiary
amine salt groups that can bind anionic contaminants, such as the chromate, arsenic, and
uraniumoxidespecies.Itcanbedesignedforsitespecificneedstocontainacationthatforms
ahighlyinsolublesolidwiththeanionofinterest.Anotheradvantageisitshighporosityand
flexibilitywhichallowsitscompressibilityintoanextremelysmallvolumetofacilitatedisposal.
1.10.THEFENTONREACTION
Some of the above described ion exchange materials, can be employed for the
treatment of a wide range of organic compounds detected in industrial and municipal
wastewater. Some of these compounds (both synthetic organic chemicals and naturally
occurring substances) pose severe problems in biological treatment systems due to their
resistancetobiodegradationor/andtoxiceffectsonmicrobialprocesses.Asaresult,theuseof
alternative treatment technologies, aiming to mineralize or transform refractory molecules
into others which could be further biodegraded, is a matter of great concern. Among them,
advancedoxidationprocesses(AOPs)arealreadybeenusedforthetreatmentofwastewater
containing recalcitrant organic compounds such as pesticides, surfactants, dyes,
pharmaceuticals and endocrine disrupting chemicals. Moreover, they have been successfully
used as pretreatment methods in order to reduce the concentrations of toxic organic
compoundsthatinhibitbiologicalwastewatertreatmentprocesses[128]
Advancedoxidationprocesses(AOPs)arebasedonthegenerationofthehighlyoxidative
hydroxyl radical which attacks nonselectively all present organic compounds [129].A great
numberofmethodsareclassifiedunderthebroaddefinitionofAOPs(Table10).Mostofthem
29
1.Introduction
useacombinationofstrongoxidizingagents(e.g.H2O2,O3)withcatalysts(e.g.transitionmetal
ions)andirradiation(e.g.ultraviolet,visible).
HOMOGENEOUS
PROCESSES
HETEROGENEOUS
PROCESSES
Table1.10.ListofmainAOPprocesses
Ozonationunderalkalineconditions(O3/OH)
Ozonationassistedbyhydrogenperoxide(O3/H2O2)and
Withoutexternal
(O3/H2O2/OH)
energysupply
Hydrogenperoxideandironcatalysts(Fentonprocess,
H2O2/Fe2+)
Ozonationandultravioletradiation(O3/UV)
Hydrogenperoxideandultravioletradiation(H2O2/UV)
Ozone,hydrogenperoxideandultravioletradiation
(O3/H2O2/UV)
Withexternal
PhotoFenton(Fe2+/H2O2/UV)
energysupply
Ozonationassistedbyultrasounds(O3/US)
Hydrdogenperoxideassistedbyultrasounds(H2O2/US)
Electrochemicaloxidation
Anodicoxidation
ElectroFenton
Catalyticozonation(O3/Cat.)
Photocatalyticozonation(O3/TiO2/UV)
Heterogeneousphotocatalysis(H2O2/TiO2/UV)
Among AOPs, the Fenton reaction has been widely applied in treating contaminated
wastewaters containing organic volatile compounds, persistent organic pollutants and dyes
[130,131, 132].TheFentonreactionconsistsonthegenerationofthehydroxylradicalfrom
hydrogenperoxideandFe(II)ionsinmildconditions(reactions1and2):
Fe2++H2O2 Æ
OH+Organicmatter Æ
Æ
Fe3++H2O2 FeOOH2+
Æ
Fe2++HO2
Æ
Æ
Fe3++HO2 Æ
2HO2 Fe3++OH+OH
k1=107M1s1
OxidizedProducts FeOOH2++H+ k3=0.0010.01M1s1
Fe2++HO2 Fe3++HO2 Fe2++O2+H+ H2O2+O2
(Eq.1)
(Eq.2)
(Eq.3)
(Eq.4)
(Eq.5)
(Eq.6)
(Eq.7)
As iron is catalytically cycled between Fe(II) and Fe(III) (reaction 1 and 3 to 6), the
hydroxylradicalscanbegeneratedalsowithFe(III),whichiscalledFentonlikereaction[133].
ThereactionusingFe(III)isslowerthanwithFe(II)althoughtheuseofFe(III)insteadofFe(II)
presentsomeadvantagesmainlyrelatedtotheworkingpHthatcanbebroadenedfrom3.0to
4.5. [134] Other advantages concern with the reduction on the reactants costs due to the
lowerexpenditureofFe(III)saltscomparedtoFe(II)salts.However,theuseofFe(II/III)saltsas
ahomogeneouscatalysthassomedrawbacksconcerningtheremovalofFedueto:
x
Chelating pollutants as well as some inorganic components of wastewater solutions
(e.g.phosphate)
30
1.Introduction
x
Lossofthecatalystduetoironhydroxideprecipitationwhichcausesredmudsludge
that should be removed from the solution, sometimes requiring further treatment
thusincreasingthecostofthewholeprocess.
Toovercomethesedrawbacks,severalheterogeneousFentoncatalystsbearingFe(II/III)
ions,clustersoroxideshavebeendeveloped[135,136,137,138,139,140,141].Inthissense,
severallayeredandporousaluminosilicatessuchasclaysandzeoliteshavebeenproposedasa
supportforthecatalyticFeduetotheirhighspecificsurface,highthermalstability,exchange
capacityandhomogeneousdistributionofactivesites(142).Withregardtozeolites,Feloaded
syntheticcommercialzeolitessuchasZSM5zeolites[143,144,145]andYzeolites[146,147,
148] have been widely reported in the literature to provide similar catalytic activities as the
homogeneouscatalysis.However,inadditiontotheelevatedcostofthesesyntheticmaterials
andthepreparationoftheseFeloadedmaterials,longandtediousproceduresarerequired.
1.11.ARSENICSORPTIONUSINGFELOADEDMATERIALS
Arsenic is a naturally occurring metal released into the environment by natural and
anthropogenic(industrialandcommercial)processes.Ithasreceivedhugepublicandscientific
attentionduetoenvironmentalandpublichealthdisastersaroundtheworld[149,150,151].
1.11.1.ARSENICTOXICITY
Arsenic compounds can be classified into three major forms: inorganic, organic, and
arsine gas. Inorganic arsenic may be formed with either trivalent (arsenite) or pentavalent
(arsenate) arsenic. Trivalent arsenic compounds tend to be more toxic than pentavalent
arsenic compounds although pentavalent species predominate and are stable in oxygen rich
aerobic environments [152]. Inorganic arsenic is more toxic than the organic forms although
very high doses of certain organic compounds may be metabolized to inorganic arsenic and
result in some of the same effects derived from an exposure to inorganic compounds.
Arsenobetaine, an organic form of arsenic, is found in seafood and is nontoxic. On the
contrary,arsinegashavethehighesttoxicityofAscompoundsanditisformedbythereaction
ofhydrogenwitharsenic,duringthesynthesisoforganicarseniccompounds,andgenerated
accidentally during the smelting and refining of nonferrous metals in mining processes. High
levelsofnaturallyoccurringarsenicarefoundinsoilandrocksleadingtounacceptablelevels
ofarsenicindrinkingwatersuchasinBangladesh.
Thetoxicityofarsenicvarieswidelybasedontherouteofexposure,theform,thedose,
the duration of exposure, and the time elapsed since the exposure. Ingestion and inhalation
31
1.Introduction
aretheprimaryroutesofbothacuteandchronicexposures.Arsinegasisoneofthemosttoxic
formsandisreadilyabsorbedintothebodybyinhalation.
Effects of acute inorganic arsenic poisoning include fever, anorexia, hepatomegaly,
melanosis, cardiac arrhythmia and eventual cardiovascular failure, upper respiratory track
symptoms, peripheral neuropathies, gastrointestinal and hematopoietic effects. Dermal
contactwithhighconcentrationsofinorganicarseniccompoundsmayresultinskinirritation,
redness, and swelling and high acute exposures may cause choleralike gastrointestinal
symptomsofvomiting(oftentimesbloody)andseverediarrhoea(oftenbloody).Ingestionof
largedosesofinorganicarsenic(70to180mg)maybefatal.
Arsenichasbeenclassifiedasaknownhumancarcinogenbymultipleagenciesbasedon
theincreasedprevalenceoflungandskincancerobservedinhumanpopulationsexposedto
arsenic.
Everyday,lackofaccesstocleanwaterandsanitationkillsthousandsofpeople,leaving
others with reduced quality of life and as cities and slums grow at increasing rates, the
situationworsens.Nowadayscleanwaterisascarceresourceandarsenicremovalfromwaters
hasemergedasamajorconcernincertaindevelopingcountries.
1.11.2.ARSENICSORBENTS
Several types of adsorbents have been used for the removal of arsenic from aqueous
effluents, many of them taking advantage of Fe(III) compounds affinity towards inorganic
arsenic species. In this regard, various methodologies for arsenic removal involve the use of
ironhydroxyoxidessuch asgoethite(either naturalorsynthetic)[153,154,155],ferrihydrite
[156,157,158]orhematite[159,160]anddifferentFebearingmaterialssuchasFe(III)loaded
zeolites [161], aluminosilicates [162] or resins[163]. To predict the longterm fate of arsenic
anddesignnewmaterialswithimprovedcapacityandefficiencyforAssorption,themolecular
understanding of the sorption of arsenic by iron (oxy)hydroxides and Febearing materials is
required.Feloadedzeoliteshavebeensuccessfullyemployedforarsenicremoval[164].Inthis
sense, arsenate and arsenite adsorption from water was carried on by using iron treated
activatedcarbonandnaturalzeolite,comparingtheirefficiencywiththeresultsobtainedusing
Faujasite (13X) and Linde type A (5A) molecular sieves. Irontreated activated carbon and
chabazite were promising as lowcost arsenic adsorbents removing approximately 60% of
arsenate and arsenite and 50% of arsenate and 30% of arsenite, respectively [165]. Besides,
aqueousarsenicsorptionbynaturalzeolites,volcanicstone,cactaceouspowderCACMMand
clinoptilolitecontainingrockswithdifferentclinoptilolite,erioniteandmordenitepercentages
have been also reported [166, 167]. Each zeolite sample in the 0.1–4 mg/L Fe concentration
32
1.Introduction
range removed more arsenate than arsenite at equivalent arsenic concentrations. The
saturation capacity of the materials was inversely related to the silicon dioxide content and
directlytotheironcontentintheacidwashedzeolite.Moreover,theadsorptionofAs(V)from
drinkingwaterbyanaluminumloadedShirasuzeolite(AlSZP1)wasstudiedobtainingresults
equivalenttothatofactivatedalumina.AligandexchangemechanismbetweenAs(V)ionsand
surface hydroxide groups on AlSZP1 was presumed [168]. Furthermore, an ironconditioned
zeolite was prepared and used for arsenic removal from groundwater at pH 7.8 and
temperature145ºC[169].Ontheotherhand,ForagerSpongeandotheradsorbentsponges
havebeensuccessfullyusedinthetreatmentofheavymetalsolutions[128,170]butscarcely
employedforthearsenicremovalafterbeingloadedwithiron[171].
Several researchers have investigated the structure of the As adsorbed onto such
materials using Extended Xray Absorption Fine Structrue (EXAFS) and IR spectroscopic
techniquesandpreviousstudieshaveshownthatAs(V)oxyanionsarestronglyadsorbedtothe
surfacesofsuchironoxidesasgoethite,ferrihydrite,andhematite[172,173,174,175].The
assessmentandcharacterizationoftheseFebearingmaterialscanshedlightonthesorption
mechanisms taking part on these materials to provide useful data concerning the As uptake
mechanism. Thus, modifications concerning the As chemical and electronic structures
dependingonthetypeofadsorbentcanbeofgeneralinterest.
1.12. WEAKNESSES AND NEEDS OF INDUSTRIALLY CONTAMINATED
WATERS
x Weaknesses:
x
Conventional treatment for tailing ponds waters consists in the depuration of the
water in water treatment plants to be afterwards discharged to nearby rivers or
creeks.However,thesetreatmentplantsareexpensiveandtheprocesscostsarenot
recovered.
x
TheFentonreactioninvolvesthepresenceofironsaltsascatalystalthoughtwomain
drawbacks arise, the first mainly concerning the loss of the catalyst due to iron
hydroxideprecipitationanditsconsequentredmudsludgegeneration.
x
Feloaded materials employed as Fenton catalysts involves long and tedious
proceduresinadditiontotheelevatedcostofthesyntheticmaterialsused.
x
Severalironcompoundshavebeenemployedforthesorptionofarsenic.Itsefficiency
isbelievedtobestronglyinfluencedbyitsstructure.
33
1.Introduction
x Needs:
x
The recovery of metals contained on tailing ponds by existing methodologies can
provideeconomicalvaluetotheseresidueswhilesolvinganenvironmentalproblem.
x
Newironloadedbasedcatalystsintodifferentmaterialsshouldbetestedtoavoidthe
removalofthecatalyst,thegenerationofredsludgederivedfromtheprecipitationof
ironhydroxyoxidesandtoimprovetheefficiency.
x
Nobel Feloaded materials more economical should be tested as well as simpler
preparationmethodologiesfortheFeloadingofthesematerials.
x
To predict the longterm fate of arsenic and to design new materials with improved
capacityandefficiencyforAssorption,themolecularunderstandingofthesorptionof
arsenicbyironhydroxyoxidesandFebearingmaterialsisrequired.
34
1.Introduction
ANALYTICALTECHNIQUES
Through the following section, the general aspects of the techniques employed in this
workareoverviewed.Inthissense,theanalysisofheavymetalshasbeencarriedoutbymeans
ofhandheldXrayfluorescencetechniquewhilesynchrotronbasedtechniquessuchasXray
AbsorptionNearEdgeStructure(XANES)andExtendedXrayAbsorptionFineStructure(EXAFS)
havebeenthemaintoolsappliedforthedeterminationofmercuryspeciesaswellastothe
characterizationoftheadsorptionofarsenicontoFeloadedmaterials.
1.13.XRAYFLUORESCENCE
XRF spectrometry can easily and quickly identify and quantify elements over a wide
dynamicrange,fromppmlevelsuptovirtually100%w/w,withoutdestroyingthesampleand
with little, if any, sample preparation. These factors lead to a significant reduction in the
sampleanalyticalcostcomparedtootherelementalanalysistechniques.
1.13.1.XRAYINTERACTIONWITHMATTER
Recording the image of a given structure requires the use of a wavelength equal to or
smaller than the size of the structure. Xrays are actually electromagnetic waves between
ultravioletlightandgammaraysonthewavelengthscale.Theirwavelengthiscomparableto
interatomicdistances,soitcanbeusedto“see”interatomicdistances(Figure1.8).
Figure1.8.Electromagneticspectrum
Xraysinteractwithatomsinessentiallytwoways:scatteringandXrayabsorptionorthe
photoelectriceffect(Figure1.9).Scatteringcausesthephotontochangeitsdirectionanditcan
be elastic (Rayleigh scattering) or inelastic (Compton scattering). In Rayleigh scattering the
energy of the photon is conserved and occurs when Xray photons interact with strongly
boundelectrons;whereasComptonscatteringoccurswhenXrayphotonsinteractwithweakly
35
1.Introduction
bound electrons and the energy of the photon is conserved after the interaction. Rayleigh
scatteringformsthebasisofXraydiffraction(Figure1.10).
Fluorescence
Incident Xray beam
Transmitted Xrays
Rayleigh scattering
Compton scattering
MATERIAL
Figure1.9.InteractionofXrayswithmatter
COMPTON SCATTERING
(Incoherent scattering)
RAYLEIGH SCATTERING
(Coherent scattering)
(3) Ef <E0
(3) Ef =E0
(1)
(1)
(2)
(2)
Electron
Energy E0
Electron
Energy E0
Nucleus
Nucleus
(1) Incoming Xray photon
(2) Energy is partially
transferred to electron
(3) Scattered photon
Loss ofenergy
(1)Incoming Xray photon
(2) Oscillating electron
(3) Scattered photon
Noloss ofenergy
Figure1.10.RayleighandComptonscattering
Ontheotherhand,XrayabsorptionoccurswhenanatomacquirestheenergyofanX
ray to excite electrons into higher energy electron orbitals that are unoccupied, or into the
continuumwheretheelectronisnolongerassociatedwiththeatom.Tofillthevoidcreatedin
theinnershell,anelectronfromahigherenergyshelldropdownalmostinstantaneously.The
excess energy resulting from this transition can be released either in the form of an Xray
photonwithawavelengthcharacteristicoftheatom(fluorescence)ortoanelectronfroman
outershellthatreceivessufficientenergytoleavetheatom(Augerelectronemission)(Figure
1.11).
AUGER ELECTRON
FLUORESCENCE
(3)
Ejected Kshell
electron
Ef =EL EK
(2)
(4)
(1)
LÆ Ktransition
Nucleus
Kshell
(1)Incoming Xray photon
(2) AKshell electron is ejected (photoelectron)
(3) Outer shell electron moves to the inner shell
hole created
(4) Energy excess emitted asfluorescence
36
(2)
(3)
(5)
(4)
(1)
Electron
Energy E0
LÆ Ktransition
Ejected Kshell
electron
Lshell
Energy E0
(1)Incoming Xray photon
(2) AKshell electron is ejected (photoelectron)
(3) Outer shell electron moves to the inner shell hole
created
(4) Energy excess is transferred to electron
(5) Electron ejected from atom (Auger electron)
Nucleus
Electron
Kshell
Lshell
1.Introduction
Figure1.11.ReleaseofenergyprocessafterXrayabsorptionbymatter
Allelements emitXraysattheirowncharacteristic energies.TheseXraysarecalledK
linesiftheyresultfromanelectronfillingtheKshell,andLlinesiftheyresultfromfillingthe
nextelectronshellout,theLshell.TheenergyoftheemittedfluorescentXraysidentifythe
elements present in the sample and, in general, the intensities of the Xray lines are
proportional to the concentration of the elements in the sample, allowing quantitative
chemicalanalysisbyXrayFluorescence(XRF)spectrometers.
1.13.2.XRAYFLUORESCENCE
XRF is a nondestructive, simultaneous multielement technique that covers a wide
dynamicrangefrom100%downtotheμg/glevelwithtypicalrelativeprecisionapproaching
1% [176]. This wellestablished analytical method has been applied to environmental,
geological,archaeological,metalandalloysamples.
Inaddition,inthelast40yearshandheldXRFequipmentshavebeenemergedasvery
profitable tool given that the application of such technique, let to quickly delineate metals
contamination at a screening level in situ [177], as well as to determine contamination
patterns.Withminimalsamplepreparationrequirements,XRFmayprovidequickqualitative,
semiquantitative,orevenquantitativeanalysisofliquids,powder,solidorthinfilmsamples
[178]. In addition, high volume of field test can be monitored to determine the spatial
distributionanddegreeofheterogeneityofheavymetalsinanundisturbedpositionwhileoff
siteanalyticalcostsareminimizedwithoutdestructionofthesamples[179,180].
1.13.4.FIELDPORTABLEXRFINSTRUMENTATION
A typical XRF system has three major components: an excitation source, a
spectrometer/detector and a data collection/processing unit. In FieldPortable XRay
Fluorescence(FPXRF)equipments,theexcitationsourceandthedetectordevicearegenerally
assembled in a fixed position in order to reduce the size and weight to facilitate
transportation.Theinstrumentdevicemaybeverycompactbyincorporationofanembedded
microcomputeroritmayberenderedmoreflexiblebyusingastandardnotebookcomputer
(Figure1.12).
37
1.Introduction
Figure1.12.SchemeofaFPXRF
Various excitation sources may be used to irradiate a sample although the more
employed are radioisotopes sources and Xray tubes. In a radioisotope source, the
characteristicXraysemittedfromasealedradioisotopesourcesuchas 55Fe, 57Co, 109Cd, 241Am
and 244Cmareemployed.However,theintensityoftheseexcitationsourcesgraduallyfallsas
theisotopedecaysandtheemittedXraywavelengthsandintensitiesarenotadjustable.On
theotherhand,Xraytubeshaveincreasedsensitivityandanalyticalrange.Xraytubeoffera
fasteranalyticaltimebecausetheXrayfluxcan be higher than mostisotopebasedsources.
They can also be used over a wider range of excitation energies, eliminating the need for
multipleisotopicsourcestoproduceXraysovertheentireexcitationsspectrum.Inaddition,it
isworthmentioningthattransportationofminiatureXraytubesinvolveslessproblemsthan
whentravelingwithradioisotopesources.ThecathodeintheminiatureXraytubeisheatedby
a filament, and it then emits electrons that are accelerated by a high electric field. The
accelerated electrons hit the anode, which emits an Xray continuum accompanied by the
characteristiclinesofanodicmetal.Dependingupontheapplication,theanodematerialmay
be Cd, Cu, Mo, Rh, Ag, W, Pt or Au. These sources are powered with an external AC power
supply,oraninternalrechargeablebattery[181].
Thereareseveraldetectorsavailablesuchasgasflowproportionalcounters,scintillation
counters and solid state detectors being the latter the most employed given their high
resolution. Solidstate detectors have improved energy resolution dramatically, thereby
reducing spectral interferences and offering a three to fourfold speed advantage over a
scintillation detector. Various types of solid state detectors exist such as Germanium, Si(Li)
(lithiumdriftedsilicon),SiPIN(siliconpositiveintrinsicnegative),CCD(chargecoupleddevice),
PDA (photo diode array), PIPS (passivated implanted planar silicon) and SSB (silicon surface
38
1.Introduction
barrier).Thesemiconductordetectorstypicallyrequirecryogeniccoolingtoimprovethesignal
tonoiseratio.Besides,thedevelopmentofpersonalcomputerswithhighspeedandmemory
has also allowed fundamental parameter algorithms to be quickly performed using multiple
standards, resulting in rapid and more accurate standardization and analyses for
multicomponent,complexmatricesoverstandardempiricalmethods[182].
1.14.SYNCHROTRONBASEDTECHNIQUES
1.14.1.SYNCHROTRONLIGHTSOURCES
Thefirstaccelerators(cyclotrons)werebuiltbyparticlephysicistsinthe1930’stostudy
collisionsbetweenhighenergyparticles.Inthisroletheywereverysuccessful,andtheLarge
Hadron Collider at CERN is based on this technology. But scientist soon noticed that these
machines also had a byproduct: they generated very bright light. The emitted light was first
considered an inconvenient because it caused the particles to lose energy. The first
experimentscarriedoutusingsynchrotronlightwereperformedatCornell(USA)in1956and
overtheyears,thenumberofexperimentsincreased,allusingmachinesbuiltforhighenergy
particlephysics.Thischangedin1980whentheUKbuilttheworld’sfirstsynchrotronspecially
devoted to produce synchrotron light for experiments. Nowadays, there are around 70
synchrotron light sources around the world, carrying out a huge range of experiments with
applications in engineering, biology, materials science, cultural heritage, chemistry,
environmentalsciencesandmanymore.
1.14.2.DESIGNANDOPERATIONOFASYNCHROTRONLIGHTSOURCE
A schematic overview of a synchrotron facility is depicted in Figure 1.13. Bunches of
elementary particles such as electrons or positrons are initially accelerated by a linear
accelerator (LINAC) and then accelerated further in a booster ring that injects the particles
travelingnearthespeedoflightintoastoragering.Theparticleswithinthestorageringare
forced to change its trajectory by bending magnets so that they travel in a closed loop. This
causesXrayswithabroadspectrumofenergies(whitelight)tobeemittedtangentialtothe
storage ring. Therefore, a synchrotron storage ring is an Nsided polygon, where N is the
numberofbends.
39
1.Introduction
X-ray
Experimental
beam line
station
Electron beam
Storage ring
Electron beam
LINAC
Booster ring
Electron gun
Insertion
device
Bending
magnet
Figure1.13.Synchrotronschematicoverview
Wigglersandundulatorsaretwotypesofspecializedinsertiondevicesthatareplacedin
thestraightsectionsofthestoragering.Awigglerconsistsofseveralcloselyspacedbending
magnets that increase the intensity of the Xray pulse. An undulator oscillates the charged
particlesusingcarefullyspacedmagnetssuchthattheinterferencebetweentheirpolesaffects
theemittedXrayspectrum.Thisinterferenceisadditiveatparticularwavelengths,producing
anintenseXraybeamatawavelengththatcanbeselectedbyvaryingthegapbetweenthe
polesofthemagnets(Figure1.14).Beamlinesareplacedtangentialtothestorageringtouse
theXraysemittedbybendingthechargedparticles[183].
Bending magnet
Wiggler
Undulator
Free electron laser
Electron beam
X-ray radiation
Magnetic structures
Figure1.14.Bendingmagnetsandinsertiondevices
40
1.Introduction
Synchrotronlightpresentsveryspecialcharacteristics:
ƒ
High intensity or flux (photons per second) over a continuous wavelength spectrum
from microwaves to hard Xrays and gamma radiation. In contrast to laser light,
synchrotronradiationisnonmonochromatic.
ƒ
Highbrightness,thousandsofmillionfoldhigherthanconventionalXraysources.
ƒ
Linearly polarized light, the light oscillates only within certain planes. The light is
emitted in very short (picoseconds) pulses with a periodic structure (microseconds),
thereforeshowingahighpotentialforstudiesoftransientphenomena.
ƒ
Light source remaining stable along the time. Depending on the facility, each bunch
refillshowsalifetimebetween4and24hours.
Despite the strong potential shown by synchrotronbased techniques and the
spectacularincreaseoftheirpossibleuses,thesetechniquespresentaswellsomedrawbacks:
ƒ
Poordetectionlimits
ƒ
Limitednumberofsynchrotronfacilities
ƒ
Complexdatatreatment
1.14.3.XRAYABSORPTIONSPECTROMETRY
The general aspects of XRay Spectroscopy (XAS) have been presented in a number of
reviews papers and books [184, 185], as well as its applications to soils, minerals, and other
geochemical matrices so the lector is addressed to the extense literature available on XAS
applications, synchrotron facilities, and specialized techniques involving synchrotron Xrays
[186,187,188,189].Also,anumberofreviewspapersandbooksectionsdescribetechniques
and applications of XAS in geochemistry andsoil science [190, 191, 192, 193, 194, 195]. The
principlesofXASanddataanalysishavebeenalsowidelydescribed[196,197,198]whilemore
details on the physics of XAS appear in several books [199, 200, 201, 202]. Therefore, only
some basics on this spectroscopy will be presented here. The reader is referred to the
abovementionedreviewsformoredetailedinformation.
As aforementioned, when the Xrays interact with matter the radiation can be either
scatteredbytheelectronsorabsorbedandexcitetheelectrons(Figure1.9).Whentheenergy
oftheincidentphotonsissufficientenough,acoreelectronoftheabsorbingatomisexcitedto
acontinuumstate(i.e.producingaphotoelectron)causingawavethatisbackscatteredbythe
neighboringatomsproducingthecharacteristicfeaturesofatransmissionXASspectra.Tofill
the created vacancy, an electron from a higher shell drops emitting fluorescence of
characteristic wavelength. Several detection setups have been developed for XAS studies,
41
1.Introduction
depending on the nature of the absorber and the matrix type. In this sense, the most
commonlyusedinvolvethemeasurementofeitherthetransmissionofXraysortheemitted
fluorescence.
The absorption spectrum of an element in the vicinity of an absorption edge can be
dividedinfourmainregions:Preedge,Edge(orwhiteline),XANESandEXAFS.(Figure1.15).
Figure1.15.TypicalXrayabsorptionspectrum.
1)Preedge:E~250eVbelowthemainabsorptionedge.Inthisregionthereisnosignificant
absorption phenomena, only localized electronic transitions to unfilled (or partially filled)
atomiclevels(e.g.,sÆp,orpÆd).
2) Edge (White line): E from ~ 2 eV below to ~ 2 eV above the absorption edge. Electronic
transitions occur with high probability from the core level to unoccupied bound states with
closeenergyorcontinuumstates.Asuddenriseofabsorptionisobserved.
3) XANES: E from ~2 to 50 eV above the edge. Lowenergy photoelectrons are strongly
scatteredandmultiplescatteringdominates[203].Theresultingfeaturesareintense,andcan
be interpreted in terms of multiple scattering from atoms in the first coordination shells
aroundtheabsorber,yieldinginformationaboutinteratomicdistancesandangles(Figure9).
Given the complexity of the theoretical approach to phenomena occurring in the XANES
region, speciation concept in XANES is usually based on the comparison of an unknown
spectrum with a database of reference spectra. The fitting process looks for the best linear
combinationofreferencespectraabletoappropriatelyreproducetheunknownspectrum.In
theseterms,XANEShasbeenwidelyemployedasaspeciationtechnique.
4) EXAFS: This region lies from ~ 50 to ~ 1000 eV above the edge. In the EXAFS region, the
photoelectrons have high kinetic energy and normally dominates single scattering by the
nearest neighboring atoms. In the EXAFS region, the most important feature is oscillations.
42
1.Introduction
Whenaphotoelectroninteractswithitsneighboringatoms,itwillbescattered(Figure16).In
the XANES, multiple scattering patterns will be dominant whilst in the EXAFS region single
scattering pattern would be the main pattern. The interactions among the scattering and
backscatteringphotoelectronwavesproducetheEXAFSoscillations(Figure16).EXAFSregion
can be analyzed to obtain information about the distance between the absorber and the
neighboringatoms,extendingouttoseveralshellsofligands.
Backscatter photoelectron
Atom with neighbor
Oscillation
Photoelectron
Isolated atom
Singlescattering
Twolegs
E
Multiple scattering
Threelegs
Photoelectron
Backscatterer
Figure1.16.OriginoffinestructureofEXAFS
The number and type of backscatterers can be also assessed through the analysis of
EXAFS region. The frequency of EXAFS oscillations is inversely related to average absorber
backscattererdistance,andtheamplitudeoftheoscillationsisdirectlyrelatedtothenumber
ofbackscatteringligands.
Regardlessofthecomplexityofthesample,theXASsignalcomesfromalloftheatoms
ofasingleelementasselectedbytheXrayenergy.Thestructuralinformationobtainedfrom
XAS is useful for identifying the chemical speciation of an element, including mineral,
noncrystallinesolidoradsorbedphases.
Inthisregard,XAStechniqueshavebeenshowntoprovidereliableinformationonthe
speciationofseveralelementsbeingespeciallyinterestingthecaseofmercury,thetoxicityof
which strongly depends on its speciation. In this sense, several studies dealt with mercury
speciationwithoutrequiringsamplepretreatment[204,205,206,207].Moreover,amongXAS
techniques, both EXAFS (extended Xray absorption fine structure) and XANES (Xray
absorption nearedge) spectroscopies have been used for the speciation of mercury in
different matrices, such as mine ores and wastes [206, 208], fish [209], contaminated soils
[210] and hyacinths [211], and in studies of interactions between mercury and soil minerals
[212].
43
1.Introduction
1.15.OBJECTIVES
ThisPhDthesishasbeenfocusedontwomainobjectivesbothregardingtheapplication
to environmental problems concerning: i) contaminated soils surrounding mine areas and ii)
industrialcontaminatedwaters.
Inthissense,morespecificgoalsofthepresentthesisare;
x
The application of different analytical techniques such as FieldPortable XRay
Fluorescence and XRay Absorption Spectroscopy to the study of highly impacted
environmentsfocusedon:
ƒ
the characterization of soils surrounding four different mine areas from
Marrakech. Study of heavy metal distribution throughout abandoned mine
areasandassessmentofheavymetalmobility.
ƒ
the study of mercury speciation through synchrotron techniques and
estimation of its mobility from soil samples from three of the main mercury
mineareasinEurope.
x
The study of a process at laboratory and pilot plant scale to recover Zn from a real
mine effluent in order to produce an economically effective output while solving an
environmentalproblem.
x
ToassessthefeasibilityofnovelFeloadedmaterials
ƒ
as catalysts to degrade different organic pollutants by means of Fenton
reaction.
ƒ
as arsenic sorbents. In this regard, the knowhow gained on synchrotron
techniques can be applied to shed light onto the sorption mechanisms of
arsenicontothesematerials.
44
1.Introduction
1.16.REFERENCES
[1] Hartman, H.L. (1992). SME Mining Engineering Handbook. Vol.1, 2nd ed. Society for Mining,
Metallurgy,andExploration,Inc.USA.
[2]Hartman,H.L.;Mutmansky,J.M.(2002).Introductoryminingengineering.JohnWiley&Sons,USA.
[3] Mbendi World Mining Overview, http://www.mbendi.co.za/indy/ming/p0005.htm (Last visit: April
2011).
[4]Wilmoth,R.C.;Hubbard,S.J.;Burckle,J.O.;MartinJ.F.Productionandprocessingofmetals:their
disposalandfuturerisks.In:MerianE.,(ed.)(1991).Metalsandtheircompoundsintheenvironment.
Occurrence,analysisandbiologicalrelevance.1965.VCH.USA.
[5]Adriano,D.C.(2001).TraceElementsinTerrestrialEnvironments;2nded,SpringerVerlag.USA.
[6] Kertes, N. (2007). US abandoned mine count still a mystery. General Accounting Office report.
AmericanMetalMarket.USA.
[7] Martin, T.E.; Davies, M.P. (2000). Trends in the stewardship of tailings dams. AGRA Earth &
EnvironmentalLtd.Canada.
[8] Rico, M.; Benito, G.; Salgueiro, A.R.; DiezHerrero, A.; Pereita, H.G. (2000). Reported tailings dam
failures.AreviewoftheEuropeanincidentsintheworldwidecontext.J.Hazard.Mater.152(2)846852.
[9]Grimalt,J.O.;Ferrer,M.;Macpherson,E.(1999).TheminetailingaccidentinAznalcollar.Sci.Total
Environ.242:311.
[10]Osan,J.;Kurunczi,S.;Török,S.;VanGrieken,R.(2002).XRayanalysisofriverbanksedimentofthe
Tisza (Hungary): identification of particles from a mine pollution event. Spectrochim. Acta Part B 57:
413422.
[11] Kibblewhite, M. (2001) Identifying and dealing with contaminated land, In: Hester, R.E. and
Harrison,R.M.(Eds),AssessmentandReclamationofContaminatedland16,RoyalSocietyofChemistry
(RSC),England.
[12]ElGharmali,A.;Rada,A.;ElAdnani,M.;Tahlil,N.;ElMeray,M.;Nejmeddine,A.(2004).Impactof
acid mining drainage on the quality of superficial waters and sediments in the Marrakesh region,
Morocco.EnvironmentalTechnology.25:14311442.
[13]Hakkou,R.;Benzaazoua,M.;Bussière,B.(2008).AcidminedrainageattheabandonedKettaramine
(Morocco):1.Environmentalcharacterization.MineWaterEnviron.27:145159.
[14]Hakkou,R.;Benzaazoua,M.;Bussière,B.(2008).AcidminedrainageattheabandonedKettaramine
(Morocco):2.Minewastegeochemicalbehavior.MineWaterEnviron27:160170.
[15] El Adnani, M.; RodriguezMaroto, J.M.; Sbai, M.L., Loukili, I.L.;Nejmeddine, A. (2007). Impact of
polymetallicmine(Zn,Pb,Cu)residuesonsurfacewater,sedimentsandsoilsatthevicinity(Marrakech,
Morocco).Environ.Technol.28:96985.
[16]Thompson,K.C.;Nathanail,C.P.(Eds.)(2003).Chemicalanalysisofcontaminatedland.CRCPress,
USA.
[17] Gillman, G.P; Bell, L.C. (1978). Soil solution studies on weathered soils from tropical North
Queensland.AustJSoilRes,16:6777.
[18] APHA Part 4110. (1998). Determination of ions by ion chromatography. In Standard Methods for
theExaminationofwaterandwastewater,16thEd.AmericanPublicHealthAssociation,USA.
[19] Bengtsson, L.; Enell, M. (1986). Chemical analysis, in: Berglund, B.E. (Ed.), Handbook of Holocene
PalaeoecologyandPalaeohydrology.JohnWiley&SonsLdt.,Chichester,England,423451.
45
1.Introduction
[20] Matthiesen, M.K.; Larney, F.J.; Selinger, L.B.; Olson, A.F. (2005). Influence of LossonIgnition
TemperatureandHeatingtimeonAshcontentofcompostandmanure.CommSoilSciPlantAnal,36:.
25612573.
[21]Kennedy,I.R.(1992).Acidsoilandacidrain,2nded.ResearchStudiesPress,England.
[22]Tatzber,M.;Stemmer,M.;Spiegel,H.;Katzlberger,C.;Haberhauer,G.;Gerzabek,M.H.(2007).An
alternativemethodtomeasurecarbonateinsoilsbyFTIRspectroscopy.Environ.ChemLett:912.
[23] Prasad, H. (1999). Heavy metals stress in plants. From molecules to ecosystems. SpringerVerlag,
Germany.
[24] Kalnicky, D.J.; Singhvi R. (2001). Field portable XRF analysis of environmental samples. J. Hazard.
Mater.83:93122.
[25] Kilbride, C.; Poole, J.; Hutchings, T.R. (2006). A comparison of Cu, Pb, As, Cd, Zn, Fe, Ni and Mn
determinedbyacidextraction/ICP–OESandexsitufieldportableXrayfluorescenceanalyses.Environ.
Pollut.143:1620.
[26] Potts, P.J.; Ellis, A.T.; Kregsamer, P.; Streli, C.; Vanhoof, C.; West, M.; Wobrauschek, P. (2005).
Atomicspectrometryupdate.XRayfluorescencespectrometry.J.Anal.At.Spectrom.20:1124–1154.
[27]Melquiades,F.L.;Appoloni,C.R.(2004).ApplicationofXRFandfieldportableXRFforenvironmental
analysis.J.Radioanal.Nucl.Chem.262(2):533541.
[28] Van Straalen, N.M. (2002). Assessment of soil contamination – a functional perspective.
Biodegradation13(1):4152.
[29]TheMinistryofHousing,SpatialPlanningandtheEnvironment.EnvironmentalQualityStandardsin
theNetherlands(1999).Areviewofenvironmentalqualitystandardsandtheirpolicyframeworkonthe
Netherlands.Netherlands..
[30] Darmendrail, D. (2001). The French approach to contaminatedland management.
http://www.sanaterre.com/guidelines/pdf/Darmendrail_2001.pdf(Lastvisit:May2011).
[31]Directive2004/35/CEoftheEuropeanparliamentandofthecouncilof21April2004.
http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2004:143:0056:0075:EN:PDF (Last visit:
May2011)
[32] Kersten M., Förstner U. (1989). Speciation of trace elements in sediments. In: Batley, G.E.(Ed.),
TraceElementSpeciation:AnalyticalMethodsandProblems.CRCPress,USA.
[33] Pickering W.F. (1995). I.2. General strategies for speciation. In: Ure, A.M.; Davidson, C.M. (Eds.),
ChemicalSpeciationintheEnvironment,BlackieAcademicandProfessional,UK.
[34] Valors dels NGR per metalls i metalloides i protecció de la salut humana aplicables a Catalunya.
http://www20.gencat.cat/portal/site/arc/menuitem.60fb2478680e61fd624a1d25b0c0e1a0/?vgnextoid
=fd370431b17d6210VgnVCM1000008d0c1e0aRCRD&vgnextchannel=fd370431b17d6210VgnVCM10000
08d0c1e0aRCRD&vgnextfmt=default(Lastvisit:June2011)
[35]Templeton,D.M.;Ariese,F.;Cornelis,R.;Danielsson,LG.;Muntau,H.;VanLeeuwen,H.P.;Lobinski,
R. (2000). Guidelines for terms related to chemical speciation and fractionation of trace elements.
Definitions, structural aspects, and methodological approaches (IUPAC Recommendations 2000). Pure
Appl.Chem.72:14531470.
[36]Ure,A.M.(1990).Methodsofanalysisforheavymetalsinsoils,in:Alloway,B.J.(Ed.),Heavymetals
insoils,4073.Blackie&Son,UK.
[37]Tack,F.M.;Verloo,M.G.(1995).Chemicalspeciationandfractionationinsoilandsedimentheavy
metalanalysis:areview.Int.J.Environ.Analyt.Chem.59:225238.
[38] Fitfield, F.W.; Haines, P.J. (1997). Environmental analytical chemistry. Blackie Academia &
Professional,UK.
46
1.Introduction
[39] Quevauviller, P. (1998). Method performance studies for speciation analysis. Royal Society of
Chemistry,UK.
[40]Tessier,A.;Campbell,P.G.C.;Bisson,M.(1979).Sequentialextractionprocedureforthespeciation
ofparticulatetracemetals.Anal.Chem.51:844851.
[41] Quevauviller, P.; Van der Sloot, H. A.; Ure A.; Muntau H.; Gomez A.; Rauret, G. (1996).
Harmonizationofleaching/extractiontestsforenvironmentalriskassessment.Sci.Total.Environ.178(1
3):133139.
[42]Kennedy,V.H.;Sanchez,A.L.;Oughton,D.H.;Rowland,A.P.(1997).Useofsingleandsequential
Chemical Extractants to Assess Radionuclide and Heavy Metal Availability from Soils for Root Uptake.
Analyst.1997,122,89100.
[43]Sahuquillo,A.;Rigol,A.;Rauret,G.(2003).Overviewoftheuseofleaching/extractiontestsforrisk
assessmentoftracemetalsincontaminatedsoilsandsediments.TrendsinAnal.Chem.22(3):152159.
[44]Rauret,G.(1998).Extractionproceduresforthedeterminationofheavymetalsincontaminatedsoil
andsediment.Talanta46(3):449455.
[45] Quevauviller, P.; Rauret, G.; LopezSanchez, J.F.; Rubio, R.; Ure, A.M.; Muntau, H. (1997). The
ScienceoftheTotalEnvironment:205,223234.
[46] Quevauviller, P.; Rauret, G.; LopezSanchez, J.F.; Rubio, R.; Ure, A.M.; Muntau, H. (1997). Report
EUR17554EN,EuropeanComission,Belgium.
[47] GomezAriza, J.L.; Giraldez, I.; SánchezRodas, D.; Morales, E. (2000). Metal sequential extraction
procedureoptimizedforheavilypollutedandironoxiderichsediments.Anal.Chim.Acta414:151164.
[48] Chester, R.; Kudoja, W.M.; Thomas, A.; Thomas, J. (1985). Pollution reconnaissance in stream
sedimentsusingnonresidualtracemetals.Environ.Pollut.B10:213–238.
[49]Agemian,H.;Chau,A.S.Y.(1977).Astudyofdifferentanalyticalextractionmethodsfornondetrital
heavymetalsinaquaticsediments.Arch.Environ.Contam.Toxicol.6:69–82.
[50] LopezSanchez, J.F.; Sahuquillo, A.; Rauret, G.; Lachica, M.; Barahona, E.; Gomez,A.; et al. (2002).
Extraction procedures for soil analysis. In Ph. Quevauviller (Ed.), Methodologies in soil and sediment
fractionationstudies(pp.28–65).RoyalSocietyofChemistry.Cambridge,UK.
[51] Novozamski, I.; Lexmon, T.M.; Houba, V.J.G. (1993). A single extraction procedure of soil for
evaluationofuptakeofsomeheavymetalsbyplants.Inter.J.Environ.Anal.Chem.:51,47.
[52]Colinet,E.;Gonska,H.;Griepink,B;Muntau,H.(1993).ThecertificationofthecontentsofCd,Cu,
Hg,Ni,PbandZninalightsandysoil.EURReport8833EN,57.CEC.Luxembourg.
[53] Perez, G.; LopezMesas, M.; Valiente, M. (2008). Assessment of Heavy Metals Remobilization by
Fractionation: Comparison of Leaching Tests Applied to Roadside Sediments. Environ. Sci. Technol. 42
(7):2309–2315.
[54]Ure,A.M,;QuevauvillerPh,;MuntauH.;GriepinkB.;1993,Speciationofheavymetalsinsolidsand
sediments. An account of the improvement and harmonization of extraction techniques undertaken
under the auspices of the BCR of the Commission of the european Communitie. Int. J. Environ. Anal.
Chem.51,135151.
[55] Mulchi, C.L.; Adamu, C.A.; Bell, P.F.; Chaney, R.L. (1992). Residual heavy metal levels in sludge
amendedcoastalplainsoils–II.Predictingmetallevelsintobaccofromsoiltestinformation.Commun.
SoilSci.PlantAnal.23,1053–1069.
[56]Lindsay,W.L.;Norvell,W.A.(1978).DevelopmentofaDTPAsoiltestforzinc,iron,manganeseand
copper.SoilSci.Soc.Am.J.42:421428.
[57] Melich, M. (1984). Melich3 Soiltest Extractant: A Modification of the Melich2 Extractant.
CommunicationsinSoilScienceandPlantAnalysis15:1409–1416.
47
1.Introduction
[58]Gupta,S.K.;Aten,C.(1993)Comparisonandevaluationofextractionmediaandtheirsuitabilityina
simplemodeltopredictthebiologicalrelevanceofheavymetalconcentrationsincontaminatedsoils.
Intern.J.Environ.Anal.Chem.,51,2647.
[59] Hughes, J.C.; Noble, A.D. (1991). Extraction of chromium, nickel and iron and the availability of
chromium and nickel to plants from some serpentinitederived soils from the eastern Transvaal as
revealedbyvarioussingleandsequentialextractiontechniques.Commun.SoilSci.PlantAnal.22:1753
1766.
[60] Juste, C.; Solda, P. (1988). Jnfluence de l’addition de differentes mat&es fertilisantes sur la
biodisponibilitcducadmium,dumanganese,dunickeletduzinccontenusdansunsolsableuxamend6
pardesbouesdestationd’epuration,Agronomie,8(10):897904.
[61] Sahuquillo, A.; Rigol, A.; Rauret, G. (2002). Comparison of leaching tests for the study of trace
metalsremobilisationinsoilsandsediments.J.Environ.Monit.4:1003–1009.
[62] Van der Sloot, H.A.; Heasman, L.; Quevauviller, P. (1997). Harmonization of leaching/extraction
tests.StudiesinEnvironmentalScience70.Elsevier.
[63] DIN (Deutsches Institut for Normung) (1993). Bodenbeschaffenheit, Vornorm, DIN v 19730, in
Boden–ChemischeBodanuntersuchungsverfahren,DIN,Berlin,4.
[64]AFNOR(AssociationFrancaisedeNormalization).(1994).AFNOR,Paris.
[65]UNICHIM(EnteNazionale_ItalianodeUnificazione).(1991)UNICHIM,Milan,Italy.
[66]Houba,V.J.G.;Novozamsky,I.;Lexmond,T.M.;VanderLee,J.J.(1990).Applicabilityof0'01MCaCl2
as a single extraction solution for the assessment of the nutrient status of soils and other diagnostic
purposes.Commun.SoilSci.PlantAnal.21:22812290.
[67] Perez, G. (2005). Disponibilidad de metales tóxicosen sitios contaminados. Aplicaciones y
limitacionesdelafraccionaciónenladeterminacióndegradientesdepolución.PhDthesis.Universitat
AutonomadeBarcelona,Spain.
[68]Zhai,L;Liao,X.;Chen,T.;Yan,X.;Xie,H;Wu,B.;Wang,L.(2008).Regionalassessmentofcadmium
pollutioninagriculturallandsandthepotentialhealthriskrelatedtointensiveminingactivities:Acase
studyinChenzhouCity,China.J.Environ.Sci.20:696–703.
[69] Carr, R.; Zhang, C.; Moles, N.; Harder, M. (2008). Identification and mapping of heavy metal
pollution in soils of a sports ground in Galway City, Ireland, using a portable XRF analyser and GIS.
EnvironGeochem.Health30:4552.
[70]Franco,C.;Soares,A.;Delgado,J.(2006).Geostatisticalmodellingofheavymetalcontaminationin
thetopsoilofGuadiamarrivermargins(SSpain)usingastochasticsimulationtechnique.Geoderma136:
852864.
[71]Shi,W.;Liu,J.;Du,Z.;Song,Y.;Chen,C.;Yue,T.(2009).SurfacemodellingofsoilpH.Geoderma150
12:113119.
[72]Panagopoulos,T.;Jesus,J.;Antunes,M.D.C.;Beltrao,J.(2006).Analysisofspatialinterpolationfor
optimisingmanagementofasalinizedfieldcultivatedwithlettuce.Eur.J.Agron.24(1):110.
[73] Jongman, R.H.G.; Ter Braak, C.J.F.; Van Tongeren, O.F.R. (1995). Data Analysis in Community and
LandscapeEcology.CambridgeUniversityPress.U.K.
[74]Zhai,L.;Liao,X.;Chen,T.;Yan,X.;Xie,H.;Wu,B.;Wang,L.(2008).Regionalassessmentofcadmium
pollutioninagriculturallandsandthepotentialhealthriskrelatedtointensiveminingactivities:Acase
studyinChenzhouCity,China.J.Environ.Sci.20:696–703.
[75] Pearson, K. (1901). "On Lines and Planes of Closest Fit to Systems of Points in Space" (PDF).
PhilosophicalMagazine2(6):559–572.
[76]Jolliffe,I.T.(1986).PrincipalComponentAnalysis.SpringerVerlag.USA
48
1.Introduction
[77]Shaw,P.J.A.(2003).MultivariatestatisticsfortheEnvironmentalSciences.HodderArnold.UK.
[78] Wang, X.M.; Dong, Z.B.; Zhang, C.X.; Qian, G.Q.; Luo, W.Y. (2009). Characterization of the
composition of dust fallout and identification of dust sources in arid and semiarid north China.
Geomorphology112:144–157.
[79] Contini, D.; Genga, A.; Cesari, D.; Siciliano, M.; Donateo, A.; Bove, M.C.; Guascito, M.R. (2010).
CharacterisationandsourceapportionmentofPM10inanurbanbackgroundsiteinLecce.Atmos.Res.
95:40–54.
[80]Chen,X.H.;Duan,Z.H.(2009).Changesinsoilphysicalandchemicalpropertiesduringreversalof
desertificationinYanchiCountyofNingxiaHuiautonomousregion,China.Environ.Geol.57:975–985.
[81]Sielaff,K.;Einax,J.W.(2007).Theapplicationofmultivariatestatisticalmethodsfortheevaluation
ofsoilprofiles.J.Soils&Sediments7:45–52.
[82]Pirkle,F.L.;Pirkle,E.C.;Pirkle,W.A.;Dicks,S.E.(1985).Evaluationthroughcorrelationandprincipal
componentanalysesofadeltaoriginfortheawthorneandCitronellesedimentsofpeninsularFlorida.J.
Geol.93:493–501.
[83] Mostert, M.M.R.; Ayoko, G.A.; Kokot, S. (2010). Application of chemometrics to analysis of soil
pollutants.TrendAnal.Chem.29:430445.
[84] Martin, T.E:; Davies, M.P. (2000). Trends in the stewardship of tailings dams. AGRA Earth &
EnvironmentalLimited.Canada:
[85]Hodgson,F.(1977).Proceedingsofthe1stAnnualBritishColumbiaMineReclamationSymposium
in Vernon. The Technical and Research Committee on Reclamation. Water quality and Mine Process
effluent.Canada.
[86]Bell,A.V.(1976).Wastecontrolsatbasemetalmines.Environ.Sci.Technol.10(2):130–135.
[87]http://www.iza.com/Documents/Communications/Publications/ZincGuide2004.pdf(Lastvisit:June
2011).
[88] Sayilgan, E.; Kukrer, T.; Civelekoglu, G.;Ferella F.;Akcil, A.;Veglio, F.; Kitis, M. (2009). A review of
technologiesfortherecoveryofmetalsfromspentalkalineandzinc–carbonbatteries.Hydrometallurgy
97:158166.
[89]Devi,N.B.;Nathsarma,K.C.;Chakravortty,V.(1997).ExtractionandseparationofMn(II)andZn(II)
fromsulfatesolutionsbysodiumsaltofCyanex272.Hydrometallurgy45(12):169179.
[90]Jha,M.K.;Kumar,V.;Singh,R.J.(2002).Solventextractionofzincfromchloridesolutions.Solv.Extr.
IonExch.20(3):389405.
[91]Salgado,A.L.;Veloso,A.M.O.;Pereira,D.D.;Gontijo,G.S.;Salum,A.;Mansur,M.B.(2003).Recovery
of zinc and manganese from spent alkaline batteries by liquidliquid extraction with Cyanex 272. J.
PowerSources115:367373.
[92] Alamdari, E.K. (2004). Synergistic effect of MEHPA on coextraction of zinc and cadmium with
DEHPA.Miner.Eng.17,89–92.
[93]Owusu,G.(1998).SelectiveextractionsofZnandCdfromZn–Cd–Co–Nisulphatesolutionusingdi
2ethylhexylphosphoricacidextractant.Hydrometallurgy47:205–215.
[94]ElNadi,Y.A.;Daoud,J.A.;Aly,H.F.(2007).Leachingandseparationofzincfromtheblackpasteof
spentMnO2Zndrycellbatteries.J.Hazard.Mater.143:328–334.
[95] Kyuchoukov, G.; Zhivkova, S. (2000). Options for the separation of Copper (II) and Zinc (II) from
chloridesolutionsbyKELEX100.Solv.Extr.IonExch.18(2):293305.
[96]Ritcey,G.M.;Lucas,B.H.(1971).Purificationofmanganesesolutionscontainingcopperandzincby
liquidliquidextraction,usingbis(2ethylhexyl)phosphoricacid.Can.Metall.Quart.10(3),2238.
49
1.Introduction
[97] Kunzmann, M; Kolarik, Z. (1992). Extraction of zinc(II) with di(2ethylhexyl) phosphoric acid from
perchlorateandsulfatemedia.Solv.Extr.IonExch.10(1):3549.
[98] Sastre, A.M.; Miralles, N.; Aguilar, M. (1984). The extraction of copper(III) from sulfate and
perchloratesolutionsbydi(2ethylhexyl)phosphoricaciddissolvedinIsoparH.Chem.Scripta24(1):44
48.
[99] Grimm, R.; Kolarik, Z. (1974). Acidic organophosphorus extractants. XIX. Extraction of copper(II),
cobalt(II),nickel(II),zinc(II),andcadmium(II)bybis(2ethylhexyl)phosphoricacid.J.Inorg.Nucl.Chem.
36(1):189192.
[100] Ritcey, G.M.; Ashbrook, A.W. (1984). Solvent Extraction. Principles and Applications to Process
Metallurgy.PartIandII.Elsevier.TheNetherlands
[101] Cheng, C.Y. (2000). Purification of synthetic laterite leach solution by solvent extraction using
D2EHPA.Hydrometallurgy56(3):369386.
[102]Rickelton,W.A.;Boyle,R.J.(1990).Theselectiverecoveryofzincwithnewthiophosphinicacids.
Solv.Extr.IonExch.8(6):78397.
[103]Lanagan,M.D.;Ibana,D.C.(2003).ThesolventextractionandstrippingofchromiumwithCyanex
272.Miner.Eng.16(3):237245.
[104]Rickelton,W.A.;Flett,D.S.;West,D.W.(1984).Cobaltnickelseparationbysolventextractionwith
bis(2,4,4trimethylpentyl)phosphinicacid.Solv.Extr.IonExch.2(6):81538.
[105] Reddy, B.R.; Bhaskara Sarma, P.V.R. (2001).. Separation and recovery of cobalt and nickel from
sulfatesolutionsofIndianOceannodulesusingCyanex272.Miner.ProcessMetall.18(3):172176.
[106]Grinbaum,B.(2006).IntegratedApproachtotheDevelopmentofSolventExtractionFlowSheets.
TsinguahSci.Technol.11,2006.207211.
[107]GrinbaumB.(2001).TheintegralapproachtodevelopmentofSXprocesses.In:MarcusY,ed.Ion
ExchangeandSolventExtraction.Dekker.USA.
[108]Mampton,F.A.(1997).MineralogyandGeologyofNaturalZeolites.SouthernPrintingCompany:
Blacksburg.USA.
[109] Earl, D.J.; Deem, M.W. (2006). Toward a database of hypothetical zeolite structures. Ind. Eng.
Chem.Res.45:5449–5454.
[110]PerezRamirez,J.;Kapteijn,F.;Mul,G.;Moulijn,J.A.(2002).NOassistedN2Odecompositionover
Febasedcatalysts.Effectsoffeedcompositionandcatalystconstitution.J.Catal.208:211223.
[111] Pieterse, J.A.Z.; Booneveld, S.; van den Brink, R.W. (2004). Evaluation of Fezeolite catalysts
preparedbydifferentmethodsforthedecompositionofN2O.Appl.Catal.B51:215–228.
[112] Chen, H.Y.; Sachtler, W.M.H. (1998). Activity and durability of Fe/ZSM5 catalysts for lean burn
NOxreductioninthepresenceofwatervapor.Catal.Today42:73–83.
[113]Vradman,L.;Herskowitz,M.;Capek,L.;Wichterlova,B.;Brosius,R.;Martens,J.A.(2005).Kinetic
experimentsandmodelingofNOoxidationandSCRofNOxwithdecaneoverCuandFeMFIcatalysts.
Ind.Eng.Chem.Res.44,4523–4533.
[114]GuzmanVargas,A.;Delahay,G.;Coq,B.;Lima,E.;Bosch,P.;Jumas,J.C.(2005).Influenceofthe
preparation method on the properties of FeZSM5 for the selective catalytic reduction of NO by n
decane.Catal.Today,107–08:94–99.
[115]Delahay,G.;Valade,D.;GuzmanVargas,A.;Coq,B.(2005).Selectivecatalyticreductionofnitric
oxidewithammoniaonFeZSM5catalystspreparedbydifferentmethods.Appl.Catal.B:55,149–155.
[116] Qi, S.; Yang, R.T. (2005). Ultraactive Fe/ZSM5 catalyst for selective catalytic reduction of nitric
oxidewithammonia.Appl.Catal.B.60:13–22.
50
1.Introduction
[117]Schwidder,M.;Kumar,M.S.;Bruckner,A.;Grunert,W.(2005).SelectivereductionofNOwithFe
ZSM5catalystsoflowFecontent:I.Relationsbetweenactivesitestructureandcatalyticperformance.
Chem.Commun.805–807.
[118] Panov, G.I.; Kharitonov, A.S.; Sobolev, V.I. (1993). Oxidative hydroxylation using dinitrogen
monoxide:apossibleroutefororganicsynthesisoverzeolites.Appl.Catal.A,98:1–20.
[119]Jia,J.;Pillai,K.S.;Sachtler,W.M.H.(2004).Onestepoxidationofbenzenetophenolwithnitrous
oxideoverFe/MFIcatalysts.J.Catal.221:119–126.
[120]PerezRamirez,J.;GallardoLlamas,A.ImpactofthepreparationmethodandironimpuritiesinFe
ZSM5 zeolites for propylene production via oxidative dehydrogenation of propane with N2O. Appl.
Catal.A279:117–123.
[121]Bulanek,R.;Wichterlova,B.;Novoveska,K.;Kreibich,V.(2004).Oxidationofpropanewithoxygen
and/ornitrousoxideoverFeZSM5withlowironconcentrations.Appl.Catal.A264:13–22.
[122] Halasz, J.; Hodos, M.; Hannus, I.; Tasi G.; Kiricsi, I. (2005). Catalytic detoxification of C2
chlorohydrocarbonsoverironcontainingoxideandzeolitecatalysts.ColloidsSurf.A265:171–177.
[123] Idelaouad, R.; Valiente, M.; Yaacoubi, A.; Tanouti, B.; LopezMesas, M. (2010). Rapid
decolourizationandmineralizationoftheazodyeC.I.AcidRed14byheterogeneousFentonreaction.J.
Hazard.Mater.186:745750.
[124]Doval,M.;Garcia,E.;Luque,j.;Martín,J.L.;Rodas,M.(1991).ArcillasIndustriales:Yacimientosy
Aplicaciones.Ed.CentrodeEstudiosRamónAreces,S.A.Spain..
[125] EPA, Demonstration Bulletin: Forager Sponge technology, EPA/540/MR94/522. U.S.
EnvironmentalProtectionAgency,SuperfundInnovativeTechnologyEvaluation.1994.USA.
[126]AlBazi,S.J.;Chow,A.(1983).Mechanismoftheextractionofpalladium(II)thiocyanatecomplexby
polyetherfoam.Talanta30:487492.
[127]Baghai,A.; Bowen, H.J.M. (1976).Separation of rhodium and iridium using siliconerubber foam
treatedwithtrinoctalymine.Analyst1976,101,661665.
[128] Stasinakis, A.S.; (2008). Use of selected advanced oxidation processes (AOPs) for wastewater
treatment–Aminireview..GlobalNESTJournal10:376385.
[129]Buxton,G.V.,Greenstock,C.L.,Helman,W.P.,Ross,W.P.(1988).Criticalreviewofrateconstants
for reactions of hydrated electrons, hydrogens atoms and hydroxyl radicals (•OH/•O) in aqueous
solution.J.Phys.Chem.Ref.Data17:513886.
[130]Katsumata,H.;Kawabe,S.;Kaneco,S.;Suzuki,T.;Ohta,K.(2004).DegradationofbisphenolAin
waterbythephotFentonreaction.J.Photochem.Photobiol.A162:297305.
[131]Nogueira,R.F.P.;Trovó,A.G.;Modé,D.(2002).Solarphotodegradationofdichloroaceticacidand
2,4dichlorophenolusinganenhancedphotoFentonprocess.Chemosphere48:385391.
[132]Chen,A.;Ma,X.;Sun,H.DecolorizationofKNRcatalysedbyFecontainingYandZSM5zeolites.J.
Hazard.Mater.156:568575.
[133] Fu, F.; Wang, Q.; Tang, B. (2009). Fenton and Fentonlike reaction followed by hydroxide
precipitationintheremovalofNi(II)fromNiEDTAwastewater:Acompartivestudy.Chem.Eng.J.:769
774.
[134]Barrault,J.,Tatibouet,J.M.,Papayannakos,N.(2000).Catalyticwetperoxideoxidationofphenol
overpillaredclayscontainingironorcopperspecies.C.R.Acad.Sci.Paris,Chemistry3,777–783.
[135]Lv,X.J.;Xu,Y.M.;Lv,K.L.;Zhang,G.C.(2005).Photoassisteddegradationofanionicandcationic
dyesoveriron(III)loadedresininthepresenceofhydrogenperoxide,J.Photochem.PhotobioA:Chem.
173:121127.
51
1.Introduction
[136]Cheng, M.M.;Ma,W.H.; Li, J.; Huang, Y.P.; Zhao, J.C. (2004). Visiblelightassisted degradation of
dye pollutants over Fe(III)loaded resin in the presence of H2O2 at neutral pH values. Environ. Sci.
Technol.38:15691575.
[137]Gumy,D.;FernandezIbañez,P.;Malato,S.;Pulgarin,C.;Enea,O.;Kiwi,J.(2005).SupportedFe/C
andFe/Nafion/CcatalystsforthephotoFentondegradationofOrangeIIundersolarirradiation,Catal.
Today101:375382.
[138]Lei,J.;Liu,C.;Li,F.;Li,X.;Zhoub,S.;Liu,T.;Gua,M.;Wu,Q.(2006).PhotodegradationoforangeI
in the heterogeneous iron oxideoxalate complex system under UVA irradiation, J. Hazard. Mat. 137:
10161024.
[139]Chen,J.;Zhu,L.(2007).HeterogeneousUVFentoncatalyticdegradatuibifdyestuffinwaterwith
hydroxylFepillaredbentonite.Catal.Today126:463470.
[140]Li,F.B.;Li,X.Z.;Li,X.M.;Liu,T.X.;Dong,J.(2007).HeterogeneousphotodegradationofbisphenolA
withironoxidesandoxalateinaqueoussolution,J.ColloidInterfaceSci.311:481490.
[141] Parra, S.; Henao, L.; Mielczarski, E.; Mielczarski, J.; Albers, P.; Suvorova, E.; Guindet, J.; Kiwi, J.
(2004).Synthesis,testing,andcharacterizationofanovelNafionmembranewithsuperiorperformance
inphotoassistedimmobilizedFentoncatalysis,Langmuir20:56215629.
[142] Thomas, J.M.; Thomas, W.J. (1997). Principles and practice of heterogeneous catalysis. VCh.
Germany.
[143]Kuznetsova,E.V.;Savinov,E.N.;;Vostrikova,L.A.;Parmon,V.N..(2004).Heterogeneouscatalysisin
theFentontypesystemFeZSM5/H2O2,Appl.CatalB:Environ.51:165170.
[144]Phu,N.H.;Hoa,T.T.K.;Tan,N.V.;Thang,H.V.;Ha,P.L.(2001).CharacterizationandactivityofFe
ZSM5catalystsforthetotaloxidationofphenolinaqueoussolutions,Appl.Catal.B:Environ,34:267
275.
[145] Chen, A.; Ma, X.;Sun, H. (2008). Decolorization of KNR catalyzed by Fecontaining Y and ZSM5
zeolites.JournalofHazardousMaterials156:568575.
[146]Neamtu,M.;Zaharia,C.;Catrinescu,C.;Yediler,A.;Macoveanu,M.;Kettrup,A.(2004).FeloadedY
zeoliteascatalystforwetproxideoxidationofreactiveazodyeProcionMarineHEXL.AppliedCatalysis
B:Environmental48:287294.
[147]Noorjahan,M.;Kumari,V.D.;Subrahmanyam,M.;Panda,L.ImmobilizedFe(III)HY:anefficientand
stablephotoFentoncatalyst.Appl.Catal.B57:291298.
[148]Rani,V.R.;Kishan,M.R.;Kulkarni,S.J.;Raghavan,K.V.(2005).Immobilizationofmetalloporphyrin
complexesinmolecularsievesandtheircatalyticactivity.Catal.Commun.6:531538.
[149] Smedley, P.L.; Kinniburgh, D.G. A review of the source, behaviour and distribution of arsenic in
naturalwaters.Appl.Geochem.2002,17,517–568.
[150]TowardsaMoreOperationalResponse:ArseniccontaminationinSouthandEastAsianCountries.
Technical
Report
No.
31303.
WHO,
(World
Health
Organization),
2005.http://siteresources.worldbank.org/INTSAREGTOPWATRES/Resources/ArsenicVolI_WholeReport.p
df.
[151]Bagla,P.;Kaiser,J.(1996).India’sspreadinghealthcrisisdrawsglobalarsenicexperts.Science274:
174–175.
[152]Greenwood,N.N.;Earnshaw,A.(1984).ChemistryofElements.(Chapter13).PergamonPress,UK.
[153] Fendorf, S.; Eick, M.J.; Grossl, P.; Sparks, D.L. (1997). Arsenate and chromate retention
mechanismsongoethite:I.Surfacestructure.Environ.Sci.Technol.31:315320.
[154] Sherman, D.M.; Randall, S.R.(2003).Surface complexation of arsenic(V) to iron(III)(hydr)oxides:
Structural mechanism from ab initio molecular geometries and EXAFS spectroscopy. Geochim.
Cosmochim.Acta67:42234230.
52
1.Introduction
[155]Farquhar,M.L.;Charnock,J.M.,Livens,F.R.;Vaughan,D.J.(2002).Mechanismsofarsenicuptake
from aqueous solution by interaction with goethite, lepidocrocite, mackinawite, and pyrite: An XRay
AbsorptionSpectrescopyStudy.Environ.Sci.Technol.36:17571762.
[156]Waychunas,G.A.;Rea,B.A.;Fuller,C.C.;Davis,J.A.(1993).Surfacechemistryofferrihydrite:Part1.
EXAFS studies of the geometry of coprecipitated and absorbed absorbed arsenate. Geochim.
Cosmochim.Acta57:22512269.
[157]Jessen,S.;Larsen,F.;Koch,C.B.;Arvin,E.(2005).Sorptionanddesorptionofarsenictoferrihydrite
inasandfilter.Environ.Sci.Technol.39:80458051.
[158]Jia,Y.;Xu,L.;Fang,Z.;Demopoulos,G.P.(2006).Observationofsurfaceprecipitationofarsenate
onferrihydrite.Environ.Sci.Technol.40:32483253.
[159]Singh,D.B.;Prasad,G.;Rupainwar,D.C.(1996).AdsorptiontechniqueforthetreatmentofAs(V)
richeffluents.ColloidsSurf.A:4956.
[160] Gimenez, J.; Martinez, M.; Pablo, J.; Rovira, M.; Duro, L. (2007). Arsenic sorption onto natural
hematite,magnetiteandgoethite.J.Hazard.Mater.141:575580.
[161]Li,Z.;Jean,J.S.;Jiang,W.T.;Chang,P.H.;Chen,C.J.(2011).RemovalofarsenicfromwaterusingFe
loadedFeloadedFeloadedFeloadednaturalzeolite.J.Hazard.Mater.187(13):318323.
[162] Dousova, B.; Grygar, T.; Martaus, A.; Fuitova, L.; Kolousek, D.; Machovic, V. (2006). Sorption of
As(V)onaluminosilicatestreatedwithFe(II)nanoparticles.J.ColloidInterfaceSci.302:424431.
[163] Rau, I.; Gonzalo, A.; Valiente, M. (2003). Arsenic(V) adsorption by immobilized iron mediation.
Modelingoftheadsorptionprocessandinfluenceofinterferinganions.React.Funct.Polym.54:85–94.
[164]Mohan,D.;PittmanJr.C.U.(2007).Arsenicremovalfromwater/wastewaterusingadsorbentsA
criticalreview.J.Hazard.Mater.142:1–53.
[165] Payne, K.B.; AbdelFattah, T.M. (2005). Adsorption of arsenate and arsenite by irontreated
activatedcarbonandzeolites:effectsofpH,temperature,andionicstrength,J.Environ.Sci.HealthA40:
723749.
[166]ElizaldeGonzalez,M.P.;Mattusch,J.;Einicke,W.D.;Wennrich,R.(2001).Sorptiononnaturalsolids
forarsenicremoval,Chem.Eng.J.81:187–195.
[167] ElizaldeGonzalez, M.P.; Mattusch, J.; Wennrich, R.; Morgenstern, P. (2001). Uptake of arsenite
andarsenatebyclinoptiloliterichtuffs.Micropor.Mesopor.Mater.46:277–286.
[168]Xu,Y.H.;Nakajima,T.;Ohki,A.(2002).Adsorptionandrenovalofarsenic(V)fromdrinkingwaterby
aluminumloadedshirasuzeolite.J.Hazard.Mater.92:275–287.
[169]Onyango,M.S.;Matsuda,H.;Ogada,T.(2003).Sorptionkineticsofarsenicontoironconditioned
zeolite.J.Chem.Eng.Jpn.36:477–485.
[170]Hollerman,W.;Holland,L.;Ila,D.;Hensley,J.;Southworth,G.;Klasson,T.;Taylor,P.;Johnston,J.;
Turner, R. (1999). Results from the low level mercury sorbent test at the Oak Ridge Y12 Plant in
Tennessee.J.Hazard.Mater.68:193203.
[171] Muñoz, J.A.; Gonzalo, A.; Valiente, M. (2002). Arsenic sorption by Fe(III)loaded opencelled
cellulosesponge.Thermodynamicandselectivityaspects.Environ.Sci.Technol.36:34053411.
[172]Hsia,T.H.;Lo,S.L.;Lin,C.F.;Lee,D.Y.(1994).Characterizationofarsenateadsorptiononhydrous
ironoxideusingchemicalandphysicalmethods.ColloidsSurf.A85:17.
[173] Grossl, P.R.; Eick, M.; Sparks, D.L.; Goldberg, S.; Ainsworth, C.C. (1997). Arsenate and chromate
retention on goethite II. Kinetic evaluation using a pressurejump relaxation technique. Environ. Sci.
Technol.31:321326.
[174]Manceau,A.(1995).Themechanismofanionadsorptiononironoxides:Evidencesforthebonding
ofarsenatetetrahedraonfreeFe(O,OH)6edges.Geochim.Cosmochim.Acta59:36473653.
53
1.Introduction
[175]Fuller,C.C.;Davis,J.A.;Waychunas,G.A.(1993).Surfacechemistryofferrihydrite,Part2,Kinetics
ofarsenateadsorptionandcoprecipitation:Geochim.Cosmochim.Acta57:22712282.
[176] Piorek, S. (1994). Principles and applications of manportable Xray fluorescence spectrometry.
TrAC,TrendsAnal.Chem13(7):281286.
[177] Kilbride C.; Poole, J.; Hutchings, T.R. (2006). A comparison of Cu, Pb, As, Cd, Zn, Fe, Ni and Mn
determinedbyacidextraction/ICP–OESandexsitufieldportableXrayfluorescenceanalyses.Environ.
Pollut.143(1):1620.
[178] Bernick, M.B.; Kalnicky, D.J.; Prince, G.; Singhvi, R. (1995). Results of fieldportable Xray
fluorescenceanalysisofmetalcontaminantsinsoilandsediment.J.Hazard.Mater.43(12):101110.
[179]Harper,M.;Pacolay,B.;Hintz,P.;Andrew,M.E.(2006).AcomparisonofportableXRFandICPOES
analysisforleadonairfiltersamplesfromaleadoreconcentratormillandaleadacidbatteryrecycler.
J.Environ.Monit.8:384392.
[180] Argyraki, A.; Ramsey, M.H.; Potts, P.J. (1997) Evaluation of portable Xray fluorescence
instrumentationforinsitumeasurementsofleadoncontaminatedland.Analyst122:743–749.
[181]Dost,A.A.(1996)Monitoringsurfaceandairboneinorganiccontaminationintheworkplacebya
fieldportableXrayfluorescencespetrometer.Ann.Occup.Hyg.40(5):589610.
[182] Goldstein, S.J.; Slemmons, A.K.; Canavan, H.E. (1996) EnergyDispersive Xray Fluorescence
MethodsforEnvironmentalCharacterizationofSoils.Environ.Sci.Technol.30:23182321.
[183]Brown,G:E.;Calas,G.;Waychunar,G.A.;Petiau,J.(1988).XrayAbsorptionSpectroscopyamdots
Applications in Mineralogy amd Geochemistry, in: Reviews in Mineralogy. Spectroscopic methods in
mineralogy and geology, Hawthorne, F.C: (ed.), Vol.18,Chap.11, 431512. Mineral Society of America,
Washington,USA.
[184] Stern, E.A.; Heald, S.M. (1983). Basic Principles and applications of EXAFS, in: Handbook on
SynchrotronRadiation,Koch,E.E.(ed.),Vol.1b,9951014.NewYork,USA..
[185]Teo,B.K.(1986).EXAFS:basicprinciplesanddataanalysis,in:InorganicChemistryConcepts9,1
349.SpringerVerlag,NewYork,USA..
[186] Fenter, P.A.; Rivers, M.L.; Sturchio, N.C.; Sutton, S.R (Eds.). (2002). Reviews in Mineralogy and
GeochemistryVol.49.MineralogicalSocietyofAmerica,WashingtonDC.USA.
[187]Waychunas,G.A.;Brown,G.E.(1984).ApplicationofEXAFSandXANESspectroscopytoproblems
in mineralogy and geochemistry, in: EXAFS and NearEdge Structure III, Hodgson, K.O.; Hedman, B.;
PennerHahn, J.E: (eds.), Springer Proceedings in Physics, Vol.2, 336342. SpringerVerlag, New York,
USA.
[188]Brown,G.E.Jr.;Waychunas,G.A.;Ponader,C.W.;Jackson,W.E.;McKeown,D.A.(1986).EXAFSand
NEXAFS studies of cation environments inoxide glasses. Journal de Physique Colloque, Paris, 47,661
668,.
[189]Calas,G.;Brown,G.E:;Waychunas,G.A.;Petiau,J.(1987).Xrayabsorptionspectroscopicstudies
ofsilicateglassesandminerals.Phys.Chem.Miner.15:1929.
[190] Brown, G.E., Jr. (1990). Spectroscopic studies of chemisorption reaction mechanisms at oxide–
water interfaces. p. 309–364. In M.F. Hochella, Jr. and A.F. White (ed.) Mineral–water interface
geochemistry.ReviewsinMineralogy23.MineralogicalSoc.ofAmerica,Washington,DC.USA.
[191] Manceau, A.; Charlet, L.; Boisset, M.C.; Didier, B.; Spadini, L. (1992). Sorption and speciation of
heavy metals on hydrous Fe and Mn oxides. From microscopic to macroscopic. Appl. Clay Sci. 7:201–
223.
[192]Fendorf,S.E.,Sparks,D.L.;Lamble,G.M.;Kelley,M.J.(1994).Applicationsofxrayabsorptionfine
structurespectroscopytosoils.SoilSci.Soc.Am.J.58:1583–1595.
54
1.Introduction
[193]Schulze,D.G.;Bertsch,P.M.(1995).SynchrotronXraytechniquesinsoil,plant,andenvironmental
research.Adv.Agron.55:1–66.
[194] Brown, G.E.; Parks, G.A. (2001). Sorption of trace elements on mineral surfaces: Modern
perspectives from spectroscopic studies, and comments on sorption in the marine environment. Int.
Geol.Rev.43:963–1073.
[195] Bertsch, P.M.;Hunter, D.B. (2001). Applications of synchrotronbased Xray microprobes. Chem.
Rev.101:1809–1842.
[196]Stern,E.A.(1978).StructuraldeterminationbyXrayabsorption.Contemp.Phys.19:239–310.
[197] Sayers, D.E.; Bunker, B. (1988). Data analysis. In D.C. Koningsberger and R. Prins (ed.) Xray
absorption: Principles, applications, techniques of EXAFS, SEXAFS and XANES, 211–256. John Wiley &
Sons,NewYork.USA.
[198]Fendorf,S.E.;Sparks,D.L.(1996).Xrayabsorptionfinestructurespectroscopy.InD.L.Sparks(ed.)
Methodsofsoilanalysis.Part3,357–375.SSSABookSer.5.SSSA,Madison.USA.
[199] Stern, E.A.; Heald, S.M. (1983). Basic principles and applications of EXAFS. In E.E. Koch (ed.)
Handbookofsynchrotronradiation.Vol.10,995–1014.NorthHolland,Amsterdam.Holland.
[200]Koningsberger,D.C.;Prins,R.(ed.)(1988).Xrayabsorption:Principles,applications,techniquesof
EXAFS,SEXAFSandXANES.JohnWiley&Sons,NewYork.USA
[201] Teo, B.K. (1986). EXAFS spectroscopy: Basic principles and data analysis. SpringerVerlag, New
York.USA.
[202]Stöhr,J.(1992).NEXAFSSpectroscopy.SpringerVerlag,NewYork.USA
[203] Bianconi, A. (1983). In: EXAFS and Near Edge Structure, Bianconi, A.; Inococcia, L.; Stipcich, S.
(eds.),118129.SpringerVerlag,Berlin,Germany.
[204] Kim, C.S.; Rytuba, J.J.; Brown, G.E. (2004). Geological and anthropogenic factors influencing
mercuryspeciationinminewastes:anEXAFSspectroscopystudy.Appl.Geochem.19:379393.
[205] Slowey, A.J.; Johnson, S.B.; Rytuba, J.J.; Brown, G.E. (2005). Role of organic acids in promoting
colloidtransportofmercuryfromminetailings.Environ.Sci.Technol.39:7869–7874.
[206]Slowey,A.J.;Rytuba,J.J.;Brown,G.E.(2005).SpeciationofMercuryandModeofTransportfrom
PlacerGoldMineTailings.Environ.Sci.Technol.39:1547–1554.
[207] Bernaus, A.; Gaona, X.;Valiente, M. (2005). Characterisation of Almadén mercury mine
environmentbyXAStechniques.J.Environ.Monit.7:771–777.
[208]Kim,C.S.;BrownJr,G.E.;Rytuba,J.J.(2000).Characterizationandspeciationofmercurybearing
minewastesusingXrayabsorptionspectroscopy(XAS).Sci.Tot.Environ.261:157–168.
[209]Harris,H.H.;Pickering,I.J.;George,G.N.(2003).Thechemicalformofmercuryinfish.Science301:
1203.
[210]Bernaus,A.;Gaona,X.;Esbri,J.M.;Higueras,P.;Falkenberg,G.;Valiente,M.(2006).Microprobe
techniquesforspeciationanalysisandgeochemicalcharacterizationofmineenvironments:themercury
districtofAlmadéninSpain.Environ.Sci.Technol.40:4090–4095.
[211] Riddle, S. G.; Tran, H. H.; Dewitt, J. G.; Andrews, J.C. (2002). Field, Laboratory, and Xray
AbsorptionSpectroscopicStudiesofMercuryAccumulationbyWaterHyacinths.Environ.Sci.Technol.
36:1965–1970.
[212] Bernaus, A.; Gaona, X.; Ivask, A.; Kahru, A.;Valiente, M. (2005). Analysis of sorption and
bioavailability of different species of mercury on model soil components using XAS techniques and
sensorbacteria.Anal.Bioanal.Chem.382:1541–1548.
55
56
2
METHODOLOGY
MINESITESCHARACTERIZATION ......................................................................................................59 2.1.STUDIEDMINESDESCRIPTION.........................................................................................................59
2.2.SAMPLING........................................................................................................................................64
2.3.CHARACTERIZATION ........................................................................................................................65
2.4.DATATREATMENT ...........................................................................................................................70
REMEDIATIONTECHNIQUESOFINDUSTRIALCONTAMINATEDWATERS .................................73
2.5.ZINCSOLVENTEXTRACTION ............................................................................................................73
2.6. FeLOADED MATERIALS FOR THE REMEDIATION OF ORGANIC AND INORGANIC POLLUTED
WATERS ..................................................................................................................................................76
2.7.REFERENCES .....................................................................................................................................79
57
58
2.Methodology
FollowingthepatternoutlinedintheIntroductionchapter,theMethodologyisdivided
intotwomainparts:I)MinesitescharacterizationandII)Remediationtechnologiesappliedto
aquaticsourcescontainingorganicandinorganicpollutants.
MINESITESCHARACTERIZATION
InthissectionthemethodologiestocharacterizedifferentmineareasfromMoroccoand
thespeciationofmercuryinthreeEuropeanminesaredescribed.
2.1.STUDIEDMINESDESCRIPTION
The characterization of different abandoned mines located in Marrakech region
(Morocco)hasbeenaccomplishedbydeterminingphysicalandchemicalparametersofmine
area soils, whilst soils from three well characterized European mercury mines have been
studiedtodetermineitsmercuryspeciationtoassessitstoxicity.
2.1.1.MARRAKECHMINES:DRAALASFAR,KETTARA,SIDIBOUOTHMANEANDBIRNEHASS
(MOROCCO)
Thestudiedminesarelocated35kmnorthwestofMarrakeshinthecoreofthecentral
Jebiletmountains(Figure2.1).TheclimateisMediterranean,borderingaridandsemiaridwith
an average annual precipitation of 231 mm (10 years). Temperatures are characterized by
greatdailyandseasonalvariationwithanaveragevalueof11.5CinJanuaryand28.8CinJuly
[1].CentralJebiletmineralizedbodyconsistsofmajorandminorlensesofmassivepyrrhotite
(Fe2+0.95S), with small amounts of sphalerite (Zn0.95Fe2+0.05S), galena (PbS), chalcopyrite
(CuFe2+S2),pyrite(Fe2+S2),arsenopyrite(Fe3+AsS)andglaucodot(Co0.75Fe2+0.25AsS)[2].
TheDraaLasfarmineislocatedafewhundredmetersfromtheTensiftRiver,closetoa
ruralcommunityofabout5790ha,which65%areoccupiedbyfarmland.DraaLasfarconsists
onadepositofpyritemineraldiscoveredin1953althoughtheircommercialexploitationdid
notbeginuntil1979.Mineralwasprocessedbyflotationafterprimaryandsecondarycrushing
and grinding, producing 60 Mt of products in the first two years (19791980) [1]. Industrial
activitystoppedinMarch1981,althoughitrestartedin1999duetoitsgreatresourceofpoly
59
2.Methodology
metalliccomponents(As,Cd,Cu,Fe,Pb,Zn).Duringitsexploitation,tailingsweredischargeall
aroundthemineareaposingariskfortheenvironment.
Draa Lasfar
Figure2.1.LocationofDraaLasfar,Kettara,SidiBouOthmaneandBirNehassmines
TheKettaramineproducedmorethan5.2Mtofpyrrhotitefrom1964to1981,although
itwasclosedin1982duetodifficultiesduringtheproductionofthepyrrhotiteanditsusein
theroastingunit[3].Duringtheexploitation,morethan3Mtofminewastewerestockpiled
overanareaof16hawithoutconcernforenvironmentalissues(Figure2.2).
Figure2.2.PicturesofKettaraminesite(Photo:GustavoPérez)
TheBirNehasszincmineandtheSidiBouOthmane(SBOthmane)mineconsistonold
graphiteminescharacterizedbyanintensemetamorphismwithirregularmassesofgrenatites
and marble. SBOthmane mine is located close to a rural district and surrounded by
agricultural lands with estimated reserves of 0.02 Mt of graphite ore deposit (3050% of
graphite). Their exploitation started on 1953, treating 115 tons per day of mineral (0.5% Pb,
7.4% Zn and 6% pyrite) by flotation processes until its closure on 1980. Bir Nehass mine
reserveswereevaluatedtobe0.25Mtcontaining20%ofthemineralenrichedinPb(3.83%)
and Zn (4.85%). Their exploitation started on 1972 with an output of 90 tons per day that
increased to 130 tons per day after the implementation of a flotation circuit in 1985. It was
closedattheendofthe20thcentury.
60
2.Methodology
2.1.2.EUROPEANMERCURYMININGDISTRICTS:ALMADÉN,MIERESANDIDRIJA
ThelocationofthethreeminingdistrictsisdepictedinFigure2.3.
Main mercury mines
Sampling sites
Figure2.3.Metallurgicalsitesofthethreemercuryminingdistricts,Almadén,AsturiasandIdrija
TheAlmadénminingdistrictislocatedinCiudadReal,Spain(Figure2.3)andoccupies30
2
km . It is located within an area sparsely populated (population density of less than 25
inhabitants/km2) with an average village population of about 2000 inhabitants (Almadén
populationis7000inhabitants).Othertraditionalactivitiesareagricultureandsheepfarming.
Hunting and incipient rural tourism are the only alternatives to traditional activities [4].
Almadén is the largest cinnabar (HgS) deposit in the world and it has been active since the
Roman times until the present days, having accounted for about one third of the total Hg
world production [5, 6]. Metallurgical processing evolved from Bustamante furnaces, with
roasting temperatures over 600 ºC, to Pacific furnaces in the last century, reaching
temperaturesofupto800ºC.SoilsatAlmadénareaaremainlyrepresentedbyquartzanda
diversity of claytype minerals such as chlorite, illite, kaolinite and pyrophyllite and high
contents of carbonates which correspond to a region with shales and quartzites as main
componentsofthestratigraphicsequence[7].
ElEntredichoopenpitmine
ElEntredichodump
Figure2.4.PicturesofAlmadénminesites(Photo:JoséMªEsbrí)
61
2.Methodology
The mercury mine of Idrija is located 50 km west of Ljubljana, Slovenia, in the narrow
valley of the Idrijca River. The Idrija mine has been the second largest mercury mine in the
worldsurpassedonlybytheAlmadénmine.After500yearsofminingactivityproducingatotal
ofabout105,000tonsofHg,frommorethan3106m3oforeandgangue,themineofIdrija
closedin1995[8].Takingintoaccountlossesduringminingandinefficientsmelting,thetotal
volumeofminedHgisestimatedtobeatleast140,000tons(9,10).Idrijaminingdistrictis,like
Almadén, a monometallic ore deposit, with high amounts of native mercury hosted in
carbonate rocks. The mineralization appears as two main species: cinnabar and native
mercury. Other minerals appearing in its paragenesis are metacinnabar, pyrite, marcasite,
dolomite, calcite, kaolinite, epsomite and melanterite. The mineralogical characterization of
Idrijasamplesrevealscarbonatebedrocksasmaincomponentsofthestratigraphicsequence,
with the exception of the meadow soil from the Pront Hill, which was developed on
carboniferous clastic rocks. River bed and suspended sediments are composed of silica, clay
minerals,FeandAloxides,hydroxidesandcarbonatesasaresultofweatheringofcarbonate
andclasticrockintheIdrijacatchment[11].MetallurgicalprocessingwassimilartoAlmadén
during thelastcentury,usingPacificfurnacesable toreach up to800ºC.Duetothemining
andoreprocessingoperations,IdrijaanditssurroundingshavebeenpollutedwithHg.
Idrijamine
Miéresmine
Figure2.5.PicturesofIdrijaandAsturiasmine(Photo:JoséMaríaEsbrí)
On the other hand, La PeñaEl Terronal, in Miéres (Asturias) is a region located in
northern Spain (Figure 2.3) with abundant Hg deposits that has been an important Hg
producer on the global scale. This site has an intense metallurgical activity with an average
annualproduction ca.517tonesofkg [12].Mercuryispresentascinnabar,but withvariable
metacinnabar and metallic mercury proportions and with other metallic minerals such as
orpiment, realgar, melnikovite, chalcopyrite, arsenopyrite, stibnite and galena [13]. To
summarize,LaPeñaElTerronalminehasamorecomplexmineralogythanAlmadénandIdrija,
withhighamountsofarsenicinitsparagenesis,Inaddition,theirrotaryfurnacesachievelower
calcinationstemperatures(over600ºC)thantheotherminingdistricts[14].
62
2.Methodology
The total mercury concentration in soils and sediments of the three mining districts is
welldocumented[15,16,17,18,19],althoughonlyafewstudiesdealtwithinorganicmercury
speciation[20,21,22,23,24].
2.1.3.AZNALCÓLLARTAILINGPOND
Aznalcóllar mine is located in a pyriterich formation following the Bethic Chain which
extends from the central south of Spain to Portugal (Figure 2.6). It has been active since
Roman times due to their high grade silver, lead and zinc ores. In this type of mine, ore is
milled, washed, and after treatment with several reagents, the valuable metal sulfides were
separated by flotation. In this process, huge volumes of acidic wastes and tailings generated
arestockpiledinatailingspond.ThetailingsreservoirinAznalcóllarissituatedneartheAgrio
River,asmalltributaryoftheGuadiamarRiver.Thewatersusedintheminingoperationsare
currently dumped, after depuration in the mine, in this small tributary. The reservoir was
constructed in 1974 using jetty materials. At that time the dam was approximately 5m high
althoughitwasenlargedseveraltimesusingtailingmaterials.Atthetimeofthetailingsdam
failureaccident,thedamwasapproximately25mhigh[25].
Figure2.6.Aznalcóllarminelocation
Figure2.7.Aznalcollartailingponds(Photo:BaruchGrinbaum)
63
2.Methodology
2.2.SAMPLING
Different sampling strategies were undertaken for the mines of Marrakech and the
Europeanminesdependingonthespecificpurposeofeachstudy.
2.2.1.MARRAKECHMININGDISTRICTS:DRAALASFAR,KETTARA,SIDIBOUOTHMANEANDBIRNEHASS
(MOROCCO)
Samples were taken every 50 meters from the mining area towards specific receptor
media(rivercreeks,hills,villages,farms,etc).Afterremovingthefirstlayerofsurfacesoil(2
cm),samplesweretakenfromtheupper20cmwithinanareaof100cm2persample.Residue
samples were taken on the stockpiled dykes, piles or ponds where tailings were deposited.
Additionally,3representativebackgroundsampleswerecollectedat1kmfromtheminingsite,
far enough to avoid disturbance from mining operations. After airdrying during 48h at 30C,
samples were sieved below 2mm through a stainless steel sieve to remove large debris and
storedinplasticbottlesatroomtemperature.
2.2.2.EUROPEANMERCURYMININGDISTRICTS:ALMADÉN,ASTURIAS(SPAIN),IDRIJA(SLOVENIA)
Samples of soils, mine tailings, calcines and riparian soils from the Almadén site were
takenatadepthof0–20cmandstoredinpolyethylenebags.Samplesofsuspendedparticles
were collected from the water column and let to sediment in a clean room. All the samples
wereairdriedtopreventmercurylosses,homogenized,milledandsievedbelow2mm.
SoilsamplesfromIdrijaweretakenusingastainlesssteelaugeratadepthof0–10cm
and stored in polyethylene bottles. Suspended river sediment was sampled during a flood
eventoftheIdrijcariverbymeansofanetdriftsamplerand,afterremovalofgravel,stones
and plant residues, river bed and suspended sediments, samples were dried at 30C during
threedaysuntilconstantweightinthedark,homogenizedinanagatemortarandseparatedin
twograinsizefractions:fractionbelow0.063mmandfraction0.063–2mm.
SamplesfromMiereswerecollectedinLaPeñaElTerronalminesite,nearthetownof
Mieres. The sampling included samples from dumps, calcines, contaminated soils and a
chimneychannelusedtotransportroastingsmoketothetopofamount.Soils,ripariansoils
andminetailingssamples(~1.5kg)werecollectedat10–30cmdepth,storedinpolyethylene
bags,airdriedinacleanroomandsievedbelow0.1mm.
64
2.Methodology
2.3.CHARACTERIZATION
ThecharacterizationoftheMarrakechminesoilshavebeenperformedbydetermining
its physicochemical parameters such as pH, EC, LOI and carbonate content as well as heavy
metal concentration and mobility. Regarding the mercury mines, the characterization of the
samples has been performed in order to assess its speciation using synchrotron techniques
that,inturn,werealsoappliedtothecharacterizationofarsenicsorptionontoFeexchanged
materials.
2.3.1.PHYSICOCHEMICALPARAMETERS
The physical characterization consisted in the measurement of the pH, the electrical
conductivity (EC), loss on ignition (LOI) and the carbonate content of the samples following
standardmethodologies[26].
Thus,pHmeasurementsweredoneinasoilsuspension(2g/5mlofdistilledwaterstirred
vigorously) after 2 h of deposition using a pHmeter WTW Multiline P4 Universal pHmeter
cabledSenTix92TpHelectrode(Germany).
TheECwasdeterminedinasoilsaturatedpaste(1gsoil/5mlofdistilledwater)witha
conductimeter WTW Multiline P4 Universal Standard Conductivity Cell TetraCon® 325
(Germany)oncecorrectedtotheworkingtemperature(20C).
Loss on ignition (LOI) was determined gravimetrically after volatilization of organic
matter on a furnace at 550°C during 4h. For the total carbonate content three replicates of
each soil were stirred during 6 h in an HCl 4 mol/L solution (1.0g of soil/20 ml of HCl 4.0 M
solution)and,afterfiltering,calciumwasmeasuredusingaJENWAYPFP7flamephotometer.
2.3.2.TOTALMETALCONCENTRATION
For the determination of the total metal concentration, aliquots of each sample were
encapsulatedintenmilliliterpolyethylenesamplecups(Chemplex,FL,USA)andsealedusing
precutMylar®circlesfilmpriortotheiranalysiswithaFPXRFequipmentAlpha6500R,Innov
XSystems(USA).Thesamplethicknessinthecupshouldbeatlest1.2cmsoastheXrayscan
penetratethesample.
Thisequipmentisatubetypeenergydispersiveinstrumentwithatungstencathodeand
asilveranodethat cangenerateXraysin theenergyrange10to40keVand1050μA. The
2
instrument is provided with a circular probe window (1.54 cm area) and employs a SiPiN
diodesdetectorwithanenergyresolutionof230eVatthefullwidthathalfmaximumintensity
ofthemanganese(Mn)KXrayline.
65
2.Methodology
Thestandardizationconsistsinthecollectionofaspectrumofaknownspectrum(Alloy
316)andthecomparisonofavarietyofparameterstovaluesstoredwhentheinstrumentwas
calibratedat thefactory. Thisproceduretakesabout1minuteandshouldbedoneanytime
thehardwareisinitiatedorrestartedandmustberepeatediftheinstrumentisoperatingfor
morethan4hours.
Theanalyzingtimeforeachsamplewassetto120sfortheheavyelementsand90sfor
thelightelements.Thistimeperiodisestablishedasthebesttradeoffbetweenaccuracyand
speedofanalysis.Foraccuracy,aninstrumentblankandacalibrationverificationcheck(NIST
2710) was checked each working day before and after analyses are conducted and once per
everytwentysamplesfollowingEPAMethod6200[27].
Figure2.8.InnovXFPXRFmodelALPHA6500andstandforlaboratorymeasurements(Photo:
ElenaPeralta)
Aninstrumentblankisusedtoverifythatnocontaminationexistsinthespectrometeror
on the probe window. As instrument blank we employed silicon dioxide although it can be
used also a polytetraflurorethylene (PTFE) block, a quartz block, "clean" sand, or lithium
carbonate.Aninstrumentblankshouldalsobeanalyzedwhenevercontaminationissuspected
bytheanalyst.
Acalibrationverificationchecksampleisusedtochecktheaccuracyoftheinstrument
andtoassessthestabilityandconsistencyoftheanalysisforthetargetanalytes.
The check sample should be a well characterized soil sample from the site that is
representative of site samples in terms of particle size and degree of homogeneity and that
containscontaminantsatconcentrationsneartheactionlevels.Ifasitespecificsampleisnot
available,thenanNISTorotherreferencematerialthatcontainstheanalytesofinterestcan
beusedtoverifytheaccuracyoftheinstrument.Toverifythecalibration,themeasuredvalue
for each target analyte should be within ±20% of the true value. In this sense, NIST 2710
66
2.Methodology
(Montana soil) standard reference sample was employed as calibration verification check,
providingresultswithinspecifiedtolerances(Table2.1).
Element
Aluminum
Calcium
Iron
Magnesium
Phosphorus
Potassium
Silicon
Sodium
Sulfur
Titanium
Table2.1.NIST2710Certifiedvalues
Massfraction(%)
Element
6.44±.0.08
1.25±0.03
3.38±0.10
0.0853±0.042
1.01±0.04
2.11±0.11
28.97±0.18
1.14±0.06
0.240±0.006
0.283±0.010
Antimony
Arsenic
Barium
Cadmium
Copper
Lead
Mercury
Nickel
Silver
Vanadium
Zinc
Massfraction(%)
38.4±3
626±38
707±51
21.8±0.2
2950±130
5532±80
32.6±1.8
14.3±1.0
35.3±1.5
76.6±2.3
6952±91
2.3.3.TOTALMERCURYCONTENT
The detection of mercury at trace levels is a complex analytical task because of its
specificphysicalandchemicalproperties.Manytechniquesexistformercurydeterminationin
different matrix, and almost all of them involve an intermediate stage of mercury
preconcentration in absorption traps [28, 29, 30] or acid mixtures for the digestion process
prior to determination by Cold Vapor Atomic Absorption Spectrometry (CVAAS) [31, 32, 33,
34]. All of them were more or less prone to analyte losses and/or contamination. Total
mercury content of all solid samples corresponding to the European mercury mines was
determined by Zeeman atomic absorption spectrometry using high frequency modulation of
lightpolarization(ZAASHFM)withaLumexRA915+analyzer[35].
In this mercury analyzer, the mercury contained in the sample is atomized by a glow
dischargemercurylampplacedinapermanentmagneticfield.Thismagneticfieldsplitsthe
254nm mercury resonance line into three polarized components: one linear and two
circularly polarised in the opposite directions (+ and ). Only components are detected.
After passing through a polarization modulator, which modulates the polarization at a
frequencyof50kHzandthustriggersthelinecomponentsinturn,theradiationthenpasses
throughamultipathcell,whoseequivalentopticallengthisabout10m.Beingequippedwith
narrowband high reflectivity mirrors, the cell isolates solely the 254nm resonance line and
suppresses all the nonresonance and stray radiation. A logarithm of the intensity ratio of +
and , which is proportional to the mercury atom concentration in the cell, is determined
upondetectingtheradiationbyaphotodetectorandsubsequentanalogdigitalconversionof
its electric signal by a microprocessor. The measurement results are read out from a LC
display. In this measurement technique, the analytical signal depends only on mercury
67
2.Methodology
concentration and is independent of the presence of dust, aerosols, and other foreign
contaminantsintheanalyticalcell.
-1
Thedetectionlimitofthistechniqueforsoilsandsedimentssamplesis0.5mgHgkg .
Foraccuracy,acertifiedreferencematerial(CRM025)wassimultaneouslyanalyzed.
Figure2.9.LumexRA915+analyzerformercurydeterminations
2.3.4.MOBILITYOFTHEMINESAMPLES
Mobility assays were performed by applying established methodology of single
extraction procedure [36] consisting on metal extraction of soil samples with HCl 0.5 M at
solid:water ratio 1g/20 ml during 1h under magnetic stirring. After each extraction, the
suspensionwascentrifuged10minat3500rpmandthesupernatantwasfilteredusing0.22
μm Millipore Millex GS filters (Ireland). The extracts were analyzed by means of Inductively
Coupled
PlasmaOptical
Emission
Spectroscopy
(ICPOES)
using
an
equipment
ThermoElementalIntrepidIIXLS(USA)(Figure2.10).
Agitation (1h) Sample
+ HCl (0,5M) (1:20)
Centrifuge 10min
4000rpm
Filtration of the
extract
ICP-OES
Figure2.10.Singleextractionprocedurescheme
TheICPOESanalyticaltechniqueallowsmultielementalanalysisofmetalsinsoils,with
anexcellentperformanceandawideanalyticrange.Inthistechnique,aplasma(ionizedgas,
electricallyneutral)isusedtoexcitetheatomsofthesamplesothatwhenrelaxedtheyemit
electromagnetic radiation at wavelengths characteristic of each element (in the region of
correspondingUVvisiblespectrum)withanintensityproportionaltoitsconcentration.
The plasma, maintained by the interaction between RF frequency and ionized argon,
reachestemperaturesupto10000K.Thesampleisintroducedthroughaperistalticpumpinto
68
2.Methodology
the instrument through a nebulizer using a flow of argon, that disperses the liquid into
dropletsthatarecarriedtoacyclonicchamber.Atthecyclonicchamberthelargerdropletsare
separatedfromthesmallerdrops,whicharemovedtowardstheplasmabyaflowofargon.
Intable2.2.canbefoundthecharacteristicwavelengthsusedforthemeasurementof
thetargetelements.
Table2.2.Characteristicwavelengthsofelementsmeasured,limitofdetectionandlinearity
Element
Wavelength(nm)
Limitofdetection(μg/L)
Linearity(μg/L)
As
Cu
Pb
Zn
193.759
324.754
220.353
213.856
1
0.5
1
0.5
150
0.550
150
0.550
2.3.5.XASMEASUREMENTS
All solid samples from the European mercury mines were prepared mixing an aliquot
withpolyethylene(IRquality),homogenizedwithavortexfor2min,pressedasapelletwith5
toncm2ofpressureusinganIRpressandsealedbetweenKapton™tape.
Its XANES measurements were performed at the HASYLAB synchrotron facility
(Germany) at A1 bendingmagnet beamline. All measurements were carried out at room
temperature.ThebeamlinesetupconsistedofaSi(111)doublecrystalmonochromator,three
ionization chambers as transmission detectors and a 7pixel Ge fluorescence detector. The
absorptionofmercurywasrecordedatitsLIIIenergy(12284eV)(Figure2.11).
A)
B)
Figure2.11.A)Sampleholdercontaining6pelletsofthesamples.B)SchematicXAFSsetup
ReferencesforXANESfingerprintadjustmentsincludedthefollowingmineralsandpure
compounds: HgCl2, HgSO4, HgO, CH3HgCl, Hg2Cl2 (calomel), HgS red (cinnabar), HgS black
(metacinnabar), Hg2NCl0.5(SO4)0.3(MoO4)0.1(CO3)0.1H2O (mosesite), Hg3S2Cl2 (corderoite),
Hg3(SO4)O2(schuetteite)andHg2ClO(terlinguaite).Thisselectionwasundertakenonthebasis
ofourpriorknowledgeofthegeochemistryofthedifferentstudiedareas[16,17,18,19,20,
21],aswellasthepossibleweatheringandanthropogenicprocessestakingplaceineachsite.
69
2.Methodology
Ontheotherhand,thelocalenvironmentofarsenicadsorbedontodifferentmaterials
was investigated by both Xray absorption nearedge structure (XANES) and extended Xray
absorptionfinestructure(EXAFS)spectroscopies.Sampleswerepreparedfollowingthesame
methodologyasdescribedforminesoilsamples.ArsenicspectrawerecollectedatitsKedge
energy (11867 eV) at beamline C of DORIS III HASYLAB facilities. This beamline is essentially
equaltoA1beamline(Figure2.11).ThemonochromatorconsistedinaSi(111)doublecrystal
and the detection was measured either by adsorption and fluorescence using a 7pixel Si(Li)
detectorovertheenergyrange1170012700eV.Themonochromatorwascalibratedusingthe
LIIIedgeofagoldfoil(11919eV).AsastandardfortheEXAFSfitting,Na2HAsO47H2O(Panreac)
wasselected.Thus,bythecomparisonoftheempiricalresultswiththetheoreticalresultsfor
Na2HAsO47H2Oanestimationofthegoodnessoftheselectedpathscanbeobtained.
2.4.DATATREATMENT
Collecteddatawiththedifferentexperimentaltechniqueshavebeentreatedtoextract
appropriate information to characterize target samples and corresponding processes in the
presentstudies.Thefollowingtoolswereusedtothispurpose:
2.4.1.CONCENTRATIONENRICHMENTRATIOS
In order to avoid the limitation of using total concentrations of pollutants without
considering the geochemical variability of the geological substrate or the particle size effect,
some indicators can be employed. Concentration enrichment ratios (CER), also called
enrichmentfactors,wereusedinthestudiesconcerningthecharacterizationofmineareasoils
fromMoroccoandareusedtoidentifyandquantifytheextentofhumaninterferenceinsoils
that,byextension,arealsoanindicatorofsoilcontamination.
CERwereinitiallydevelopedtospeculateontheoriginofelementsintheatmosphere,
precipitation or seawater [37, 38, 39]. This use was progressively extended to the study of
soils, lake sediments, peat, tailings and other environmental materials [40, 41] comparing a
targetpollutantwithabackgroundelement.TheformulatocalculateCERcanbegeneralized
as:
CER=
[El]sample /[X]sample
[El]background /[X]background
(Equation2.1)
where“El”istheelementunderconsideration,“X”isthechosenreferenceelementand the
subscripts“sample”or“background”indicatewhichmediumtheconcentrationrefersto[42].
Thereferenceelement“X”shouldbelittleaffectedbyweatheringprocessesandshouldshow
70
2.Methodology
littlevariabilityofoccurrence.Inthissense,themostcommonreferenceelementsemployed
in the literature are aluminum (Al), zirconium (Zr), iron (Fe), scandium (Sc), and titanium (Ti)
[43, 44, 45], although there have been also attempts at using other elements such as
manganese[41],chromium[46],lithium[47]andcalcium[48].Inthiswork,Zrwasselectedas
lithogenicelementduetohomogeneityofZrconcentrationinallsamplesandbackground.
The interpretation of CERs can be employed to determine the anthropogenic
contribution[49](Table2.3).
Table2.3.AnthropogeniccontributionatdifferentCERvalues
CER
Anthropogeniccontribution
<2
25
520
2040
>40
Minimalornule
Moderate
Significant
Strong
Extreme
2.4.2.GEOGRAPHICINFORMATIONSYSTEMS
Usedinthestudiesconcerningthecharacterizationofthemine sitesfromMarrakech.
Contour maps of CER values of target elements were done by GIS representation using
Miramonv6.4CompleteGeographicalInformationSystemandRemoteSensingsoftware[50]
choosingIDWinterpolatorasthemostsuitableduetotheirregularsamplingrealizedonthe
minesites.
2.4.3.STATISTICALTOOLS
BoxplotsgraphsofKettara,BirNehassandSidiBouOthmanewereobtainedusingSPSS
Statistics17.0software.PCA/APCSwasrealizedbyusingExceladdinXLStatDataAnalysisand
Statistical Software [51]. Samples were scaled by using the standard normal variate (SNV)
algorithmandBartlettsphericitytestwascheckedinordertoconfirmthatthevariableswere
uncorrelated.Kaisercriterionwasusedtoselectprincipalcomponents,andonlyfactorswith
eigenvaluesgreaterthan1wereconsidered.
2.4.4.XASDATATREATMENT
XANESspectraofsamplesfromEuropeanmercurymineswereprocessedusingSixPACK
dataanalysissoftwarepackage[52,53,54,55].Spectraprocessingincludedenergycorrection,
signal normalization and background correction. After data correction and normalization,
principal component analysis (PCA) was applied to the set of unknown spectra to determine
thenumberofprincipalcomponentsrequiredtodescribethevariationinthedata.Then,the
PCA results were used with a target transformation, which projected the spectrum from a
71
2.Methodology
referencecompoundontothevectorspacedefinedbythecomponents.Ifthetargetvectorlay
withinthiscomponentspace(abovethe95%confidencelevel),thenthisreferencecompound
was determined to be present in the corresponding sample. Finally, a linear leastsquares
approach was used to determine the fractional amount of each reference compound in the
samples [56, 57, 58]. The quality of the target transformation was given by the reduced 2
value, which represents the goodness of the fit to the spectra data using the linear
combinationprocedure[59]andisdefinedas:
reduced 2
1 N obs
6 ( F i F i fit ) 2
i
N -P 1
(Equation2.2)
where
Fiobs istheordinateoftheXANESspectrummeasuredfromthesampleattheithenergy
point,
F i fit istheordinateofthefittedXANESspectrum,Nisthenumberofdatapointsinthe
fitted XANES energy range and P is the number of fitted components. A higher reduced2
denotes that the Hg compounds compared possess a lower degree of similarity. This 2
representsthegoodnessofthemodelfit.
EXAFSdatatreatmentwasperformedwithVIPERsoftware[60].InVIPER,theextracted
EXAFSsignalorfunctionwasconvertedtofrequency(k)space,weightedbyk2,andFourier
transformed to produce the Rspace EXAFS paircorrelation function, which is similar to a
radial distribution function. The program includes an option to iterate the postedge
background to obtain peaks with good resolution and to minimize spurious peaks at small
radial distances. After backtransforming the first and second peaks in the raw Fourier
transform into frequency (k) space, an ordinary fitting analysis was performed to obtain
interatomic distances and coordination numbers. Phase and amplitude functions were
extracted from sodium arsenate and scorodite standards (FeAsO44H2O). The DebyeWaller
factor()fortheunknownsampleswereconstrainedto=0.003forthefirstshell,and=0.008
forthesecondshell.
72
2.Methodology
REMEDIATIONTECHNIQUESOFINDUSTRIAL
CONTAMINATEDWATERS
Severalremediationtechnologieshavebeenstudiedinthisthesistotheirapplicationon
thetreatmentofindustrialwaterscontainingdifferenttypesofpollutants.Inthisconcern,two
differenttechniqueshavebeenstudied:
1)Conventionalmethodologiessuchassolventextractionhasbeenemployedtotherecycling
of zinc from a mine tailing pond to provide an economical benefit while diminishing the
volumeofhazardousmaterialscontainedintheminetailingatlaboratoryandpilotplantscale.
2)Feexchangedmaterialshavebeenemployedascatalystsforthedegradationofpersistent
organicpollutants(POPs)bymeansofFentonprocessesaswellastothesorptionofinorganic
contaminants.
2.5.ZINCSOLVENTEXTRACTION
2.5.1.LABORATORYEXPERIMENTS
InordertogetaZnsulphaterichliquortobeusedlaterinelectrowinningprocess,the
performance of a newer commercial extractant, Ionquest 290 (Bis(2,4,4trimethylpentyl)
phosphinicacid),iscomparedwiththeresultsofmoreconventionalextractantsDEHPA(Di2
(ethylhexyl) phosphoric acid) and Cyanex 272 (Bis(2,4,4trimethylpentyl) phosphinic acid) for
thesolventextractionofaZnrichmineeffluent.
Intherelatedtailingminesamples,FewasremovedfromtheminewaterpriortotheSX
treatmentbymeansofabiooxidationprocessusingThiobacillusferrooxidansandaselective
alkalineprecipitationstep[61,62]toobtainapregnantleachsolution(PLS)withoutiron,since
therearenoreagentscommerciallyavailablecapabletoextractZnselectivelyfromasolution
containingFe.
TheextractantsDEHPA(Batchref.0063829)andIonquest290(BatchRef.G05A1)were
kindly supplied by Rhodia UK Ltd. and Cyanex 272 was purchased from Cytec Industries BV,
Netherlands. Ionquest 290 has the same active ingredient as Cyanex 272 but has a lower
contentofinactiveimpurities,thephosphineoxideimpurityis<5%inIonquest290butaround
15%inCyanex272[63].
Two type of kerosene with different flash point were also studied as solvents for the
extractants. Commercial grade extrapure aliphatic kerosene Ketrul D80 and Ketrul D100
73
2.Methodology
(Batchref.20062016and20061560,respectively)werekindlysuppliedbyTotalFluidesFrance.
Ketrul D80 and Ketrul D100, have a flash point of 72 ºC and 100ºC or superior (ISO 2719),
respectively.Itmustbepointedoutthatthehighertheflashpointthelessertheflammability
ofthekerosene,and,thereforethehigherthesecurityofthesolventextractionprocess.
Sulfuric Acid 9598% was purchased from J.T. Baker, Phillipsburg, NJ, USA and it was
usedtostripthezincfromtheorganicenrichedphase.Allthereagentswereusedasreceived
withoutanyfurtherpurification.Stopperedglasstubesof50mLwereusedforthetwophases
contactandtheagitationtookplaceinarotatingrack.
Forthekineticexperiments10mLofDEHPA40%(v/v),Cyanex2725%(v/v)orIonquest
2905%(v/v)wereagitatedwith10mlofPLS(ratioA:O=1)inarotatingrackduring5,10,20,
30,40or60min.Theorganicphaseloadedwiththetargetmetal/s(OP)wasstrippedwith5
mLofH2SO42.0Mduring3htoensurecompletestripping.DEHPAconcentrationwashigher
duetoefficiencyrelatedtoextractionyieldandextractantcost.
Todetermineselectivity,isothermsvaryingtheA:Oratiofrom0.1to10wererealized.
DifferentvolumesofCyanex2725%(v/v),Ionquest2905%(v/v)orDEHPA40%(v/v)ineach
typeofkerosenewereequilibratedwiththePLSduring15minandthereafterOPwasstripped
with5mLH2SO42.0M.Nocentrifugationofthedualphasesystemwasrequiredbecauseof
theclearphaseseparationobtained.SelectivityofthesolventstowardsZnwasdeterminedby
the recovery of each metal (equation 2.3) and by the amount of metal remaining in the OP
(Equation2.5)whichiscalculatedbythedifferentamountsofmetalintheraffinate(Equation
2.4)andintheOP.
§ Znstrip ·
%Recovery= ¨
¸ ×100
© ZnPLS ¹
(Equation2.3)
§ Znraffinate ·
%Remaining R= ¨
¸ ×100
© ZnPLS ¹
(Equation2.4)
%Remaining OP = 100 - %Recovery - %Remaining R
(Equation2.5)
Major elements present in the PLS, in the strip liquor and in the raffinate were
determinedbyICPOESThermoIrisIntrepidIIXLS(USA).
2.5.2.SCALINGTHESXTOAPILOTPLANT
Thebiooxidationreactorconsistedofa150cmhighand70cmdiameterstainlesssteel
columndividedtothreezones:a30cmdeepbottomspacewhereairandsolutionwerefedin,
asiliceousstonepackedbedcontainingtheinoculumsupportedbyastainlesssteelscreenand
anairspaceatthetoptopHandEhcontrolanda50mmpipetoletthesolutionoverflow.The
effluent circulated through a tank where pH was initially adjusted and, after pH adjustment,
74
2.Methodology
thesolutionwastransferredtothebioreactorwherebiooxidationofferrousionstookplace,
tobefinallytransferredtoaprecipitationtankfedbyalimesolutionfromaseparatedtank.
After precipitation and sedimentation of iron compounds, the supernatant was directly used
asfeedsolutiontothesolventextractionstage(Figure2.12).
The pilot plant process was undergone with Ionquest 290. Ionquest 290 (Purity>95%)
was supplied by Rhodia UK Ltd. and commercial grade extrapure aliphatic kerosene Ketrul
D100(bp 100ºC) by Total Fluides France. A solution of Na2CO3 was used for pH adjustment
during the solvent extraction experiments. For the stripping step, the loaded solvent was
contacted with 2M H2SO4 at a phase ratio O:A=10, the initially expected phase ratio in the
planttoachievetherequiredzinctransferintheEWplantof20g/L.Inpractice,itwasfound
that the required transfer in the EW plant was 40 g/L Zn, consequently, the phase ratio was
modified to O:A=20. No laboratory tests were undertaken at this phase ratio, but directly
appliedinthepilotplant.
Figure2.12.BiooxidationandSXflowsheetatthepilotplant
TwoBatemanPulsedColumns(BPC)wererequiredfortheSXandstrippingprocessesat
thepilotplantduetotheirdemonstratedfeasibilityinseveralSXplants[64,65,66].BPCare
large diameter vertical pipes filled alternately with disk and doughnut shaped baffles to
promotecontactbetweentheorganicandaqueousphasesthroughthecolumn.Adecanterat
eachendofthecolumnallowstheliquidstocoalesceandbedecantedseparately.Whenthe
solvent phase is continuous, the interface between the phases is in the lower decanter and
whentheaqueousphaseiscontinuous,itisintheupperdecanter.Thecolumnsarepulsedby
blowing air at the required amplitude and frequency of the pulses [67]. An 80mm diameter
BPC,7mhigh(equivalentto3theoreticalmixersettlerstages)waschosenfortheSXprocess
75
2.Methodology
anda40mmdiameterBPC6meterhighforthestripping.Thecompletepipingoftheplantis
showninFigure2.12.
EXTRACTION
COLUMN
EXTRACTION
COLUMN
Figure2.13.SectionofthecolumnslayoutatAznalcollarpilotplant(Photo:BaruchGrinbaum)
Allflowswerefedthroughmeteringpumpsandtheflowratesofallinletsandaqueous
outletsweremeasuredbyrotameters.Thepilotwasrunfor12workingdays,10hoursaday
onaverage,atotalof120hours.Theaverageflowrateoftheaqueousfeedwas150L/h,so,
3
about18 m oftailingsolution,afterFeprecipitation,were treated. Thetotalvolumeofthe
solventwas300Landithad5%Ionquest290dissolvedinkerosene(20%aromaticand80%
2+
aliphatic);theweakelectrolyte(WE,stripsolution)consistedof190g/LH2SO4with50g/LZn .
Asolutionof50100g/LNa2CO3waspreparedperiodicallyina60Lbarrelandusedtoadjust
thepH.
The concentration of Zn was determined using a Perkin Elmer 3110 AAS at the pilot
plantlaboratory.TheZnintheraffinateandSEwasdetermined directly,whiletheZnin the
barrenandloadedsolvent(BSandLS)solutionweredeterminedafterstrippingusingH2SO4.
2.6. FEEXCHANGE MATERIALS FOR THE REMEDIATION OF ORGANIC
ANDINORGANICPOLLUTEDWATERS
SeveralmaterialshavebeenstudiedasFesupportstobeusedeitherasFentoncatalysts
orarsenicsorbents.Thesematerialsincluded,USYzeolite(ZeolystInternational).Inthiscase,
3+
conditions for Fe immobilization have been modified with respect to the ones present on
Neamtuetal.,[68],i.e.3cyclesof6hoursofFeexchangingwithanexcessofFe(NO3)31Mat
80C. These modifications resulted on a faster Feexchanging preparation and diminish the
employed Fe(III) solution concentration. In addition, the whole process was done at room
76
2.Methodology
temperature, so, our process minimizes time, reagents and energy consumption. Likewise,
zeolite Y (namely ZY, Grace Davison), Forager sponge type M (namely Sp, Dynaphore Inc.,),
clinoptilolite (namely Clino; Natural zeolite, origin: Cuba) and montmorillonite K10 (namely
MMT; Aldrich) were conditioned at pH=4 to be afterwards loaded with Fe(III) using the
indicatedconditionsoptimizedforUSY.
The Fe(III)bearing materials were prepared by ion exchange, contacting each material
with a solution of Fe(NO3)3 0.05M (Panreac) at pH=2 at room temperature. After 1h of
contacting, the materials were filtered, washed with milliQ water and dried in an oven at
60ºC. The amount of Fe on each supporting material before and after the Fe loading was
measured with a FieldPortable XRay Fluorescence (FPXRF) equipment InnovX Systems,
modelAlpha6500R.
2.6.1.FENTONREACTION
The commercial azo dye Acid Red 14 (Chromotrope FB 50%, C.I. 14720, Aldrich) was
selectedasamodeldye,sinceitisacommontextileandleatherindustrydye,alsoemployed
fordyingnylon,woolandsilk[69,70,71].
-3
Thedecolorisationtestswereperformedadding0.25gofcatalystand8.75x10 mmols
of hydrogen peroxide (H2O2 35%, Fluka) to 100 ml of AR14 0.05 mM at 75ºC. Decolorisation
andmineralizationofAR14wasmeasuredat=516nmand=324nm,respectively,usingan
UnicamUV/VisSpectrometerUV2(Unicam)atregulartimeintervalssoastoestablishkinetics
correlations.Finally,theanalysisofthedegradationproductsinthefinalsolutionwasdoneby
GCMS HP6890 (column 30m x 0.25 mm x 250 μm) following the methodology described in
Zheming et al. [72]. The heterogeneous catalysis experiments were compared with those
performedinhomogeneouscatalysismodeusingthesameamountofFeloadedintheUSY.
In addition, acetic acid (CH3COOH 96%, Panreac) and phenol (C6H5OH 99.5%, Panreac)
wereselectedasmodelcompoundsduetotheirrefractorinesstodegradationbyconventional
oxidationmethods[73].Thereactionwasperformedover100mlsolutionofeitheraceticacid
0.5%orphenol0.5%at75Cduring1h,using0.25gofeachFeloadedsupportedmaterialasa
catalystand8.75x103mmolsofH2O2.ToavoidinterferencesbyH2O2 inCODmeasurements,
0.2gofMnO2wereaddedtoremoveresidualH2O2[74].CODwasmeasuredbeforeandafter
Fenton reaction, following the procedure described in section 5220 C of Standard Methods
[75]. The amount of Fe released from each support, after the Fenton reaction, was finally
analyzedbymeansofanICPOESThermoIrisIntrepidIIXLS(USA).
The performance of the Feloading process as well as the Fenton reaction was also
testedincontinuouscolumnprocessbyusing1cmdiameterglasscolumnsfilledwiththeFe
77
2.Methodology
exchanged materials. The Feloading process was also done in countercurrent at 2ml/min at
thesameconditionsofthebatchprocess.Washingofthematerialswasdonecirculatingmilli
Qwaterduring3hand,afterdryinginanovenat60Covernight,theamountofFeloadedinto
each material was measured by FPXRF. The Fenton reaction in column was performed over
100mlofAR140.05mM,100mlofaceticacid0.5%and100mlofphenol0.5%.
2.6.2.ARSENICREMOVAL
Each Fe(III)bearing material was slowly added into a solution containing 1000 ppm of
Na2HAsO47H2O(Panreac)atpH=4.Thesolutionwasagitatedinarotatingrackduring4hat30
rpm. The suspension was filtered, and the solids were thoroughly washed three times with
distilledwateranddriedinanovenunderairat60ºCovernight.Pelletsofeachsamplewere
doneusinganIRpressandsealedbetweenKapton™tape.TheamountofFeandAscontained
inthepelletsofeachmaterialwasfinallydeterminedbyFPXRF.
78
2.Methodology
2.7.REFERENCES
[1] ONEM (Observatoire Nationale de l’Environnement du Maroc). (1997). Monographie locale de
l’environnementdelavilledeMarrakech.
[2] Hibti, M.; Bouabdelli, M.; Mouttaqi, A.; Sagon, J.P. (1999). L’effet du métamorphisme sur les
minéralisations sulfurées de la province hercynienne (Meseta sudoccidentale, Maroc). Exemple des
gisementssulfurésdeHajjaretdeKettara.ChroniqueRechrecheMinière536–537:23–37.
[3]Hakkou,R.;Benzaazoua,M.;Bussière,B.(2008).AcidminedrainageattheabandonedKettaramine
(Morocco):1.Environmentalcharacterization.MineWaterEnv.27:145159.
[4]McMahon,G.;Remy,F.Largeminesandthecommunity:socioeconomicandenvironmentaleffects
inLatinAmerica,CanadaandSpain.The,WorldBank,WashingtonD.C.USA.
[5] Hernández, A.; Jébrak, M.; Higueras, P.; Oyarzun, R.; Morata, D.;Munha, J. (1999). The Almadén
mercuryminingdistrict,Spain.Miner.Deposita34:539548.
[6]Saupé,F.(1990).ThegeologyoftheAlmadenmercurydeposit.Econ.Geol.85:482510.
[7] GarcíaSansegundo, J., Lorenzo, S. & Ortega, E. (1987). Mapa geológico de España. Servicio de
Publicaciones,MinisteriodeIndustriayEnergía,Madrid,p.60.
[8]Mlakar,I.,Drovenik,M.,1971.StructuralandgeneticparticularitiesoftheIdrijamercuryoredeposit.
Geologija14:67–128.
[9] Gosar, M.; Pirc, S.; Bidovec, M. (1997). Mercury in the Idrijca River sediments as a reflection of
miningandsmeltingactivitiesoftheIdrijamercurymine.J.Geochem.Explor.58:125–131.
[10]Biester,H.;Gosar,M.;Muller,G.(1999).MercuryspeciationintailingsoftheIdrijamercurymine.J.
Geochem.Explor.65:195–204.
[11]Kanduc,T.;Kocman,D.;Ogrinc,N.(2008).Hydrogeochemicalandstableisotopecharacteristicsof
the River Idrijca (Slovenia), the boundary watershed between the Adriatic and Black Seas. Aquat
Geochem14:239–262.
[12]Loredo,J.;Ordóñez,A.;Baldo,C.;GarcíaIglesias,J.(2003).Arsenicmobilizationfromwastepilesof
theElTerronalmine,Asturias,Spain.Geochem–Explor.Env.A.3(3):229237.
[13] Loredo, J.; Ordoñez, A.; Gallego, J.R.; Baldo, C.;GarcíaIglesias, J. (1999). .Geochemical
characterisation of mercury mining spoil heaps in the area of Mieres (Asturias, northern Spain). J
GeochemExplor67:377–90.
[14] Luque, C.; Gutierrez, M. (2006). La Minería del Mercurio en Asturias: Rasgos Históricos, 1st ed.
Oviedo,Spain.
[15]BerzasNevado,J.J.;GarciaBermejo,L.F.;RodríguezMartínDoimeadiós,R.C.(2003).Distributionof
mercuryintheaquaticenvironmentalatAlmadén,Spain.Environ.Pollut122:261271.
[16] Higueras, P.; Oyarzun, R.; Biester, H.; Lillo, J.;Lorenzo, S. (2003). A first insight into mercury
distributionandspeciationinsoilsfromtheAlmadenminingdistrict,Spain.J.Geochem.Explor.80:95–
104.
[17] Higueras, P.; Oyarzun, R.; Lillo, J.; SánchezHernández, J.C.; Molina, J.A.; Esbrí, J.M.; Lorenzo, S.
(2006).TheAlmadéndistrict(Spain):Anatomyofoneoftheworld’slargestHgcontaminatedsites.Sci.
Tot.Environ.356:112–124.
[18] Gray, J. E.; Hines, M.E.; Higueras, P.; Adatto, I.; Lasorsa, B.K. (2004). Mercury Speciation and
Microbial Transformations in Mine Wastes, Stream Sediments, and Surface Waters at the Almadén
MiningDistrict,Spain.Environ.Sci.Technol.38,4285–4292.
79
2.Methodology
[19]Horvat,M.;Jereb,V.;Fajon,V.;Logar,M.;Kotnik,J.;Faganeli,J.;Hines,M.;Bonzongo,J.C.(2002)
Mercurydistributioninwater,sedimentandsoilintheIdrijcaandSocariversystems.Geochem.Explor.
Environ.Anal.2:287–296.
[20]Bernaus,A.;Gaona,X.;Valiente,M.(2005).CharacterisationofAlmadénmercurymineenvironment
byXAStechniques.J.Environ.Monit.7:771–777.
[21] Bernaus, A.; Gaona, X.; Esbri, J.M.; Higueras, P.; Falkenberg, G.; Valiente, M. (2006). Microprobe
techniquesforspeciationanalysisandgeochemicalcharacterizationofmineenvironments:themercury
districtofAlmadéninSpain.Environ.Sci.Technol.40:4090–4095..
[22]Kocman,D.,Horvat,M.&Kotnik,J.(2004).Mercuryfractionationincontaminatedsoilsfromthe
Idrijamercurymineregion.J.Environ.Monitor.6:696–703.
[23]Biester,H.;Gosar,M.;Muller,G.(1999).MercuryspeciationintailingsoftheIdrijamercurymineJ.
Geochem.Explor.65:195–204.
[24] Biester, H.; Gosar, M.; Covelli, S. Occurrence and Fractionation of Mercury Species Derived from
DumpedMiningResiduesinSedimentsoftheIdrijaMiningArea.(2000).Environ.Sci.Technol.34:3330–
3336.
[25]Grimalt,J.O.;Ferrer,M.;Macpherson,E.TheminetailingaccidentinAznalcollar.Sci.TotalEnviron.
242:311.
[26]AubertG.(1978).Méthodesd’analysesdessols.G.R.D.P.:Marseille.France.
[27]Method6200.FieldPortableXRayFluorescenceSpectrometryforthedeterminationofelemental
concentrationsinsoilandsediment.
http://www.epa.gov/osw/hazard/testmethods/sw846/pdfs/6200.pdf(Lastvisit:June2011)
[28]Ebinghaus,R.;Kock,H.H.;Schmolke,S.R.(2001).Measurementsofatmosphericmercurywithhigh
timeresolution:recentapplicationsinenvironmentalresearchandmonitoring,FreseniusJ.Anal.Chem.
371:806815.
[29] Logar, M.; Horvat, M.; Akagi, H.; Pihlar, B. (2002). Simulateneous determination of inorganic
mercuryandmethylmercuryinnaturalwaters,Anal.Bioanal.Chem.374:10151021.
[30]Liang,L.;Lazoff,S.;Horvat,M.,Swain,E.;Golkeson,J.(2000).Determinationofmercuryincrudeoil
byinsituthermaldecompositionusingasimplelabbuiltsystem,J.Anal.Chem.367:811.
[31]Welz,B.;Sperling,M.(1999).AtomicAbsorptionSpectrometry,3rded.WileyVCH.USA.
[32] Puk, R.; Weber, J.H. (1994). Critical review of analytical methods for determination of inorganic
mercuryandmethylmercurycompounds.Appl.Organomet.Chem.8:293302.
[33]Clevenger,W.L.;Smith,W.B.;Winefordner,J.D.(1997).Tracedeterminationofmercury:areview.
CriticalRev.Anal.Chem.29:126.
[34]McIntosh,S.;Baasner,J.;Grosser,Z.;Hanna,C.(1994).Improvingthedeterminationofmercuryin
environmentalsamplesusingadedicatedflowinjectionmercurysystem.At.Spectrosc.15:161163.
[35]Sholupov,S.E.;Ganeyev,A.A.(1995).Zeemanatomicabsorptionspectrometryusinghighfrequency
modulatedlightpolarization.SpectrochimicaActaPartB:AtomicSpectroscopy50:12271236.
[36] Perez, G.; LopezMesas, M.; Valiente, M. (2008). Assessment of heavy metals remobilization by
fractionation:comparisonofleachingtestsappliedtoroadsidesediments.EnvironSciTechnol42:2309–
2315.
[37] Chester, R.; Stoner, J.H. (1973). Pb in particulates from the lower atmosphere of the eastern
Atlantic.Nature:245:27–8.
[38] Zoller, W.H.; Gladney, E.S.; Duce, R.A. (1974). Atmospheric concentrations and sources of trace
metalsattheSouthPole.Science183:199–201.
80
2.Methodology
[39] Peirson, D.H.; Cawse, P.A.; Cambray, R.S. (1974). Chemical uniformity of airborne particulate
material,andamaritimeeffect.Nature251:675–9.
[40]Ragaini,R.C.;Ralston,H.R.;Roberts,N.(1977).EnvironmentaltracemetalcontaminationinKellogg,
Idaho,nearaleadsmeltingcomplex.Environ.Sci.Technol.11:773–781.
[41] Loska, K.; Cebula, J.; Pelczar, J.; Wiechula, D.; Kwapilinski, J. (1997). Use of enrichment and
contaminationfactorstogetherwitheoaccumulationindexestoevaluatethecontentofCd,Cu,andNi
intheRybnikwaterreservoirinPoland.WaterAirSoilPoll.93:347–365.
[42]Reimann,C.;Filzmoser,P.;Garrett,R.G.(2005).Backgroundandthreshold:criticalcomparisonof
methodsofdetermination.Sci.TotalEnviron.46:1–16.
[43] Blaser, P.; Zimmermann, S.; Luster, J.; Shotyk, W. (2000) Critical examination of trace element
enrichments and depletions in soils: As, Cr, Cu, Ni, Pb, and Zn in Swiss forest soils. Sci. Total Environ.
249:257280.
[44] Schiff, K.C.; Weisberg, S.B. (1999). Iron as a reference element for determining trace metal
enrichmentinSouthernCaliforniacoastalshelfsediments.Mar.Environ.Res.48:161176.
[45]Schropp,S.J.;Lewis,F.G.;Windom,H.L.;Ryan,J.D.;Calder,F.D.;Burney,L.C.(1990)Interpretation
of Metal Concentrations is Estuarine Sediments of Florida Using Aluminum as a Reference Element.
Estuaries13:227–235.
[46] McMurtry, G.M.; Wiltshire, J.C.; Kauahikaua, J.P. (1995) Heavy Metal Anomalies in Coastal
SedimentsofOahu,Hawaii.PacificScience,49:452–470.
[47]Loring,D.H.(1990)Lithium:ANewApproachfortheGranulometricNormalizationofTraceMetal
Data.MarineChemistry,29:155–168.
[48] Loska, K.; Wiechua, D.; Pelczar, J. (2005). Application of enrichment factor to assessment of zinc
enrichment/depletioninfarmingsoils.Comm.SoilSci.PlantAnal.36:1117—1128.
[49]Bourennane,H.;Douay,F.;Sterckeman,T.;Villanneau,E.;Ciesielski,H.;King,D.;Baize,D.(2010).
Mapping of anthropogenic trace elements inputs in agricultural topsoil from Northern France using
enrichmentfactors.Geoderma157:165–174.
[50]Pons,X.MIRAMONwebsite:http://www.creaf.uab.es/miramon(Lastvisit:April2010)
[51]Fahmy,T.XLSTATwebsite:http://www.xlstat.com(Lastvisit:April2010)
[52] SIXPack (2004). SIXPack–Sam’s Interface for XAS Analysis Package, powered by IFEFFIT 1.2.6.
CopyrightMattNewville,UniversityofChicago,USA.
[53] Catalano, J.G., Trainor, T.P., Eng, P.J., Waychunas, G.A.;Brown, G.E. (2005). CTR diffraction and
grazing incidence XAFS study of U(VI) adsorption to a Al2O3 and a Fe2O3 (1102) surfaces. Geochim.
Cosmochim.Acta69(14):35553572.
[54]Slowey,A.J.;Rytuba,J.J.;Brown,G.E.(2005).SpeciationofMercuryandModeofTransportfrom
PlacerGoldMineTailings.Environ.Sci.Technol.39:1547–1554.
[55] Arai, J.; Lanzirotti, A.; Sutton, S.R.; Newville, M.; Dyer, J.;Sparks, D.L. (2006). patial and temporal
variability of arsenic solidstate speciationin historically lead arsenatecontaminated soil. Environ.Sci.
Technol.40:673–679.
[56]Malinowski,E.R.(1991).FactorAnalysisinChemistry,2nded.Wiley,USA.
[57]Ressler,T.;Wong,J.;Roos,J.;Smith,I.L.(2000).QuantitativespeciationofMnbearingparticulates
emitted from autos burning (methylcyclopentadienyl)manganese tricarbonyladded gasolines using
XANESspectroscopy.Environ.Sci.Technol.34,950–958.
[58] Wasserman, S.R.; Allen, P.G.; Shuh, D.K.; Bucher, J.J.; Edelstein, N.M. (1999). EXAFS and Principal
ComponentAnalysis:ANewShellGame.J.SynchrotronRad.6:284–286.
81
2.Methodology
[59] Rehr, J.J.; Albers, R.C.; Zabinsky, S.I. (1992). Highorder multiplescattering calculations of xray
absorptionfinestructure.Phys.Rev.Lett.69,3397–3400.
[60]Klementiev,K.V.(2001).VIPERforWindows,freeware;K.V.Klementev,J.Phys.D:Appl.Phys.34:
20917.
[61] Mazuelos, A.; Carranza, F.; Palencia, I.; Romero, R. (2000). High efficiency reactor for the
biooxidationofferrousiron.Hydrometallurgy58(3):269275.
[62] Carranza, F.; Iglesias, N.; Romero, R.; Palencia, I. (1993). Kinetics improvement of highgrade
sulfidesbioleachingbyeffectsseparation.FEMSMicrobiol.Rev.11(13):12938.
[63] Barnard, K.R.; Shiers, D.W. (2011). Mechanism and rate of butyl phosphinate formation from
reactionofphosphinicacid(Cyanex272)andtributylphosphate.Hydrometallurgy106:7683.
[64]Ferreira,A.N.;Agarwal,S.;Machado,R.M.;Gameiro,M.L.F.;Santos,S.M.M.;Reis,M.T.A.;Ismael,
M.R.C.;Correia,J.N.;Carvalho,J.M.R.(2010).ExtractionofcopperfromacidicleachsolutionwithAcorga
M5640usingapulsedsieveplatecolumn.Hydrometallurgy104:6675.
[65] Gameiro, M.L.F.; Machado, R.M.; Ismael, M.R.C.; Reis, M.T.A.; Carvalho, J.M.R. (2010). Copper
extractionfromammoniacalmediuminapulsedsieveplatecolumnwithLIX84I.J.Hazard.Mater.183:
165175.
[66] Sole, K.C.; Feather, P A.M.; Cole, M. (2005). Solvent extraction in southern Africa: An update of
somerecenthydrometallurgicaldevelopments.Hydrometallurgy78:5278.
[67]Ritcey,G.M.(2006).Solventextractionprinciplesandapplicationstoprocessmetallurgy,Vol.1,336
348.G.M.Ritcey&Associates.Canada.
[68]Neamtu,M.;Zaharia,C.;Catrinescu,C.;Yediler,A.;Macoveanu,M.;Kettrup,A.(2004).FeloadedY
zeoliteascatalystforwetproxideoxidationofreactiveazodyeProcionMarineHEXL.AppliedCatalysis
B:Environmental48:287294.
[69]Shen,Z.;Wang,W.;Jia,J.;Feng,X.;Hu,W.;Peng,A.(2002).Catalyticallyassistedelectrochemical
oxidationofdyeAcidRedB.WaterEnviron.Res.74:117121.
[70]Lin,J.J.;Zhao,X.S.;Liu,D.;Yu,Z.G.;Zhang,Y.;Xu,H.(200).Thedecolorationandmineralizationof
azo dye C.I. Acid Red 14 by sonochemical process: rate improvement via Fenton's reactions. J Hazard
Mater.157:5416.
[71]Daneshvar,N.;Khataee,A.R.(2006).RemovalofAzoDyeC.I.AcidRed14fromContaminatedWater
using Fenton, UV/H2O2, UV/H2O2/Fe(II), UV/H2O2/Fe(III) and UV/H2O2/Fe(III)/Oxalate Processes: A
ComparativeStudy.J.Environ.Sci.Health,PartA:,41:315–328.
[72]Zheming,Xiaofang,Yanming,Xiuling.(2005).Dualelectrodesoxidationofdyewastewaterwithgas
diffusioncathode.Environ.Sci.Technol.39:18191826.
[73]Sinha,A.;Chakrabarti,S.;Chaudhuri,B.;Bhattacharjee,S.;Ray,P.(2007).OxidativeDegradationof
StrongAceticAcidLiquorinWastewaterEmanatingfromHazardousIndustries.Ind.Eng.Chem.Res.46:
31013107.
[74]Pardieck,D.;Bouwer,E.J.;Stone,A.T.(1992).Hydrogenperoxideusetoincreaseoxidantcapacity
forinsitubioremediationofcontaminatedsoilsandaquifers.J.Contam.Hydrol.9:221242.
[75]APHAAWWAWPCF(1995).StandardMethodsfortheExaminationofWaterandWastewater.19th
ed.,AmericanPublicHealthAssociationUSA.
82
3
RESULTSANDDISCUSSION
MINESITESCHARACTERIZATION ......................................................................................................85 3.1.HEAVYMETALCONTAMINATIONANDMOBILITYATTHEDRAALASFARMINEAREA.....................85
3.2.CHARACTERIZATIONOFKETTARA,SIDIBOUOTHMANEANDBIRNEHASSMINEAREAS ...............92
3.3. XANES SPECIATION OF MERCURY IN THREE MINING DISTRICTS: ALMADEN (SPAIN), ASTURIAS
(SPAIN)ANDIDRIJA(SLOVENIA) ...........................................................................................................104
REMEDIATIONTECHNIQUESOFINDUSTRIALCONTAMINATEDWATERS ...............................111
3.4. EXTRACTANT AND SOLVENT SELECTION TO RECOVER ZINC FROM A MINING EFFLUENT: FROM
LABORATORYSCALETOPILOTPLANT ..................................................................................................111
3.5. FeLOADED MATERIALS FOR THE REMEDIATION OF ORGANIC AND INORGANIC POLLUTED
WATERS ................................................................................................................................................119
3.6.REFERENCES...................................................................................................................................128
83
84
3.Resultsanddiscussion
The current chapter describes the results obtained from the studies carried out in the
present thesis including the characterization of samples from polluted mine areas as well as
the study of processes applied to remediate wastewaters containing either organic or
inorganicpollutants,i.e.,thebasicandscaleupprocessforthevalorizationonanacidicmine
water residue. Accordingly to this content, results include two main sections: Mine sites
CharacterizationandRemediationTechnologies.
MINESITESCHARACTERIZATION
Thephysicochemicalparametersandtheconcentrationofheavymetalsoffourmines
locatedinMarrakechregion(Morocco)wereevaluatedtocharacterizetheareaanditslevelof
contamination,aswellasitstoxicologicalriskderivedfromtargetheavymetalsmobility. On
the other hand, the results focused in the speciation of mercury samples from three main
Europeanmercuryminesarealsooutlinedinthefollowingsection.
3.1. HEAVY METAL CONTAMINATION AND MOBILITY AT THE DRAA LASFAR MINE
AREA(SEEANNEXI)
ThemainresultsofafirstinsightintotheDraaLasfarmine(Marrakech)areoutlinedin
this section. To characterize the degree of pollution in this mine area, the present study
includes the geochemical distribution maps of the pollutants, the particle size effects (i.e.,
intraparticle concentration affecting metals distribution) and the mobility of the main
pollutantsbyemployingsingleleachingteststopredicttheriskoftheirmobility.
3.1.1.PHYSICOCHEMICALPARAMETERS
TheresultsobtainedforthesoilpH,electricalconductivity(EC),lossonignition(LOI)and
CaCO3 content measurements corresponding to Draa Lasfar mine area are resumed in Table
3.1.
Itisrevealedthat,ingeneral,allsampledpointsshowedneutraltoalkalinepHranging
from7to9,similartopHofbackgroundsampleswiththeexceptionofaveryacidicsample
correspondingtosample#48withpH3.5justbesidetheminesite,mainlyrelatedtodeposits
85
3.Resultsanddiscussion
ofsulfidicresidues,whichbyoxidationandformationofsulfuricacid,cancausesuchdecrease
ofthepH.
Table3.1.SummaryofphysicochemicalparametersforDraaLasfarsamples
pH
CE(μS/cm) LOI(g/kg) CaCO3(mg/g)
Minearea
samples
(81samples)
Background
samples
(4samples)
min
3.5(#48)
96.0(#53)
12.7(#2)
7.9(#50)
max
9.6(#2)
14,160(#31)
75.8(#45)
209.9(#26)
mean
8.3
1463
32.6
43.2
st.dev.
0.7
2170
14
32
Mean
8.5
136.3
30.7
24.9
St.dev.
0.5
20.4
6.8
2.6
EC showed higher variability than pH, with values ranging from 100 to 15,000 μS/cm
(Table3.1)which,ingeneral,areinaccordancewithpreviousstudiesperformedonMorocco
soils[1].LikewisepH,thesamplewithhigherECvalue(sample#31)islocatedjustbesidethe
mine site and corresponds to the sample with the higher amounts of metals in the area. In
addition,thevaluesobtainedforthemineareasamplesaresignificantlyhigherthanthoseof
thebackgroundsamples,thusindicatinghighamountsoflabileionsclosetotheminesiteas
consequenceofminingactivities.
LOIvaluesformineareaandbackgroundsamples(Table3.1)valuesaresimilarandquite
homogeneous(around30g/kg)althoughsomespecificpointshaveLOIreaching76g/kgdue
tosomecloselocalizedagriculturalactivities.
Theobservedcarbonatecontentrangedfrom10to210mg.g1(Table3.1)althoughthe
majority of the samples present similar CaCO3 content to background samples. The highest
content of carbonates is observed for samples #26 and #71, located 400 m away from the
mine site. Together with basic pH values, the presence of carbonates in the soil lead to an
increase in the retention of heavy metals, mainly as carbonate salts as a consequence of
relatedhydroxyoxidesprecipitation,theprincipalretentionmechanismforheavymetals[2].
3.1.2.HEAVYMETALCONCENTRATIONINTHEMINEAREA
RegardingthemetalconcentrationmeasuredbyFPXRFanditscorrespondingcalculated
CER values, elements have been classified as either pollutants (when the samples have CER
valuesabove5)orlithogenicelementsforCERvaluesbelow5.Inthissense,As,Cu,PbandZn
can be classified as pollutants whereas the rest of elements measured are considered
lithogeniccomponentsofthesoil(Table3.2).
86
3.Resultsanddiscussion
Lithogeniccomponents
Pollutants
Table3.2.Minimum,maximumandmeanconcentrationandCERvaluesforthemeasured
elementsinmineareaandbackgroundsamples
Background
Mineareasamples
samples
Min
Max
Mean
Mean
Conc.
8.9
3108(#48)
70±400
13±5
As
CER
0.4
20.0
2.2±4
1.0±0.3
Conc. 16.6
172(#45)
35±20
34±8
Cu
CER
0.3
5.9
1.1±1
1.0±0.3
Conc.
6.7
2309(#48)
70±300
17±3
Pb
CER
0.3
45.9
3±6
1.0±0.1
Conc.
30
1114(#45)
125±200
82±7
Zn
CER
0.3
13.6
2±2
1.0±0.1
Conc. 221.5
541(#17)
389±70
449±60
Ba
CER
0.4
1.4
0.9±0.3
1.0±0.1
Conc. 19632 121652(#48) 32721±10000 35555±3000
Fe
CER
0.4
4.8
0.9±0.5
1.0±0.1
Conc. 6071
32976(#81)
23327±5000
38244±1500
K
CER
0.3
1.4
0.8±0.3
1.0±0.1
Conc.
290
1119(#21)
607±150
699±50
Mn
CER
0.4
2.0
0.9±0.3
1.0±0.1
Conc.
47
106(#10)
76±13
81±6
Rb
CER
0.5
1.7
1.0±0.3
1.0±0.1
Conc.
86
322(#26)
144±40
131±9
Sr
CER
0.5
3.1
1.1±0.5
1.0±0.1
Conc. 2802
5860(#17)
4460±700
5229±600
Ti
CER
0.4
1.3
0.9±0.2
1.0±0.1
Zr Conc.
112
335(#67)
215±50
210±17
Concisgiveninmg/kg.Inparenthesisisgiventhesamplewithmaximumconcentration.
3.1.3.GISCONTOURMAPSOFTHEMAINPOLLUTANTS
Althougharseniccannotbeconsideredametalitwillbereferredtoasbelongingtothe
heavy metals group for reasons of convenience. Arsenic distribution of CER values using GIS
contour maps, depicted in Figure 3.1, showed two hot spots beside the mine site
corresponding to samples #48 (3108 ppm, CER=280) and #31 (203 ppm, CER=19.4). Sample
#48 represents an arsenic concentration 100 fold higher than background levels which
indicatesthatremediationismandatoryforthisspecificarea.Atincreasingdistancesfromthe
minesite,arsenicconcentrationdecreasestovaluessimilartobackgroundsamples,exceptfor
samples #45 (203 ppm, CER=15.9) and #46 (125 ppm, CER=9.2). An anomalous result is
observed for sample #21 (72 ppm, CER=7.1). This sample is located at the other side of the
rivercreekanditsarsenicconcentrationishigherthanneighboringsamples.Itisprobablydue
to a waste deposit when mining was active. Given the proximity of this area to the creek
waters,itisforemosttomonitorthisarea.
CopperCERdistributionmapalongtheminingarea(Figure3.2)followedatrendsimilar
totheoneexpressedbyarsenic,beingthesamplesclosetotheminesitetheoneswithhigher
87
3.Resultsanddiscussion
copperCERvalues.Likewisearsenic,sample#21(51ppm,CER=1.9)locatedattheothersideof
theriverbasin,hashighcopperconcentrationdespitebeingfarfromtheminearea.Thiscan
beexplainedbythefactthatminoramountsofCuarefoundtypicallyadsorbedinarsenopyrite
(FeAsS)ores.
Theleaddistributionaroundthemine(Figure3.3)showedfourhotspotslocatedaround
samples #31 (180 ppm, CER=13.0), #45 (770 ppm, CER=45.9), #48 (2310 ppm, CER=130) and
#58(420ppm,CER=30).Itisalsonoteworthytohighlightsample#21(62ppm,CER=4.6)given
itshighCERvaluesandproximitytocreekwaters.
CERdistributionmapforZn(Figure3.4)followedthesametrendastheonedepictedby
Pb with 4 hot spots located at samples #20 (630 ppm, CER=8.5), #45 (1110 ppm, CER=13.6),
#48(30ppm,CER=10.8)and#58(930ppm,CER=10.8).
Thus, taking into account the GIS maps obtained by using CER values for the main
pollutantsoftheDraaLasfarminearea,itcanbestatedthatthemostpollutedsitesarefound
beside the mine site towards the river creek whilst samples closed to Koudiyat hill reported
values similar to background. Hence, the pollution over the mine area of Draa Lasfar can be
mainlyattributedtoweatheringeffectsandthetopographyoftheterrainthatfacilitatesthe
disposal of mine residues towards descendent areas such as the river creek and reduce the
depositiononelevatedareassuchashills.
Other measured elements showed CER values close to background samples, hence
consideredaslithogeniccomponentsofthesoil.ThisgroupofelementsisformedbyBa,Fe,K,
Rb,Sr,TiandZr(Table3.2).Inthissense,themeanconcentrationofBa,Fe,K,Mn,Rb,Sr,Ti
and Zr is similar to background samples and therefore its correspondent CER values range
between0and2,exceptingsample#48.SuchsamplescontainhighFecontentthat,giventhe
high As content can be related to an arsenopyrite (FeAsS) deposit. Thus, no anthropogenic
enhancementoftheseelementsisobserved.
Finally,somespecificsamplespresentextremelyhighconcentrationonsomeelements.
High sulfur concentrations were found in samples #19 (18400 ppm), #31 (14500 ppm), #33
(15500ppm),#45(36800ppm),#48(113700ppm),#58(5300ppm),#59(14800ppm)and#70
(32400ppm)alsorelatedtoarsenopyritedepositsthussupportingthearsenopyritenatureof
themineraloresextracted.
88
3.Resultsanddiscussion
Cu
As
CER=6
CER=6
Mine
Mine
area
area
CER=3
CER=3
CER=0
CER=0
Scale1:25000
Background
samples
Figure 3.1. GIS contour map of arsenic
distributionaroundtheminearea.
Pb
Scale1:25000
Background
samples
Figure 3.2. GIS contour map of copper
distributionaroundtheminearea.
Zn
CER=20
Mine
CER=20
area
Mine
area
CER=10
CER=10
CER=0
CER=0
Scale1:25000
Background
samples
Scale1:25000
Background
samples
Figure3.3.GIScontourmapofleaddistribution
aroundtheminearea.
Figure 3.4. GIS contour map of zinc
distributionaroundtheminearea.
3.1.4.EFFECTOFPARTICLESIZEANDMOBILITY
SampleswithhighCERvaluesand/orwithspatialsignificancewereselectedtostudythe
effectofparticlesizeandthemobilityofpollutants.Thus,samples#20,#31,#46,#48,#58and
#70wereselected.RelatedresultsarecollectedinTable3.3.
The results obtained for target fractions show a generalized increase on As,Pb and Zn
concentrationsaftermillingthesamplesbelow100μm.Inthissense,samples#20,#31,#46,
#48and#58hadanenrichmentonAs,PbandZnwhenmilled.Therefore,itcanbestatedthat,
89
3.Resultsanddiscussion
ingeneral,theseelementsarepartoftheparticlecore.Ontheotherhand,adecreaseofthe
concentrationoncopperasthesoilismilledisobservedthusindicatingcopperisadsorbedat
the surface of the soil particles instead of forming part of the mineral ore revealing an
anthropogenicinputofcopper.
Regarding the results obtained for the pollutants mobility in selected samples, also in
Table 3, it can be observed arsenic, lead and zinc in the mobile phase of some samples
(samples #31, #46). Sample #46 shows the highest mobility of pollutants, its pH is alkaline
(pH=8.1),withhighEC(EC=2151μS/cm)andrelativelyhighcarbonatecontent([CaCO3]=58.4
mg/g).Intheseconditions,mobilityisnotspeciallyfavoredalthoughgiventherelativelylow
LOIvalue(LOI=39.3g/kg)itcanbesupposedthatthisfactorenablestheavailabilityofcations
from the mine ore to the mobile phase. Therefore it can be stated that the leading factor
regarding mobility of the samples at Draa Lasfar mine area is concentration of metals and
organicmatter(basedonLOIdeterminations).
Itisalsoimportanttohighlightsample#48,whichaccountsforbeingthemostpolluted
and acidic sample (pH=3.5), with high EC (EC=4873 μS/cm) and low carbonate content (25.8
mg/g). According to literature [3] these conditions favor the availability of cations, however
sample#48hasalsoahighorganicmattercontentasindicatesitsLOIvalue(LOI=56.0mg/g),
which benefits the adsorption of soil labile ions, thus explaining the relatively low mobility
observed.Thatleadstoloweritsenvironmentalriskwhenconsideringonlytotalconcentration
values. In this sense, soil organic matter is considered one of the primary immobilizing
processesfortraceandtoxicpollutants[4].
Inthissense,GIScontourmapsofpollutantsusingCERdatahavebeenavaluabletoolto
characterize pollutants distribution around the mine area and to determine sources of
contamination.
90
3.Resultsanddiscussion
Table3.3.Particlesizeeffectsandmobilityassays.Concentrationofpollutantsatfraction<2mm
andfraction<100μmandamountofpollutantsmobile
SAMPLE
As
Cu
Pb
Zn
2mm(mg/kg)
125
80
55
628
#20
100μm(mg/kg)
167
72
66
713
Mobility(mg/L)
<0.5
<0.5
<0.5
<0.5
2mm(mg/kg)
72
51
62
144
#21
100μm(mg/kg)
67
48
61
150
Mobility(mg/L)
<0.5
<0.5
<0.5
<0.5
2mm(mg/kg)
203
43
180
481
#31
100μm(mg/kg)
268
77
313
734
Mobility(mg/L)
49
2
6
18
2mm(mg/kg)
125
60
375
774
#46
100μm(mg/kg)
172
59
477
933
Mobility(mg/L)
54
1
17
23
2mm(mg/kg)
3,108
144
2,309
631
#48
100μm(mg/kg)
3,569
167
2,614
704
Mobility(mg/L)
5
1
<0.5
4
2mm(mg/kg)
113
71
425
925
#58
100μm(mg/kg)
149
77
537
1,087
Mobility(mg/L)
<0.5
<0.5
<0.5
<0.5
2mm(mg/kg)
15
33
24
97
#70
100μm(mg/kg)
15
50
20
91
Mobility(mg/L)
29
<0.5
<0.5
<0.5
91
3.Resultsanddiscussion
3.2. CHARACTERIZATION OF KETTARA, SIDIBOU OTHMANE AND BIR NEHASS MINE
AREAS
FollowingthemethodologyemployedforthecharacterizationofDraaLasfarminearea,
threeadditionalabandonedminesof theareaatsitesof:Kettara,SidiBouOthmaneand Bir
Nehasswerecharacterizedfortheirpotentialpollutantimpact.
3.2.1.PHYSICOCHEMICALCHARACTERIZATION
Theresultsobtainedforthemeasuredphysicochemicalparametersaresummarizedin
Table3.4.Sampleshavebeendistinguishedbetweenresiduessamples,thesamplestakenat
specificpointswereresidueswerestoredandmineareasamples,sampledatregulardistances
fromtheminesite.
Table3.4.SummaryofpH,conductivityandcarbonatecontentforthesamplestakenatKettara,
SidiBouOthmaneandBirNehass
Kettara
MineArea
(58samples)
Residues
(7samples)
Background
(3samples)
SBOthmane
MineArea
(30samples)
Residues
(17samples)
Background
(3samples)
BirNehass
MineArea
(33samples)
Residues
(4samples)
Background
(3samples)
92
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
pH
EC(μS/cm)
LOI(mg/g)
CaCO3(mg/g)
2.0(S10)
8.2(S46)
6.3±1.9
2.0(R2)
2.4(R3)
2.2±0.2
7.8
8.5
8.1±0.4
7.1(S1)
8.1(S21)
7.8±0.2
3.2(R20)
8.4(R9)
7.3±1.1
6.8
8.0
7.6±0.8
8.0(S20)
8.4(S11)
7.7±0.5
1.8(R1)
3.5(R3)
2.9±0.7
7.4
7.7
7.5±3
0.1(S37)
4,900(S10)
680±900
2,686(R1)
7,295(R7)
3,618±1700
153
100
126±30
226(S30)
823(S16)
349±130
159(R17)
5,469(R12)
1,898±1200
324
670
447±200
58(S32)
2,005(S19)
250±400
2,119(R3)
9,011(R1)
4,135±3000
87
94
91±4
6(KS15)
71(KS41)
34±20
5(KR1)
11(KR6)
7.6±2
41
53
48±7
37.5(S2)
93.7(S1)
63.4±13
23(R17)
50(R12)
35±8
30.5
35.3
33±2
23.8(S31)
46.5(S8)
31±5
18.4(R1)
35.4(R2)
29±7
29.9
36.7
33±4
2.0(S2,S9)
167(S48)
37±60
1.25(R3)
7.5(R4)
4±3
217
275
240±30
3.7(S20)
439(S12)
690±1100
7.5(R17)
299.6(R16)
78±70
2
10
7±5
3(S31)
165(S1)
24±40
5.0(R1)
24(R3)
13±8
3
4
3.7±0.3
3.Resultsanddiscussion
ThemeanpHvalueobtainedforKettaramineareasamplesisslightlyacidicasaresultof
theoxidationofpyritethatreleasessulfuricacidandlowersthepH.Inthissense,23outof58
sampleshavepHbelow7andthepHiscomprisedbetween2and4in13samples.Fortherest
of the samples the pH ranged from 7 to 9, which is a normal pH also observed for the
backgrounddata.Ontheotherhand,samplestakenattheresiduesdeposits,areveryacidic,
allofthemintherangeofpHof22.4.TheselowpHvaluesarealsorelatedtotheoxidationof
high contentsofsulfur.UnlikeKettara,BirNehassandSidiBouOthmanemineareasamples
haveneutraltoalkalinepHforthemajorityofthesamples,beingthesevaluesaresimilarto
thebackgroundsamples.Regardingthesamplingcorrespondingtotheresidues,pHatKettara
and Bir Nehass residues were strongly acidic (pH from 2 to 3.5) related to pyrite deposits
whereasallSidiBouOthmaneresidues(exceptonesamplingpoint)werequitebasicwithapH
similartobackgroundsamples.
In this sense, the most acidic samples have also the higher EC values. This correlation
betweenlowpHandhighECvaluecanbeexplainedbythepresenceofhighamountsofsulfur
ions that causes an increase of the EC and by oxidation lowers the pH by acid sulfuric
formation.Inthisregard,KettaramineareasampleshadhigherECthanthemineareasamples
ofSBOthmaneandBirNehass,andmoreover,theECfoundfortheresiduesweremuchhigher
thanmineareasamples.
LOIvaluesaresimilarforminearea,residuesandbackgroundsamplesforallthestudied
mines(around30g/kg)althoughsomespecificpointsofSbOthmanemineareassampleshad
higherLOIvaluesmainlyrelatedtosomecloselocalizedagriculturalactivities.
RegardingthecarbonatecontentitcanbestatedthatsoilswithapHof7.5andhigher
generally have a high calcium carbonate content. In this sense, as stated in previous section
3.1.1., alkaline soils together with high amounts of organic matter and the presence of
carbonatesincreasetheretentionofheavymetalsinsoils.
3.2.2.HEAVYMETALCONCENTRATIONINTHEMINEAREA
Taking into account the CER values of the metals measured for all three mines and by
comparing samples, residues and backgrounds values, it isobserved As, Cu, Pb and Zn to be
the main pollutants of the studied mine areas since most of the samples exceed CER values
above5,thusindicatingthesignificantanthropogeniccontribution(Table3.5).
93
3.Resultsanddiscussion
BirNehass
SidiBouOthmane
Kettara
Table3.5.SummaryofAs,Cu,Pb,ZnandZrconcentrationandCERvaluesforthesamplesand
residuesofKettara,SidiBouOthmaneandBirNehass
As
Cu
Pb
Zn
Min
10
27
17
49
Conc.
Max
237
1362
486
243
Mean
34±40
256±300
76±100
106±40
MineArea
(58samples)
Min
0.5
0.35
0.7
0.7
CER
Max
18.8
27.6
34
4.2
Mean
2±3
4±6
4±6
1.4±0.6
Min
28
364
105
93
Conc.
Max
104
2113
349
337
Mean
64±40
1,287±500
233±100
197±100
Residues
(7samples)
Min
3.6
16.5
14
2.6
CER
Max
7.8
74
36
10.0
Mean
6±2
38±17
23±7
5±3
Min
11
44
12
49
Background
Conc.
Max
24
52
15
76
(3samples)
Mean
16±7
48±4
14±2
60±14
Min
10
25
23
101
Conc.
Max
112
46
6706
36267
Minearea
Mean
27±30
31±8
1,467±2000 5,018±9000
(30samples)
Min
0.6
0.9
0.8
0.6
CER
Max
11
1.7
391
414
Mean
2±3
1.1±0.1
62±100
43±90
Min
6
109
76
85
Conc.
Max
203
36140
13044
57380
Mean
98±60
10,653±16000 4,112±4000 19,238±1500
Residues
(17samples)
Min
4.6
6.7
2.7
0.5
CER
Max
26
1405
786
638
Mean
14±6
380±20
249±200
242±180
Min
13
22
30
144
Background
Conc.
Max
17
29
37
167
(3samples)
Mean
15±2
27±8
32±4
156±12
Min
8
24
19
86
Conc.
Max
113
46
1495
29732
Mean
23±20
29±6
148±300
1,744±6000
Minearea
(33samples)
Min
0.5
0.9
1.1
1.2
CER
Max
11
2.1
94
366
Mean
2±2
1.2±0.4
8±20
22±70
Min
92
52
1776
8521
Conc.
Max
760
310
29559
23309
Mean 282±300
149±140
9,423±13000 15,075±6000
Residues
(4samples)
Min
11
3.3
164
141
CER
Max
70
14.8
2010
350
Mean
29±30
8±6
803±1000
273±80
Min
9
22
19
68
Background
Conc.
Max
16
29
23
99
(3samples)
Mean
13±4
27±8
19±4
84±16
M.A. Mining Area, Bkg. background, R. Residue. (The number of samples is given in Table 3.4).
After the evaluation of the results of the main pollutants concentration (Table 3.5) it can be
considered that the amount of mineral extracted per day and the exploitation time of each
mineaffectsthelevelofcontamination.Inthissense,Kettaraminesitewaslessexploitedand
94
3.Resultsanddiscussion
hencelesspollutedthanSBOthmaneandBirNehassminesitesbothexploitedwithinaperiod
ofapproximately30yearsandwithexploitationoutputsof115and90tonsperday.
3.2.3.APPLICATIONOFCHEMOMETRICS
Chemometrics tools have been applied to establish relationships between the three
mines as well as patterns and spatial distribution of the main target pollutants identified. In
this sense, boxplot figures of the data for the mine area samples and the residues of each
minecanprovideamoreunderstandingrepresentationoftheobtaineddata.
As depicted from Box plots given in Figure 3.5, CER values are higher for the residues
than for the mine area samples, which is logical since the wastes resulting from mining and
milling processes were stockpiled in these specific areas. However, none of the metals
analyzed in Kettara (mine area or residues) has very strong anthropogenic contribution
(CER>40).ConcerningboxplotfiguresformineareaandresiduesforSBOthmane(Figure3.6)
and Bir Nehass (Figure 3.7) it can be observed that CER values are much higher for residues
thanformineareasamplesespeciallyregardingPbandZn.Giventhesimilaritiesbetweenbox
plot figures for both SBOthmane and Bir Nehass it can be stated that both mines are
mineralogicallycomparable.Inthissense,boxplotfiguresofCERvalueshighlightdifferences
between mine area and residues samples as well as to graphically determine the degree of
contaminationregardingacertainelement.
Principal components analysis (PCA) was also applied to the CER data to point out
differences within. From the representation of loadings and scores of PC1 and PC2 for the
threemines(Figure3.8,3.9,and3.10)itcanbedistinguishedineachmine:mineareasamples
grouped all together at the center of the figures and residues samples at the left of the
representation more dispersed. In this sense, given the proximity of the residues samples
representation and the main pollutants in the figure it can be considered that residues are
more influenced by the pollutants than mine area samples. However, from the scores
representationofeachmine,itcanbeobservedthatmanyofthesampleswithhighCERvalues
(andhencewithhighconcentrationonpollutants)canbeclearlydistinguishedfromthebulkof
the mine area samples (S8 to S10 in Kettara, S2 in SBOthmane and S8, S19 and S20 in Bir
Nehass).Inthisregard,itcanbeconcludedthatPCAisabletopointoutdifferencesbetween
mineareasamplesandresiduesandeventodistinguishthemostpollutedfromsamplesless
pollutedaswellastoindicatetheparametersthataffectthedistinctionbetweensamples.In
generalitcanbestatedthatforthethreeminesstudied,PC1ismainlyassignedtothemain
pollutants(As,Cu,PbandZn)whilelithoghenicelementssuchasBa,K,Ca,Rb,MnandSror
the physicochemical variables charge the rest of PC. On the other hand, PCA is not able to
95
3.Resultsanddiscussion
establish patterns for the three mines since loading distribution is different for the three
mines.
Figure3.5.BoxplotdistributionofCERvaluesforAs,Cu,PbandZnforKettaraminearea(M.A.)
andresiduesamples.CER=5(dottedline),CER=20(lines)andCER=40(straightline)
Figure3.6.BoxplotdistributionofCERvaluesforAs,Cu,PbandZnforSBOthmaneminearea
(M.A.)andresiduesamples.CER=5(dottedline),CER=20(lines)andCER=40(straightline)
Figure3.7.BoxplotdistributionofCERvaluesforAs,Cu,PbandZnforBirNehassminearea(M.A.)
andresiduesamples.CER=5(dottedline),CER=20(lines)andCER=40(straightline)
96
3.Resultsanddiscussion
Biplot(PC1andPC2:51.75 %)
15
KR6
10
PC2(15.39 %)
Mn
5
KR7
pH KS53
K
KS48
KS38
KS58
KS29
KS22
KS44
KS21
KS37
KS49
KS31
Ba
KS52
Ca
KS19
KS30
KS20
KS56
KS32
KS18
KS23
KS16
KS55
KS47
KS40
KS46
KS24
KS34
KR2
Zn KR5 KR4
CE Cu KR1
Rb
CaCO3As
Pb SR3
Sr KS1
KS14
KS33
KS12
KS6
KS5
KS41
KS42
KS43
KS25
KS39
KS4
KS13 Fe
KS45
KS17
KS50
KS27
KS11
KS15
KS2
KS26
KS8
KS3
KS51
KS28
KS36
KS7
Ti KS35
0
5
KS9 KS10
10
15
10
5
0
5
10
15
20
25
30
PC1(36.36%)
Figure3.8.KettarabiplotrepresentationofloadingsandscoresofPC1andPC2
Biplot(PC1andPC2:59.66 %)
20
RS3
15
Sr Ba
PC2(20.69 %)
10
Cu As
K
5
SR8
Pb
R4B19
SB16
SB14
SB21
SB13
SB20
R4B14
SB24
R4B7
SB25 SR20R4B17
SB19
SB10
R4B12
SB4
SB26
SB17
Rb CE
Fe
R4B10
SB9
SB7
SB27
SB23
SB6
SB8
SB5
R4B6
SB3
R4B18 Zn pH
R4B9
SB18
SB11SR13
SB1
SB22
Mn
SB12
R4B2
Ti SB2
CaCO3Ca SR1
0
5
10
15
20
20
15
10
5
0
5
10
15
20
25
30
PC1(38.97 %)
Figure3.9.SBOthmanebiplotrepresentationofloadingsandscoresofPC1andPC2
Biplot(PC1andPC2:55.14 %)
20
BS8
15
pH
PC2(19.36 %)
10
Sr
5
0
Rb
K
Mn
Pb BR5
Ca
BR4
BN4
BN24
BN1
BN10
BN9
CaCO3
BN32
BN16
Ba BS19 AsBR3
BN5
BN11
Fe Cu
Ti
CE
BN15
BN33
BN3
Zn
BN2
BN13
BN6
BN14
BR1
BN25
BN30
BN28
BN17
BN23
BN21
BN22
BN29
BN31
BN26
BN12
BN7
BN18
BN27
5
BS20
10
15
20
20
15
10
5
0
5
10
15
20
25
30
35
PC1(35.78 %)
Figure3.10.BirNehassbiplotrepresentationofloadingsandscoresofPC1andPC2
97
3.Resultsanddiscussion
3.2.4.GISCONTOURMAPSOFTHEPOLLUTANTS
Withseparatesamplevaluesandinordertodelineatethedistributionoftargetmetals
aroundthemineareaaGISrepresentationofcorrespondingCERvalueshasbeencarriedout.
The obtained information can be applied on the management of the target areas, i.e.,
monitoring,isolationorpollutionremoval.
Kettara GIS contour maps (Figures 3.11 to 3.14) show two well separated areas: a big
areaatthecenterofthe mineareaandasmallareaatthenorth.Bothareasarelocatedat
specific sampling points with localized contamination, especially at residues sampling points.
Given that the area is flat, the distribution of pollutants does not follow any geographic
consideration but only specific points were residues were stored. Considering the maps for
eachelementitcanbestatedthatthedistributionofpollutantsissimilarforarsenic,copper
andlead,whilstzincdistributionismorehomogeneousalongtheminearea.Inaddition,lead
canbeconsideredthemainpollutantregardingitshighCERvalues.
RegardingSBOthmanemineareaGISmapsitcanbepointedoutthatthemineareais
highly contaminated, being observed samples with CER values above 200 (Figures 3.17 and
3.18). Arsenic and copper GIS maps (Figures 3.15 and 3.16) of SBOthmane present two hot
spotslocatedatdepositsofresidueswhilePbandZnGISmapsindicateamoreriskysituation
withCERvaluesabove200(Figures3.17and3.18).Thedistributionofcontaminantsisaffected
mainlybythelocationofthedepositsofresidues.
With respect to Bir Nehass mine area it can be stated that, likewise SBOthmane, the
mine is less contaminated with arsenic (Figure 3.19) and copper (Figure 3.20) being lead
(Figure3.21)andzinc(Figure3.22)themainpollutants.Inthissense,auniquehotspotcanbe
observedforarsenicandleadaroundasamplelocatedataresiduedepositwhileseveralhot
spotswithCER=200canbeseenforleadandzinc,relatedtospecificresiduedeposits.Thearea
is flat and the distribution is only owed to the location of the deposit of residues from the
mine.
Once detected the most polluted areas within each mine, a real evaluation of their
potentialriskcanbeobtainedfromtheresultsofmetalsmobility,showninTable3.6,byusing
thesingleextractionprocedure.
ThemobilityresultslettopointoutthatallthemineareasamplestakenatKettaramine
areahaveverylowmobility,withtheexceptionoftheresidues(morethan30mgCu/Lonthe
mobilefraction).ThesesampleshaveanacidicpH(around2)andveryloworganicmatter(LOI
~10),insuchsituation,mobilityofheavymetalsisfavored.
98
3.Resultsanddiscussion
On the other hand, SBOthmane and Bir Nehass mine area samples are highly
concentrated on Pb and Zn showing an extremely high content on Pb and Zn in the mobile
phaseformineareasamplesandevenhigherfortheresidues,thusrepresentingathreatenfor
theenvironment.Thephysicchemicalcharacteristicsofthesesamples(pHaround7,andhigh
contentonorganicmatter)donotfavormobilityoftheelementsascanbeobservedforthe
lowlevelsofarsenicandcopperfoundinthemobilephase.However,giventhehighcontent
ofleadandzincitislikelythattheconcentrationofmetalsexceedthecapacityofthesoilto
retain them and the migration to a mobile phase may take place. In this sense, the residues
sampling points which are associated with waste disposal sites are even more concentrated
thanmineareasamplesand,giventhehighamountofmetals,theimmobilizationprocessesin
soils to retain metals are also overwhelmed. In this regard, the level of metals found in the
mobile phase for SBOthmane and Bir Nehass are extremely high (especially for zinc) so
remediation treatments should be applied to these areas if the soil is intended for further
purposes.
99
3.Resultsanddiscussion
Table3.6.Totalconcentration(inmg/Kg)andmobility(inmg/L)forAs,Cu,PbandZnofthemost
contaminatedsamplesofKettara,SidiBouOthmaneandBirNehassmineareas
As
Cu
Pb
Zn
Total
237±12
1,360±50
307±15
243±18
S8
Mobile
<0.5
6.1±0.2
<0.5
1.1±0.1
Total
<15
810±40
406±19
49±15
S9
Mobile
<0.5
2.5±0.1
<0.5
1.7±0.1
Minearea
Total
117±14
956±50
486±22
67±16
S10
Mobile
<0.5
18±0.1
<0.5
<0.5
Total
51±7
643±20
328±11
213±12
S28
Mobile
<0.5
6.3±0.1
2.2±0.2
1.3±0.1
Total
<15
1,257±50
341±18
99±17
R4
Mobile
<0.5
21.1±0.1
0.5±0.1
3.8±0.1
Total
<15
1,248±50
303±17
<10
Residues
R5
Mobile
<0.5
11.1±0.1
0.5±0.1
1.1±0.1
Total
<15
2,113±60
164±12
337±20
R6
Mobile
<0.5
33.0±0.1
0.7±0.1
11.4±0.1
Total
96±28
<25
6,542±89
36,267±400
S2
Mobile
1.6±0.1
2.2±0.1
393±3
1071±30
Total
<15
<25
2,665±37
10,991±120
S3
Mobile
1.2±0.1
1.1±0.1
184±2
563±8
Minearea
Total
<15
<25
3,723±50
13,555±160
S11
Mobile
1.4±0.1
1.5±0.1
280±3
719±50
SidiBou
Total
<15
<25
6,706±87
22,641±300
S30
Othmane
Mobile
1.4±0.1
3.2±0.1
483±3
932±30
Total
<15
26,400±300
103±3
135±3
R3
Mobile
2.7±0.2
2.3±0.2
179±2
359±4
Residues
Total
<15
<25
6,428±80
17,490±200
R5
Mobile
3.7±0.2
4.3±0.3
733±2
772±6
Total
197±40
240±20
11,004±150 31,487±400
R8
Mobile
2.8±0.2
2.5±0.2
523±6
1240±20
Total
22±4
<25
166±7
1,309±23
S4
Mobile
<0.5
<0.5
4.6
54±4
Total
46±13
<25
1,390±30
29,732±350
S19
Mobile
<0.5
0.6±0.1
54±3
1071±140
Minearea
Total
113±13
<25
1,500±30
13,140±160
S20
Mobile
<0.5
<0.5
40±3
369±70
Bir
Total
36±5
<25
259±8
1,960±30
S21
Nehass
Mobile
<0.5
<0.5
10±2
77±16
Total
93±20
<25
3,705±60
15,143±200
R1
Mobile
<0.5
2.1±0.1
18.3±0.5
474±5
Total
164±18
52±14
2,640±40
8,521±115
Residues
R3
Mobile
<0.5
1.0±0.1
88±2
99.8±0.8
Total
112±17
86±18
1,776±34
13,325±190
R5
Mobile
<0.5
3.0±0.1
55.7±1.4
639±7
100
3.Resultsanddiscussion
Copper
Arsenic
CER=20
CER=80
CER=10
CER=40
CER=0
CER=0
Scale 1:25000
Scale 1:25000
Figure3.11.KettaramineareaGIScontour
mapofarsenic.
Figure3.12.KettaramineareaGIScontour
mapofcopper
Zinc
Lead
CER=40
CER=20
CER=20
CER=10
CER=0
CER=0
Scale 1:25000
Figure3.13.KettaramineareaGIScontour
mapoflead.
Scale 1:25000
Figure3.14.KettaramineareaGIScontourmap
ofzinc.
Arsenic
CER=20
CER=10
Scale 1:15000
CER=0
Figure3.15.SidiBouOthmanemineareaGIScontourmapofarsenic
101
3.Resultsanddiscussion
Copper
CER=20
CER=10
Scale 1:15000
CER=0
Figure3.16.SidiBouOthmanemineareaGIScontourmapofcopper
Lead
CER=400
CER=200
Scale 1:15000
CER=0
Figure3.17.SidiBouOthmanemineareaGIScontourmapoflead
Zinc
CER=200
CER=100
Scale 1:15000
CER=0
Figure3.18.SidiBouOthmanemineareaGIScontourmapofzinc
102
3.Resultsanddiscussion
Arsenic
Copper
Scale 1:5000
CER=80
Scale 1:5000
CER=20
CER=40
CER=10
CER=0
CER=0
Figure3.19.BirNehassmineareaGIScontour
mapofarsenic
Figure3.20.BirNehassmineareaGIScontour
mapofcopper
Zinc
Lead
Scale 1:5000
Scale 1:5000
CER=200
CER=200
CER=100
CER=100
CER=0
CER=0
Figure 3.21. Bir Nehass mine area GIS contour Figure3.22.BirNehassmineareaGIScontour
mapoflead
mapofzinc
103
3.Resultsanddiscussion
3.3. XANES SPECIATION OF MERCURY IN THREE MINING DISTRICTS: ALMADÉN,
ASTURIAS(SPAIN),IDRIA(SLOVENIA)(ANNEX2)
Mercuryisoneofthemosttoxicelementsassomeofitscompoundscanbeabsorbedby
livingtissuesinlargedosesandthesecompoundsortheirderivativescanconcentrateandbe
storedoverlongperiodsoftimecausingchronicoracutedamages[5].Thetoxicityofheavy
metalsismainlycontrolledbythedoseanditschemicalspeciation.Hence,theassessmentof
mercury species on the environment is of great relevance since many health problems are
related to specific Hg species. Following previous studies by Brown and coworkers on the
characterization of mercury mines in north America [6, 7], this work aimed at providing a
general perspective on the speciation of mercury in three of the most important mercury
mining districts in Europe. In this study, XANES has been complemented with a single
extractionprotocolforthedeterminationofHgmobilitytodeterminetoxicityofthesamples.
3.3.1.CHEMICALANALYSISOFTHESAMPLES
A list of samples from the metallurgical plants and drainage network of the three
districts,theircorrespondingacronymsandashortdescriptionofthesamplingsiteisprovided
inTable3.7.TheirlocationonthemineisdepictedinFigure3.23.
Figure3.23.Samplinglocations,minesandmetallurgicalsitesofthethreemercuryminingdistricts:
Almadén,AsturiasandIdria.
104
3.Resultsanddiscussion
Location
Table3.7.Samplescollectedatthethreeminingdistricts
Id
Samplingarea
Material
ALMADENSITE
Almadén
HR
Almadenejos
Valdeazoguesriver
SanQuintín
CH
AZG
ALM
RD
SQ
HuertadelRey
Soilsfromanoldmetallurgicalplantofthe
17thcentury
MaindumpofAlmadénmine
Dumpmaterial,sedimentsandripariansoils
Azogadoriverstream
Ripariansoilsandstreamsediments
Decommissionedmetallurgicalplant Soilsfromthemetallurgicalplant
DownstreamofElEntredichopit
Suspendedparticles
DecommissionedPbZnAgmine
Minewastesandsoilsfromandoldflotation
plantusedtotreatcinnabar
ASTURIASSITE
Minetailings
Calcines
Soil
ForestSoils
TRRmn
TRRc
TRRs
TRRfs
Mineandmetallurgicalplant
Mineandmetallurgicalplant
Metallurgicalplant
ElTerronalmine
Dumpsinthevicinityofrotaryfurnaces
Calcinationwaste
Soilfromanabandonedchimneychannel
Forestsoilsfromtheminingarea
IDRIASITE
Soils
S1S3
S2
S4
S5S6
Sediments
RS
SS
Vicinityofthemetallurgicalplant
ProntHill
ConfluenceoftheIdrijcaandBaca
rivers
ConfluenceofIdrijcaandBacarivers
Idrijcariver,35kmdownstream
fromtheminebeforeBacariver
inflow
Idrijcariver,35kmdownstream
fromtheminebeforeBacariver
inflow
Soils
Meadowsoils
AlluvialsoilsamplescollectedalongtheIdrijca
river40kmdownstreamfromthemine
Soilsfromadeepprofileat50cmdepth(S5)
and100cm(S6)
Riverbedsedimentsofacompositesample
takenwithinadistanceof50mwithgrainsize
<0.063mm(RS1)and0.0632mm(RS2)
Suspendedriversedimentsofacomposite
sampletakenwithinadistanceof50mwith
grainsize<0.063mm(SS1)and0.0632mm
(SS2)
Highmercuryconcentrationsinsoilsamplesfrommetallurgicalsiteswerefoundatthe
Almadén district (Table 3.8) that can be mainly attributed to the inefficient metallurgical
techniquesusedintheoldplantsofAlmadenejosandHuertadelRey[8].Intheseplants,the
roasting temperatures were below 500 ºC. Also high mercury concentrations were found in
sedimentsandripariansoilsfromValdeazoguesriver(RD)andespeciallyfromAzogadostream
(AZG)(2,816mgHgg1).Theseresultscoincidewithpreviousstudiesundertakenatthesame
sampling site [9]. Other heavy metals are also found in Almaden site samples although in
minorconcentrations,andespeciallyhighamountsofleadandzincwerefoundinsamplesof
SanQuintínarea(SQ).
LikewiseAlmaden,thetotalmercurycontentofsoilanddumpsamplesofAsturiasmine
(Table 3.9) show the high mercury content (the highest from the three mines studied) with
27,350 mg Hg g1 in dump samples (TRRmn116) and 18,000 mg Hg g1 in soils from the
chimneychannel,withalsohighamountsofarsenic(from735mgAsg1to187,218mgAsg1).
Ontheotherhand,Idriasamples(Table3.10)revealedminoramountsofthemetalsanalyzed
comparedtoAlmadenandAsturias,beingthesamplesneartheformersmeltingfacilitiesthe
mostpollutedcausedbythesettlingdownofHgenrichedparticlesintheimmediatevicinityof
105
3.Resultsanddiscussion
thesmokestackofthesmelter.ItisimportanttohighlightthehighHgconcentrationobserved
in Idria sediments (RS) and in alluvial soils (S4) 40 km downstream from the mine probably
linkedtomercurybearingrocks,wastesfromcombustionprocessesorcontaminatedriverbed
sediments. These inputs to the aquatic environment remain in the area even a decade after
theendingofminingoperations.
Table 3.8. Almaden average metal content
(giveninμgg1)
SAMPLE
Hg
As
Pb
Zn
CH127
989
<15
<10
112
HR108
976
<15
214
96
HR109
404
<15
111
104
HR110
200
<15
130
185
RD124
105
<15
<10
<10
CH125
1,800
<15
<10
112
AZG105
2,816
23
139
233
CH128
450
<15
102
185
ALM101
2,720
<15
74
153
ALM102
2,629
<15
102
193
CH126
2,230
<15
<10
365
SQ111
902
<15
15,837 6,877
SQ112
1,730
<15
2,154 1,221
SQ113
1,935
<15
19,049 7,134
SQ114
390
Table 3.9. Asturias average metal content
(giveninμgg1)
SAMPLE
Hg
As
Pb
Zn
TRRmn115 1,470 39338
<10
<15
TRRmn116 27,350 11,7553 <10
<15
TRRs118
3,280
735
<10
<15
TRRs121
18,000 12,133
<10
<15
TRRmn122 5,785 4,2300
<10
<15
TRRfs3
1,570 1,6826
107
173
TRRfs4
1,080 1,120
53
137
TRRc5
34
187,218 <10
<15
TRRc55
54
25,876
<10
<15
Table3.10.Idriaaveragemetalcontent(given
inμgg1)
SAMPLE
Hg
As
Pb
Zn
S1
333
21
<10
112
S2
47
26
<10
102
S4
76
<15
<10
64
S5
175
<15
47
145
S6
144
<15
73
496
RS1
6,540
<15
302
270
RS2
1920
<15
14
<15
SS–1
96
<15
<10
449
SS1
11
<15
<10
24
S3
95
27
46
130
3.3.2.XANESSPECIATIONANDMOBILITYRESULTS
In Figure 3.24 the spectra corresponding to mercury standards and to the samples for
each mine are given. Considering the number of sample XANES spectra, PCA was performed
separatelyforeachminingdistrict.Anexampleoffittingforaselectedsampleofeachmineis
giveninFigure3.25.
PCA results for Almaden district indicated that five components are required to
reconstructeachoftheexperimentalspectra(cinnabar,Cb(redHgS);metacinnabar,Mc(black
HgS);HgCl2;calomel(Hg2Cl2)andschuetteite,Sc(Hg3(SO4)O2))withabove95%ofconfidence.
The most common species found in almost all samples were mercury sulfides (cinnabar and
metacinnabar) but also nonsulfide phases like schuetteite, calomel (Hg2Cl2) and mercury
chloride(HgCl2)whichwerefoundinsoilandsedimentsamples.
106
3.Resultsanddiscussion
Figure3.24.XANESspectraofselectedHgpurecompoundsandsamplesfrom Almaden,Idriaand
Asturiasminingdistricts(allspectraaredeliberatelystackedtoshowdifferences).Eachspectrum
correspondstothemeanvalueoffivereplicates.
Figure3.25.XANESspectraofselectedsamplesfromthethreeminingdistrictswithreconstructed
spectrashownasdashedlines.
107
3.Resultsanddiscussion
IDRIA
ASTURIAS
ALMADEN
Table3.11.Mainmercuryspecies(in%)andmobilemercury(inmgL1and%).Abbreviations:Cb:
cinnabar;Mc:metacinnabar;Sc:schuetteite;Co:corderoite
Mobility
Red.Chi
Sample
Cb Mc Sc Co HgO HgSO4 Hg2Cl2 HgCl2
mgL1(%)
Sq.(103)
CH127
0.4
62
0
0
0
0
0
38
0
1.4±0.3
HR108
0.6
37 23 0
0
0
0
40
0
0.6±0.2
HR109
0.7
33 24 0
0
0
0
43
0
0.2±0.1
HR110
0.6
41 22 0
0
0
0
37
0
<0.2
RD124
0.5
0
0 94 0
0
0
0
6
<0.2
CH125
0.4
7
0 83 0
0
0
0
10
<0.2
AZG105
0.3
0
0 80 0
0
0
20
0
<0.2
CH128
0.4
24 22 0
0
0
0
35
19
<0.2
ALM101
0.3
38 39 23 0
0
0
0
0
10.8±0.3
ALM102
0.7
39 31 0
0
0
0
30
0
21.3±0.5
CH126
0.3
33 32 35 0
0
0
0
0
<0.2
SQ111
0.2
54
0 17 0
0
0
29
0
0.6±0.1
SQ112
0.2
51
0 21 0
0
0
28
0
3.7±0.2
SQ113
0.2
59
0 17 0
0
0
24
0
<0.2
SQ114
0.3
47
0 20 0
0
0
33
0
<0.2
TRRmn115 29 24 0
1
0
0
0
0
47
0.4±0.1
TRRmn116 28 22 0
0.9
0
0
0
0
50
73±2
TRRs118
0.8
28 22 0
0
0
0
0
50
20.1±1.3
TRRs121
0.7
29 22 0
0
0
0
0
49
56.5±2
TRRmn122 30 24 0
0.7
0
0
0
0
46
43.6±2
TRRfs3
3
44 28 0
0
10
18
0
0
0.7±0.2
TRRfs4
3
50 36 0 14
0
0
0
0
<0.2
TRRc5
8
52 30 0 18
0
0
0
0
<0.2
TRRc55
7
57 43 0
0
0
0
0
0
<0.2
S1
6
44
0 32 0
0
24
0
0
<0.2
S2
2
55
0
0
0
0
45
0
0
0.2±0.1
S4
4
85 15 0
0
0
0
0
0
<0.2
S5
4
90
0
0
0
10
0
0
0
<0.2
S6
5
58
0
0
0
0
42
0
0
<0.2
RS1
2
57
0
0
0
0
43
0
0
<0.2
RS2
3
100 0
0
0
0
0
0
0
<0.2
SS1
4
90
0
0
0
0
10
0
0
<0.2
SS1
9
55
0
0
0
0
45
0
0
<0.2
S3
07
66
0 26 0
8
0
0
0
0.3±0.1
Regarding Almaden mine area samples, XANES analyses from San Quintín area (Table
3.11) indicated high amounts of cinnabar (47–59%) and minor amounts of relatively more
soluble species like calomel (24–33%) and schuetteite (17–21%) that can be attributed to
weathering processes. The absence of metacinnabar phases in that samples, a metastable
polymorphofcinnabarthatoccurswhentheroastingprocessofmercuryoresisnotcomplete
oritisdoneinthepresenceofimpurities[10],isassociatedtothehistoricaluseofthesite,as
this site was used to perform flotation tests and no furnaces were employed. On the other
hand,metacinnabarhasbeenidentifiedinsoilsamplesfromAlmadenejos(ALM)(31–39%)and
HuertadelRey(HR)(~23%),locationswithhistoricalmetallurgicalactivity.
108
3.Resultsanddiscussion
Othernonsulfidephaseslikemercurouschloride(24–43%)havealsobeenidentifiedat
SanQuintínandHuertadelRey,attributabletotheprocessofsoilformation.Highamountsof
schuetteite have been identified in ore stockpile in San Quintín and Almadenejos area.
Schuetteite is a mineral phase typically linked to the presence of Hg(0) that appears in the
sunlightexposedsideoftherocksurface,anditisfrequentlyfoundnearoldfurnacesandore
dumps[11].Relativelymoresolublephaseshavebeenidentifiedinsoilandsedimentsamples
fromValdeazoguesRiver(100%)andAzogadostream(100%)(Hg2Cl2,HgCl2andHg3(SO4)O2)as
a result of weathering processes caused by the drainage network of the mining district. The
mobilityofmercuryinthisdistrictisclearlylinkedwithmetallurgicalactivityandformationof
secondary chloride phases. The highest mobility was found in soil samples from an old
metallurgicalprecinct(ALM)(21.3mgL1;Table3.11)relatedtothepresenceofHg2Cl2.
InAsturiasminingdistrict,allsamplesfromthedecommissionedmineandmetallurgical
facilityshowedhighmercurycontentsinsoils(TRRfs),dumpmaterials(TRRmn)andchimney
soils (TRRs) (Table 3.9), and a predominance of sulfides species (50–100%) with significant
presence of metacinnabar in all samples (Table 3.11). Cinnabar and metacinnabar in these
samplesishigherthaninAlmadénareasinceinAsturiasthemetallurgywaslessefficientthan
inIdriaand Almadénarea,withlowerroastingtemperatureandpoorestrecoveryrates.The
contentsofothermercuryspeciessuchaschloridesaresignificant,withhighamountsonsoils
samplesfromthefacilityandthechimneyexhaustingroastingsmokesandthusthemobilityof
mercury in this district is higher than in Almadén. In qualitative terms, the mobile mercury
determinediscorrelatedwiththepresenceofHgCl2(exceptforTRRmn115),amobilephase
of mercury and, in a lesser account, to the presence of metacinnabar resulting from the
incompletecombustion.
At the Idria mining district, cinnabar is the most common form of mercury in soil,
sedimentsandsuspended particles,whilemetacinnabarisalsofoundinsoilsampleS4,and
sulfatesinsoilsandsediments(S,RS,SS).Thelackofmetacinnabarinmostofthesesamplesis
duetothereuseofcalcinesandmetallurgicalwastesintherefillingofminegalleriesresulting
in a minor dispersion of this material throughout the surrounding environment. High
proportions of sulfates were found in soil samples (S), but the mobility of mercury in this
district was clearly reduced, mainly by the major proportions of cinnabar in soils, sediments
and suspended particles. This low mobility of mercury (0.2–0.3 mg L1, see Table 3.11) is in
agreement with former studies on the area [12] that described low watersoluble mercury
speciesinsedimentsandsuspendedparticles.
Consideringthethreedistricts,themainprocessesaffectingmercuryspeciationareore
composition,mininghistoryandroastingprocess.Thetypeofmetallurgicalprocessingarises
109
3.Resultsanddiscussion
as one of the most important factors in defining mercury availability. In this sense, mercury
mobilityishigherinAsturiasdistrictowingtoitsroastingtreatmentwaslessefficientthanin
AlmadenorIdria(lowerroastingtemperaturesandpoorerrecoveringrates)thatincreasesthe
presence of metacinnabar and, principally, HgCl2 phases responsible for the mobility of
mercury. Despite the complex and lengthy history of mining and metallurgical activity, the
mobility is significantly lower in the Almaden district given its better roasting processes
achieved with better furnaces (only in the last century) and likewise Almaden, even lower
mobilityvalueswerefoundinIdriadistrictrelatedtoitsefficientmetallurgicalprocess(similar
to Almadén area), together with the appropriate management of calcines that were used to
refilloldgalleriesaswellastheshortermininghistoryofthisdistrict.
Ratherinsolublemercurycompounds(cinnabar,metacinnabar,schuetteite,corderoite)
have been shown to prevail in dumps and wastes from mines and metallurgical plants,
whereas more soluble Hg phases (mainly HgCl2 but also HgO and HgSO4) were found in soils
and sediments from all target areas. A qualitative relationship between mobile mercury and
the presence of mercury chlorides or sulfates compounds has been established for samples
from the three districts. Nonetheless, the absolute mobility remains relatively low in most
cases, inherently suggesting that kinetic effects and availability of the soluble phases might
alsobeconsideredintheassessmentofmercurybehavior.
110
3.Resultsanddiscussion
REMEDIATIONTECHNOLOGIES
This section includes results of treatment processes for industrial water from two
different sectors: mining activities and textile industry. These results constitute specific
examplesofinnovationinwatertreatmentprocessforbothinorganicandorganicpollutants.
Thus,resultsoflaboratoryandpilotplantscaleforrecyclingofwaterfromaminetailing
pond are reported here. On the other hand, the results achieved by Feexchanged materials
for the degradation of persistent organic pollutants (POPs) by means of Fenton processes as
wellastothesorptionofinorganiccontaminantsarealsosummarized.
3.4. EXTRACTANT AND SOLVENT SELECTION TO RECOVER ZINC FROM A MINING
EFFLUENT:FROMLABORATORYSCALETOPILOTPLANT
Inatailingpondfromanabandonmineisstoredahugestreamofeffluent,estimatedto
be10,000m3/dayandcontainingabout1g/LofZnandsignificantamountsofferrous,ferric,
calcium,copper,aluminumandmanganeseions.Inthissense,topreventdambreachesfrom
thetailingpondthatcancausehugehazardstohumansandtheenvironmentitisrequiredto
reducetheamountofwastewatercontainedintheminetailingpond.Inaddition,therecovery
ofzinccanprovideeconomicvaluetotheprocesswhilesolvinganenvironmentalproblem.
3.4.1.SXLABORATORYRESULTS
To accomplish for a valuable Zn recovery, separation of Zn from Fe and Ca must be
obtainedsinceanyfurtheruseoftheZnliquorproduct,i.e.,Electrowining(EW)process,will
requireofsuchconditions.TherecoveryofZnwasinvestigatedtoselecttheextractantwith
higher efficiency and selectivity between DEHPA, Cyanex 272 or Ionquest 290. Additionally,
twotypesofkerosenewerealsoevaluated.
SincetherearenoreagentscommerciallyavailableabletoextractZnselectivelyfroma
solutioncontainingFe,Fewasremovedfromthe minewaterpriortothe SXtreatmentbya
biooxidationprocessfollowedbyanalkalineprecipitationstep[13,14]toobtainapregnant
leachsolution(PLS)withoutiron.
After the precipitation step, Fe was completely removed and also the amount of Al
decreaseddrasticallyandCudroppedbyhalf(from45.0mg/Lto21.7mg/L).Theprocessdid
notalterthecontentofZn,sothewholeprocessofzincrecoverydoesnotloseeffectiveness
111
3.Resultsanddiscussion
duetotheironremovalstep.Theconcentrationoftheothermetalsremainedsimilartothe
initial(Table3.12).
Table3.12.Solutioncomposition,beforeandafterbiooxidationtreatment
Concentration(mg/L)
Element Initialsolution Afterbiooxidation Afterprecipitation
[Fe2+]
254
0
0
[Fe3+]
446
690
0.2
[Zn]
1,020
1,020
1,010
[Al]
292
250
20
[Mn]
265
260
200
[Cu]
45
45
21.7
[Ca]
600
600
600
[Pb]
1.6
1.6
1.6
pH
3.0
1.9
4.8
Regardingtheselectivityexperimentsperformedwiththethreeextractantsstudied,the
obtainedresults(Figure3.26,3.28and3.30)depictedarationalreductionoftherecoveryof
zinc as the A:O phase ratio increases due to a saturation of the extractant. In this sense,
Cyanex272andIonquest290,usedinalesserconcentrationthanDEHPA(5%vs40%(v/v)for
DEHPA),presentaplateauataA:O>1whichsuggeststhattheextractantissaturated,whilst
zincrecoveryforDEHPAisstilldiminishing.
TherecoveryofmetalsachievedbyDEHPAwasZn>Ca>Mn>Al>Cu,andatA:O=1the
recovery of Zn was around 75%, but also other metal impurities were also recovered,
especiallyCaandMn(60%and30%recovered,respectively)pointingoutthatDEHPAispoorly
selectivetowardsZnextraction(Figure3.26).Inaddition,around80%oftheAlremainedinthe
organicphase(OP)afterthestrippingstep(Figure3.27)limitingthereuseoftheextractant.
The recovery of metals obtained for Cyanex 272 at 5% (v/v) was Zn>>Cu>Mn~Ca~Al
(Figure 3.28), although Mn, Ca and Al are slightly recovered. In this sense, Cyanex 272
selectivity towards zinc is higher than DEHPA and, moreover, negligible amounts of metals
(around 1%) were found in the organic phase (Figure 3.29) so the organic phase employing
Cyanex272canbereusedseveralcycleswithpracticallynoregeneration.
TherecoveryofmetalsforIonquest290wasZn>>Al>Cu~Mn~Ca(Figure3.30).Thetrend
issimilartoCyanex272sincetherecoveryofZincatA:O=1isaround40%andotherimpurities
arepracticallynotrecovered.Inaddition,lessthan5%oftheelementsanalyzedremaininthe
organicphaseafterthestrippingstep(Figure3.31).Thus,contrarytoDEHPA,Cyanex272and
Ionquest290selectivelyextractZnfromasolution containinghighamounts ofCaandother
metalswithoutfoulingoftheOP.
112
3.Resultsanddiscussion
(b)RemainingOPDEHPA
(a)RecoveryDEHPA
100
100
ZnKD80
CaKD80
AlD80
MnKD80
CuKD80
90
80
ZnKD80
CaKD80
AlD80
MnKD80
CuKD80
90
80
70
60
60
50
50
%
%
70
ZnKD100
CaKD100
AlD100
MnKD100
CuKD100
40
40
30
30
20
20
10
10
0
0
0
2
4
RatioA/O
6
8
0
10
Figure3.26.%RecoveryatdifferentA:Oratios
forDEHPA40%(v/v)
2
RatioA/O
6
8
10
(b)RemainingOPCyanex
100
ZnKD80
CaKD80
AlKD80
MnKD80
CuKD80
90
80
70
ZnKD100
CaKD100
AlKD100
MnKD100
CuKD100
ZnKD80
CaKD80
AlKD80
MnKD80
CuKD80
90
80
70
60
60
50
50
%
%
4
Figure3.27.%RemainingOPatdifferentA:O
ratiosforDEHPA40%(v/v)
(a)RecoveryCyanex272
100
40
40
30
30
20
20
10
10
0
ZnKD100
CaKD100
AlKD100
MnKD100
CuKD100
0
0
2
4
RatioA/O
6
8
10
0
Figure3.28.%RecoveryatdifferentA:Oratios
forCyanex2725%(v/v)
ZnKD80
CaKD80
AlD80
MnKD80
CuKD80
90
80
70
2
4
RatioA/O
6
8
10
Figure3.29.%RemainingOPatdifferentA:O
ratiosforCyanex2725%(v/v)
(b)RemainingOPIonquest
(a)RecoveryIONQUEST
100
100
ZnKD100
CaKD100
AlD100
MnKD100
CuKD100
ZnKD80
CaKD80
AlD80
MnKD80
CuKD80
90
80
70
60
60
50
50
ZnKD100
CaKD100
AlD100
MnKD100
CuKD100
%
%
ZnKD100
CaKD100
AlD100
MnKD100
CuKD100
40
40
30
30
20
20
10
10
0
0
0
2
4
6
8
10
RatioA/O
Figure3.30.%RecoveryatdifferentA/Oratios
forIonquest2905%(v/v)
0
2
4
RatioA/O
6
8
10
Figure3.31.%RemainingOPatdifferentA:O
ratiosforCyanex2725%(v/v)
The differences observed on the recovery trends between DEHPA and the other two
extractantscanbeassociatedtotheirchemicalnature,giventhatphosphoricextractants(as
DEHPA)havehigheraffinityforcalciumthanphosphinicextractants(suchasCyanex272and
Ionquest 290). The small differences observed between Cyanex 272 and Ionquest 290 are
113
3.Resultsanddiscussion
explained by both the different phosphinic acid concentration (Ionquest 290 is 510% more
concentratedthanCyanex272)andalsooweddifferentproductimpuritiesineachextractant.
Asasummary,DEHPAreportedpoorselectivitytowardszincduetothecoextractionof
manganese and calcium (that resulted in a gypsum precipitate in the stripping solution) and
highamountsofaluminumremainedintheorganicphaseafterthestrippingstepreducingits
reusability. On the contrary, Cyanex 272 and Ionquest 290 provided high zinc selectivity
towards calcium and negligible amounts of metals were found in the organic phase so no
extractant regeneration step will be required. Regarding Cyanex 272 and Ionquest 290, the
latter achieved a zinc recovery 510% higher and therefore Ionquest 290 is considered the
most appropriate extractant. Considering the two different kerosene employed (Ketrul D80
and Ketrul D100), no significative differences were observed and thus both of them can be
equallyfeasiblefortherecoveryofzinc.However,fromanengineeringpointofview,theuse
ofKetrulD100isrecommendedduetoitslowerflammabilitycomparedtoKetrulD80,andfor
thatreasonitwasselectedasdissolvent.
3.4.2.SXPILOTPLANTPROCESS
Tofulfilltherequirements,thepilotplantmayproduceaneconomicallyeffectiveoutput
and the overall process should be environmentally friendly. The pilot plant process layout is
depictedinFigure3.32.
Pregnant
Leach
Solution
Loaded
Solvent
Strong
Electrolyte
Raffinate
SOLVENT
EXTRACTION
SOLVENT
STRIPPING
ELECTROWINNING
Barren
Solvent
Weak
Electrolyte
Zinc
Figure3.32.Inputsandoutputsatthepilotplant
To satisfy the environmental requirements at least 95% of the Zn must be recovered
fromtheeffluent,whereastoproducetheeconomiceffectiveoutputthezinccontainedinthe
stripping solution must be converted to metallic zinc, that must be treated in an
electrowinning (EW) plant. To fulfill the operating conditions for the EW plant, the SX plant
shouldprovideafinalproductstreamof90g/LZninthestrippingstep(strongelectrolyte)by
usingaweakelectrolytewith50g/L.
114
3.Resultsanddiscussion
Previouslytothepilotplantoperations,computersimulationwasperformedtoestimate
the required pilot plant inputs and outputs, to calculate the distribution coefficients (D) and
the number of stages. Experience has shown that computer simulation is a more flexible
designtoolthanMcCabeThielediagramsforpulsedcolumns[15,16,17].Theresultsobtained
inthesimulation,collectedinTable3.13,determinedthatataphaseratioO:A=0.50.6,atwo
stage column is enough to recover more than 95% of the Zn. The addition of a third stage
enableseithertodecreasethephaseratioO:Ato0.4ortoworkwithaphaseratioofO:A=0.5
andobtainarecoveryofZnnearto99%,i.e.<10mg/LZnintheraffinate.Theconcentrationof
Zn in the loaded solvent should be in the range of 2.22.8 g/L, that is around 7085% of the
totaltheoreticalloadingof3.3gZn/LforIonquest2905%(v/v),whichisquitereasonable.In
ordertogetafinalsolutionof90g/LZn,theZntransferfromtheorganicphasetothestrip
phaseshouldbeof40g/L;toachievethatvaluethestrippingshouldberunataphaseratioof
O:A=20,soonlyoneequilibriumstageisrequiredforthestripping.
Table3.13.RecoveryofZndependingontheplantconfigurationusing5%Ionquest290
No.Stages PhaseratioO:A Zninraff.(mg/L)
%Recovery
0.50
51
94.7
2
0.60
24
97.6
0.35
75
92.1
0.40
30
96.8
3
0.45
11
98.9
0.5
4
99.6
The maximum loading obtained experimentally at limiting conditions (by contacting 3
timesthesolventwithcorrespondingfreshportionsoftheeffluentatphaseratioO:A=0.1)was
2.9gZn/L.Sincethisresultwassimilartotheobtainedafterasinglecontact,itrevealedthat
thelimitingconditionscouldbeachievedbyasinglecontact.
Experiments performed at the pilot plant without pH control (Table 3.14) shown that
without pH control, the extraction was quite selective. In this sense, no Mn, Cu or Al were
extractedandonlyasmallamountofCawasextracted.Suchfactisalsoconfirmedbythehigh
values regarding separation factors. However, the distribution ratio of Zn (DZn) was small,
especially at the dilute end of the process (phase ratio O:A=10). In addition, the pH of the
raffinate (final pH) dropped from 2.6 to 2.1 as O:A increased, despite the suitable pH for Zn
extraction by Ionquest 290 is above 2.5 [18]. Furthermore, to avoid Ca coextraction, pH
shouldbearound3asindicatedbytheisothermsgivenintheonlineUserManual,page5from
CytecCorporationforCyanex272andconsideringthesamecompositionofbothCyanex272
and Ionquest 290. (http://www.cytec.com/specialtychemicals/PDFs/CYANEX%20272.pdf,
accessed26thDecember2010).
115
3.Resultsanddiscussion
Table3.14.ExtractionexperimentswithoutpHcorrection,22°C
Phaseratio
O:A
Final
pH
Aqueous(mg/L)
Organic(mg/L)
Dvalues&Separationfactors
Zn
Zn
Mn
Ca
Mn
Ca
DZn
DZn/DCa
DZn/DMn
PLS
5.0
962 206
763
0.1
2.58
792 208
618
1595
0
8
2.0
154.5
1104
0.3
2.50
692 208
613
910
0.2
12
1.3
66.4
1350
0.5
2.31
621 205
605
668
0.1
15
1.1
44.4
2260
1
2.18
536 206
624
444
0.1
13
0.8
38.4
1600
2
2.16
467 207
613
273
0
9
0.6
40.9
4200
3
2.32
402 202
597
178
0.3
10
0.4
23.9
270
5
2.25
342 203
599
132
0.1
6
0.4
39.9
800
10
2.1
270
601
76
0.1
8
0.3
22.5
610
203
When adjusting to pH=3 (Table 3.15) higher amounts of zinc were extracted and the
distributioncoefficientofzinc(DZn)washigherthanwithoutpHadjustment.Theextractionof
MnandCastillremainedquitelowatpH=3asisalsoindicatedbythehighseparationfactors
obtained. Therefore, given that higher distribution coefficient for zinc is obtained at this pH,
thepilotplanteffluentshouldbemaintainedaroundpH3.Inpractice,thepHadjustmentwas
achievedbydirectneutralizationofboththeacidicraffinateandtheorganicsolvent(bypre
equilibrationwithaqueoussolution)usingNa2CO3,withandaverageconsumptionof1.62kg
Na2CO3perkgofzinctreated.
Table3.15.ExtractionexperimentsatpH3,22°C
Aqueous(mg/L)
Organic(mg/L)
Dvalues&Separationfactors
Phaseratio
O:A
Zn
Mn
Ca
Zn
PLS(pH5.0)
963
213
583
0.1
784
231
531
0.3
343
233
0.5
182
1
Mn
Ca
DZn
DZn/DCa
DZn/DMn
2878
0
76
3.7
26.4
9104
509
2073
0.4
72
6.0
42.9
3103
211
536
1700
0.5
40
9.3
133.0
5103
49
213
547
875
1.5
38
17.9
199.9
3103
2
22
194
534
502
3.4
43
22.8
285.0
1103
3
14
127
532
297
2.7
46
21.2
235.6
1103
5
4
181
584
192
3
42
48
685.7
2103
10
1
183
579
90
1
48
90
1125
2104
Shake out stripping experiments were carried out at O:A=10, by contacting 200 mL of
loadedsolvent(LS)containing1.95g/LZnwith20mLofH2SO4200g/L(weakelectrolyte,WE)
containingdifferentzincconcentrationsrangingfrom40to90g/L(Table3.16).Toachievethe
required transfer, the concentration of Zn should increase by ~20 g/L, which was consistent
withtheresultsshowninTable3.16.Inallcases,only517mg/LofZnremainedinthebarren
116
3.Resultsanddiscussion
solvent (BS), so almost all zinc was recovered. Therefore, one stage of stripping is enough
regardlesstheconcentrationofZninthestrippingsolution.
Table3.16.StrippingexperimentsatphaseratioO:A=10,22ºC
AqueousIn
Aqueousout
Zn(g/L)
40
50
60
70
80
90
H2SO4(g/L)
200
194
188
176
200
200
Zn(g/L)
58.8
68.2
77.8
91.0
102.8
115.4
Additional laboratory tests carried out at the mine site during the pilot plant
experimentsatphaseratioO:A=20,revealedthattheloadedsolventfromthepilotplantwas
efficientlystrippedinonecontact,i.e.onestage,bythestripsolutionusedinthepilotplant
experiments,usingaweakelectrolytewith~50gZn/L,producinganSEcontaining90g/LZn,
i.e.azinctransferof40g/L,asitwasrequiredfortheEWplant.
Preliminaryhydraulictestsatthepilotplantshowedthattheavailablefluxisabove30
3
2
m /m /h in both columns. Given that it was proven that only one stage is required for the
stripping, this step was not further optimized and was run mainly to produce BS. It was
operatedatafluxof40m3/m2/h(35l/hsolvent).Thepulsingofthecolumnshadanamplitude
of15mmandafrequencyof1Hz.TheflowrateoftheWEthroughthepumpwas57L/h.The
averagevalueofZnintheBSwasabout20mg/LZn.
Table3.17.Extractioninorganicandaqueousdispersioncontinuities
Zn(mg/L)
pH
Feed(L/h) BS(L/h) Flux(m3/m2/h)
Raff. Raff.
LS
110
55
33
2.7
11
1,910
Organiccontinuous
130
60
38
2.8
55
1,880
dispersion
130
60
38
2.9
11
1,800
150
70
44
2.9
1,880
Aqueouscontinuous
150
70
44
3.1
2,520
dispersion
150
70
44
2.9
2,240
Three tests with both organic continuous and aqueous continuous dispersion (Table
3.17)wereundertakentodeterminethepreferreddispersion.Duringbothorganiccontinuous
andaqueouscontinuousruns,thetemperaturerosefrom25°Cinthemorningto34°Cinthe
evening,facilitatingthecomparisonbetweenbothdispersionsresults.Everytesttook5hours,
longenoughtoreachsteadystateandthephaseratiowaskeptatA:O=2.1duringallthetest
work. The results were similar for both dispersions. The concentration of Zn in the LS was
around2,000mg/Landintheraffinatebelow50mg/L,indicatingthanmorethan95%ofthe
Znwasrecovered.Thus,theextractionprocessoperatedsuccessfullywithbothaqueousand
117
3.Resultsanddiscussion
organic continuous dispersions at 2334°C. As the available flux and recovery with both
dispersionsweresimilar,itispreferabletousetheaqueouscontinuousdispersionasthereisa
lowerexpenditureonsolvent.Usinganaqueouscontinuousdispersionthedangeroffiredue
tokeroseneignitionisalsodiminished.
ThestrippingoftheLS(containingaround2g/LZn)achievedaSEwith3040g/LZn(a
zinctransferof3040g/L)whilstlessthan50mg/LZnintheraffinateatafluxof45m3/m2/h
using an aqueous continuous dispersion at O:A=20. The stripping column worked well and
suppliedtherequiredBStotheextraction.Giventhatthelaboratorytestsprovedthatthere
was no need for an extra column, one stage of mixersettler was sufficient to obtain the
requiredzinctransferof40g/Lwithbarrensolventcontaining~50mg/LZn.
Asasummary,giventhattherecyclingoftheorganicphaseleadtoarelativeimportance
of the extractant costs, Ionquest 290 was selected as the most suitable extractant for the
targetstreamduetoitshigherselectivityandloadingcapacitytowardsZnextraction.Ionquest
290avoidsthenecessityofscrubbingthegypsumprecipitateinthestripliquoraswellasthe
regenerationofthesolventafterhighamountsofaluminumarenotstrippedifcomparedwith
DEHPA (a cheaper extractant compared to Ionquest 290 and Cyanex 272). As both solvents,
Ketrul D80 and Ketrul D100, showed similar behavior, Ketrul D100 was the solvent
recommendedowingitslowervolatilityandflammability.Thepilotplantprovedthefeasibility
oftheprocess,obtainingazincrecoveryof95%andleavinglessthan50mg/Lintheraffinate.
ThestrippingwasefficientandonlyasinglestageatO:A=20wasrequiredtoachieveatransfer
of40g/L.ForaZnpriceaboveUS$2/kgtheoperatingcostsarecoveredwhile,additionally,a
seriousenvironmentalproblemissolved.
118
3.Resultsanddiscussion
3.5. FELOADED MATERIALS FOR THE REMEDIATION OF ORGANIC AND INORGANIC
CONTAMINATEDWASTEWATERS
Here, are summarized the results obtained by using Feloaded materials to remediate
organic and inorganic wastewaters. In this sense, organic pollutants such as dyes and
persistentorganicpollutantsweredegradedbyfollowingFentontreatmentusingasacatalyst
Feloaded materials. Such Feloaded materials were also applied to the removal of arsenic
from polluted wastewaters taking advantage of the affinity of arsenic with iron compounds.
The first step on these processes includes the loading of the material with Fe. The results
obtainedforloadingtheUSYzeolitewithFe(III)arepresentedinFigure3.33andindicatedaFe
loadingincreasewithtime.Onehourwasselectedasappropriateloadingtime.
TimeofFe3+exchange
ConcentrationoftheFeloadingsolution
1.8
3
1.6
2.5
1.2
[Fe3+](Wt.%)
[Fe3+ ](Wt.%)
1.4
1
0.8
0.6
2
1.5
1
0.4
0.5
0.2
0
0
1h
3h
6h
3*6h
3days
Figure3.33.FeconcentrationonUSYzeoliteat
1h,3h,6h,3daysand6cyclesof3hofFe
exchange(errorbarscorrespondtothe
standarddeviationonthedetermination)
0.01M
0.05M
0.1M
0.2M
0.5M
Figure3.34.FeconcentrationofUSYzeolite
between0.01to0.5MinitialFe(NO3)3
concentration(errorbarscorrespondtothe
standarddeviationonthedetermination)
AdecreaseontheFecontentisobservedwhentheconcentrationoftheloadingsolution
isincreased(seeFigure3.34)beingexplainedbytheformationofpolynuclearFecomplexesat
high Fe concentration, so less Fe is available for the exchange with USY [19]. On the other
hand,ataverylowFeconcentration(0.01M),notenoughFeisavailabletooccupyallthesites,
thusbeingthemaximumloadingataconcentrationofFe(NO3)30.05M,hencebeingselected
as the most appropriate loading concentration. Under this conditions, at room temperature,
theamountofFeintroducedintotheUSYzeolitewas2.7±0.2wt.%.AccordingtoNeamtuetal.
[20]1.69wt.%ofFecanbeintroducedwhenexchangingUSY threetimes during6husing an
excess of Fe(NO3)3 1M at 80ºC. Our new process achieved 2.7±0.2 wt.%.Fe by using milder
conditions. Therefore these conditions were also implemented to other materials with high
exchangepropertiessuchaszeoliteY,clinoptilolite,montmorilloniteandForagersponge.
119
3.Resultsanddiscussion
3.5.1.FELOADEDMATERIALSAPPLIEDASFENTONCATALYSTS
Two different low cost materials such the natural zeolite clinoptilolite and the clay
montmorillonite K10 (MMT) along with the commercial synthetic zeolite USY were Fe
exchangedfortheirevaluationasFentoncatalysts.TheamountofFeoneachmaterialbefore
andafterloadingisshowninFigure3.35.Fromtheobtainedresultsitcanbepointedoutthat
beforetheexchangingprocessnoFewasdetectedintheUSYwhilstMMTstructurecontained
2.1±0.1 wt.% and clinoptilolite 1.0±0.1 wt.% of Fe. The initial Fe content of MMT and
clinoptiloliteisstructuralandduetotheirnaturaloriginrelatedtosoilsusuallyrichinFe.After
the loading process, the amount of Fe on MMT was 4.3 wt.%, so 2.4 wt.% of Fe were
introduced onto MMT. On the other hand, the clinoptilolite amount of Fe after the loading
withFewas2.1wt.%,soonly1.0wt.%Fewasintroducedontotheclinoptilolite.Thesevalues
arestronglyrelatedtothesurfaceareaofeachmaterial,asUSYhasbiggersurfaceareathan
montmorillonite and, montmorillonite bigger than clinoptilolite (Specific surface area:
USY=730 m2/g; MMT=271 m2/g; clinoptilolite=31.7 m2/g). Overall, this process has
demonstratedtobesuitableforloadingFelowcostmaterialssuchasnaturalzeolitesandclays
withdifferentsurfaceareas.
3.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Afterloading
Beforeloading
=218nm
3.0
2.5
Absorbance
[Fe3+ ](Wt.%)
4.0
2.0
=516nm
=324nm
1.5
1.0
0.5
0.0
200
USY
MMT
Clinoptilolite
Figure3.35.Fecontentofthematerialsbeforeand
aftertheloadingwithFe(NO3)3
250
300
350
400
450
500
550
600
Wavelength (nm)
Figure3.36.UVVisspectraofAR140.05
mM
ToevaluatethefeasibilityofsuchFeloadedmaterialsasheterogeneouscatalystsonthe
degradationoforganiccompounds,thedecolorisationofamodeldyehasbeenemployed.The
acid dye used, Acid Red 14 (AR14), has two peaks at UV region (218 nm and 324 nm) and a
characteristic peak at visible region at 516 nm (Figure 3.36). The peak at 218 nm can be
attributedtotheabsorbanceofthenaphthalenegroups,thepeakat324nmcorrespondsto
the electron conjugation of naphthalene rings with the N=N group, whereas the peak at
516nmisrelatedtothehighconjugatedstructureofthewholedyemoleculethatconfersits
characteristiccolortothedye[21].
120
3.Resultsanddiscussion
%Decolorization=516nm
%Degradation=324nm
100
100
90
90
80
80
Hom.Cat.
FeUSY
FeMMT
FeClinoptilolite
70
60
50
Hom.Cat.
FeUSY
FeMMT
FeClinoptilolite
70
60
50
40
40
30
30
20
20
10
10
0
0
0
10
20
30
40
50
Time(min)
60
70
80
90
0
Figure3.37.KineticsofAR14decolorisation
(=516nm)forFeUSY,FeMMT,Fe
Clinoptiloliteandhomogeneouscatalysis(Hom.
Cat.)
10
20
30
40
50
60
70
80
90
Time(min)
Figure3.38.KineticsofAR14mineralization
(=324nm)forFeUSY,FeMMT,Fe
Clinoptiloliteandhomogeneouscatalysis(Hom.
Cat.)
The absorbance was measured at regular intervals of time at its characteristic
wavelength (=516 nm) to determine kinetics of decolorisation of the dye. The degree of
mineralizationofthedyewasfollowedthroughthecharacteristicwavelengthrelatedtobond
breaking(=324nm).Fromtheobtainedresults(Figure3.37)itcanbestatedthatallFeloaded
materials are able to discolor AR14 0.05mM in less than 60 min. Moreover, according to
literature[22,23]theFentonreactionfollowsapseudofirstorderequation[ln(C/Co)=Kat],so
thelinearregionofthekineticexperimentscanbeusedtoobtaintheapparentfirstorderrate
constant(Ka)foralltheFeloadedmaterialsstudied.Thus,takingintoaccounttheKavaluesfor
the Feloaded materials together with the homogeneous catalysis (Table 3.18), it can be
pointedoutthatthecatalyticactivitiesfollowedtheorderHomogeneouscatalysis>FeUSY>Fe
MMT>FeClinoptilolite.
Table3.18.Apparentfirstorderrateconstant(Ka)fortheexchangedmaterials
Catalyst
Ka(min1)
R2
[Fe3+]solution(mg/L)
Homogeneouscatalysis
0.368±0.02
0.995
17.6±0.5
FeUSY
0.311±0.009
0.998
<0.2
FeMMT
0.143±0.003
0.998
0.5±0.2
FeClinoptilolite
0.041±0.002
0.994
1.4±0.2
In this regard, the reaction kinetics for FeUSY is comparable to the homogeneous
catalysis, whilst FeMMT and Feclinoptilolite showed slower kinetics. These results can be
associatedwiththeamountofFeloadedaftertheFeexchanging(nottothetotalFecontent),
thatindicatesthatthestructuralFepresentinMMTandclinoptiloliteisnotaccessibletoactas
catalyst of the Fenton reaction. Moreover, negligible amounts of Fe were released from the
Feloadedmaterials,assmallamountsofFewerefoundinsolutionafterthereaction,sothe
reactionmainlyoccursduetotheFelinkedtothesupport(Table3.18).
121
3.Resultsanddiscussion
Fromthevaluesobtainedat324nm(Figure3.38),mostlyrelatedtothedegradationof
naphthalene,andthustothemineralizationofthedye,itcanbeobservedthatthemaximum
degradationachievedwas95%,thusthemineralizationofAR14wasnotcomplete,evenafter
90minofreaction.Inthisregard,theanalysisbyGCMSofthesolution,afterbeingtreated,
revealed only oxalic acid and malonic acid as degradation products. These compounds are
refractorytooxidationbyFentonprocessesasithasbeendemonstratedthatlowchainacids
aredifficulttodegradebytheradicalhydroxyl[24].ItisalsonoteworthythatFeUSYisableto
achievesimilarkineticstothehomogeneouscatalysisprocess(maximummineralizationat20
min for both) whereas FeMMT and Feclinoptilolite lasted 25 and 50 min respectively to
achievemaximummineralization.
To complement the feasibility of the Feloaded materials studied to degrade organic
pollutants,thedegradationoftworefractoryorganiccompounds,aceticacidandphenol,was
alsoevaluated.Suchdegradationwasfollowedbymeasuringchemicaloxygendemand(COD)
as a measure of the amount of organic compounds in water. The results obtained for the
degradation of the model acetic acid solution (COD=5300 ppm) and phenol solution
(COD=11900 ppm) for the Feloaded catalysts together with the results obtained for the
homogeneouscatalysisandtheamountofFeinsolutionaregiveninTable3.19.
Table3.19.AceticacidandphenolremovalbyFesupportedmaterialsandhomogeneouscatalysis
AceticCODremoval PhenolCODremoval
[Fe3+]solution(mg/L)
USY
34.6±5%
93±2%
0.9±0.4
MMT
37.8±3%
94±4%
2.5±0.4
Clinoptilolite
30.5±4%
87±2%
1.2±0.4
Homogeneouscatalysis
25±4%
85±3%
18±2
Mineralization of acetic acid is partly achieved for all heterogeneous catalysts and the
homogeneouscatalysis,withaCODdiminutionof2040%.Comparingthevaluesobtainedfor
each of the Feexchanged materials it can be observed a higher performance over the
homogeneous catalysis. All the Feloaded materials reached almost 30% of COD degradation
whilst homogeneous catalysis only achieved 25% of COD removal. Regarding the values
obtained for phenol, almost complete mineralization of the solution was achieved reaching
about 90% of COD removal. Again, the results obtained for the Feexchanged catalysts were
higher than for the homogeneous catalysis. Among the supported catalysts, FeUSY and Fe
MMTachievedhigherCODremovalthanclinoptilolitemainlyduetotheirhigherFecontent.
Thesevaluesaresimilartothosereportedbefore[25],where20%and96%ofCODremoval
wereachievedforaceticacidandphenol,respectively,indicatingthattheprocessisalsoviable
for the removal of persistent organic pollutants. Moreover, iron hydroxyoxides were not
122
3.Resultsanddiscussion
formed, so there was no need to remove the red sludge caused by iron hydroxyoxides as it
happenedwhenusingthehomogeneouscatalysis.
Finally,columntestsapplyingsimilarconditionstothoseemployedinbatchexperiments
wereperformed.Giventhesmallparticlesizeofthematerialsusedinbatchexperiments,the
columnblockedavoidingthecirculationofthesolution.However,giventhenaturaloriginof
the clinoptilolite zeolite, it was able to be grind and milled to obtain different grain sizes
allowingitsuseincolumn.As,theclinoptiloliteatgrainsize0.22mmdidnotblockthecolumn,
itwasemployedforcolumnexperiments.Inthissense,threecolumnswerefilledwith4.6gof
clinoptilolitegrainsize0.22mmandloadedwithironbycirculating100mlofFe(NO3)30.05M
at room temperature at 2mL/min in countercurrent. The amount of Fe introduced into the
clinoptilolitewas1.0±0.1%wt,equaltotheobtainedforthebatchprocessusingclinoptilolite
finegrainsize.
Discoloringof100mLAR140.05mMwasdoneoveroneofthecolumnsobtainingtotal
discoloring of the dye in less than 15 minutes and achieving kinetics of discoloring
(Ka=0.365±0.02,R2=0.993)comparabletothehomogeneouscatalysisandtheFeUSYmaterial.
This high discoloring kinetic is explained by the fact that in column processes the solution
contacts several times with fresh catalyst along the column. In that case, the amount of Fe
loaded into the zeolite is not the key factor for the Fenton reaction, due to in column
processesthecontactofsolutionandcatalystisenhanced.Moreover,whenthereactionwas
done over 100 mL of acetic acid or 100 mL of phenol, COD removal was 29±4 and 92±4,
respectively, thus providing similar COD removal than the batch process with the other
materialsstudiedandthehomogeneouscatalysis.ThisfactdemonstratesthefeasibilityofFe
loadedclinoptiloliteasheterogeneousFentoncatalystsalsoincolumn.
123
3.Resultsanddiscussion
3.5.2.FELOADEDMATERIALSAPPLIEDTOARSENICREMOVAL
Arsenic contamination in groundwater generates widespread human health disasters
around the world (especially in Southeast Asia). In this sense, besides their application as
catalysts in Fenton processes, Feloaded materials can be also employed for the removal of
arsenic given the affinity of Fe compounds with arsenic. In this sense, three different Fe(III)
bearing materials namely zeolite USY (UltraStable Steamed Y zeolite), zeolite Y (ZY) and
Foragersponge(Sp)havebeentestedasarsenicsorbents.Inthisregard,thecharacterization
of these materials by FPXRF and XAFS techniques can shed light onto the different sorption
mechanismsofarsenicintosuchmaterials.
Zeolite USY (USY), zeolite Y (ZY) and Forager sponge (Sp) were loaded with Fe(III)
following the methodology described in section 3.5.1 to obtain the materials USY3, ZY3 and
Sp3.Inthissense,underthesameconditions,ZYachievedgreaterFecontentthanUSYorSp
(Table3.20).Asitwasconcludedintheprevioussection,theloadingofFeintothematerialsis
stronglyrelatedtoitsspecificsurfacearea,thusaszeoliteYhasaspecificsurfaceareahigher
than zeolite USY, its Fe loading was superior (Specific surface area: USY=730 m2/g; ZY=900
m2/g).AlthoughspecificsurfaceareaplaysanimportantroleontheloadingcapacityofFeon
the materials, it has to be taken into account also the number of functional groups. Thus,
although Forager spongeis has less surface area than zeolites (Specific surface area= 1015
m2/gaccordingtoproducer)thehighcontentoffunctionalgroupsallowshigherFeloadings.
Afterthearsenicsorptionprocess,itcanbeobservedthatforbothstudiedzeolites,arelation
between the As sorbed and the content of iron can be depicted (equal As:Fe ratio).
Nevertheless,Foragersponge,hasanAs:Feratiohigherthanforthezeolitesmainlyowedto
tertiaryaminesaltgroupscontainedinthespongethatcanbindanioniccontaminants,suchas
arsenic,chromateoruraniumoxidespecies.
Material
USY3As
ZY3As
Sp3As
Table3.20.FeandAscontentoftheUSY,ZYandsponge
[Fe](mg/Kg)
[As](mg/Kg)
%ArsenicAdsorption As:Feratio
41±3
46,000±50
16,560±30
0.4
90±5
88,930±70
36,140±40
0.4
74±4
46,730±50
29,640±40
0.6
A better understanding of the differences regarding arsenic sorption onto those
materials is given by the analysis of EXAFS spectra. In this sense, the theoretical paths from
scorodite (FeAsO42H2O) were used to determine bond lengths and coordination numbers
regarding to the presence of FeAs bonds in its structure. The goodness of these paths was
validated by the calculation of bond lengths and coordination number for rösslerite
(MgHAsO47H2O)andferrihydrite(Fe2O30.5H2O),thestandardsmeasuredatthesynchrotron
124
3.Resultsanddiscussion
as scorodite was not available. The results obtained by using the theoretical paths for the
fitting of the experimental spectra of scorodite were concordant with the theoretical known
values (Table 3.20 and Figure 3.38). In this sense, the theoretical values for rösslerite are 2
coordinationshellscontaining2Oxygenatomseachat1.66
and1.70
,respectively,while
theexperimentalresultsobtainedwere1coordinationshellcontaining3.6±0.3Oxygenatoms
at1.70
.Giventhatthedistancesandthecoordinationnumberareverysimilar,itcanbesaid
thatthepathsarecorrectandcanbeusedtofitthespectraoftheunknownsamples.
Table3.20.Theoreticalandfitvaluesforrösslerite
Theoreticalvalues
Fitresults
R(
)
CN
R(
)
CN
(103
2)
1.66
2
1.70±0.01 3.6±0.3
3.7±1
1.70
2
AsO
AsO
E0(eV)
1.4±1.7
2.5
Rosslerite, As(V) standard
2
1.5
1.5
1
0
0.5
-0.5
x(k)·k2
FT(X(k)·k2)
1
0.5
-1
-1.5
Rosslerite, As(V) standard
R2=8.7%
0
-0.5
-1
-2
-1.5
-2.5
0
1
2
3
4
5
6
7
r, A
-2
8
2
4
6
8
10
12
k, A
2
Figure3.38.EXAFSspectraforRosslerite.a)Fourier–transformedspectra(k weighted)andb)AsK
edgespectra.(mink=3.89;maxk=11.54;minR=0.57;maxR=2.04)
ThedistancesandcoordinationshellsobtainedforeachoftheFeloadedmaterialsusing
the rösslerite paths are given in Table 3.21. The spectra and fit spectra for arsenic adsorbed
ontoUSY3aregiveninFigure3.39,forarsenicadsorbedontoZY3inFigure3.40andforarsenic
adsorbedontoFeloadedspongeinFigure3.41.
Table3.21.FitresultsforSp3,USY3andZY3firstandsecondcoordinationshells
Coordination
Material
R()
CN
(103Å2)
E0(eV)
shell
st
1 =AsO
1.69±0.02
4.2±0.2
3
2±2
Sp3As(R=14.6%)
2nd=AsFe
3.23±0.07
2.4±0.9
8
3±7
1.69±0.02
4.7±0.2
3
2±2
1st=AsO
USY3As(R=12.0%)
2nd=AsFe
3.19±0.05
3.3±0.8
8
8±5
1.69±0.02
4.4±0.2
3
3±2
1st=AsO
ZY3As(R=12.3%)
2nd=AsFe
3.21±0.05
3.4±0.8
8
5±5
The first coordination shell around As (AsO) is at similar distance and coordination
numbersarealmostequalforallthematerials(Sp3,USY3andZY3).Themaindifferencesare
observed for the second coordination shell (AsFe), which is at the same distance for all the
125
3.Resultsanddiscussion
materialsalthoughthecoordinationnumberisslightlyhigherforbothzeolites(USY3andZY3)
than for the sponge (Sp3). Such fact can be attributed to the As in the sponge which is not
coordinated to the Fe loaded but coordinated to the amine groups, so the coordination
numberisdecreased.
4
USY3-As
3
2
2
USY3-As
R2=8.7%
1.5
1
0.5
0
x(k)·k2
FT(X(k)·k2)
1
-1
0
-0.5
-2
-1
-1.5
-3
-2
-4
0
1
2
3
4
5
6
7
-2.5
8
2
r, A
4
6
8
10
12
k, A
Figure3.39.USY3As.a)Fouriertransformedspectra(k2weighted)andb)AsKedgespectra.
Themodelfitsareshownasgreyline.(mink=3.89;maxk=11.54;minR=0.57;maxR=3.07.
Constraints:1=0.003
2;2=0.08
2)
4
ZY3-As
3
2
2
ZY3-As
R2=8.7%
1.5
1
0.5
0
x(k)·k2
FT(X(k)·k2)
1
-1
-2
0
-0.5
-1
-1.5
-3
-2
-4
0
1
2
3
4
5
6
7
-2.5
8
2
r, A
4
6
8
10
12
k, A
Figure3.40.ZY3Asa)Fouriertransformedspectra(k2weighted)andb)AsKedgespectra.
Themodelfitsareshownasgreyline.(mink=3.89;maxk=11.54;minR=0.57;maxR=3.07.
Constraints:1=0.003
2;2=0.08
2)
4
Sp3-As
3
2
2
Sp3-As
1.5
1
0
2
0.5
x(k)·k2
FT(X(k)·k2)
1
-1
-2
0
-0.5
-1
-1.5
-3
-2
-4
0
1
2
3
4
r, A
5
6
7
8
-2.5
2
4
6
8
10
12
k, A
Figure3.41.Sp3Asa)Fouriertransformedspectra(k2weighted)andb)AsKedgespectra.
Themodelfitsareshownasgreyline.(mink=3.89;maxk=11.54;minR=0.57;maxR=3.07.
Constraints:1=0.003
2;2=0.08
2)
126
3.Resultsanddiscussion
Different surface species have been observed from EXAFS studies concerning arsenate
adsorption on iron oxides (Figure 3.42). Arsenate can be adsorbed on iron oxides mainly as
bidentate complexes resulting from cornersharing between AsO4 tetrahedra and two FeO6
octahedra (namely 2C). Furthermore monodentate complexes from cornersharing between
AsO4tetrahedraandFeO6octahedra(namely 1V)werealsoinferred[26].Severalotherstudies
proposedalsobidentateedgesharingbetweenAsO4tetrahedraandafreeedgeofthesame
FeO6 octahedra (namely 2E) [27, 28]. Each type of coordination has a different bond length
(Table 3.22). In this sense, given the distances obtained for the Feloaded materials studied
and the bond length for each type of coordination, it can be inferred that the arsenate is
complexedwiththeFeoftheFeloadedmaterialsasabidentatecornersharingbond.
Figure3.42.Possiblesurfacecomplexesonironoxidehydroxides
Table3.22.Interatomicdistancesaccordingthetypeofcomplex
Nameofcomplex
Typeofcomplex
Bondsharing
RAsFe(Å)
1
V
Monodentatecomplex
Cornersharing
3.6
2
C
Bidentatecomplex
Cornersharing
3.26
2
E
Bidentatecomplex
Edgesharing
2.8
127
3.Resultsanddiscussion
3.6.REFERENCES
[1] El Hachimi, M.L.; El Founti, L.; Bouabdli, A.; Saidi, N.; Fekhoui, M.; Tasse, N. (2007). Pb and As in
miningalkalinewaters:contamination,comportmentandrisks(theZeidaabandonedmine,Morocco).
Rev.Sci.Eau20:113.
[2]Plassard,F.;Winiarski,T.;PetitRamelM.(2000).Retentionanddistributionofthreeheavymetalsin
acarbonatedsoil:comparisonbetweenbatchandunsaturatedcolumnstudies.J.Contam.Hydrol.42:
99–111.
[3]Chuan,M.C.;Shu,G.Y.;Liu,J.C.(1996).Solubilityofheavymetalsinacontaminatedsoils:effectof
redoxpotentialandpH.Water,AirSoilPollut.90:543556.
[4] Gerritse, R.G.; Van Driel, W. (1984). The relationship between adsorption of trace metals, organic
matter,andpHintemperatesoils.J.Environ.Qual.13:197204.
[5]Förstner,U.(1998).IntegratedPollutionControl,editedbyA.WeissbachandH.Boeddicker,81–130.
SpringerVerlag.Germany.
[6] Kim, C. S.; Brown Jr, G.E.;Rytuba, J. J. (2000). Characterization and speciation of mercurybearing
minewastesusingXrayabsorptionspectroscopy.Sci.Tot.Environ.261:157–168.
[7] Kim, C S.; Rytuba, J.J.;Brown Jr, G.E. (2004). Geological and anthropogenic factors influencing
mercuryspeciationinminewastes:anEXAFSspectroscopystudy.Appl.Geochem.19:379–393.
[8] Sumozas, R. (2005). Arquitectura industrial en Almaden: Antecedentes, Génesis y Extensión de un
modelo.PhDthesis,CastillaLaManchaUniversity,Spain.
[9]Gray,J.E.;Hines,M.E.;Higueras,P.;Adatto,I.;Lasorsa,B.K.(2004).MercurySpeciationandMicrobial
TransformationsinMineWastes,StreamSediments,andSurfaceWatersattheAlmadénMiningDistrict,
Spain.Environ.Sci.Technol.38:4285–4292.
[10]Dickson,F.W.;Tunell,G.(1959).Thestabilityrelationsofcinnabarandmetacinnabar.Am.Mineral.
44,471–487.
[11] Higueras, P.; Oyarzun, R.; Biester, H.; Lillo, J.; Lorenzo, S. (2003). A first insight into mercury
distributionandspeciationinsoilsfromtheAlmadenminingdistrict,Spain.J.Geochem.Explor.80,95–
104.
[12]Kocman,D.(2008).MassbalanceofmercuryintheIdrijcarivercatchment.PhDthesis,JozefStefan
InternationalPostgraduateSchool,Slovenia.
[13] Mazuelos, A.; Carranza, F.; Palencia, I.; Romero, R. (2000). High efficiency reactor for the
biooxidationofferrousiron.Hydrometallurgy58(3):269275.
[14] Carranza, F.; Iglesias, N.; Romero, R.; Palencia, I. (1993). Kinetics improvement of highgrade
sulfidesbioleachingbyeffectsseparation.FEMSMicrobiol.Rev.11(13):129138.
[15] Grinbaum, B. (1992). How to fit a simulator to a real liquidliquid extraction plant, in: Sekine, T.
(Ed.),SolventExtraction.12351240.Elsevier.TheNetherlands.
[16] Grinbaum, B. (1993). The development of liquidliquid extraction processes using simulation”, in:
Longsdail, D.H. and Slater, M.J. (Eds.), Solvent Exttraction in the Process Industries. 715722. Elsevier.
TheNetherlands.
[17]Gottliebsen,K.;Grinbaum,B.;Chen,D.;Stevens,G.(2000).Recoveryofsulfuricacidfromcopper
electrolytebleeds.Hydrometallurgy.56:293307.
[18] Tsakiridis, P.E.; Oustadakis, P.;Katsiapi, A.; AgatziniLeonardou, S. (2010). Hydrometallurgical
processforzincrecoveryfromelectricarcfurnacedust(EAFD).PartII:Downstreamprocessingandzinc
recoverybyelectrowinning.J.Hazard.Mater.179:8–14.
[19] Schwertmann, U.; Cornell, R.M. (2000). Iron oxides in the laboratory. Preparation and
characterization.2ndedition.VileyVCH.Germany.
[20]Neamtu,M.;Zaharia,C.;Catrinescu,C.;Yediler,A.;Macoveanu,M.;Kettrup,A.(2004).FeloadedY
zeoliteascatalystforwetproxideoxidationofreactiveazodyeProcionMarineHEXL.AppliedCatalysis
B:Environmental48:287294.
[21]Pretsch,E.;Bühlmann,P.Affolter,C.;Herrera,A.;Martínez,R.(2001).Determinaciónestructuralde
compuestosorgánicos.Springer,Barcelona.
128
3.Resultsanddiscussion
[22] Gumy, D.; FernandezIbañez, P.; Malato, S.; Pulgarin, C.; Enea, O.; Kiwi, J. Supported Fe/C and
Fe/Nafion/C catalysts for the photoFenton degradation of Orange II under solar irradiation, Catal.
Today101:375382.
[23]Lei,J.;Liu,C.;Li,F.;Li,X.;Zhou,S.;Liu,T.;Gu,M.;Wu,Q.(2006).PhotodegradationoforangeIin
theheterogeneousironoxideoxalatecomplexsystemunderUVAirradiation,J.Hazrad.Mat.137:1016
1024.
[24]Franch,M.I.;Ayllón,J.A.;Peral,J.;Doménech,X.(2004).Fe(III)photocatalyzeddegradationoflow
chaincarboxylicacidsimplicationsoftheironsalt.AppliedCatalysisB:Environmental50:8999.
[25] Hermosilla, D; Cortijo, M.; Huang, C.P. (2009). The role of iron on the degradation and
mineralizationoforganiccompoundsusingconventionalFentonandphotoFentonprocesses.Chemical
EngineeringJournal155:637646.
[26]Waychunas,G.A.;Rea,B.A.;Fuller,C.C.;Davis,J.A.(1993).Surfacechemistryofferrihydrite:Part1.
EXAFS studies of the geometry of coprecipitated and absorbed absorbed arsenate. Geochim.
Cosmochim.Acta57:22512269.
[27]Fendorf,S.;Eick,M.J.;Grossl,P.;Sparks,D.L.(1997).Arsenateandchromateretentionmechanisms
ongoethite:I.Surfacestructure.Environ.Sci.Technol.31:315320.
[28]Fuller,C.C.;Davis,J.A.;Waychunas,G.A.(1993).Surfacechemistryofferrihydrite,Part2,Kineticsof
arsenateadsorptionandcoprecipitation:Geochim.Cosmochim.Acta57:22712282.
129
130
4
CONCLUSIONS
131
132
4.Conclusions
Considering the objectives of this thesis and after the studies conducted, the results
described throughout the present research are new contributions on solving real
environmental problems: contaminated soils surrounding mine areas and industrial
contaminatedwaters.Thefollowingarethemostimportantconclusionstobedrawnfromthe
resultsobtainedinthedifferentworkscontainedherein.
x
FieldPortableXRayFluorescence(FPXRF)spectrometryhasbeenaneffectivetoolto
characterizesoilsamplesfromfourMoroccanminesites.Inthissense,theapplication
ofGeographicInformationSystems(GIS)let toproducemapsrevealing the pollution
trends in these areas. Likewise, XRay Absorption Spectroscopy (XAS) has been
successfully applied to determine the mercury speciation in soil samples from three
mainEuropeanmercurymines.
x
The pilot plant study to recover zinc from a mine tailing pond has been carried out,
obtainingproperresultsforaZnpricequotationaboveUS$2/kg.Thevalueofthezinc
productcoversthewholetreatmentwhileanenvironmentalproblemissolved.
x
Various Feloaded materials have been tested as Fenton catalysts and arsenic
sorbents.TheresultsobtainedwhenusedasFentoncatalystswerecomparabletothe
homogeneous catalysis while avoiding the loss of the catalyst and the generation of
red mud. Its application as arsenic sorbents achieved high rates of arsenic sorption.
The application of XAS techniques applied to the adsorption of arsenic by Feloaded
materialslettocharacterizethesorptionofarsenicontheseFeloadedmaterials.
More specific conclusions driven from the obtained results for each of the studies
performedinthisthesisaresummarizedas:
HeavyMetalContaminationandMobilityattheDraaLasfarminearea:
x
RegardingCERvaluescalculatedusingtheFPXRFresults,arsenic,copper,leadandzinc
canbedistinguishedasthemainpollutantsofthemineareawhilstBa,Fe,K,Rb,Sr,Ti
canbeconsideredlithogeniccomponents.
x
Themostpollutedsitesarefoundbesidetheminesitetowardstherivercreekwhilst
samplesclosedtoKoudiyathillreportedvaluessimilartobackground
x
GIScontourmapsshowedasimilardistributionforAsandCu,aswellasforPbandZn.
The most contaminated sites were at the vicinity of the mine, especially at the
northwestarea,probablylinkedtoweatheringeffectsandtopographyofthearea.
133
4.Conclusions
x
The leading factor regarding mobility of the samples at Draa Lasfar mine area is
concentrationofmetalsandorganicmatter(basedonLOIdeterminations).However,
giventhelowmetalcontentonthemobilephase,itmaybeconsideredlowerriskthan
expectedwhentakingintoaccountonlytotalconcentrationvalues.
CharacterizationofKettara,SidiBouOthmaneandBirNehassmines:
x
LikewiseDraaaLasfar,As,Cu,PbandZnarethemainpollutantsatthethreeminesites
regardingitsCERvalues.
x
Thelevelofcontaminationofeachmineisstronglydependentontheexploitationtime
sincetheannualextractionforeachminewassimilar.
x
Samplestakenatresiduesdepositsarehighlypollutedcomparedtosamplestakenat
theminearea.TheseresultswerecorroboratedbyboxplotrepresentationsandPCA.
x
The samples with high content of lead and zinc present high concentration of these
elements in the mobile phase, so it can be concluded that the high concentration of
metalsexceedthecapacityofthesoiltoretainthem.
XANES speciation of mercury in three mining districts: Almadén, Asturias (Spain), Idria
(Slovenia):
x
This work represents the first interregional study of mercury speciation of the two
main European Hgmining districts (Almaden and Idria), and a polymetallic district
locatedinAsturias.
x
XANES revealed that rather insoluble mercury compounds (cinnabar, metacinnabar,
schuetteite, corderoite) prevail in dumps and wastes from mines and metallurgical
plants,whereasmoresolubleHgphases(mainlyHgCl2butalsoHgOandHgSO4)were
foundinsoilsandsedimentsfromalltargetareas.
x
It can be established from the results from the three districts, that the presence of
mercurychloridesorsulfatescanberelatedtomobilemercury.
x
The type of metallurgical processing arises as one of the most important factors in
defining mercury mobility as less efficient roasting treatment (lower roasting
temperatures and poorer recovering rates) increases the presence of metacinnabar
and,principally,HgCl2phasesresponsibleforthemobilityofmercury.
134
4.Conclusions
x
Nonetheless, the absolute ‘mobility’ remains relatively low in most cases, inherently
suggesting that kinetic effects and availability of the soluble phases might also be
consideredintheassessmentofmercurybehavior.
Extractantandsolventselectiontorecoverzincfromaminingeffluent:fromlaboratoryscaleto
pilotplant:
x
OpposedtoDEHPA,Cyanex272andIonquest290providehighzincselectivitytowards
calciumandnegligibleamountsofmetalsarefoundintheorganicphaseavoidingthe
regenerationoftheorganicphasestep.
x
Ionquest 290 is considered the best extractant amongst the three studied due to its
higherselectivitycomparedtoDEHPAandahigherZincrecovery(5–10%)thanCyanex
272.
x
Amongst the studied solvents Ketrul D80 and Ketrul D100, the latter is the
recommendedduetoitslowervolatilityandflammability.
x
The pilot plant has proven the feasibility of the process as the zinc recovery is up to
95%andlessthan50mg/Lareleftintheraffinate.Thestrippingisefficientandonlya
singlestageatO:A=20isrequiredtoachieveatransferof40g/L.
FeloadedmaterialsappliedasFentoncatalysts:
x
An enhanced methodology using mild conditions to achieve high Feloadings into
zeoliteUSYispresentedinthisthesis.Inthissense,1hofcontactwithzeoliteUSYand
a 0.05M Fe(NO3)3 solution at room temperature reached higher loadings than when
increasedcontacttimesandconcentrationsolutionswereemployed.
x
Other low cost materials with exchange properties such as montmorillonite clay and
natural zeolite clinoptilolite have also been loaded with the enhanced methodology
appliedtozeoliteUSYobtainingalsohighFeloadings.
x
Thetreatmentofasyntheticdyesolution(AcidRed14)byFentonreactionusingthe
aforementionedFeloadedmaterialsachievedtotaldecolorization.
x
Moreover,usingsuchFeloadedmaterialsasFentoncatalyst,theremovalofCODfrom
solutions containing acetic acid and phenol is ca. 30% and 90%, respectively, results
thatareevenhigherthanthoseobtainedwhenusinghomogeneouscatalysis.
135
4.Conclusions
x
The process in column was also tested for the clinoptilolite grain size 0.22mm
obtainingdegradationkineticsandCODremovalfromasolutioncontainingaceticacid
andphenolsimilartothehomogeneouscatalysis
x
NosignificantamountsofFearestrippedfromthematerialsattheemployedreaction
conditions.
Feloadedmaterialsappliedtoarsenicremoval:
x
Zeolite USY, zeolite Y and Forager sponge loading of Fe is strongly related to the
surface area, specific sites and functional groups. In this regard, zeolite Y achieved
greaterFecontentthanzeoliteUSYortheForagersponge.
x
Arelationbetweentheadsorbedarsenicandthecontentofironcanbeobservedfor
both zeolites (As:Fe=0.4). Nevertheless, Forager sponge As:Fe ratio is higher mainly
due to the presence of tertiary amine salt groups in the sponge can bind further
arsenic,inadditiontothearsenicalreadylinkedtoFe.
x
EXAFS spectra inferred that the arsenate is complexed with Fe as a bidentate corner
sharingcomplex.
With this thesis, the line of work of our investigation group concerning environmental
problemsandtheuseofnoveltechniquestoitscharacterizationisbroadenedby:
x
TheuseofGeographicInformationSystemstodeterminespatialvariabilityofsamples.
x
TheapplicationFPXRFandEXAFStechniquestothecharacterizationofsoilsandsolid
materials.
x
136
TheemploymentofFeloadedmaterialsasFentoncatalysts.
I
HEAVYMETALCONTAMINATION
ANDMOBILITYATTHEMINEAREA
OFDRAALASFAR(MOROCCO)
MartaAvila,GustavoPerez,MouhsineEsshaimi,LailaMandi,
NaailaOuazzani,JoseL.BriansoandManuelValiente.
TheOpenEnvironmentalPollution&ToxicologyJournal.
AcceptedManuscript
ACCEPTED MANUSCRIPT
Heavy Metal Contamination and Mobility at the Mine Area of Draa
Sfar (Morocco)
Marta Avilaa, GustavoPereza, Mouhsine Esshaimib, Laila Mandib,c, Naaila Ouazzanib, Jose L. Briansod
and Manuel Valiente*a
a
5
Centre GTS. Chemistry Department, Universitat Autonoma de Barcelona, 08193 Spain.
b
Laboratoire d'Hydrobiologie, Ecotoxicologie et Assainissement (LHEA), Faculté des Sciences Semlalia, Université Cadi Ayyad,
Marrakech (Morocco)
c
National Center for Studies and Research on Water and Energy, University Cadi Ayyad, BP511, 40 000 Marrakech (Morocco)
d
10
15
20
Geology Department. Universitat Autonoma de Barcelona, 08193 Spain
*Corresponding author phone: +34-935812903; fax: +34-935811985; e-mail: Manuel.Valiente@uab.es.
The present study represents a first insight into the Draa Sfar mine (Marrakech) to assess the possible diffusion of heavy metals
and to predict the risk of their mobility in the surroundings of the mine area. The edaphological parameters pH, electrical
conductivity (EC), loss on ignition (LOI) and CaCO3 were measured according to standard methods, whilst heavy metals
concentration was determined by Field Portable X-ray Fluorescence. Concentration enrichment ratios (CER) were calculated in
order to estimate the anthropogenic contribution of target pollutants determining As, Cu, Pb and Zn as the main pollutants,
whereas Ba, Ca, Fe, K, Mn, Rb, Sr, Ti and Zr were considered lithogenic components. GIS contour maps of pollutants using
CER data, showed the most polluted areas at the vicinity of the mine, especially at the northwest area, probably linked to
weathering effects and topography of the area. Particle size studies established that As, Pb and Zn are part of the mineral ore
while Cu behaviour corresponded to an anthropogenic origin. Additionally, mobility assays employing single leaching tests
indicated a greater mobility of As and Zn rather than that of Pb and Cu due to their lower adsorption process in the soil,
independently of their respective concentration.
Introduction
25
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45
The presence of heavy metals in soils originates
considerable impact on the environment causing damages to
microflora, flora and fauna, and thus restricting soil use [1].
As a consequence of mining and mineral processing huge
amounts of heavy metals are deposited in waste dumps and
tailings requiring management and monitoring once the
activity has stopped [2]. In Marrakech region, mining activity
represents a high area of activity thus constituting a great
hazard due to the presence of high amounts of heavy metals
related to functioning or abandoned mines. In this concern,
few studies have been done in this area to determine the
heavy metal concentration around mine areas and their impact
on surrounding soil and water resources [3]. In addition, no
detailed investigation has been carried out in the region to
assess the possible mobility of heavy metals in order to
predict the toxicological risk in the surroundings of Draa Sfar
mine area.
In the last years the systematic control of contaminated
areas has become a key issue to define healthcare policies,
cost effective environmental planning and risk assessment
tools [4]. To this purpose the last decade Field Portable X-ray
Fluorescence (FP-XRF) equipments have been applied given
their reliable and rapid heavy metal measurement which
50
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65
70
allows to quickly delineate in situ metal contamination at a
screening level [5, 6]. In addition, high volume of field test can
be monitored to determine the spatial distribution and degree
of heterogeneity of heavy metals in an undisturbed position
while off-site analytical costs are minimized without
destruction of the samples [7, 8]. FP-XRF results can be
applied together with Geographic Information Systems (GIS)
to determine spatial variability in a mine area. Such tools let to
produce maps which are helpful in identifying the sources and
spatial patterns of the pollutants [9, 10, 11].
Moreover, concentration enrichment ratios (CER), also
called enrichment factors, have been used to obtain
complementary reliable information on site risk assessment
[12, 13]. CER, was a concept developed in the early seventies
to derive the origin of elements in the atmosphere,
precipitation or seawater, and was progressively applied to
other environmental materials, such as lake sediments or soils
[14]. In many cases, it was used to determine the contribution
of anthropogenic emissions to trace element fluxes [15] (Table
1).
Besides the concentration, toxicity and impact of heavy
metals in soils and sediments is mostly determined by its
mobility and availability [16]. The fate and transfer of these
metals is a complex process that depends on the soil
mineralogy as well as to physicochemical transport processes.
Over the last 30 years, sequential extraction schemes (SES)
ACCEPTED MANUSCRIPT
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20
25
30
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40
have been the main tools employed to evaluate the
availability of the contaminants in soils, sediments and sludge
[17, 18, 19, 20]. SES represent a chemical scheme which tries
to mimic the various natural conditions under which soils
may release metals into the water resources thereby providing
an indication of the potential bioavailability of those metals.
On the other hand, leaching tests such as (NH4)2SO4 or HCl
single non-selective extractions methods, can provide also a
useful assessment for screening purposes to identify labile or
mobile phases [21, 22]. The main advantages of these single
leaching tests against SES are mainly related to their cost
efficiency, easy to use and a reduction on bias induced by
sequential translation and accumulation of procedural errors.
In this sense, the main aims of the present study focuses on
(i) a geochemical characterization of the Draa Sfar mine area
in order to identify pollutants and lithogenic components
present in the soils affected by the mining activity; (ii) the
generation of distribution maps of pollutants at the mine area,
(iii) the evaluation of particle size effects, such as
intraparticle concentration affecting metals distribution and
(iv) the assessment of the pollutants mobility employing
single leaching tests.
Table 1. Anthropogenic contribution at different CER values
CER
<2
2-5
5-20
20-40
>40
Sampling description
Experimental
In order to assess the impact of the Draa Sfar mine residues
on the surrounding environment, a total of 85 samples were
collected in the vicinity of the mine covering 230 ha through 8
sampling lines oriented towards specific receptor media
(Tensift river creek, Koudiyat Tazakouit hill, village, farms,
etc.). Two samples were taken at the other side of Tensift river
creek (samples 21 and 22) and 4 representative background
samples (from 82 to 85) at 1 km from the mining site in order
to avoid mining contamination.
Samples were taken every 50 meters from the upper 20 cm
after removing the first layer of surface soil (2 cm) within an
area of 100 cm2 per sample. Collected samples were air-dried
at 30 C during 48 hours, sieved to remove large debris through
a 2 mm stainless steel sieve and stored in plastic bottles for
their transportation to the laboratory.
Site description
Sample analysis and data treatment
Draa Sfar mine is located a few hundred meters from the
Tensift River, close to a rural community of about 5790 ha of
which 65% are occupied by farmland. The climate is
Mediterranean, bordering arid and semi arid with an average
annual precipitation of 231 mm (10 years). Temperatures are
characterized by great daily and seasonal variation with an
average value of 11.5 C in January and 28.8 C in July. Draa
Sfar mine, involves a deposit of pyrite mineral located 10 km
west of Marrakech city (Fig. 1) can pose a risk for the
environment due to discharge of tailings all around the mine
area. Draa Sfar was discovered in 1953 although their
commercial exploitation did not begin until 1979. Mineral
was processed by flotation after primary and secondary
crushing and grinding producing 59516 tons of products in
the first two years (1979-1980) [23]. Industrial activity
stopped in March 1981, although activity restarted in 1999
due to its great resource of poly-metallic components (As, Cd,
Cu, Fe, Pb, Zn).
50
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60
65
70
75
80
85
90
45
Anthropogenic contribution
Minimal or nule
Moderate
Significant
Strong
Extreme
Figure 1. Location of Draa Sfar mine.
95
The physical characterization consisted in the determination
of the soil pH, the electrical conductivity (EC), the loss on
ignition (LOI) and the carbonate content of the samples
according to standard methods [24]. The pH was measured in a
soil suspension (2g/5 ml of distilled water stirred vigorously)
after 2 h of deposition using a pH-meter (Model WTW
Multiline P4 Universal pH-meter cabled Sen-Tix 92T pH
electrode, Germany). EC was determined in a soil saturated
paste (1g/5 ml of distilled water) with a conductimeter (Model
WTW Multiline P4 Universal Standard Conductivity Cell
TetraCon® 325, Germany), once corrected to the working
temperature (20 ºC). LOI was determined gravimetrically after
volatilization of organic matter on a furnace at 550°C during
4h. For the total carbonate content three replicates of each soil
were stirred during 6 h in HCl 4 mol/L solution (1.0g of soil
per 20 ml of HCl 4.0 mol/L solution) and, after filtering,
calcium was measured by flame spectroscopy (Model
JENWAY-PFP7, UK). For the chemical characterization, an
aliquot of each sample was encapsulated and covered with
Mylar® film prior to their analysis with FP-XRF (Innov-X
Systems, model Alpha-6500R, Woburn, MA, USA). A soil
standard NIST 2710 and a SiO2 blank were measured for
corrections and three replicates were measured for each
sample. The most contaminated samples were selected for the
particle size effect and mobility assays studies. For the particle
size effect studies, samples were milled and sieved below 100
μm for analysis of the fraction below 100 μm by FP-XRF.
Mobility assays were performed by applying a established
methodology [25] consisting on sample extraction with HCl
0.5 M during 1h under magnetic stirring. After each extraction,
the suspension was centrifuged and the supernatant was
filtered using 0.22 μm filters (Millex GS, Millipore, Ireland).
ACCEPTED MANUSCRIPT
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The extracts were analyzed by means of Inductively Coupled
Plasma-Optical
Emission
Spectroscopy
(ICP-OES)
(ThermoElemental model Intrepid II XLS, Franklyn, MA,
USA). In order to assess the impact of the Draa Sfar mine
residues on the surrounding environment, a total of 85
samples were collected in the vicinity of the mine covering
230 ha through 8 sampling lines oriented towards specific
receptor media (Tensift river creek, Koudiyat Tazakouit hill,
village, farms, etc.). Two samples were taken at the other side
of Tensift river creek (samples 21 and 22) and 4
representative background samples (from 82 to 85) at 1 km
from the mining site in order to avoid mining contamination.
CER indicators were calculated considering the
concentration of a given element, namely Cn, in both target
and background samples, normalized with respect to a
lithogenic conservative element such as Al, Zr or Ti, which is
accurately determined in each sample. Rubio et al. [12]
recommended the use of regional background values. While
the geochemical background values are constant, the levels of
contamination vary with time and places. Background values
are distinctly different among different soil types, especially
with respect to Na, Mg, Al, K, Ca, Ba, Sc, Ti, Fe and Zr [13].
Zr was selected as lithogenic element due to homogeneity of
Zr concentration in all samples and backgrounds.
60
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75
80
25
CER
30
35
40
45
50
n
ª C sample º
« n
»
«¬ C Zr sample »¼
ª C Background º
»
« n
C Background »
¼
¬« Zr
The evaluation of the extent and distribution of
contamination was carried out by using Geographic
Information Systems (GIS) [26, 27, 28] which in addition,
allowed the detection of the areas requiring monitoring or
even treatment. GIS maps for the distribution of target metals
around the mine area were done by Miramon v6.4 - Complete
Geographical Information System and Remote Sensing
software [29]. Different interpolation methods can be used to
determine spatial variability such as inverse distance
weighting (IDW), Kriging and spline functions [30]. While
spline method involve a considerable interpolation error when
there are large changes in the surface values within a short
horizontal distance, kriging method may not be met in
practice unless employing 100 samples in order to obtain a
reliable variogram that correctly describes spatial structure. In
contrast, IDW interpolator assumes that each input point has a
local influence that diminishes with distance [31], and no
assumptions are required for the data, being this method the
most suitable for our irregular sampling [32].
85
Heavy metals content
90
95
100
105
Results and discussion
Soil properties
55
pH, LOI and carbonate content [33] are geochemical soil
characteristics able to provide sufficient information to
understand the soils capacity to retain heavy metal pollutants.
(Numerical values on pH, EC, LOI and CaCO3 for each
sample can be found on Table S1 of Supplementary material).
The results obtained for the soil pH measurements, depicted in
GIS (Fig. 2) revealed that, in general, all sampled points
presented a neutral to alkaline pH ranging from 7 to 9, similar
to background samples with the exception of a very acidic
sample corresponding to sample D48 with a pH 3.47. pH
variations seemed to be related to heterogeneous deposits of
sulfidic residues in the surroundings of the mine which by
oxidation and formation of sulfuric acid can cause a decrease
of the pH.
EC showed more variability than the pH, with EC values
ranging from 100 to 15.000 μS/cm (Fig. 3). In general, these
results are correlated with previous studies carried on Morocco
soils [34]. A decreasing salinity gradient was also observed
and the values obtained for the mine area samples are
significantly higher than for the background samples which
indicate high amounts of labile ions close to the mine area. A
hot spot located at sample D31 with an EC of 14.160 μS/cm
was observed mainly due to high amounts of metals present in
this area. Mine area and background samples LOI values (Fig.
4) have similar values ranging from 13 to 75 g/Kg, except
some points where LOI could reach 76 g/kg due to some close
localized agricultural activities. The observed carbonate
content ranged from 10 to 210 mg.g-1 (Fig. 5) although the
majority of the samples present similar CaCO3 content to
background samples. The highest values are observed for
samples D26 and D71, located at 400 m of the mine. Together
with basic pH values, the presence of carbonates in the soil
lead to an increase in the retention of heavy metals, mainly as
carbonate salts as a consequence of precipitation, the principal
retention mechanism of heavy metals [35].
110
From the obtained results employing FP-XRF and the
corresponding CER values, elements can be classified into
pollutants (elements anthropogenically enhanced) or lithogenic
elements (those with CER values similar to background
samples). In this concern, most of the samples have CER
values above 5 for As, Cu, Pb and Zn (Table S2), thus being
considered the main pollutants of the mine area.
Arsenic distribution around the mine area, given in Fig. 6,
showed two hot spots located just beside the mine area
corresponding to samples D48 (3108 ppm, CER=280) and D31
(203 ppm, CER=19,4). Moving away from this area, samples
showed lower As concentration with values similar to
background samples, except samples D45 (203 ppm,
CER=15,9) and D46 (125 ppm, CER=9,2). Sample D48 depicts
a very high arsenic concentration (more than 100 fold higher
than background levels) indicating that remediation is
mandatory for this specific area. An anomalous sampling point
is represented by sample D21 (72 ppm, CER=7,1), proceeding
from the other side of the river, with arsenic concentration
much higher than samples closer to the mine site. Thus, this
area should be under monitoring since is in contact with the
creek waters.
Regarding Cu CER distribution map along the mining area
(Fig. 7) it can be stated that distribution of pollutants followed
the similar trend as As although in a lesser degree of pollution.
ACCEPTED MANUSCRIPT
5
10
Thus, the highest polluted samples are those located close to
the mine, such as samples D20 (80 ppm, CER=2.6), D48 (144
ppm, CER=5.9) and D45 (173 ppm, CER=5.1). Again, sample
D21 (51 ppm, CER=1.9) at the river basin, has a relatively
high copper concentration despite being far from the mine
area. Also, the area close to Koudiyat Tazakouit hill, present
copper concentration similar to background samples, thus
indicates no anthropogenic contribution with copper. The lead
distribution around the mine (Fig. 8) showed 4 hot spots
located around samples D31 (180 ppm, CER=13.0), D45 (770
ppm, CER=45.9), D48 (2310 ppm, CER=130) and D58 (420
15
20
ppm, CER=30). Sample D21 (62 ppm, CER=4.6) should be
also considered due to their high CER values and proximity to
creek waters. CER distribution map for Zn (Fig. 9) followed
the same trend as Pb with 4 hot spots located at samples D20
(630 ppm, CER=8.5), D45 (1110 ppm, CER=13.6), D48 (30
ppm, CER=10.8) and D58 (930 ppm, CER=10.8).
Generally, GIS contour maps of CER for the pollutants
showed the most contaminated at the vicinity of the mine,
especially at the northwest area, probably linked to weathering
effects and topography of the area.
Figure 2. GIS contour map of pH at the mine area
Figure 3. GIS contour map of the electrical conductivity at the mine area
Figure 4. GIS contour map of the loss on ignition (LOI) at the mine area
Figure 5. GIS contour map of the CaCO3 content of the mine area
ACCEPTED MANUSCRIPT
5
10
15
Figure 6. GIS contour map of arsenic distribution around the mine area
Figure 7. GIS contour map of copper distribution around the mine area
Figure 8. GIS contour map of lead distribution around the mine area
Figure 9. GIS contour map of zinc distribution around the mine area
Other elements measured by FP-XRF presented values
close to background samples and, accordingly. a mean CER
value lower than 2, thus considering their origin as lithogenic.
The values obtained for Ba, Fe, K, Rb, Sr, Ti and Zr are
shown on Table 2.
As can be seen from the results on Table 2, mean values of
mine area samples are similar to those of background samples
and, in addition, mean CER values are between 0 and 2
(excepting sample 48 with high Fe content and sample 26
with high Sr content), thus indicating no anthropogenic
enhancement of these elements in the soils analyzed.
Finally, other elements were detected at extremely high
concentration in some samples. High sulphur concentrations
were found in samples D19 (18400 ppm), D31 (14500 ppm),
D33 (15500 ppm), D45 (36800 ppm), D48 (113700 ppm),
D58 (5300 ppm), D59 (14800 ppm) and D70 (32400 ppm).
20
25
30
High arsenic concentrations are also found in some of these
samples supporting the consideration of the arseno-pyrite
nature of the mineral ores. Other elements such as Ag, Au, Bi,
Br, Cd, Co, Cr, Ni, P, Sb or Se were not detected due to the
limits of detection of the FP-XRF.
For the mobility and particle size effect studies, 7 samples
were selected due to their high content on pollutants (samples
D20, D31, D46, D48, D58 and D70) or for their spatial
significance (sample at the other side of the river creek, D21).
Results obtained along with some drinking water quality
standards, are depicted in Table 3.
Results given in Table 3, indicate an increase on both As and
Pb concentration when the samples are milled and sieved
below 100 μm, i.e., samples D20 (from 125 to 167 ppm), D31
(from 203 to 268 ppm), D46 (from 125 to 172 ppm), D48
(from 3108 to 3569 ppm) and D58 (from 113 to 149 ppm) had
ACCEPTED MANUSCRIPT
5
an enrichment on As and also for Pb samples D31 (from 180
to 313 ppm), D46 (from 375 to 477 ppm) and D48 (from 2309
to 2614 ppm) showed an enrichment when milled and sieved.
On the rest of samples, slight differences were found. Thus, it
can be stated that, in general, these elements are forming part
of the particle core which is in agreement of the arseno-pyrite
nature of the mineral ore. The trend followed for Cu is a
diminution of the concentration as the soil is being milled
indicating that instead of forming part of the mineral, Cu is
10
15
adsorbed at the surface of the soil particles thus indicating and
anthropogenic origin. A few exceptions are found such as
samples D31 (from 43 to 77ppm), D48 (from 144 to 167 ppm),
D70 (from 33 to 50 ppm) which, in these cases, may constitute
part of the arseno-pyrite mineral ore. For zinc, slightly higher
concentrations are found when diminishing the particle size of
the soil that support its presence forming part of the mineral
ores.
Table 2. Minimum, maximum and mean concentration and CER values for the lithogenic elements in mine area and background samples.
Ba
Fe
K
Mn
Rb
Sr
Ti
Zr
Conc.
CER
Conc.
CER
Conc.
CER
Conc.
CER
Conc.
CER
Conc.
CER
Conc.
CER
Conc.
Min
221,5
0,4
19,632
0,4
6,071
0,3
290
0,4
47
0,5
86
0,5
2,802
0,4
112
Mine area samples
Background samples
Max
Mean
Min
Max
Mean
541 (D17)
389±70
404
530
449±60
1,4
0,9±0,3
0,9
1,2
1,0±0,1
121,652 (D48)
32,721±10,000
30,769
38,106
35,555±3,000
4,8
0,9±0,5
0,9
1,1
1,0±0,1
32,976 (D81)
23,327±5,000
26,770
30,263
38,244±1,500
1,4
0,8±0,3
0,9
1,1
1,0±0,1
1,119 (D21)
607±150
631
737
699±50
2,0
0,9±0,3
0,9
1,0
1,0±0,1
106 (D10)
76±13
73,4
86,4
81±6
1,7
1,0±0,3
0,9
1,1
1,0±0,1
322 (D26)
144±40
119
140
131±9
3,1
1,1±0,5
0,9
1,0
1,0±0,1
5,860 (D17)
4,460±700
4,431
5,741
5,229±600
1,3
0,9±0,2
0,9
1,1
1,0±0,1
335 (D67)
215±50
199
235
210±17
Conc is given in mg/kg. Max concentration is in parenthesis.
20
25
30
35
40
Regarding the results obtained for the mobility of selected
samples, collected in Table 3, it can be stated that the content
of arsenic, lead and zinc of some samples is higher than the
quality standard regulations while Cu concentration on the
mobile phase is bordering quality standards. Despite being the
most acidic sample (pH=3.5), with high EC (EC=4873
μS/cm) and low CaCO3 content (25,8 mg/g), the most
polluted sample (D48) did not present high metal content on
the mobile phase, thus indicating less danger than expected
when taking into account only total concentration values.
According to literature [36] these conditions favor availability
of cations, but sample D48 has also a high LOI value which
benefits the adsorption of labile ions at the soil what explains
the relatively low mobility of sample D48. The sample
presenting most mobility of pollutants is sample D46, which
is a soil sample alkaline (pH=8.1), with an EC of 2,151
uS/cm, CaCO3 content of 58.4 mg/g and a LOI of 39.3 g/kg.
In these conditions mobility is not favoured but the relatively
low value of LOI regarding sample D48 enable the
availability of cations from the mine ore to the mobile phase.
Therefore, it can be stated that the physico-chemical
parameteres analysed does not correlate with the mobility
results, thus, to assess the toxicological risk of the Draa Sfar
mine area additional specific measurements are required.
Table 3. Results for < 2 mm and <100 μm particle size and fraction of
metal mobile.
50
D20
D21
D31
D46
D48
D58
D70
45
55
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
100µm (mg/kg)
2mm (mg/kg)
Mobility (mg/L)
As
125
167
BDL
72
67
BDL
203
268
49
125
172
54
3,108
3,569
5
113
149
BDL
15
15
29
Cu
80
72
BDL
51
48
BDL
43
77
2
60
59
1
144
167
1
71
77
BDL
33
50
BDL
Pb
55
66
BDL
62
61
BDL
180
313
6
375
477
17
2,309
2,614
BDL
425
537
BDL
24
20
BDL
Zn
628
713
BDL
144
150
BDL
481
734
18
774
933
23
631
704
4
925
1,087
BDL
97
91
BDL
ACCEPTED MANUSCRIPT
Conclusions
5
10
15
20
Draa Sfar mine area has been characterized by determining
various physico-chemical parameters of edaphological
importance, including pH, electrical conductivity (CE),
CaCO3 content and loss on ignition (LOI). Anthropogenic
pollution has been assessed by the use of CER. Thus, As, Cu,
Pb and Zn can be distinguished as the main pollutants of the
mine area. CER values obtained for Ba, Fe, K, Rb, Sr, Ti
indicated its lithogenic characteristic. GIS contour maps of
pollutants using CER data have been a valuable tool to
characterize pollutants distribution around the mine area and
determine sources of contamination. GIS maps showed a
similar distribution for As and Cu, as well as for Pb and Zn.
The most contaminated sites were at the vicinity of the mine,
especially at the northwest area, probably linked to
weathering effects and topography of the area. No
contamination was found in and around Koudiyat Tazakouit
hill. Concerning mobility studies, As, Pb and Zn
concentration in some samples exceeded water quality
standard regulations while Cu concentration on the mobile
phase is on the border. Nevertheless, the most polluted
samples did not present high metal content on the mobile
phase, thus indicating lower risk than expected when taking
into account only total concentration values.
55
60
65
70
75
25
Acknowledgements
30
The present work has been carried with support of the
Spanish Ministry of Science and Innovation (Grant
CTQ2009-07432), the Morocco-Spanish project N°
A/011433/07 “Estudio de la movilidad de metales pesados en
suelos contaminados” and the pole of competences on Water
and Environment (Morocco).
80
85
Notes
90
†Electronic Supplementary Information (ESI) available.
35
40
45
50
References
[1] Adriano DC. Trace elements in the terrestrial
environment, Springer Verlag, New York, U.S., 1986.
[2] Osan J, Kurunczi S, Török S, Van Grieken R. X-Ray
analysis of riverbank sediment of the Tisza (Hungary):
identification of particles from a mine pollution event.
Spectrochim. Acta 2002; Part B 57: 413- 422.
[3] El Adnani M, Rodriguez-Maroto JM, Sbai ML, Loukili
IL,Nejmeddine A. Impact of polymetallic mine (Zn, Pb, Cu)
residues on surface water, sediments and soils at the vicinity
(Marrakech, Morocco). Environ. Technol. 2007; 28: 969-85.
[4].Ferguson C, Kasamas H. Risk assessment for
contaminated sites in Europe. LQM Press University of
Nottingham, Nottingham, U.K., 1999.
[5] Kalnicky DJ, Singhvi R. Field portable XRF analysis of
environmental samples. J. Hazard. Mater. 2001; 83: 93-122.
[6] Kilbride C, Poole J, Hutchings TR. A comparison of Cu,
Pb, As, Cd, Zn, Fe, Ni and Mn determined by acid
extraction/ICP–OES and ex situ field portable X-ray
95
100
105
110
fluorescence analyses. Environ. Pollut. 2006; 143: 16-20.
[7] Harper M, Pacolay B, Hintz P, Andrew ME. A comparison
of portable XRF and ICP-OES analysis for lead on air filter
samples from a lead ore concentrator mill and a lead-acid
battery recycler. J. Environ. Monit. 2006; 8: 384 - 392.
[8] Argyraki A, Ramsey MH, Potts PJ. Evaluation of Portable
X-ray Fluorescence Instrumentation for in situ Measurements
of Lead on Contaminated Land. Analyst 1997; 122: 743 – 749.
[9] Zhai L, Liao X, Chen T et al. Regional assessment of
cadmium pollution in agricultural lands and the potential
health risk related to intensive mining activities: A case study
in Chenzhou City, China. J. Environ. Sci. 2008; 20: 696–703.
[10] Carr R, Zhang C, Moles N, Harder M. Identification and
mapping of heavy metal pollution in soils of a sports ground in
Galway City, Ireland, using a portable XRF analyser and GIS.
Environ Geochem. Health. 2008; 30: 45-52.
[11] Franco C, Soares A, Delgado J. Geostatistical modelling
of heavy metal contamination in the topsoil of Guadiamar river
margins (S Spain) using a stochastic simulation technique.
Geoderma 2006; 136: 852-864.
[12] Sutherland RA, Tolosa CA. Multi-element analysis of
road-deposited sediment in an urban drainage basin, Honolulu,
Hawaii. Environ Pollut. 2000; 110: 483-495.
[13] Bhuiyana MAH, Parvezb L, Islamc MA, Dampared SB,
Suzukia S. Heavy metal pollution of coal mine-affected
agricultural soils in the northern part of Bangladesh. J. Hazard.
Mater. 2010; 173: 384–392.
[14] Reimann C, Filzmoser P, Garrett RG. Background and
threshold: critical comparison of methods of determination.
Sci. Total Environ. 2005; 46: 1–16.
[15] Bourennane H, Douay F, Sterckeman T et al.. Mapping of
anthropogenic trace elements inputs in agricultural topsoil
from Northern France using enrichment factors. Geoderma
2010; 157: 165–174.
[16] Kersten M, Förstner U. Trace element speciation:
analytical methods and problems, ed G.E., Batley, CRC Press,
Florida, 1995; 245-317.
[17] Tessier A, Campbell PGC, Bisson M. Sequential
extraction procedures for the speciation of particulate trace
metals. Anal. Chem. 1979; 51: 844–851.
[18] Kennedy VH, Sanchez AL, Oughton DH, Rowland AP.
Use of single and sequential chemical extractants to assess
radionuclide and heavy metal availability from soils for root
uptake. Analyst 1997; 122: 89-100.
[19] Rauret G, López-Sánchez J, Sahuquillo F, Muntau AH,
Quevauviller P. Indicative values for extractable contents
(mass fractions) of Cd, Cr, Cu, Ni, Pb and Zn in sediment
(CRM 601) following the modified BCR-sequential extraction
(three-step) procedure – Addendum to EUR-report 17554 EN.
Belgium: European Commission. BCR Information Reference
Materials, EUR 19502 EN. 2000.
[20] Sahuquillo A, Rigol A, Rauret G. Overview of the use of
leaching/extraction tests for risk assessment of trace metals in
contaminated soils and sediments. Trends Anal. Chem. 2003;
22: 152-159.
[21] Moreno-Jimenez E, Peñalosa JM, Manzano R, CarpenaRuiz RO, Gamarra R, Esteban E. Heavy metals distribution in
soils surrounding an abandoned mine in NW Madrid (Spain)
ACCEPTED MANUSCRIPT
5
10
15
20
25
and their transference to wild flora. J. Hazard. Mater. 2009;
162: 854–859.
[22] El Gharmali A, Rada A, El Adnani M, Tahlil N, El
Meray M, Nejmeddine A. Impact of acid mine drainage on
the quality of superficial Waters and sediments in the
Marrakesh region, Morocco. Environ. Technol. 2004; 25:
1431-1442.
[23] Ministère de l’énergie et des mines. Délégation
Régionale de Marrakech 1996. Monographie de la province
de
Marrakech
(Géologie,
Mines,
Energie).
[www.minenv.gov.ma/onem/synt_monog_locales/mono_loc_
marrakech.pdf]. [updated and cited 2010 November 1]
[24] Aubert G. Méthodes d’analyses des sols. G.R.D.P.,
Marseille, 1978, 80-83,123-127.
[25] Pérez G, López-Mesas M, Valiente M. Assessment of
heavy metal remobilization by fractionation: comparison of
leaching tests applied to roadside sediments. Environ. Sci.
Technol. 2008; 42: 2309–2315.
[26] Zhai L, Liao X, Chen T et al. Regional assessment of
cadmium pollution in agricultural lands and the potential
health risk related to intensive mining activities: A case study
in Chenzhou City, China. J. Environ. Sci. 2008; 20: 696–703.
[27] Carr R, Zhang C, Moles N, Harder M. Identification and
mapping of heavy metal pollution in soils of a sports ground
in Galway City, Ireland, using a portable XRF analyser and
GIS. Environ. Geochem. Health. 2008; 30: 45-52.
[28] Franco C, Soares A, Delgado J. Geostatistical modelling
of heavy metal contamination in the topsoil of Guadiamar
river margins (S Spain) using a stochastic simulation
30
35
40
45
50
55
technique. Geoderma 2006; 136: 852-864.
[29]
Pons
X.
MIRAMON
website:
[http://www.creaf.uab.es/miramon] (updated and cited 19 April
2010).
[30] Shi W, Liu J, Du Z, Song Y, Chen C, Yue T. Surface
modelling of soil pH. Geoderma 2009; 150: 113-119.
[31] Panagopoulos T, Jesus J, Antunes MDC, Beltrao J.
Analysis of spatial interpolation for optimising management of
a salinized field cultivated with lettuce. Eur. J. Agron 2006;
24: 1-10.
[32] Jongman RHG, Ter Braak CJF, Van Tongeren OFR. Data
analysis in community and landscape ecology, Cambridge
University Press, Cambridge, U.K, 1995.
[33] de Matos AT, Fontes MPF, da Costa LM, Martínez MA.
Mobility of heavy metals as related to soil chemical and
mineralogical characteristics of Brazilian soils. Environ.
Pollut. 2001; 111: 429–435.
[34] El Hachimi ML, El Founti L, Bouabdli A, Saidi N,
Fekhoui M, Tasse N. Pb and As in mining alkaline waters:
contamination, comportment and risks (the Zeida abandoned
mine, Morocco). Rev. Sci. Eau 2007; 20: 1-13.
[35] Plassard F, Winiarski T, Petit-Ramel M. Retention and
distribution of three heavy metals in a carbonated soil:
comparison between batch and unsaturated column studies. J.
Contam. Hydrol. 2000; 42: 99–111.
[36] Chuan MC, Shu GY, Liu JC. Solubility of heavy metals in
a contaminated soils: effect of redox potential and pH. Water,
Air Soil Pollut. 1996; 90: 543-556.
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SUPPLEMENTARY MATERIAL
Table S1. pH, electrical conductivity (CE), loss on ignition (LOI) and CaCO3 content for Draa Sfar mine area samples.
Sample
pH
CE
CaCO3
LOI
Sample
pH
CE
CaCO3
1
8.6
135
34.0
15.6
44
8.0
2565
118.6
64.4
2
9.6
172
26.1
12.7
45
7.5
8632
55.1
75.8
3
8.7
205
11.2
14.3
46
8.1
2151
58.4
39.3
4
8.7
294
21.2
24.4
47
8.2
2095
34.0
25.1
5
9.1
337
24.5
20.6
48
3.5
4873
25.8
56.0
6
8.5
1152
30.8
31.7
49
7.3
124
15.7
18.3
7
8.5
1708
28.5
27.9
50
8.0
102
7.9
19.1
8
9.0
747
38.2
32.1
51
8.3
107
25.8
17.4
LOI
9
8.0
4599
39.3
56.4
52
8.7
125
18.4
28.2
10
8.4
779
39.3
63.5
53
8.8
96
47.4
25.6
11
8.1
3452
27.6
26.2
54
8.6
136
48.3
21.2
12
8.6
536
31.5
22.4
55
8.8
132
79.8
23.0
13
8.3
489
25.3
17.8
56
8.7
203
36.8
24.3
14
8.4
2651
20.2
25.9
57
7.8
102
24.9
20.5
15
8.9
171
33.7
34.9
58
7.9
655
24.9
23.5
16
8.2
983
33.2
37.3
59
8.2
5275
55.2
32.7
17
7.8
2034
43.9
64.5
60
8.7
212
44.9
35.5
18
8.9
451
32.0
13.3
61
8.1
274
55.1
34.0
19
8.2
3376
30.8
23.3
62
8.2
240
52.8
48.3
20
8.3
852
15.8
24.5
63
7.9
456
37.1
61.8
21
7.9
1838
26.1
26.5
64
8.2
299
45.9
33.6
22
8.2
2210
26.2
17.9
65
8.4
261
43.8
27.2
23
8.2
1659
41.4
22.6
66
8.5
1663
20.7
25.6
24
8.4
922
23.7
14.6
67
8.5
106
20.0
22.3
25
8.1
1098
25.4
14.1
68
8.3
163
22.5
26.8
26
8.3
1797
209.9
54.4
69
7.9
2120
25.0
28.7
27
8.6
775
128.1
32.5
70
8.1
2570
93.3
29.5
28
8.5
576
79.1
36.8
71
8.3
1616
149.4
49.7
29
8.2
3152
21.9
21.7
72
8.5
692
98.9
40.7
30
8.0
5460
19.1
22.8
73
8.0
2699
62.9
44.9
31
7.6
14160
16.5
27.4
74
8.8
517
112.7
44.0
32
8.8
199
16.6
13.3
75
8.6
879
35.6
32.2
33
7.8
6940
29.7
52.6
76
8.1
2330
23.7
31.8
34
8.6
779
33.0
38.6
77
8.2
1298
36.8
27.9
35
8.4
375
29.7
40.1
78
8.1
1339
41.5
39.6
36
8.2
373
33.7
38.2
79
8.3
559
36.8
36.3
37
8.6
278
37.1
28.4
80
8.3
333
28.5
33.5
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Sample
pH
CE
CaCO3
LOI
Sample
pH
CE
CaCO3
LOI
38
8.5
345
24.4
24.4
81
8.3
425
39.1
29.4
39
8.5
415
57.3
44.7
82
8.7
113
21.3
23.5
40
8.5
552
35.6
42.8
83
8.1
805
26.1
29.1
41
9.0
214
72.9
39.5
84
8.6
145
24.9
39.9
42
8.6
371
55.1
46.7
85
8.6
151
27.3
30.1
43
8.5
493
61.7
48.5
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Table S2. Concentration and CER values of pollutants As, Cu, Pb and Zn along with CER reference element Zr.
As
Cu
Pb
Zn
Zr
Sample
CER
CER
CER
CER
(mg/kg)
(mg/kg)
(mg/kg)
(mg/kg)
(mg/kg)
1
BDL
16.7
0.3
13.2
0.5
39.7
0.3
302.6
2
BDL
16.6
0.5
9.4
0.6
31.6
0.4
204.8
3
9.2
0.5
24.6
0.5
7.6
0.3
40.1
0.4
272.8
4
BDL
37.8
1.1
9.6
0.6
44.2
0.6
199.5
5
13.2
0.7
31.9
0.6
11.2
0.4
46.2
0.4
308.0
6
12.4
0.7
26.3
0.6
10.5
0.4
48.8
0.4
285.8
7
BDL
BDL
13.0
0.6
44.0
0.4
263.1
8
11.3
0.6
38.2
0.8
12.8
0.5
46.9
0.4
283.1
9
11.3
0.6
24.2
0.5
12.8
0.5
58.4
0.5
315.0
10
20.2
1.8
52.7
1.7
20.1
1.3
98.1
1.3
182.1
11
14.1
1.0
29.2
0.8
15.8
0.9
76.1
0.9
222.5
12
36.4
3.0
41.4
1.3
23.1
1.5
126.1
1.6
192.3
13
BDL
24.1
0.5
8.4
0.4
36.0
0.3
277.5
14
BDL
23.7
0.5
12.1
0.5
48.3
0.4
276.3
15
BDL
33.9
0.7
18.4
0.8
58.3
0.5
291.1
16
15.1
1.1
30.1
0.8
15.5
0.8
70.2
0.8
229.1
17
18.1
1.6
50.3
1.6
17.6
1.2
96.8
1.3
185.5
18
13.8
1.4
28.9
1.1
12.1
0.9
65.9
1.0
157.3
19
BDL
30.5
0.6
14.5
0.6
50.9
0.4
289.7
20
125.4
10.9
79.9
2.6
54.8
3.6
627.9
8.5
184.6
21
71.5
7.1
50.5
1.9
61.8
4.6
143.9
2.2
162.4
22
BDL
BDL
BDL
29.7
0.7
111.5
23
14.5
0.9
21.8
0.5
9.0
0.4
47.7
0.5
254.8
24
9.6
0.8
17.9
0.6
12.0
0.8
39.2
0.5
191.4
25
12.0
1.2
17.7
0.7
11.1
0.9
43.7
0.7
158.3
26
19.4
1.9
23.5
0.9
7.4
0.6
45.6
0.7
163.6
27
19.2
1.6
28.9
0.9
14.9
1.0
100.0
1.3
188.2
28
22.5
2.1
34.1
1.2
16.4
1.2
80.2
1.2
169.6
29
12.4
1.2
23.1
0.9
12.4
0.9
48.6
0.8
162.0
30
12.7
1.3
22.5
0.8
10.5
0.8
51.4
0.8
161.0
31
203.3
19.4
43.1
1.5
179.7
13.0
480.7
7.2
167.8
32
14.7
0.9
22.1
0.5
6.7
0.3
49.4
0.5
274.4
33
14.4
1.3
46.2
1.6
18.0
1.3
76.1
1.1
172.8
34
18.1
1.5
25.9
0.8
12.4
0.8
72.7
1.0
190.0
35
16.8
1.6
34.1
1.2
16.6
1.2
79.3
1.2
168.6
36
14.2
1.3
39.4
1.3
14.2
1.0
74.8
1.0
179.5
37
15.2
1.4
30.3
1.0
11.6
0.8
72.8
1.0
175.3
38
16.5
1.6
18.8
0.7
13.3
1.0
78.5
1.2
162.7
39
25.2
2.7
42.8
1.7
17.6
1.4
102.9
1.7
151.4
40
16.4
1.3
25.6
0.8
16.8
1.0
85.4
1.1
199.3
41
12.6
1.1
40.6
1.3
15.2
1.0
77.6
1.0
190.2
42
16.0
1.4
38.6
1.2
15.6
1.0
78.8
1.0
187.9
43
18.1
1.4
35.7
1.0
17.8
1.0
90.9
1.1
209.9
44
19.5
1.6
29.9
0.9
23.4
1.5
91.2
1.2
196.1
45
203.3
15.9
172.5
5.1
773.5
45.9
1113.7
13.6
204.7
46
125.4
9.2
60.4
1.7
375.1
20.8
774.3
8.9
218.9
47
19.8
1.4
BDL
23.3
1.3
81.8
0.9
225.0
48
3107.6
340.8
144.3
5.9
2309.5
191.8
631.2
10.8
146.3
49
12.7
1.0
30.8
0.9
23.7
1.4
71.5
0.8
212.4
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Sample
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
As
(mg/kg)
13.2
BDL
18.8
15.4
12.6
17.5
11.7
9.2
113.5
14.3
22.7
16.7
14.7
12.1
BDL
10.1
8.9
17.5
10.6
14.2
15.2
17.9
15.4
14.1
13.2
18.6
13.8
12.5
17.5
14.9
18.8
19.9
11.3
9.4
18.9
12.7
CER
0.8
1.2
0.8
0.7
1.2
0.8
0.6
8.5
1.1
2.2
1.4
1.3
1.0
0.7
0.5
0.8
0.6
1.0
1.5
1.7
1.5
1.2
1.1
1.4
1.2
1.1
1.4
1.2
1.4
1.8
0.9
0.8
1.3
1.0
Cu
(mg/kg)
24.7
29.7
28.4
24.8
24.0
27.9
BDL
28.2
70.9
32.0
39.2
29.4
25.5
37.9
24.4
28.4
30.3
17.3
22.4
20.3
33.0
36.2
31.8
23.4
37.1
37.3
30.6
38.4
23.3
26.3
37.9
33.4
26.7
31.5
34.0
44.7
CER
0.6
0.7
0.7
0.5
0.5
0.7
0.7
2.0
0.9
1.4
0.9
0.8
1.1
0.7
0.7
0.7
0.3
0.5
0.5
1.2
1.3
1.2
0.8
1.2
1.1
1.0
1.2
0.7
0.8
1.1
1.1
0.8
1.0
0.9
1.3
Pb
(mg/kg)
27.3
28.9
30.1
20.5
23.4
25.4
19.8
19.4
424.6
15.5
12.6
17.7
20.3
18.8
18.1
15.3
13.5
40.2
8.4
61.2
23.9
14.4
20.3
14.3
18.9
17.4
13.4
16.5
28.5
34.8
29.6
31.8
15.5
16.8
20.6
16.6
CER
1.3
1.4
1.4
0.8
1.0
1.3
1.1
0.9
24.1
0.9
0.9
1.1
1.3
1.1
1.0
0.8
0.6
1.5
0.4
3.2
1.8
1.1
1.5
0.9
1.2
1.0
0.9
1.1
1.7
2.2
1.7
2.2
0.9
1.0
1.1
1.0
Zn
(mg/kg)
89.0
78.0
86.5
75.8
80.9
83.4
80.0
60.1
925.2
68.2
91.2
80.3
88.0
101.0
76.4
68.4
58.8
143.6
46.2
254.0
96.9
86.6
91.2
74.7
92.1
88.8
87.5
82.2
110.0
101.9
121.7
102.5
73.8
87.7
84.9
81.6
CER
0.9
0.8
0.8
0.6
0.7
0.9
0.9
0.6
10.8
0.8
1.4
1.0
1.2
1.2
0.9
0.7
0.6
1.1
0.4
2.7
1.5
1.3
1.4
1.0
1.2
1.1
1.1
1.1
1.3
1.3
1.4
1.5
0.9
1.1
0.9
1.0
Zr
(mg/kg)
253.6
245.6
254.8
315.2
273.4
237.5
226.2
251.6
214.4
209.0
168.1
196.2
184.3
203.6
221.8
237.2
261.0
334.5
273.5
231.2
164.5
167.1
162.8
186.6
187.9
208.7
190.9
185.9
207.7
192.6
215.6
174.7
200.6
198.8
235.3
204.3
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Table S3. Concentration and CER values of lithogenic components of the mine area soils.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
[Ba] CER [Ca] CER
258 0.4 27289 0.7
281 0.6 22533 0.9
221 0.4 19581 0.6
362 0.8 22154 0.9
357 0.5 30911 0.8
357 0.6 28711 0.8
269 0.5 29235 0.9
391 0.6 34868 1.0
309 0.5 30801 0.8
524 1.3 28820 1.3
351 0.7 26997 1.0
390 0.9 32758 1.4
285 0.5 19025 0.5
351 0.6 27108 0.8
395 0.6 29798 0.8
374 0.8 26120 0.9
541 1.4 30486 1.3
425 1.3 22717 1.1
314 0.5 29023 0.8
332 0.8 26896 1.2
504 1.4 32042 1.6
268 1.1 23227 1.7
277 0.5 26142 0.8
268 0.6 24012 1.0
282 0.8 26180 1.3
254 0.7 152754 7.4
398 1.0 80860 3.4
489 1.3 57198 2.7
260 0.7 26920 1.3
358 1.0 24843 1.2
400 1.1 31011 1.5
447 0.8 26868 0.8
471 1.3 35369 1.6
417 1.0 35113 1.5
444 1.2 30086 1.4
478 1.2 31344 1.4
331 0.9 38313 1.7
419 1.2 31113 1.5
460 1.4 47652 2.5
464 1.1 28833 1.1
403 1.0 50142 2.1
391 1.0 45098 1.9
483 1.1 43702 1.7
501 1.2 71367 2.9
370 0.8 45868 1.8
456 1.0 41987 1.5
303 0.6 30427 1.1
336 1.1 24281 1.3
382 0.8 18375 0.7
[Fe]
CER [K]
CER [Mn] CER
20967
0.4 16935 0.4 366 0.4
21316
0.6 15710 0.6 331 0.5
21932
0.5 16496 0.4 359 0.4
26064
0.7 20143 0.7 589 0.9
27227
0.5 21029 0.5 473 0.4
28338
0.6 21479 0.6 509 0.5
24544
0.5 17130 0.5 417 0.5
28881
0.6 21204 0.5 531 0.5
30383
0.6 22375 0.5 536 0.5
45017
1.4 31405 1.3 953 1.5
31924
0.8 24433 0.8 681 0.9
30029
0.9 25208 1.0 552 0.8
22565
0.5 20010 0.5 379 0.4
28183
0.6 22776 0.6 523 0.6
33504
0.7 26127 0.7 622 0.6
34044
0.9 25434 0.8 646 0.8
42132
1.3 30390 1.2 873 1.4
30945
1.1 25338 1.2 560 1.0
27468
0.5 20836 0.5 477 0.5
31008
1.0 24356 1.0 816 1.3
48007
1.7 26530 1.2 1119 2.0
19632
1.0 18323 1.2 290 0.8
26535
0.6 18592 0.5 547 0.6
22180
0.7 17151 0.7 436 0.7
22429
0.8 17945 0.8 424 0.8
21726
0.8 6071
0.3 327 0.6
31861
1.0 20968 0.8 553 0.9
37649
1.3 28488 1.2 577 1.0
24892
0.9 19394 0.9 533 1.0
25017
0.9 18161 0.8 525 1.0
28026
1.0 15913 0.7 542 0.9
28776
0.6 21636 0.6 506 0.5
36671
1.2 27816 1.2 824 1.4
35246
1.1 26579 1.0 601 0.9
36326
1.2 28645 1.2 751 1.3
38300
1.2 29693 1.2 795 1.3
34242
1.1 26694 1.1 655 1.1
35236
1.2 30085 1.4 640 1.2
43267
1.6 29429 1.4 897 1.7
37955
1.1 27871 1.0 739 1.1
35597
1.1 24743 1.0 656 1.0
30991
0.9 23222 0.9 671 1.0
37225
1.0 28315 1.0 840 1.2
31805
0.9 19598 0.7 586 0.9
50814
1.4 16688 0.6 882 1.3
36602
1.0 22309 0.7 768 1.0
23885
0.6 17281 0.6 447 0.6
121652 4.8 BDL
564 1.1
30573
0.8 24754 0.9 488 0.7
[Rb] CER
60.4
0.5
54.3
0.7
58.6
0.6
64.3
0.8
66.5
0.6
70.2
0.6
61.6
0.6
68.7
0.6
73.6
0.6
106.4 1.5
78.6
0.9
76.3
1.0
66.7
0.6
69.0
0.6
82.2
0.7
87.0
1.0
100.9 1.4
71.0
1.2
67.1
0.6
82.4
1.2
102.8 1.6
56.3
1.3
65.4
0.7
56.3
0.8
54.5
0.9
54.6
0.9
74.0
1.0
96.0
1.5
63.3
1.0
57.9
0.9
62.5
1.0
65.8
0.6
89.4
1.3
80.8
1.1
91.8
1.4
97.3
1.4
84.9
1.3
87.8
1.4
98.4
1.7
89.3
1.2
84.6
1.2
75.4
1.0
87.7
1.1
77.6
1.0
70.0
0.9
78.8
0.9
58.7
0.7
47.0
0.8
67.8
0.8
[Sr]
101
111
102
114
114
121
119
140
135
143
142
157
86
119
127
128
137
126
122
163
168
114
119
148
102
322
250
201
156
213
184
125
181
167
139
149
141
146
166
142
193
171
172
224
187
172
133
132
99
CER
0.5
0.8
0.6
0.9
0.6
0.7
0.7
0.8
0.7
1.2
1.0
1.3
0.5
0.7
0.7
0.9
1.2
1.3
0.7
1.4
1.6
1.6
0.7
1.2
1.0
3.1
2.1
1.9
1.5
2.1
1.7
0.7
1.6
1.4
1.3
1.3
1.3
1.4
1.7
1.1
1.6
1.4
1.3
1.8
1.4
1.2
0.9
1.4
0.7
[Ti] CER
3396 0.4
3069 0.6
3533 0.5
3677 0.7
4441 0.6
4563 0.6
4080 0.6
4446 0.6
4730 0.6
5731 1.2
5275 0.9
4855 1.0
3589 0.5
4606 0.7
4865 0.7
4799 0.8
5860 1.3
5105 1.3
4933 0.7
4714 1.0
5277 1.3
2802 1.0
4482 0.7
3251 0.7
3351 0.8
2913 0.7
3787 0.8
4614 1.1
3712 0.9
3382 0.8
3211 0.8
4979 0.7
4770 1.1
4543 0.9
4839 1.1
4924 1.1
5110 1.2
5001 1.2
4876 1.3
4950 1.0
4939 1.0
4097 0.9
4557 0.9
3458 0.7
4497 0.9
4588 0.8
3688 0.7
3471 0.9
4809 0.9
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
478
375
376
431
386
323
430
439
423
398
473
405
411
450
458
430
434
418
382
330
453
375
473
351
391
346
454
447
343
365
456
463
423
404
440
530
0.9
0.7
0.7
0.6
0.7
0.6
0.9
0.8
0.9
0.9
1.3
1.0
1.0
1.0
1.0
0.8
0.8
0.6
0.6
0.7
1.3
1.0
1.3
0.9
1.0
0.8
1.1
1.1
0.8
0.9
1.0
1.2
1.0
0.9
0.9
1.2
9961
21304
25823
30841
30322
54544
31668
21703
21702
34736
28184
33259
34232
27892
34855
39220
19555
17613
25783
19160
73693
78690
61498
46338
80724
29240
27167
27857
46893
27641
20194
19429
22577
25740
28436
24581
0.3
0.7
0.8
0.8
0.9
1.8
1.1
0.7
0.8
1.3
1.3
1.3
1.5
1.1
1.2
1.3
0.6
0.4
0.7
0.7
3.6
3.7
3.0
2.0
3.4
1.1
1.1
1.2
1.8
1.1
0.7
0.9
0.9
1.0
1.0
1.0
36278
30194
30123
29506
29334
27301
29771
30631
40552
31449
38171
34537
33698
35943
30434
27891
28723
29975
28280
24598
34977
32739
38035
28371
32023
30191
33211
37287
31315
32822
36070
37833
30769
35658
37686
38106
0.8
0.7
0.7
0.5
0.6
0.7
0.8
0.7
1.1
0.9
1.3
1.0
1.0
1.0
0.8
0.7
0.6
0.5
0.6
0.6
1.2
1.1
1.3
0.9
1.0
0.8
1.0
1.1
0.9
1.0
1.0
1.2
0.9
1.0
0.9
1.1
25687
23103
21614
20669
20068
16463
23137
22118
24804
22958
31414
27695
26177
27599
24809
23153
28953
23603
22584
19955
25748
20540
26637
20922
21234
23764
27808
27383
24632
27028
29217
32976
26770
28047
27896
30263
0.7
0.7
0.6
0.5
0.5
0.5
0.7
0.6
0.8
0.8
1.4
1.0
1.0
1.0
0.8
0.7
0.8
0.5
0.6
0.6
1.1
0.9
1.2
0.8
0.8
0.8
1.1
1.1
0.9
1.0
1.0
1.4
1.0
1.0
0.9
1.1
549
524
503
502
470
465
495
493
658
692
660
651
753
702
547
525
562
496
497
439
839
747
689
611
802
615
675
781
628
664
708
688
631
697
730
737
0.6
0.6
0.6
0.5
0.5
0.6
0.6
0.6
0.9
1.0
1.2
1.0
1.2
1.0
0.7
0.6
0.6
0.4
0.5
0.6
1.5
1.3
1.2
1.0
1.3
0.9
1.0
1.2
0.9
1.0
1.0
1.2
0.9
1.0
0.9
1.1
79.5
67.4
67.5
67.2
66.4
65.0
70.9
70.1
74.9
80.7
96.5
82.8
80.8
85.4
72.2
71.6
76.3
70.4
66.7
66.5
98.5
80.9
92.0
70.4
78.2
77.8
80.9
88.7
74.3
83.1
90.3
92.1
73.4
78.8
86.4
84.9
0.8
0.7
0.7
0.6
0.6
0.7
0.8
0.7
0.9
1.0
1.5
1.1
1.1
1.1
0.8
0.8
0.8
0.5
0.6
0.7
1.6
1.3
1.5
1.0
1.1
1.0
1.1
1.2
0.9
1.1
1.1
1.4
0.9
1.0
1.0
1.1
93
103
108
108
117
138
108
92
103
229
154
148
148
142
139
147
113
101
120
107
179
190
179
156
171
138
133
143
158
142
128
121
119
130
140
134
0.6
0.7
0.7
0.5
0.7
0.9
0.8
0.6
0.8
1.7
1.4
1.2
1.3
1.1
1.0
1.0
0.7
0.5
0.7
0.7
1.7
1.8
1.7
1.3
1.4
1.0
1.1
1.2
1.2
1.2
0.9
1.1
0.9
1.0
0.9
1.0
4715
4870
5259
4919
5190
5222
4377
4685
4722
4863
4929
5285
4546
4933
4288
3964
3673
4757
4560
3465
3948
3823
4257
3630
4722
4660
5070
5152
4283
5042
4662
5018
4431
5143
5741
5599
0.7
0.8
0.8
0.6
0.8
0.9
0.8
0.7
0.9
0.9
1.2
1.1
1.0
1.0
0.8
0.7
0.6
0.6
0.7
0.6
1.0
0.9
1.0
0.8
1.0
0.9
1.1
1.1
0.8
1.0
0.9
1.1
0.9
1.0
1.0
1.1
II
XANESSPECIATIONOFMERCURYIN
THREEMININGDISTRICTS–
ALMADEN,ASTURIAS(SPAIN),IDRIA
(SLOVENIA)
JoseMariaEsbri,AnnaBernaus,MartaAvila,DavidKocman,
EvaM.GarciaNoguero,BeatrizGuerrero,XavierGaona,
RodrigoAlvarez,GustavoPerezGonzalez,ManuelValiente,
PabloHigueras,MilenaHorvatandJorgeLoredo.
JournalofSynchrotronRadiation
JournalofSynchrotronRadiation.(2010)17,2:179186.
soil and geosciences
Journal of
Synchrotron
Radiation
ISSN 0909-0495
Received 22 June 2009
Accepted 15 January 2010
XANES speciation of mercury in three
mining districts – Almadén, Asturias (Spain),
Idria (Slovenia)
José Maria Esbrı́,a* Anna Bernaus,b Marta Ávila,b David Kocman,c
Eva M. Garcı́a-Noguero,a Beatriz Guerrero,b Xavier Gaona,b Rodrigo Álvarez,d
Gustavo Perez-Gonzalez,b Manuel Valiente,b Pablo Higueras,a Milena Horvatc and
Jorge Loredod
a
Departamento de Ingenierı́a Geológica y Minera, Escuela Universitaria Politécnica de Almadén,
Universidad de Castilla-La Mancha, 13400 Almadén (Ciudad Real), Spain, bGrup de Tècniques de
Separació en Quı́mica (GTS), Departament de Quı́mica, Universitat Autónoma de Barcelona,
08193 Bellaterra (Barcelona), Spain, cDepartment of Environmental Sciences, Jozef Stefan Institute,
Ljubljana SI-1001, Slovenia, and dDepartamento de Explotación y Prospección de Minas,
Universidad de Oviedo, Oviedo 33004, Spain. E-mail: josemaria.esbri@uclm.es
The mobility, bioavailability and toxicity of mercury in the environment strongly
depend on the chemical species in which it is present in soil, sediments, water or
air. In mining districts, differences in mobility and bioavailability of mercury
mainly arise from the different type of mineralization and ore processing. In this
work, synchrotron-based X-ray absorption near-edge spectroscopy (XANES)
has been taken advantage of to study the speciation of mercury in geological
samples from three of the largest European mercury mining districts: Almadén
(Spain), Idria (Slovenia) and Asturias (Spain). XANES has been complemented
with a single extraction protocol for the determination of Hg mobility. Ore,
calcines, dump material, soil, sediment and suspended particles from the three
sites have been considered in the study. In the three sites, rather insoluble sulfide
compounds (cinnabar and metacinnabar) were found to predominate. Minor
amounts of more soluble mercury compounds (chlorides and sulfates) were also
identified in some samples. Single extraction procedures have put forward a
strong dependence of the mobility with the concentration of chlorides and
sulfates. Differences in efficiency of roasting furnaces from the three sites have
been found.
# 2010 International Union of Crystallography
Printed in Singapore – all rights reserved
Keywords: mercury speciation; XANES; Almadén; Idria; Asturias; bioavailability.
1. Introduction
Assessing the distribution and mobilization of heavy metals in
the environment as a result of natural processes or anthropogenic activities is of special relevance in mining districts.
Mercury (Hg) is one of the most toxic heavy metals, as some of
its compounds can be absorbed by living tissues in large doses
and these compounds or their derivatives can concentrate and
be stored over long periods of time. Through the food chain,
mercury can eventually affect human beings and cause chronic
or acute damage (Förstner, 1998). From a toxicological point
of view, the toxicity of heavy metals is primarily controlled
by the dose and the corresponding chemical speciation.
Accordingly, many recent studies have been devoted to assess
heavy metal speciation either through direct or indirect
approaches (Horvat, 2005). The most widely used methods are
based on sequential selective extractions (Bloom et al., 2003;
J. Synchrotron Rad. (2010). 17, 179–186
Kocman et al., 2004) and X-ray absorption spectroscopy
(XAS) techniques (Kim et al., 2000, 2003, 2004; Slowey et al.,
2005a,b; Bernaus et al., 2005a,b, 2006a,b). Alternative techniques are based on Hg pyrolysis followed by AAS detection,
which allows the differentiation among cinnabar, metallic Hg
and matrix-bound Hg (Biester et al., 1999, 2000). XAS techniques have been shown to provide reliable information on
the speciation of mercury without requiring sample pretreatment (Kim et al., 2004; Slowey et al., 2005a,b; Bernaus et
al., 2006a). The application of XAS to mercury speciation
provides results with good consistency in terms of Hg–S/Hg–
non-S and Hg–insoluble/Hg–soluble ratios according to wetchemistry data (Kim et al., 2003). On the other hand, one of
the main limitations of the XAS methods refers to their high
detection limits.
Among XAS techniques, both EXAFS (extended X-ray
absorption fine structure) and XANES (X-ray absorption
doi:10.1107/S0909049510001925
179
soil and geosciences
near-edge) spectroscopies have been previously used for the
speciation of mercury in different matrices, such as mine ores
and wastes (Kim et al., 2000, 2004), fish (Harris et al., 2003),
contaminated soils (Bernaus et al., 2006a) and hyacinths
(Riddle et al., 2002), and in studies of interactions between
mercury and soil minerals (Bernaus et al., 2005b). According
to data available in the literature (Webb, 2005), XANES is
particularly useful for analysis of geochemical and environmental systems and has been preferred in this study. This is
in agreement with our previous experience and the known
XANES fingerprint differences among the Hg compounds
mainly expected in mining environments (Bernaus et al.,
2005a, 2006a,b).
In this framework, mobility studies represent a good
complement to purely speciation techniques, as they represent
a more empirical approach to the understanding of mercury
transfer among inorganic, organic and biological reservoirs.
In line with the publications by Brown and co-workers on
the characterization of mercury mines in north America (Kim
et al., 2000, 2004), this work aims at providing a further
understanding and a general perspective on the role of
mercury in three of the most important mercury mining
districts in Europe, namely Almadén and Asturias in Spain
and Idria in Slovenia.
2. Materials and methods
2.1. Study sites
Among the three mining districts selected in this study,
Almadén and Idria have been the largest world mercury
producers in historic times, both having a monometallic
character. On the other hand, Asturias has a more complex
mineralization, with high proportions of arsenic in its paragenesis. It is important to highlight that Almadén is the largest
cinnabar (HgS) deposit in the world and it has been active
since the Roman times until the present days, having
accounted for about one third of the total Hg world production (Hernández et al., 1999; Saupé, 1990). Metallurgical
processing in the study area evolved from Bustamante
furnaces, with roasting temperatures over 873 K, to Pacific
furnaces in the last century, reaching temperatures of up to
1073 K.
From a mineralogical point of view, soils at Almadén area
are mainly represented by quartz and a diversity of clay-type
minerals such as chlorite, illite, kaolinite and pyrophyllite and
high contents of carbonates which correspond to a region with
shales and quartzites as main components of the stratigraphic
sequence (Garcı́a Sansegundo et al., 1987, among others). The
high content of carbonates can be explained by the presence
of mafic magmatic rocks strongly affected by propilitic,
carbonate-rich alteration processes in the stratigraphic
sequence (Hall et al., 1997; Higueras et al., 2000).
Idria mining district is, like Almadén, a monometallic ore
deposit, with higher proportions of native mercury and hosted
in carbonate host rocks. The mineralization appears as two
main species: cinnabar and native mercury. Other minerals
180
José Maria Esbrı́ et al.
XANES speciation of mercury
Figure 1
Sampling locations, mines and metallurgical sites of the three mercury
mining districts, Almadén, Asturias and Idria. Abbreviations: ALM:
Almadenejos decommissioned metallurgical plant; RD: Valdeazogues
river downstream; El Entredicho: decommissioned open pit; AZG:
Azogado stream; CH: dump of Almadén mine; HR: Huerta del Rey;
SQ: San Quintı́n (real location: 50 km to the east of Almadén); TRR:
El Terronal mine. (See Table 1 for more details.)
appearing in the paragenesis are metacinnabar, pyrite,
marcasite, dolomite, calcite, kaolinite, epsomite and melanterite.
The mineralogical characterization of Idria samples reveals
carbonate bedrocks as main components of the stratigraphic
sequence, with the exception of the meadow soil from the
Pront Hill which was developed on carboniferous clastic rocks.
River bed and suspended sediments are composed of silica,
clay minerals, Fe and Al oxides, hydroxides and carbonates as
J. Synchrotron Rad. (2010). 17, 179–186
soil and geosciences
Table 1
Samples collected at the three mining districts.
Location
Almaden site
Almadén
Almadenejos
Valdeazogues river
San Quintı́n
Idria site
Soils
Sediments
Asturias site
Mine tailings
Calcines
Soil
Forest soils
ID
Sampling area
Material
HR
CH
AZG
ALM
RD
SQ
Huerta del Rey metallurgical precinct
Main dump of Almadén mine
Azogado stream
Decommissioned metallurgical plant
Downstream of El Entredicho pit
Decommissioned Pb–Zn–Ag mine
Soils from old metallurgical plant of the 17th century
Dump material, sediments and riparian soils
Riparian soils and stream sediments
Soils from the metallurgical precinct
Suspended particles
Mine wastes and soils from an old flotation plant tested for
cinnabar treatment
S1–S3
S2
S4
Vicinity of the metallurgical plant
Pront Hill
Idrijca merges with the river Baca
S5–S6
RS
Alluvial plain confluence of Idrijca and Baca rivers
Idrijca river, 35 km downstream from the mine before
Baca river inflow
SS
Idrijca river, 35 km downstream from the mine before
Baca river inflow
Soils
Meadow soils
Alluvial soil samples collected along the river Idrijca 40 km
downstream from the mine
Soils from a deep profile at depth 50 cm (S5) and 100 cm (S6)
River bed sediments of a composite sample taken within a
distance of 50 m with grain size < 0.063 mm (RS1) and
0.063–2 mm (RS2)
Suspended river sediments of a composite sample taken
within a distance of 50 m with grain size < 0.063 mm (SS1)
and 0.063–2 mm (SS2)
TRRmn
TRRc
TRRs
TRRfs
Mine and metallurgical plant
Mine and metallurgical plant
Metallurgical plant
El Terronal mine
remnants of carbonate and clastic rock weathering products in
the Idrijca catchment (Kanduč et al., 2008).
Asturias district shows a more complex mineralogy, with
mercury present as cinnabar, but with variable metacinnabar
and metallic mercury proportions and with other metallic
minerals such as orpiment, realgar, melnikovite, chalcopyrite,
arsenopyrite, stibnite and galena (Loredo et al., 1999). This site
has an intense metallurgical activity with lower calcinations
temperatures in their rotary furnaces (over 853 K) than the
other mining districts (Luque & Gutiérrez, 2006).
The total mercury concentration in soils and sediments of
these three mining districts is well documented (Berzas
Nevado et al., 2003; Higueras et al., 2003, 2006; Gray et al.,
2004; Horvat et al., 2002), although only a few studies dealt
with inorganic mercury speciation (Bernaus et al., 2005a,
2006a; Kocman et al., 2004; Biester et al., 1999, 2000).
2.2. Sample collection, storage and preparation
Samples from the main mines, metallurgical plants and
drainage network of the three districts were considered in this
study (Fig. 1). A list of samples, corresponding acronyms used
in the text and short descriptions is provided in Table 1.
The samples of soils, mine tailings, calcines and riparian
soils from Almadén were taken at a depth of 0–20 cm, stored
in polyethylene bags and sieved at the Almadén School of
Mines to below a grain size of 2 mm. Samples of suspended
particles were collected from the water column, sedimented in
laboratory and air-dried in a clean room. The rest of the
samples were air-dried to prevent mercury losses, homogenized and ground before analysis.
J. Synchrotron Rad. (2010). 17, 179–186
Dumps in the vicinity of rotary furnaces
Calcination waste
Soil from an abandoned chimney channel
Forest soils from the mining area
Soil samples from Idria were taken with a stainless steel
auger at a depth of 0–10 cm and stored in polyethylene
containers. Suspended river sediment was sampled during a
flood event of the Idrijca river by means of a net drift sampler
(Kocman, 2008). After removal of gravel, stones and plant
residues, river bed and suspended sediments were sieved and
separated in two grain-size fractions: < 0.063 mm and 0.063–
2 mm. Before analyses, samples were dried at 303 K for three
days (to a constant weight) in the dark, then ground and
homogenized in an agate mortar and transferred into polypropylene containers.
The samples from Asturias area were collected in the La
Peña-El Terronal mine site, near the town of Mieres. The site
includes dumps, calcines, contaminated soils and a chimney
channel used to transport roasting smoke to the top of a
mount. Soils, riparian soils and mine tailings samples
( 1.5 kg) were collected at 10–30 cm depth, stored in polyethylene bags, air-dried in a clean room and sieved in the
laboratory using a 0.1 mm sieve.
All solid samples from the three mining districts were
prepared for synchrotron analysis using an aliquot, mixed with
polyethylene (IR quality), homogenized with a vortex for
2 min and pressed to a pellet with 5 ton cm2 of pressure.
2.3. Chemical characterization
Total mercury content of all solid samples was determined
by Zeeman atomic absorption spectrometry using highfrequency modulation of light polarization (ZAAS-HFM)
with a Lumex RA-915+ analyzer (Sholupov & Ganeyev, 1995).
The detection limit of this technique for soils and sediments
José Maria Esbrı́ et al.
XANES speciation of mercury
181
soil and geosciences
samples is 0.5 mg Hg kg1. For accuracy, certified reference
material (CRM-025) was analyzed simultaneously.
2.4. XANES measurements
XANES measurements were performed at the synchrotron
facility Hamburger Synchrotronstrahlungslabor (HASYLAB)
in Hamburg (Germany) at the bending-magnet beamline A1
(see further details by Bernaus et al., 2005b). All measurements were carried out at room temperature. The beamline
set-up consisted of a Si(111) double-crystal monochromator,
three ionization chambers as transmission detectors and a
seven-pixel Ge fluorescence detector.
The photon absorption of mercury was recorded at its LIII
energy (12284 eV). Fluorescence detection mode was used
for the analysis of all samples, except for the reference
compounds whose spectra were recorded in transmission
mode. References for XANES fingerprint adjustments
included minerals and pure compounds: HgCl2, HgSO4, HgO,
CH3HgCl, Hg2Cl2 (calomel), HgSred (cinnabar), HgSblack
(metacinnabar), Hg 2 NCl 0.5 (SO 4 ) 0.3 (MoO 4 ) 0.1 (CO 3 ) 0.1 H 2 O
(mosesite), Hg3S2Cl2 (corderoite), Hg3(SO4)O2 (schuetteite)
and Hg2ClO (terlinguaite). This selection was undertaken on
the basis of our prior knowledge of the geochemistry of the
different study areas (Horvat et al., 2002; Higueras et al., 2003,
2006; Gray et al., 2004; Kocman et al., 2004; Kanduč et al.,
2008), as well as the possible weathering and anthropogenic
processes taking place in each site.
XANES spectra were processed using SixPACK data
analysis software package (SIXPack, 2004; see also Catalano
et al., 2005; Slowey et al., 2005b; Arai et al., 2006). Spectra
processing included energy correction, signal normalization
and background correction. After data correction and
normalization, a principal component analysis (PCA) was
applied to the set of unknown spectra to determine the
number of principal components required to describe the
variation in the data. Then, the PCA results were used with a
target transformation, which projected the spectrum from a
reference compound onto the vector space defined by the
components. If the target vector lay within this component
space (above the 95% confidence level), then this reference
compound was selected to be present in the dataset. Finally, a
linear least-squares approach was used to determine the
fractional amount of each reference compound in the samples
(Malinowski, 1991; Ressler et al., 2000; Wasserman et al.,
1999). The quality of the target transform was given by the
reduced 2 value, which represents the goodness of the fit to
the spectra data, and is defined as
reduced 2 ¼
N
X
obs
2
1
fit
;
i
N P i¼1 i
ð1Þ
where iobs is the ordinate of the XANES spectrum measured
from the sample at the i th energy point, ifit is the ordinate of
the fitted XANES spectrum, N is the number of data points in
the fitted XANES energy range (scaled by the wavenumber k)
and P is the number of fitted components.
182
José Maria Esbrı́ et al.
XANES speciation of mercury
A higher reduced-2 denotes that the Hg compounds
compared possess a lower degree of similarity. This 2
represents the goodness of the model fit to the spectra
data using the linear combination procedure (Rehr et al.,
1992).
2.5. Mobility study (single extraction procedures)
Assays on the mobility of mercury were performed
according to the methodology reported by Perez et al. (2008).
Briefly, the methodology consisted of sample extraction with
0.5 M HCl for 1 h with magnetic stirring. The ratio solid : water
was 1 g : 20 ml. After centrifugation at 3500 r.p.m. for 10 min,
the extracts were filtered and analyzed by ICP-OES (ThermoElemental ICP-OES, model Intrepid II XLS, Franklyn,
MA, USA).
3. Results and discussion
Total mercury content (Table 2) in the Almadén district shows
high Hg concentrations in soil samples from metallurgical
sites, which can be mainly attributed to the inefficient metallurgical techniques used in the old plants of Almadenejos and
Huerta del Rey (Sumozas, 2005), with estimated roasting
temperatures below 873 K. High total mercury concentrations
have also been found in sediments and riparian soils from
Valdeazogues river, but especially from Azogado stream
(AZG) (2816 mg Hg g1). The latter is in good agreement with
previous studies undertaken at the same sampling site (Gray et
al., 2004). Other heavy metals are in low concentrations except
in samples from the San Quintı́n area (SQ), where significantly
high amounts of Pb and Zn were also found (Table 2).
In Idria samples, analysis of total mercury content revealed
high concentrations in all samples (Table 2). Those samples
taken near the former smelting facilities were the most
polluted. This observation can be explained by the settling
down of Hg-enriched particles in the immediate vicinity of the
smokestack of the smelter. Moreover, the high total Hg
concentration observed in Idria sediments (RS) and in alluvial
soils (S4) 40 km downstream from the mine indicate that
sources of mercury such as mercury-bearing rocks, wastes
from combustion processes, as well as contaminated river-bed
sediments remain the major Hg input to the aquatic environment in the area even a decade after the end of mining
operations.
The total mercury content of soil and dump samples of
Asturias mine show the highest mercury content of the three
mines studied, with 27350 mg g1 in dump samples (TRRmn116) and 18000 mg g1 in soils from the chimney channel, with
high amounts of arsenic content (from 735 mg g1 to
187218 mg g1).
PCA was performed separately for each mining district
given the significant differences expected and considering the
number of sample XANES spectra (representative enough)
available in each case. As stated in x2.4, the original set of
reference compounds included 11 mercury phases (Fig. 2). In
Fig. 2, XANES spectra of samples collected in the three
J. Synchrotron Rad. (2010). 17, 179–186
soil and geosciences
Table 2
Average heavy metals content in samples from the three mining districts
(in mg g1).
Mercury was analyzed by ZAAS-HFM and As, Zn and Pb by XRF. BDL: data
below detection limits. = grain size.
Sample
Material
Hg
As
Pb
Zn
989
976
404
200
105
BDL
BDL
BDL
BDL
BDL
BDL
214
111
130
BDL
112
96
104
185
BDL
CH-125
AZG-105
CH-128
ALM-101
ALM-102
CH-126
SQ-111
SQ-112
SQ-113
SQ-114
Dump
Soil
Soil
Soil
Suspended
particles
Sediment
Riparian soils
Riparian soils
Soil
Soil
Soil
Dump
Dump
Soil
Soil
1800
2816
450
2720
2629
2230
902
1730
1935
390
BDL
23
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
139
102
74
102
BDL
15837
2154
19049
112
233
185
153
193
365
6877
1221
7134
Asturias
TRRmn-115
TRRmn-116
TRRs-118
TRRs-121
TRRmn-122
TRRfs-3
TRRfs-4
TRRc-5
TRRc-55
Dump
Dump
Chimney soil
Chimney soil
Dump
Soil
Soil
Calcined
Calcined
1470
27350
3280
18000
5785
1570
1080
34
54
39338
117553
735
12133
42300
16826
1120
187218
25876
BDL
BDL
BDL
BDL
BDL
107
53
BDL
BDL
BDL
BDL
BDL
BDL
BDL
173
137
BDL
BDL
Soil
Meadow Soil
Alluvial soil
Soil
(50 cm depth)
Soil
(100 cm depth)
Sediment
< 63 mm
Sediment
< 2 mm
Suspended
particles
< 63 mm
Suspended
particles
< 2 mm
Soil
Ore
333
47
76
175
21
26
BDL
BDL
BDL
BDL
BDL
47
112
102
64
145
144
BDL
73
496
Figure 2
6540
BDL
302
270
1920
BDL
14
BDL
XANES spectra of selected Hg pure compounds and samples from
Almadén, Idria and Asturias mining districts (all spectra are deliberately
displaced to show differences). Each spectrum corresponds to the mean
value of five replicates.
96
BDL
BDL
449
11
BDL
BDL
24
95
27
46
130
Almadén
CH-127
HR-108
HR-109
HR-110
RD-124
Idria
S-1
S-2
S-4
S-5
S-6
RS-1
RS-2
SS-1
SS-2
S-3
Hg ore
mining districts are also reported. As examples, Fig. 3 shows
the fitted spectra for selected samples from each of the three
sites (more data are reported in Table 3).
For the Almadén district, the PCA results indicate that five
components [cinnabar (Cb), metacinnabar (Mc), HgCl2,
Hg2Cl2 and schuetteite (Sc)] can be used to reconstruct each of
the experimental spectra (depending on the sample) above the
95% confidence level. Mercury sulfides are the most common
species found in almost all samples (Table 3), especially in
those collected in abandoned metallurgical plants like Almadenejos area and Huerta del Rey (Almadén area). Non-sulfide
phases like schuetteite [Hg3(SO4)O2], calomel (Hg2Cl2) and
J. Synchrotron Rad. (2010). 17, 179–186
mercuric chloride (HgCl2) are present in different ratios in soil
and sediment samples.
XANES analyses in the samples from San Quintı́n area (see
Table 3) have shown the absence of metacinnabar but high
amounts of cinnabar (47–59%) and minor amounts of relatively more soluble species like calomel (24–33%) and
schuetteite (17–21%) which can be attributed to weathering
processes. The absence of metacinnabar, a metastable polymorph of cinnabar which occurs during the roasting process of
mercury ores in the presence of impurities (Dickson & Tunell,
1959), is due to the historical use of the site, as only flotation
tests were performed and no furnaces were used there.
On the other hand, metacinnabar has been identified in soil
samples from Almadenejos (ALM) (31–39%) and Huerta del
Rey (HR) ( 23%), locations with known historic metallurgical activity.
Other non-sulfide phases like mercurous chloride (24–43%)
have also been identified at San Quintı́n and Huerta del Rey,
and can be attributed to the process of soil formation. High
José Maria Esbrı́ et al.
XANES speciation of mercury
183
soil and geosciences
Table 3
Main mercury species (in %) and mobile mercury (in mg L1 and %).
Abbreviations: Cb, cinnabar; Mc, metacinnabar; Sc, schuetteite; Co, corderoite; BDL, below detection limits.
Sample
Cb
Mc
Sc
Co
HgO
HgSO4
Hg2Cl2
HgCl2
Reduced 2
Mobility† (mg L1) (%)
Almadén
CH-127
HR-108
HR-109
HR-110
RD-124
CH-125
AZG-105
CH-128
ALM-101
ALM-102
CH-126
SQ-111
SQ-112
SQ-113
SQ-114
62
37
33
41
0
7
0
24
38
39
33
54
51
59
47
0
23
24
22
0
0
0
22
39
31
32
0
0
0
0
0
0
0
0
94
83
80
0
23
0
35
17
21
17
20
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
40
43
37
0
0
20
35
0
30
0
29
28
24
33
0
0
0
0
6
10
0
19
0
0
0
0
0
0
0
0.0004
0.0006
0.0007
0.0006
0.0006
0.0004
0.0003
0.0004
0.0003
0.0007
0.0003
0.0002
0.0002
0.0002
0.0003
1.4 (3.2)
0.6 (1.2)
0.2 (1)
BDL
BDL
BDL
BDL
BDL
10.8 (7.9)
21.3 (16.2)
BDL
0.6 (1.3)
3.7 (4.3)
BDL
BDL
Asturias
TRRmn-115
TRRmn-116
TRRs-118
TRRs-121
TRRmn-122
TRRfs-3
TRRfs-4
TRRc-5
TRRc-55
29
28
28
29
30
44
50
52
57
24
22
22
22
24
28
36
30
43
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14
18
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
0
0
0
0
0
47
50
50
49
46
0
0
0
0
0.001
0.0009
0.0008
0.0007
0.0007
0.003
0.003
0.008
0.007
0.4 (0.5)
73.3 (5.4)
20.1 (12.3)
56.5 (6.3)
43.6 (15.1)
0.7 (0.9)
0.1 (0.2)
BDL
BDL
Idria
S-1
S-2
S-4
S-5
S-6
RS-1
RS-2
SS-1
SS-2
S-3
Hg ore
44
55
85
90
58
57
100
90
55
66
100
0
0
15
0
0
0
0
0
0
0
0
32
0
0
0
0
0
0
0
0
26
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
8
0
24
45
0
0
42
43
0
10
45
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.006
0.002
0.004
0.004
0.005
0.002
0.003
0.004
0.009
0.007
BDL
0.2 (8.5)
BDL
BDL
BDL
BDL
BDL
BDL
BDL
0.3 (6.3)
† Determined according to the method of Perez et al. (2008).
amounts of schuetteite have been identified in ore stockpile in
San Quintı́n and Almadenejos area. This is a mineral phase
typically linked to the presence of Hg0 that appears in the
sunlight-exposed side of the rock surface, and it is frequently
found near old furnaces or ore dumps (Higueras et al., 2003).
High proportions of relatively more soluble phases have
been identified in soil and sediment samples from Valdeazogues River (100%) and Azogado stream (100%). These phases
[Hg2Cl2, HgCl2 and Hg3(SO4)O2] have been considered a
result of the weathering processes taking place within the
drainage network of the mining district. The mobility of
mercury in this district is clearly linked with metallurgical
activity and formation of secondary chloride phases. The
highest mobility was found in soil samples from an old
metallurgical precinct (ALM) (10.8–21.3 mg L1; see Table 3).
At the Idria mining district the PCA analysis reveals the
presence of five components (Cb, Mc, Sc, HgO, HgSO4). In
this district, cinnabar is the most common Hg form in soil,
184
José Maria Esbrı́ et al.
XANES speciation of mercury
sediments and suspended particles, while the presence of
metacinnabar is found in a soil sample (S-4), and sulfates in
soils and sediments (S, RS, SS). The lack of metacinnabar in
most of these samples is due to the re-use of calcines and
metallurgical wastes in the refilling of mine galleries with
minor dispersion of this material throughout the surrounding
environment. High proportions of sulfates were found in soil
samples (S), but the mobility of mercury in this district was
clearly reduced, mainly by the major proportions of cinnabar
in soils, sediments and suspended particles. This low mobility
of mercury (0.2–0.3 mg L1, see Table 3) is in accordance with
Kocman (2008), describing low water-soluble mercury species
in sediments and suspended particles.
In Asturias mining district, the PCA analysis needs six
components to reconstruct samples spectra [Cb, Mc, corderoite (Co), HgCl2, HgO, HgSO4]. All samples from the
decommissioned mine and metallurgical facility show high
mercury contents in soils (TRRfs), dump materials (TRRmn)
J. Synchrotron Rad. (2010). 17, 179–186
soil and geosciences
4. Conclusions
This work represents the first inter-regional study of mercury
speciation of the two main European Hg-mining districts
(Almadén and Idria), and a polymetallic district located in
Asturias.
XANES has provided key information on the inorganic
mercury speciation of ores, calcines, dump material, soils,
sediments and suspended particles samples. Rather insoluble
mercury compounds (cinnabar, metacinnabar, schuetteite,
corderoite) have been shown to prevail in dumps and wastes
from mines and metallurgical plants, whereas more soluble Hg
phases (mainly HgCl2 but also HgO and HgSO4) were found
in soils and sediments from all target areas. A qualitative
relationship between mobile mercury and the presence of
mercury chlorides or sulfates compounds has been established
for samples from the three districts. Nonetheless, the absolute
‘mobility’ remains relatively low in most cases, inherently
suggesting that kinetic effects and availability of the soluble
phases might also be considered in the assessment of mercury
behaviour.
Figure 3
XANES spectra of selected samples from the three mining districts with
reconstructed spectra shown as dashed lines. (See Table 3 for more
details.)
and chimney soils (TRRs) (Table 2), and a predominance of
sulfides species (50–100%) with significant presence of metacinnabar in all samples (Table 3). Ratios between cinnabar and
metacinnabar in these samples are lower than in Almadén
area, where metallurgical activity was not the predominant
activity. In this mining site, metallurgy was less efficient than in
Idria and Almadén area, with lower roasting temperature and
poorest recovery rates. The contents of other mercury species
such as chlorides are significant, with high amounts on soils
samples from the facility and the chimney exhausting roasting
smokes. The mobility of mercury in this district is higher than
in Almadén. In qualitative terms, the percentage of mobile
mercury agrees well with the presence of HgCl2 except for
TRRmn-115. In general, it is important to point out that it is
likely that the methodology applied to assess Hg mobility only
extracts a fraction of the HgCl2 present, thus underestimating
Hg mobility.
If we consider the three districts, the main processes
affecting mercury speciation are ore composition, mining
history and roasting process. The type of metallurgical
processing arises as one of the most important factors in
defining mercury availability: mercury mobility is higher in
Asturias district owing to the inefficient roasting treatment
used (lower roasting temperatures and poorer recovering
rates); the mobility is significantly lower in the Almadén
district, with better furnaces (only in the last century) and
despite the complex and lengthy history of mining and
metallurgical activity. On the other hand, the even lower
mobility values found in Idria district are related to its efficient
metallurgical process (similar to Almadén area), together with
the appropriate management of calcines used for refilling old
galleries and the shorter mining history of the district.
J. Synchrotron Rad. (2010). 17, 179–186
Synchrotron experiments at HASYLAB were financially
supported by the European Community, Research Infrastructure Action under the FP6 ‘Structuring the European
Research Area’ Programme (through the Integrated Infrastructure Initiative ‘Integrating Activity on Synchrotron and
Free Electron Laser Science’). Financial contribution from the
projects PPQ2003-01902, CTQ2005-09430-C05 and CTM200613091-C02-02/TECNO funded by the Spanish Ministry of
Science and Innovation is also acknowledged.
References
Arai, J., Lanzirotti, A., Sutton, S. R., Newville, M., Dyer, J. & Sparks,
D. L. (2006). Environ. Sci. Technol. 40, 673–679.
Bernaus, A., Gaona, X., Esbrı́, J. M., Higueras, P., Falkenberg, G. &
Valiente, M. (2006a). Environ. Sci. Technol. 40, 4090–4095.
Bernaus, A., Gaona, X., Ivask, A., Kahru, A. & Valiente, M. (2005b).
Anal. Bioanal. Chem. 382, 1541–1548.
Bernaus, A., Gaona, X. & Valiente, M. (2005a). J. Environ. Monit. 7,
771–777.
Bernaus, A., Gaona, X., van Ree, D. & Valiente, M. (2006b). Anal.
Chim. Acta, 565, 73–80.
Berzas Nevado, J. J., Garcı́a Bermejo, L. F. & Rodrı́guez Martı́nDoimeadiós, R. C. (2003). Environ. Pollut. 122, 261–271.
Biester, H., Gosar, M. & Covelli, S. (2000). Environ. Sci. Technol. 34,
3330–3336.
Biester, H., Gosar, M. & Müller, G. (1999). J. Geochem. Explor. 65,
195–204.
Bloom, N. S., Preus, E., Katon, J. & Hiltner, M. (2003). Anal. Chim.
Acta, 479, 233–248.
Catalano, J. G., Trainor, T. P., Eng, P. J., Waychunas, G. A. & Brown,
G. E. (2005). Geochim. Cosmochim. Acta, 69, 3555–3572.
Dickson, F. W. & Tunell, G. (1959). Am. Mineral. 44, 471–487.
Förstner, U. (1998). Integrated Pollution Control, edited by A.
Weissbach and H. Boeddicker, pp. 81–130. Berlin: Springer-Verlag.
Garcı́a-Sansegundo, J., Lorenzo, S. & Ortega, E. (1987). Servicio de
Publicaciones, Ministerio de Industria y Energı́a, Madrid, p. 60.
Gray, J. E., Hines, M. E., Higueras, P., Adatto, I. & Lasorsa, B. K.
(2004). Environ. Sci. Technol. 38, 4285–4292.
Hall, C. M., Higueras, P. L., Kesler, S. E., Lunar, R., Dong, H. &
Halliday, A. N. (1997). Earth Planet. Sci. Lett. 148, 287–298.
José Maria Esbrı́ et al.
XANES speciation of mercury
185
soil and geosciences
Harris, H. H., Pickering, I. J. & George, G. N. (2003). Science, 301,
1203.
Hernández, A., Jébrak, M., Higueras, P., Oyarzun, R., Morata, D. &
Munhá, J. (1999). Miner. Deposita, 34, 539–548.
Higueras, P., Oyarzun, R., Biester, H., Lillo, J. & Lorenzo, S. (2003). J.
Geochem. Explor. 80, 95–104.
Higueras, P., Oyarzun, R., Lillo, J., Sánchez-Hernández, J. C., Molina,
J. A., Esbrı́, J. M. & Lorenzo, S. (2006). Sci. Tot. Environ. 356, 112–
124.
Higueras, P., Oyarzun, R., Munhá, J. & Morata, D. (2000). Rev. Soc.
Geol. Esp. 13, 10–119.
Horvat, M. (2005). Dynamics of Mercury Pollution on Regional and
Global Scales: Atmospheric Processes and Human Exposures
Around the World, edited by V. Pirrone, N. Mahaffey and R.
Kathryn, pp. 154–190. New York: Springer.
Horvat, M., Jereb, V., Fajon, V., Logar, M., Kotnik, J., Faganeli, J.,
Hines, M. & Bonzongo, J. C. (2002). Geochem. Explor. Environ.
Anal. 2, 287–296.
Kanduč, T., Kocman, D. & Ogrinc, N. (2008). Aquat. Geochem. 14,
239–262.
Kim, C. S., Bloom, N. S., Rytuba, J. J. & Brown, G. E. (2003). Environ.
Sci. Technol. 37, 5102–5108.
Kim, C. S., Brown Jr, G. E. & Rytuba, J. J. (2000). Sci. Tot. Environ.
261, 157–168.
Kim, C. S., Rytuba, J. J. & Brown Jr, G. E. (2004). Appl. Geochem. 19,
379–393.
Kocman, D. (2008). PhD thesis, Vol XIII, pp. 152, Jozef Stefan
International Postgraduate School, Slovenia.
Kocman, D., Horvat, M. & Kotnik, J. (2004). J. Environ. Monitor. 6,
696–703.
186
José Maria Esbrı́ et al.
XANES speciation of mercury
Loredo, J., Ordoñez, A., Gallego, J. R., Baldo, C. & Garcı́a-Iglesias, J.
(1999). J. Geochem. Explor. 67, 377–390.
Luque, C. & Gutiérrez, M. (2006). Editors. La Minerı́a del Mercurio
en Asturias: Rasgos Históricos, 1st ed.
Malinowski, E. R. (1991). Factor Analysis in Chemistry, 2nd ed. New
York: Wiley.
Perez, G., Lopez-Mesas, M. & Valiente, M. (2008). Environ. Sci.
Technol. 42, 2309–2315.
Rehr, J. J., Albers, R. C. & Zabinsky, S. I. (1992). Phys. Rev. Lett. 69,
3397–3400.
Ressler, T., Wong, J., Roos, J. & Smith, I. L. (2000). Environ. Sci.
Technol. 34, 950–958.
Riddle, S. G., Tran, H. H., Dewitt, J. G. & Andrews, J. C. (2002).
Environ. Sci. Technol. 36, 1965–1970.
Saupé, F. (1990). Econ. Geol. 85, 482–510.
Sholupov, S. E. & Ganeyev, A. A. (1995). Spectrochim. Acta B, 50,
1227–1238.
SIXPack (2004). SIXPack – Sam’s Interface for XAS Analysis
Package, powered by IFEFFIT 1.2.6. Copyright Matt Newville,
University of Chicago, USA.
Slowey, A. J., Johnson, S. B., Rytuba, J. J. & Brown, G. E. (2005a).
Environ. Sci. Technol. 39, 7869–7874.
Slowey, A. J., Rytuba, J. J. & Brown, G. E. (2005b). Environ. Sci.
Technol. 39, 1547–1554.
Sumozas, R. (2005). PhD thesis, Castilla-La Mancha University,
Spain.
Wasserman, S. R., Allen, P. G., Shuh, D. K., Bucher, J. J. & Edelstein,
N. M. (1999). J. Synchrotron Rad. 6, 284–286.
Webb, S. M. (2005). Phys. Scr. T115, 1011–1014.
J. Synchrotron Rad. (2010). 17, 179–186
III
EXTRACTANTANDSOLVENT
SELECTIONTORECOVERZINC
MartaAvila,GustavoPerezandManuelValiente
SolventExtractionandIonExchange(2011)29:384–397
Solvent Extraction and Ion Exchange, 29: 384–397, 2011
Copyright © Taylor & Francis Group, LLC
ISSN 0736-6299 print / 1532-2262 online
DOI: 10.1080/07366299.2011.573434
Downloaded By: [Consorci de Biblioteques Universitaries de Catalunya] At: 12:27 6 June 2011
Extractant and Solvent Selection to Recover Zinc
from a Mining Effluent
M. Avila, G. Perez, and M. Valiente
Universitat Autonoma de Barcelona, Department of Chemistry, Centre GTS,
Bellaterra, Barcelona, Spain
Abstract: The feasibility of three commercial extractants (DEHPA, Cyanex 272,
and Ionquest 290) has been assessed for the recovery of Zn from an acidic mine
effluent. Less than 5 min are required to reach equilibrium for the studied extractants. Regarding selectivity, DEHPA extracted efficiently Zn, Ca, Mn, and Al,
although Al remained in the solvent extract after stripping, hindering the solvent
reuse. Neither Ionquest 290 nor Cyanex 272 extract Al, Cu, Mn, or Ca significantly. Ionquest 290 recovery of Zn is 5–10% higher than Cyanex 272. In addition,
20%(v/v) Ionquest 290 produces higher recoveries than 40%(v/v) DEHPA, thus
Ionquest 290 has been selected as the most suitable among the extractants studied.
Keywords: Mining effluent, solvent extraction, zinc, DEHPA, Ionquest 290; Cyanex
272
INTRODUCTION
The development of viable ways of recycling industrial waters such as
mining effluents rather than the simple disposal of the effluents and their
derivate sludge as a hazardous waste in specially controlled landfills is damaging both environmentally and economically. In a currently abandoned
mine in Andalusia, in the south of Spain, a huge stream of effluent containing about 1 g/l Zn and significant amount of Ca, Cu, Al, and Mn have
to be treated before disposal. Zinc is the fourth most commonly used metal
in the world with over 7 Mt of annual production worldwide, trailing only
iron, aluminum, and copper in annual production due to its broad utility.
Address correspondence to M. Valiente, Universitat Autonoma de Barcelona,
Department of Chemistry, Centre GTS, Campus de la UAB, Edicici CN, 08193,
Bellaterra, Barcelona, Spain. E-mail: Manuel.Valiente@uab.cat
Downloaded By: [Consorci de Biblioteques Universitaries de Catalunya] At: 12:27 6 June 2011
Zinc Recovery from a Mining Effluent
385
Nearly 50% of the amount of the Zn is used for galvanizing to protect
steel from corrosion, approximately 19% is used to produce brass, and
16% goes into the production of zinc based alloys to supply the die casting industry. The rest of the zinc is employed to produce roofing, gutters,
and down-pipes, rubber in tires, sunscreen, TV screens, and luminous dials
and ointments to prevent bacteria and fungi from reproducing, amongst
others.[1] Hence, the recovery of Zn from mine waters can provide economical benefits while diminishing the volume of hazardous materials contained
in the mine tailing.
In this context, conventional treatment methods for zinc extraction
and purification include precipitation, ion exchange, adsorption, electrochemical recovery, membrane separation, and solvent extraction (SX).[2]
In this regard, SX has been widely proposed as some of the most economical and practical processes to extract Zn from waters containing Zn and
other impurities.[3–7] SX involves the extraction of a target element from
the initial solution by an extractant usually diluted in an organic solvent,
leaving other constituents in the aqueous raffinate. Then, a subsequent reextraction/stripping of the extracted elements present in the organic phase
(OP) is usually carried out with some acidic solution (stripping solution).
When the organic phase has higher affinity for some metals than the stripping solution, or undesirable metals have also been extracted, scrubbing
of the solvent prior to the stripping of the target elements or regeneration
of the extractant after the stripping process for further applications should
be done. These steps generally increase the cost of the process due to the
expenditure in both reactants and time.
Nowadays, a wide number of extractants are available for use in SX
for the recovery of metals, some of which are suitable for a specific metal,
and others must be used at certain conditions to avoid the extraction
of impurities.[8,9] In this sense, the most widely used extractants for Zn
recovery are those corresponding to the organophosphorus acids group,
that is, DEHPA and Cyanex 272, commonly used in SX. In this study, a
newer commercial extractant, Ionquest 290, is compared with the results
of DEHPA and Cyanex 272 in samples obtained from the Zn rich mine
effluent in order to get a Zn sulphate rich liquor to be used later in an
electrowinning plant.
Di-(2-ethylhexyl) phosphoric acid (DEHPA) has been successfully
used as an extractant for many metal ions including Zn due to its
great extraction capacity and low cost.[10–12] It has been used to
extract Zn more efficiently than other bivalent metal ions such as
Cu, Ni, Co, and Cd.[13] The order of extraction of eight metal ions
from a sulphate solution using DEHPA has been reported as a function of pH to be Fe3+ >Zn2+ >Cu2+ >Co2+ >Ni2+ >Mn2+ >Mg2+ >Ca2+
where Zn is extracted much earlier than Mn.[14] In a more recent
study of the separation of divalent metal ions from a synthetic
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386
M. Avila et al.
laterite leach solution, the extraction of metal ions was in the order
Zn2+ >Ca2+ >Mn2+ >Cu2+ >Co2+ >Ni2+ >Mg2+ .[15] By varying the acidic
conditions and the temperature as main parameters, the target metal (or
even different metals) can be separated from the bulk solution by changing
in various steps the conditions to get pure solutions of the target metals. Cyanex 272 has been used, as well as its thio-substituted derivatives
(Cyanex 302 and Cyanex 301), in the extraction of several metal ions.[16]
Various studies report the adequacy of Cyanex 272 to extract Fe, Zn, Cr,
Cu, and Ni from sulphuric and/or sulphate solutions.[17–19] In the present
study, to achieve greater recoveries and improved selectivity, another
commercial extractant, Ionquest 290 with the same active ingredient as
Cyanex 272, bis(2,4,4-trimethylpentyl) phosphinic acid [(C8 H17 )2 P(O)OH]
was also studied. In addition, two kerosenes with different flash points were
also studied as a solvent for the extractants.
Thus, the aim of this work was to investigate the SX processes for the
recovery of Zn from a mine effluent using either DEHPA, Cyanex 272,
or Ionquest 290 as extractants to identify the best extractant regarding
the efficiency as well as the process selectivity to recover Zn from that
mine stream. Determination of the best type of kerosene for the mentioned
extraction/stripping process was an additional goal of this study.
EXPERIMENTAL
Sample Description
Fe was removed from the mine water prior to the SX treatment by means
of a biooxidation process using Thiobacillus ferrooxidans and a precipitation step[20,21] to obtain a pregnant leach solution (PLS) without iron,
since no reagents capable of extracting Zn selectively from a solution
containing Fe are commercially available. Major elements present in the
PLS were determined by means of Inductively Coupled Plasma-Optical
Emission Spectroscopy (ICP-OES) (ThermoElemental model Intrepid II
XLS, Franklyn, MA, USA).
Reagents
The extractants DEHPA (Batch ref. 0063829) and Ionquest 290 (Batch Ref.
G05A1) were kindly supplied by Rhodia UK Ltd. and Cyanex 272 was purchased from Cytec Industries BV, Netherlands. Extractants were dissolved
in commercial grade extra-pure aliphatic kerosene Ketrul D80 or Ketrul
D100 (Batch ref. 20062016 and 20061560, respectively) kindly supplied by
Total Fluides France. Ketrul D80 and Ketrul D100, have a flash point of
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Zinc Recovery from a Mining Effluent
387
72◦ C and 100◦ C or superior (ISO 2719), respectively. It must be pointed out
that the higher the flashpoint, the lesser the flammability of the kerosene,
and, therefore the higher the security of the solvent extraction process. The
stripping of the organic enriched phase was performed using 2.0 M sulfuric acid solution. Sulfuric acid 95–98% was purchased from J.T. Baker,
Phillipsburg, NJ. All of them were used as received without any further
purification. Stoppered glass tubes of 50 mL were used for the contact of
the two phases and the agitation took place in a rotating rack. Metal content in the strip liquor and in the raffinate were determined by means of
a ThermoElemental ICP-OES model Intrepid II XLS (Thermo, Franklyn,
MA, USA).
Procedure
Kinetic Experiments
For the kinetic experiments 10 mL of DEHPA 40% (v/v), Cyanex 272
5% (v/v), or Ionquest 290 5% (v/v) were agitated with 10 mL of PLS
(ratio A/O = 1) in a rotating rack at 5, 10, 20, 30, 40, and 60 min. The
organic phase loaded with the target metal/s (OP) was stripped with 5 mL
of H2 SO4 2.0 M during 3 h to ensure complete stripping.
Selectivity Experiments
To determine selectivity, isotherms varying the ratio A/O from 0.1 to 10
were done. Different volumes of Cyanex 272 5% (v/v), Ionquest 290 5%
(v/v), or DEHPA 40% (v/v) in each type of kerosene were equilibrated with
the PLS. After 15 min of equilibration, OP was stripped with 5 mL H2 SO4
2.0 M. DEHPA concentration was higher due to efficiency related to the
extraction yield and the extractant cost. No centrifugation of the dualphase system was required because of the clear-phase separation obtained.
Selectivity of the solvents towards Zn was determined by the corresponding recovery of Zn and metal impurities and by the amount of metal not
stripped from the OP (remaining %, R); hence the recovery was expressed
as the ratio between the concentration of metal in the strip liquor and the
PLS concentration (Eq. (1)). The %Remaining R was calculated considering the concentration of the target metal in the raffinate and in the PLS
(Eq. (2)); and the %Remaining OP was considered as the amount of metal
not recovered and not remaining in the raffinate(R) (Eq. (3)).
Znstrip
×100
(1)
%Recovery =
ZnPLS
388
M. Avila et al.
Znraffinate
%Remaining R =
ZnPLS
×100
%Remaining OP = 100 − %Recovery − %Remaining R
(2)
(3)
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Effect of Extractant Concentration
After selection of the most appropriate extractant, isotherms varying the
ratio A/O from 0.1 to 10 at three different concentrations of Ionquest 290:
5%, 10%, and 20% (v/v) were studied. The dual phase was agitated during
15 min, and the organic phase was stripped afterwards with 5 mL of H2 SO4
2.0 M.
RESULTS AND DISCUSSION
The results include characterization of mining water samples, solvent
extraction kinetics, extraction selectivity, and the effect of the selected
extractant concentration.
Sample Description
After Fe removal, the solution was colorless. The content of relevant metals is listed in Table 1. After Fe removal small amounts of Fe were found
on the effluent solution but Zn concentration was not affected by this process. This solution contains big amounts of Ca, Mn, and Al as the main
impurities from the mine stream. In addition, the PLS is around pH 4.3,
which is a suitable pH for zinc extraction.[22,23]
Kinetics Experiments
Kinetics experiments were conducted in order to determine the differences between the extractants as well as to determine the time required to
reach equilibrium. The results for the three studied extractants—DEHPA,
Cyanex 272, and Ionquest 290—for Zn Recovery (%) at ratio A/O = 1
Table 1. Characteristics of the mine water after iron removal
pH
4.3±0.1
[Zn]
(mg/L)
[Ca]
(mg/L)
[Cu]
(mg/L)
[Fe]
(mg/L)
[Al]
(mg/L)
[Mn]
(mg/L)
881 ± 50
580 ± 20
45 ± 7
1.2 ± 0.9
210 ± 10
195 ± 10
Zinc Recovery from a Mining Effluent
389
Extractant kinetics
100
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% Recovery
80
60
40
20
0
0
5
10
15
20
25
30
35
Time (min)
DEHPA 40% KD80
CYANEX 272 5% KD80
IONQUEST 5% KD80
40
45
50
55
60
DEHPA 40% KD100
CYANEX 272 5% KD100
IONQUEST 5% KD100
Figure 1. Recovery kinetics of DEHPA (circles), Cyanex 272, (squares), and
Ionquest 290 (triangles) using Ketrul D80 (KD80) (solid line) and Ketrul D100
(KD100) (dashed line).
as a function of time are given in Fig. 1. An increase of recovery was
observed in the first 5 min, and after 5 min a plateau was reached indicating that less than 5 min are required to achieve equilibrium under
the given experimental conditions. Recovery achieved for DEHPA 40% is
more than twice higher than that obtained for Cyanex 272 and Ionquest
290, probably due to a DEHPA concentration 8-fold higher than the two
phosphinic extractants. Small differences were observed between Cyanex
272 and Ionquest 290, with a similar equilibration time, with a slightly
higher recovery for Ionquest 290. No relevant differences were observed
for the two types of kerosene employed (different flash point).
Selectivity
For the electrowinning process, an enriched Zn solution with low amounts
of impurities is required. This can be achieved with an extractant which
selectively recovers Zn from the PLS, leaving all the other elements in the
raffinate, or by increasing the process with further steps such as scrubbing
of the OP when a less selective solvent is used, or by further separation
processes. Moreover, it is important to determine the amount of metals
remaining in the organic phase to predict the design of the overall recovery
process, that is, additional scrubbing and washing steps. When elements
are poorly released from the OP to the strip solution, that is, when these
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390
M. Avila et al.
elements remain in the OP after stripping, hindering its possible reuse, a
solvent regeneration step is mandatory, and this regeneration involves an
increase of the economic costs and time of the entire process.
Taking into account the results obtained for the selectivity experiments
(Figs. 2–4), a logical recovery decrease as the phase ratio A/O increases,
and this is observed for all the extractants and is due to the saturation of
the extractant. For Cyanex 272 and Ionquest 290 a plateau was observed
at a phase ratio A/O > 1 for all the metals analyzed, indicating that the
extractant is saturated at this ratio. For DEHPA, the recovery is still diminishing which indicates that not all the extractant is complexed with metals
under the extraction conditions.
Metal recovery using DEHPA mostly follows the trend: Zn> Ca> Mn
> Al > Cu. At phase ratio A/O = 1 recovery of Zn was around 75%,
but the recovery of metal impurities was also significant, especially Ca and
Mn, with a recovery of 60% and 30%, respectively, indicating that DEHPA
is poorly selective for Zn extraction (Fig. 2a). Also, around 80% of the Al
remained in the OP after the stripping (Fig. 2b) when using the A/O ratio
from 0.1 to 2, having a fouling effect on the possible reuse of the extractant.
Cyanex 272 recovery (Fig. 3a) followed the trend Zn>>Cu>Mn∼
Ca∼Al. In this case, Mn, Ca, and Al are slightly recovered, indicating
Cyanex 272 to have a higher selectivity for Zn than DEHPA. Recoveries
obtained for Zn ranged from 65% (ratio A/O = 0.1) to ∼20% (ratio A/O
> 2) while the recovery of the other metals ranged from 35% (ratio A/O
= 0.1) to less than 5% (ratio A/O > 2). In addition, negligible amounts
of metals (around 1%) were found in the OP (Fig. 3b), indicating that
practically no regeneration of the solvent is required.
Ionquest 290 recovery (Fig. 4a) followed the trend Zn>>Al>Cu∼
Mn∼Ca. Zinc recoveries range from 85% (ratio A/O = 0.1) to ∼30% (ratio
A/O > 2) while the recovery of Al range from 20% (ratio A/O = 0.1) to less
than 5% (ratio A/O > 2). Recovery of the other metal analyzed was below
5% in the entire studied range. As Cyanex 272, Ionquest metal remaining in
the OP showed a similar behavior that Cyanex 272, being the concentration
of metal below 5% for all the elements analyzed at the range of the phase
ratio studied (Fig. 4b). Thus, unlike DEHPA, Cyanex 272, and Ionquest
290 selectively extract Zn from a solution containing high amounts of Ca
and other metals in fewer amounts without fouling of the OP. Given that
small amounts of Ca is found in the strip liquor, an extractant regeneration
should be taken into account if the process is conducted several times with
the same extracting OP.
On the other hand, the difference observed on the recovery trends
between DEHPA and the other two extractants can be attributed to their
chemical nature provided that, phosphoric extractants have higher affinity for calcium than phosphinic extractants. In addition, the differences in
trends between Cyanex 272 and Ionquest 290 are relatively very small and
Zinc Recovery from a Mining Effluent
391
(a) Recovery DEHPA
100
Zn KD80
Ca KD80
Al D80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al D100
Mn KD100
Cu KD100
%
50
40
30
20
10
0
0
2
4
6
8
10
Ratio A/O
(b) Remaining OP DEHPA
100
Zn KD80
Ca KD80
Al D80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al D100
Mn KD100
Cu KD100
60
%
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60
50
40
30
20
10
0
0
2
4
6
8
10
Ratio A/O
Figure 2. Recoveries (a) and Remaining OP (b) percentages at different A/O ratios
for DEHPA 40% (v/v).
in the same order of magnitude as in the case of Al, Cu, Ca, and Mn. Such
small differences can be explained by both the different phosphinic acid
concentration present in each extractant and to the presence of product
impurities.
392
M. Avila et al.
(a) Recovery Cyanex 272
100
Zn KD80
Ca KD80
Al KD80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al KD100
Mn KD100
Cu KD100
%
50
40
30
20
10
0
0
2
4
6
8
10
Ratio A/O
(b) Remaining OP Cyanex
100
Zn KD80
Ca KD80
Al KD80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al KD100
Mn KD100
Cu KD100
60
%
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60
50
40
30
20
10
0
0
2
4
Ratio A/O
6
8
10
Figure 3. Recoveries (a) and Remaining OP (b) percentages at different A/O ratios
for Cyanex 272 5% (v/v).
DEHPA showed poor selectivity towards Zn due to the co-extraction
of Ca resulting on a gypsum precipitate in the stripping solution. Besides
the high amount of Ca and Mn in the strip liquor, high amounts of Al
remained in the OP after the strip step, hence requiring regeneration of
Zinc Recovery from a Mining Effluent
393
(a) Recovery IONQUEST
100
Zn KD80
Ca KD80
Al D80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al D100
Mn KD100
Cu KD100
%
50
40
30
20
10
0
0
2
4
6
8
10
Ratio A/O
(b) Remaining OP Ionquest
100
Zn KD80
Ca KD80
Al D80
Mn KD80
Cu KD80
90
80
70
Zn KD100
Ca KD100
Al D100
Mn KD100
Cu KD100
60
%
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60
50
40
30
20
10
0
0
2
4
6
8
10
Ratio A/O
Figure 4. Recoveries (a) and Remaining OP (b) percentages at different A/O ratios
for and Ionquest 290 5% (v/v).
the solvent prior to their reuse, increasing the cost of the whole process. Cyanex 272 and Ionquest 290 showed high Zn selectivity towards Ca
and negligible amounts of metals in the OP, indicating that no extractant
regeneration step is required. When comparing Cyanex 272 and Ionquest
290 recoveries obtained for Zn, it can be pointed out that Ionquest 290
achieved 5–10% higher recoveries than Cyanex 272. These results indicate
394
M. Avila et al.
Effect of the Extractant Concentration
Because Cyanex 272 and Ionquest 290 are five to seven times more expensive than DEHPA, their concentration should be as low as possible without
diminishing recovery. Isotherms varying the concentration of Ionquest 290
were done in order to determine a proper concentration of Ionquest 290
that recovers maximum Zn without increasing extractant costs.
From the results collected in Fig. 5, an expected increase of Zn recovery is observed as the extractant concentration increases. When comparing
the results for the different concentrations of Ionquest 290 with DEHPA, it
can be highlighted that Ionquest 20% (v/v) is capable of achieving a higher
recovery than DEHPA 40%. In addition, selectivity was not modified as
the concentration increased and complete stripping of the organic phase
was ensured. Again, no significant differences are observed between the
two different kerosene diluents.
Effect of Ionquest 290 concentration
100
Zn 5% KD80
Zn 10% KD80
Zn 20% KD80
DEHPA 40% KD80
90
80
70
Zn 5% KD100
Zn 10% KD100
Zn 20% KD100
DEHPA 40% KD100
60
%
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that the most selective extractant for minimizing Ca extraction achieving
good Zn recovery is Ionquest 290. Considering the two different kerosenes
employed, no significant differences were observed independently of the
employed extractant, thus indicating that both of them can be equally feasible for this application. Thus, from an engineering point of view, the use
of Ketrul D100 is recommended due to their lower flammability.
50
40
30
20
10
0
0
2
4
Ratio A/O
6
8
10
Figure 5. Recovery obtained using Ionquest 290 5% (squares), Ionquest 290 10%
(rhombus), Ionquest 290 20% (triangles), and DEHPA 40% (circles) using Ketrul
D80 (solid line) or Ketrul D100 (dashed line).
Zinc Recovery from a Mining Effluent
395
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CONCLUSIONS
DEHPA reagent was unable to extract Zn selectively from the solution at
the target pH and temperature of the mine effluent. High amounts of Ca
were extracted, creating a gypsum precipitate in the strip solution, avoiding their use for electrowinning. In addition, Al was extracted from the
PLS but not stripped out, fouling the extractant and inhibiting their reuse.
Neither Cyanex 272 nor Ionquest 290 5% (v/v) indicated Al, Cu, Mn, or
Ca enrichment in the strip liquor, obtaining recoveries of Zn up to 85%.
Although both of them followed similar trends, Ionquest 290 recovery
of Zn is 5–10% higher than Cyanex 272. In addition, Ionquest 290 20%
(v/v) obtained recoveries comparable or even higher than DEHPA 40%
(v/v). Although Ionquest 290 is 5–7 times more expensive than DEHPA,
Ionquest 290 was selected as the most suitable extractant for the target
stream due to its higher selectivity and loading capacity towards Zn extraction, which avoids both the steps of scrubbing of the gypsum precipitate in
the strip liquor and regeneration of the solvent due to high amounts of
Al not stripped from DEHPA. Besides, the recycling of the organic phase
minimizes the importance of the extractant costs. Both the solvents Ketrul
D80 and Ketrul D100 showed similar behavior, Ketrul D100 is the solvent
recommended due to its lower volatility and flammability.
ACKNOWLEDGMENTS
Thanks are due to Dr. Baruch Grinbaum of the Bateman Company for
his valuable advice. The public company EGMASA (Andalusia, Spain) is
acknowledged for supporting the personnel expenses for the present study.
The Spanish Ministry for Science and Innovation is acknowledged for supporting the laboratory expenses (Project CTQ2009-07432 (Subprograma
PPQ)).
REFERENCES
1. Adriano, D. C. Trace Elements in Terrestrial Environments, 2nd Ed.;
Springer-Verlag: New York, 2001.
2. Sayilgan, E.; Kukrer, T.; Civelekoglu, G.;Ferella F.; Akcil, A.; Veglio,
F.; Kitis, M. A review of technologies for the recovery of metals from
spent alkaline and zinc–carbon batteries. Hydrometallurgy 2009, 97,
158–166.
3. Jha, M.K.; Kumar, V.; Singh, R.J. Solvent extraction of zinc from
chloride solutions. Solv. Extr. Ion Exch. 2002, 20(3), 389–405.
Downloaded By: [Consorci de Biblioteques Universitaries de Catalunya] At: 12:27 6 June 2011
396
M. Avila et al.
4. Salgado, A.L.; Veloso, A.M.O.; Pereira, D.D.; Gontijo, G.S.; Salum,
A.; Mansur, M.B. Recovery of zinc and manganese from spent alkaline
batteries by liquid-liquid extraction with Cyanex 272. J. Power Sources
2003, 115, 367–373.
5. Devi, N. B.; Nathsarma, K. C.; Chakravortty, V. Extraction and separation of Mn(II) and Zn (II) from sulfate solutions by sodium salt of
Cyanex 272. Hydrometallurgy 1997, 45(1–2), 169–179.
6. Kolev, S. D.; Baba, Y.; Cattrall, R.W.; Tasaki, T.; Pereira, N.; Perera,
J.M.; Stevens, G.W. Solid phase extraction of zinc(II) using a PVCbased polymer inclusion membrane with di(2-ethylhexyl)phosphoric
acid (D2EHPA) as the carrier. Talanta 2009, 78, 795–799.
7. Vahidi, E.; Rashchi, F.; Moradkhani D. Recovery of zinc from an
industrial zinc leach residue by solvent extraction using D2EHPA.
Miner. Eng. 2009, 22, 204–206.
8. El-Nadi, Y.A.; Daoud, J.A.; Aly, H.F. Leaching and separation of zinc
from the black paste of spent MnO2-Zn dry cell batteries. J. Hazard.
Mater. 2007, 143, 328–334.
9. Kyuchoukov, G.; Zhivkova, S. Options for the separation of Copper
(II) and Zinc (II) from chloride solutions by KELEX 100. Solv. Extr.
Ion Exch. 2000, 18(2), 293–305.
10. Ritcey, G. M.; Lucas, B. H. Purification of manganese solutions
containing copper and zinc by liquid-liquid extraction, using bis(2ethylhexyl)phosphoric acid. Can. Metall. Quart. 1971, 10(3), 223–228.
11. Kunzmann, M; Kolarik, Z. Extraction of zinc(II) with di(2-ethylhexyl)
phosphoric acid from perchlorate and sulfate media. Solv. Extr. Ion
Exch. 1992, 10(1), 35–49.
12. Sastre, A. M.; Miralles, N.; Aguilar, M. The extraction of copper(III)
from sulfate and perchlorate solutions by di(2-ethylhexyl)phosphoric
acid dissolved in Isopar-H. Chem. Scripta 1984, 24(1), 44–48.
13. Grimm, R.; Kolarik, Z. Acidic organophosphorus extractants. XIX.
Extraction of copper(II), cobalt(II), nickel(II), zinc(II), and cadmium(II) by bis(2-ethylhexyl) phosphoric acid. J. Inorg. Nucl. Chem.
1974, 36(1), 189–192.
14. Ritcey, G.M.; Ashbrook, A.W. Solvent Extraction. Principles and
Applications to Process Metallurgy. Part I and II. Elsevier: Amsterdam;
1984.
15. Cheng, C. Y. Purification of synthetic laterite leach solution by solvent
extraction using D2EHPA. Hydrometallurgy 2000, 56(3), 369–386.
16. Rickelton, W. A.; Boyle, R. J. The selective recovery of zinc with new
thiophosphinic acids. Solv. Extr. Ion Exch. 1990, 8(6), 783–797.
17. Lanagan, M. D.; Ibana, D. C. The solvent extraction and stripping of
chromium with Cyanex 272. Miner. Eng. 2003, 16(3), 237–245.
18. Rickelton, W.A.; Flett, D.S.; West, D.W. Cobalt-nickel separation by
solvent extraction with bis(2,4,4 trimethylpentyl)phosphinic acid. Solv.
Extr. Ion Exch. 1984, 2(6), 815–838.
Downloaded By: [Consorci de Biblioteques Universitaries de Catalunya] At: 12:27 6 June 2011
Zinc Recovery from a Mining Effluent
397
19. Reddy, B. R.; Sarma, P.V.R.B. Separation and recovery of cobalt and
nickel from sulfate solutions of Indian Ocean nodules using Cyanex
272. Miner. Process Metall. 2001, 18(3), 172–176.
20. Mazuelos, A.; Carranza, F.; Palencia, I.; Romero, R. High efficiency
reactor for the biooxidation of ferrous iron. Hydrometallurgy 2000,
58(3), 269–275.
21. Carranza, F.; Iglesias, N.; Romero, R.; Palencia, I. Kinetics improvement of high-grade sulfides bioleaching by effects separation. FEMS
Microbiol. Rev. 1993, 11(1–3), 129–138.
22. Northcott, K.; Kokusen, H.; Komatsu, Y.; Stevens, G. Synthesis and
surface modification of mesoporous silicate SBA-15 for the adsorption
of metal ions. Sep. Sci. Technol. 2006, 41, 1829–1840.
23. Tsakiridis, P.E.; Oustadakis, P.; Katsiapi, A.; Agatzini-Leonardou S.
Hydrometallurgical process for zinc recovery from electric arc furnace
dust (EAFD). Part II: Downstream processing and zinc recovery by
electrowinning. J. Hazard. Mater. 2010, 179, 8–14.
IV
ZINCRECOVERYFROMANEFFLUENT
USINGIONQUEST290:FROM
LABORATORYSCALETOPILOT
PLANT
M.Avila,B.Grinbaum,F.Carranza,A.Mazuelos,R.Romero,N.
Iglesias,J.L.Lozano,G.Perez,M.Valiente.
Hydrometallurgy(2011),107:6367
Hydrometallurgy 107 (2011) 63–67
Contents lists available at ScienceDirect
Hydrometallurgy
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / h yd r o m e t
Zinc recovery from an effluent using Ionquest 290: From laboratory scale to
pilot plant
M. Avila a, B. Grinbaum b, F. Carranza c, A. Mazuelos c, R. Romero c, N. Iglesias c, J.L. Lozano d,
G. Perez a, M. Valiente a,⁎
a
Universitat Autonoma de Barcelona, Dept de Química, 08193, Bellaterra, Spain
Bateman Litwin N.V. POB 15, Yokneam 20692, Israel
Universidad de Sevilla, Depto. de Ingeniería Química, 41092 Sevilla, Spain
d
EGMASA, Isla de la Cartuja, 41092 Sevilla, Spain
b
c
a r t i c l e
i n f o
Article history:
Received 28 September 2010
Received in revised form 17 January 2011
Accepted 17 January 2011
Available online 17 February 2011
Keywords:
Mine tailing pond
Zinc extraction
Fe bioxidation removal
Bateman Pulsed Column
Ionquest 290
a b s t r a c t
A stream of effluent from a mine tailings pond, containing zinc, ferrous ions and other metals, required
treatment to prevent pollution and recover valuable metals. A solvent extraction (SX) process using Ionquest
290 as extractant was developed to recover the Zn from the effluent. Ferrous ions were bio-oxidized and
removed by selective alkaline precipitation prior to the zinc extraction. The Fe removal as well as the SX
process were developed successfully at laboratory scale and verified in a pilot plant on-site, using two
Bateman Pulsed Columns for the extraction and stripping of Zn. The results were satisfactory obtaining above
95% recovery of the Zn.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
An abandoned mine in Andalusia, Spain, has a huge stream of
effluent, estimated to be 10,000 m3/day, flowing into a tailings pond.
Since the effluent contains about 1 g/L of Zn and significant amounts
of ferrous, ferric, calcium, copper, aluminum and manganese ions,
their removal is required in order to prevent pollution of a nature
reserve downstream from the area. The recycling of such effluents
rather than simple neutralisation and disposal as a hazardous waste
can provide an economical benefit, while diminishing the volume of
hazardous materials contained in the mine tailing. Zinc metal has a
high economical value and recycling can add economical value to
those residues.
Conventional methods for separation of pure Zn include precipitation,
ion exchange, adsorption, electrochemical recovery, membrane separation and solvent extraction (SX) (Sayilgan et al., 2009) with SX being the
most economical and practical process to extract Zn from industrial
waters (Devi et al., 1997; Jha et al., 2002; Salgado et al., 2003). In recent
years SX has become essential to the hydrometallurgical industry due to a
growing demand for high purity metals, rigid environmental regulations,
the need for lower production costs, as well as due to the diminishing
production in high-grade ore reserves (Alamdari, 2004; Owusu, 1998). In
⁎ Corresponding author. Tel.: +34 935812903; fax: +34 935811985.
E-mail address: Manuel.Valiente@uab.es (M. Valiente).
0304-386X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.hydromet.2011.01.009
this sense, the organophosphorus acids, i.e., DEHPA (Ritcey and Lucas,
1971) and Cyanex272 (Lanagan and Ibana, 2003) and their thiosubstituted derivatives Cyanex 301 and Cyanex 302 (Rickelton and Boyle,
1990) have been the most widely used extractants to recover Zn.
In the present study, a recently commercialized organophosphorus
extractant, Ionquest 290, has been employed for the selective
recovery of zinc from a mine effluent located in Aznalcollar (Sevilla,
Spain). Ionquest 290 has the same active ingredient as Cyanex 272, bis
(2,4,4-trimethylpentyl) phosphinic acid, but has a lower content of
inactive impurities, the phosphine oxide impurity is b5% in Ionquest
290 but around 15% in Cyanex 272 (Barnard and Shiers, 2010).
Iron ions are more strongly extracted than the majority of metals
(including Zn) by most of the known commercial extractants (Lupi
and Pilone, 2000; Yokoyam et al., 1996) so it needs to be removed
prior to SX process. For this purpose, a process based on the biooxidation of Fe2+, using specific bacteria, followed by selective
alkaline precipitation of Fe3+, was required (Mazuelos et al., 1999,
2010a).
EGMASA, the regional environmental government company in
Andalusia (Spain), suggested the recovery of Zn from the mentioned
effluent by combining solvent extraction (SX) and electrowinning
(EW) process. Therefore, this process should be environmentally
friendly and also to produce an economically effective output. At least
95% of the Zn must be recovered from the effluents in order to satisfy
the environmental requirements and the SX plant should provide a
final product stream of 90 g/L Zn in the stripping step (strong
64
M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
electrolyte) by using a spent electrolyte with 50 g/L Zn, to fulfill the
operating conditions of the EW plant. The whole bio-oxidation and Fe
precipitation, as well as the SX process were developed successfully at
laboratory scale and afterwards verified in a pilot plant on-site.
2. Materials and methods
lime solution from a separated tank. The bioreactor operated
continuously with a residence time of 60 min and connected to the
pilot plant in order to bio-oxidize a total volume of 56 m3 of mine
water. After precipitation and sedimentation of iron compounds, the
supernatant was directly used as feed solution to the solvent
extraction stage (Fig. 1).
2.1. Iron removal
2.2. Solvent extraction
Laboratory tests were aimed at determining the operating conditions
for the pilot plant. To reach the target concentration of b5 ppm it is
necessary to completely convert ferrous to ferric ions in the biooxidation stage.
Bio-oxidation laboratory tests were performed in a methacrylate
column packed with siliceous stone particles and inoculated with a
mixed culture of Acidithiobacillus ferrooxidans, Leptospirillum ferrooxidans
and some heterotrophic bacteria including Acidiphillium organovorum,
facilis and cryptum (Mazuelos et al., 2010b) The inoculum was obtained
from the Riotinto Mine acid mine drainage waters. The culture was
routinely maintained on a modified Silverman and Lungren 9 K nutrient
medium at pH 1.25 (adjusted with H2SO4) in the University of Seville
laboratories. The effluent solution was fed into the bottom of the column
and overflowed while air was supplied under pressure (0.5 bar) from the
bottom. Precipitation tests were carried out in stirred reactors with pHcontrolled addition of alkaline reagent. The pH and pumping rates
determined the length of tests, avoiding a rapid increase in pH that
would lead to undesirable co-precipitation phenomena. Ferrous concentration was determined by end-point automatic titration with K2Cr2O7
while total metal concentration was determined by AAS.
At the pilot plant site, the bio-oxidation process took place in a
bioreactor consisting of a 150 cm high and 70 cm diameter stainless
steel column divided to three zones: a 30 cm deep bottom space
where air and solution were fed in, a siliceous stone packed bed
containing the inoculum supported by a stainless steel screen and an
air space at the top where pH and Eh control was made, a 50 mm pipe
formed the solution overflow. The effluent circulated through a tank
where pH was initially adjusted and, after pH adjustment, the solution
was transferred to the bioreactor where bio-oxidation of ferrous ions
took place, to be finally transferred to a precipitation tank fed by a
For the laboratory investigation of the solvent extraction process, a
solution of 5% Ionquest 290 dissolved in kerosene was used. Ionquest
290 (Purity N 95%) was supplied by Rhodia UK Ltd. and commercial
grade extra-pure aliphatic kerosene Ketrul D100 (bp 100 °C) by Total
Fluides France. All reagents were used as received without further
purification. A solution of Na2CO3 was used for pH adjustment during
the solvent extraction experiments. For the stripping step, the loaded
solvent was contacted with 2 M H2SO4 at a phase ratio O:A = 10, the
initially expected phase ratio in the plant to achieve the required zinc
transfer in the EW plant of 20 g/L. In practice, it was found that the
required transfer in the EW plant was 40 g/L Zn, consequently, the
phase ratio was modified to O:A = 20. No laboratory tests were
undertaken at this phase ratio, but directly applied in the pilot plant.
Two Bateman Pulsed Columns (BPC) were required for the SX and
stripping processes at the pilot plant due to their demonstrated
feasibility in several SX plants (Ferreira et al., 2010; Gameiro et al.,
2010; Sole et al., 2005). BPC are large diameter vertical pipes filled
alternately with disk and doughnut shaped baffles to promote contact
between the organic and aqueous phases through the column. A
decanter at each end of the column allows the liquids to coalesce and
be decanted separately. When the solvent phase is continuous, the
interface between the phases is in the lower decanter and when the
aqueous phase is continuous, it is in the upper decanter. The columns
are pulsed by blowing air at the required amplitude and frequency of
the pulses (Ritcey, 2006).
An 80 mm diameter BPC, 7 m high (equivalent to 3 theoretical
mixer-settler stages) was chosen for the SX process and a 40 mm
diameter BPC 6 meter high for the stripping. The piping of the plant is
shown in Fig. 1.
EXTRACTION
COLUMN
BIO-OXIDATION
REACTOR
STRIPPING
COLUMN
Weak
Loaded
solvent electrolyte
(WE)
(LS)
PRECIPITATION
TANK
Upper
decanter
LIME
TANK
Soda
Barren
solvent (BS)
Bed containing
the inoculum
Area of distribution of
air and liquid
Diffusers
disk
doughnut
Air
pH ADJUSTMENT
TANK
Lower
decanter
Raffinate
Feed
ORGANIC
SOLVENT
TANK
Fig. 1. Bio-oxidation and SX flowsheet at the pilot plant.
Strong
electrolyte (SE)
M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
1200
400
300
800
250
600
200
150
400
100
200
0
Flow rate (L/h)
350
1000
[Fe(II)], [Zn] (ppm)
All flows were fed through metering pumps and the flow rates of
all inlets and aqueous outlets were measured by rotameters. The pilot
was run for 12 working days, 10 h a day on average, i.e. a total of
120 h. The average flow rate of the aqueous feed was 150 L/h, so,
about 18m3 of tailing solution after Fe precipitation were treated. The
total volume of the solvent was 300 L and it had 5% Ionquest 290
dissolved in kerosene (20% aromatic and 80% aliphatic); the weak
electrolyte (WE, strip solution) consisted of 190 g/L H2SO4 with 50 g/L
Zn2+. A solution of 50–100 g/L Na2CO3 was prepared periodically in a
60 L barrel and used to adjust the pH.
The concentration of Zn was determined using a Perkin Elmer
3110 AAS at the mine laboratory. The Zn in the raffinate and SE was
determined directly, while the Zn in the barren and loaded solvent (BS
and LS) solution were determined after stripping using H2SO4.
65
50
0
20
40
60
80
100
120
140
0
160
Time (h)
[Fe(II)]in
3. Results and discussion
[Fe(II)]out
[Zn]out
[Zn]in
Flow rate
Fig. 3. Bio-oxidation process at the pilot plant.
3.1. Iron removal
The representative composition of the major components in the
effluents was Zn 1000 ± 100 mg/L, Fe 500 ± 50 mg/L (36% ferric), Ca
600 ± 50 mg/L, Mn 200 ± 20 mg/L and Cu 50 ± 5 mg/L. The pH of the
effluent was always around 3.0.
After the bio-oxidation process, laboratory scale precipitation
experiments indicated that a final pH of 4.5 was reached after 65 min
and Fe precipitation was almost complete, with the remaining
concentration below 0.5 ppm. The initial Zn concentration was
practically unaffected by this process (Fig. 2). Lime consumption
was 4.2 g CaO per kg of solution.
Similar results were obtained in the continuous bio-oxidation
process at the pilot plant (Fig. 3) achieving total ferrous oxidation at
all times and flow rates tested. Table 1 shows the effect of the biooxidation – precipitation stage. Iron precipitation took 60 min in the
pilot plant. During this time, the lime addition to reach pH 3.5 took
45 min followed by intermittent dosing for the next 15 min until pH
4.7 was achieved. After the precipitation step Fe was completely
removed, the amount of Al decreased drastically, Cu dropped by half
(from 45.0 ppm to 21.7 ppm) while the concentration of the other
elements measured, including Zn, remained similar to the initial.
Precipitation and sedimentation stages accurately reproduced laboratory results, producing 56 m3 of iron-free solution with practically
all the initial zinc.
3.2. Computer simulation
1100
1080
1060
1040
1020
1000
980
960
940
920
900
0
10
20
30
40
50
time (min)
[Zn]
pH
60
70
5
4,5
4
3,5
3
2,5
2
1,5
1
0,5
0
80
[Fe]
Fig. 2. Evolution of [Zn], [Fe] and pH in laboratory precipitation tests.
pH; [Fe] (ppm)
[Zn] (ppm)
Computer simulations were performed to estimate the required
pilot plant inputs and outputs by using CurveExpert 1.3 to calculate
the distribution coefficients, D, and the number of stages). Experience
has shown that computer simulation is a more flexible design tool
than McCabe–Thiele diagrams for pulsed columns (Grinbaum, 1992;
Grinbaum, 1993; Gottliebsen et al, 2000). The results obtained in the
simulation, collected in Table 2, determined that at a phase ratio O:
A = 0.5–0.6, a two-stage column is enough to recover more than 95%
of the effluent Zn. The addition of a third stage enables either to
decrease the phase ratio O:A to 0.4 or to work with a phase ratio of O:
A = 0.5 and obtain a recovery of Zn near to 99%, i.e. b10 ppm Zn in the
raffinate. The concentration of Zn in the loaded solvent should be in
the range of 2.2–2.8 g/L extractant, around 70–85% of the theoretical
total loading of 3.3 g/L, which is quite reasonable. In order to get a final
solution of 90 g/L Zn in the SE, i.e. a Zn transfer of 40 g/L, the stripping
should be run at a phase ratio of O:A = 20, and only one equilibrium
stage is required.
3.3. Solvent extraction and stripping
The initial concentration of Ionquest 290 was chosen to be 5%.
Using a higher concentration would require an extreme O:A phase
ratio, while a lower concentration would increase the flow rate of the
solvent and, accordingly, the size of the stripping unit. The maximum
loading that was obtained experimentally at limiting conditions, i.e.,
by contacting 3 times the solvent with corresponding fresh portions of
effluent at phase ratio O:A = 0.1, was 2.9 g Zn/L Ionquest 290. Since
this result is similar to the obtained after a single contact, it reveals
that the limiting conditions can be achieved by a single contact.
Tests to determine pH control were done at laboratory. From the
results shown in Table 3, it can be seen that without pH control, the
extraction was quite selective. Thus, no Mn, Cu and Al were extracted
and only a small amount of Ca was extracted. In addition, separation
factors were high enough to support the indicated selectivity.
However, the distribution ratio of Zn (DZn) at natural pH range was
low, especially at the dilute end of the process (phase ratio O:A = 10).
In addition, the pH of the raffinate (final pH) dropped from 2.6 to 2.1
as the phase ratio O:A increased, while the suitable pH for Zn
extraction by Ionquest 290 is above 2.5 (Tsakiridis et al., 2010).
Furthermore, to avoid Ca co-extraction, pH should be around 3 as
indicated by the isotherms in the Cyanex 272 online User Manual p. 5
Table 1
Solution composition, before and after treatment.
[Fe2+] (ppm)
[Fe3+] (ppm)
[Zn] (ppm)
[Al] (ppm)
[Mn] (ppm)
[Cu] (ppm)
[Ca] (ppm)
[Pb] (ppm)
pH
Initial solution
After bio-oxidation
After precipitation
254
446
1020
292
265
45
600
1.6
3.0
0
690
1020
250
260
45
600
1.6
1.93
0
0.2
1010
20
200
21.7
600
1.6
4.78
66
M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
Table 2
Recovery of Zn vs. Plant Configuration, using 5% Ionquest 290, ZnPLS = 0.95 g/kg.
No. stages
Phase ratio O:A
Zn in raff. (ppm)
Recovery (%)
2
0.50
0.60
0.35
0.40
0.45
0.5
51
24
75
30
11
4
94.7
97.6
92.1
96.8
98.9
99.6
3
from Cytec Corporation (http://www.cytec.com/specialty-chemicals/
PDFs/CYANEX%20272.pdf, accessed 26th December 2010).
As seen in Table 4, adjusting to pH = 3 results in higher zinc than
without pH adjustment. The extraction of Mn and Ca remains quite
low as indicated by the high separation factors. Therefore, the pH at
the pilot plant should be maintained around pH 3. In practice, the pH
adjustment was achieved by direct neutralization of both the acidic
raffinate and the organic solvent (by pre-equilibration with aqueous
solution) using Na2CO3. The consumption of Na2CO3 was 1.62 kg/kg
Zn.
Shake out stripping experiments were carried out by contacting
200 mL of loaded solvent (LS) containing 1.95 g/L Zn with 20 mL of
aqueous phase containing 200 g/L H2SO4 and weak electrolyte (WE)
containing zinc concentrations of 40 to 90 g/L at 22 °C. The process
was carried out at O:A = 10, i.e. the concentration of Zn in the strong
electrolyte (SE) should increase by ~20 g/L with respect to the WE
solution, which was consistent with the results shown in Table 5. In all
cases, remaining Zn in the barren solvent (BS) was only 5–17 ppm, i.e.
almost all zinc was recovered. Thus, one stage of stripping is enough
for the Zn recovery regardless of the concentration of Zn in the
stripping solution.
Additional laboratory tests carried out at the mine site during the
pilot plant experiments at phase ratio O:A = 20, revealed that the
loaded solvent from the pilot plant was efficiently stripped in one
contact, i.e. one stage, by the strip solution used in the pilot plant
experiments, using a weak electrolyte with ~50 g/L Zn producing an
SE containing 90 g/L Zn, i.e. a zinc transfer of 40 g/L, as required for the
EW plant.
Preliminary hydraulic tests at the pilot plant showed that the
available flux is above 30 m3/m2/h in both columns. The stripping was
run mainly in order to produce BS and was not optimised. It was
operated at a flux of 40 m3/m2/h (35 L/h solvent), the pulsing had an
amplitude of 15 mm and a frequency of 1 Hz. The flow rate of the WE
through the pump was 5–7 L/h. The average value of Zn in the BS was
about 20 mg/L Zn.
Three tests with organic continuous dispersion and with aqueous
continuous dispersion were undertaken to determine the preferred
dispersion. During both organic continuous and aqueous continuous
runs, the temperature rose from 25 °C in the morning to 34 °C in the
evening, which facilitated the comparison between both dispersions.
Table 3
Extraction experiments with 5% Ionquest 290, no pH correction, 22 °C.
Phase ratio
Final
pH
O:A
PLS
0.1
0.3
0.5
1
2
3
5
10
5.0
2.58
2.50
2.31
2.18
2.16
2.32
2.25
2.10
Aqueous (mg/L)
Organic (mg/L)
D values &
separation factors
Zn
Mn
Ca
Zn
Mn
Ca
DZn
DZn/DCa
DZn/DMn
962
792
692
621
536
467
402
342
270
206
208
208
205
206
207
202
203
203
763
618
613
605
624
613
597
599
601
1595
910
668
444
273
178
132
76
0.03
0.2
0.1
0.1
0.03
0.3
0.1
0.1
8
12
15
13
9
10
6
8
2.0
1.3
1.1
0.8
0.6
0.4
0.4
0.3
154.5
66.4
44.4
38.4
40.9
23.9
39.9
22.5
1 104
1350
2260
1600
4200
270
800
610
Table 4
Extraction experiments with 5% Ionquest 290 at pH 3, 22 °C.
Phase ratio
Aqueous
(mg/L)
Organic
(mg/L)
D values &
separation factors
O:A
Zn
Mn
Ca
Zn
Mn
Ca
DZn
DZn/DCa
DZn/DMn
PLS (pH 5.0)
0.1
0.3
0.5
1
2
3
5
10
963
784
343
182
49
22
14
4
1
213
231
233
211
213
194
127
181
183
583
531
509
536
547
534
532
584
579
2878
2073
1700
875
502
297
192
90
0
0.4
0.5
1.5
3.4
2.7
3
1
76
72
40
38
43
46
42
48
3.7
6.0
9.3
17.9
22.8
21.2
48
90
26.4
42.9
133.0
199.9
285.0
235,6
685,7
1125
9·104
3·103
5·103
3·103
1 103
1 103
2 103
2·104
Every test took 5 h, long enough to reach steady state and the phase
ratio was kept at A:O = 2.1 during all the testwork.
As seen from Table 6, similar results were obtained with both
dispersions. The Zn concentration in the LS was around 2000 mg/L
and Zn in the raffinate was far below 50 mg/L, indicating than N95% of
the Zn is recovered. Hence, the extraction process was found to
operate successfully with both aqueous and organic continuous
dispersions at column fluxes of about 40 m3/m2/h, at 23–34 °C. As
the available flux and recovery with both dispersions were similar, it
is preferable to use the aqueous continuous dispersion as there is a
lower expenditure on solvent. With aqueous continuous the danger of
fire due to kerosene ignition is also significantly diminished.
The stripping of the LS containing around 2 g/L Zn, achieved a SE
with 30–40 g/L Zn above the WE, i.e. a zinc transfer of 30–40 g/L
whilst achieving b50 mg/L Zn in the BS at a flux of 45 m3/m2/h with
aqueous continuous at O:A phase ratio of 20.
While the column worked well and supplied the required BS to the
extraction, laboratory tests proved that there was no need for an extra
column, and one stage of mixer-settler was sufficient to obtain the
required zinc transfer of 40 g/L with barren solvent containing ~50 mg/L
Zn.
4. Conclusions
The results, as demonstrated by the pilot plant, proved the process
feasibility with 95% Zn recovery from the effluent. The pre-treatment
stage bio-oxidation achieved complete oxidation of ferrous in the
bioreactor, subsequent lime precipitation resulted in b1 mg/L Fe
remaining in the solution whilst not affecting the zinc tenor. The zinc
extraction stage was successfully carried out in a 80 mm diameter 7 m
high Bateman Pulsed Column leaving b50 mg/L Zn in the raffinate.
The required phase ratio was O:A = 0.5 for a solution flux of 44 m3/
m2/h. The system working with both aqueous and organic continuous
dispersions, at a temperature above 25 °C. The stripping was efficient
with only a single stage at O:A phase ratio of 20 required to achieve a
40 g/L Zn transfer into the electrolyte. The Na2CO3 consumption was
1.7 kg/m3 effluent (1.7 kg/kg Zn).
Table 5
Stripping experiments with 5% Ionquest 290 at 22 °C, at phase ratio O:A = 10.
Aqueous in
Aqueous out
Zn(g/L)
H2SO4 (g/L)
Zn (g/L)
40
50
60
70
80
90
200
194
188
176
200
200
58.8
68.2
77.8
91.0
102.8
115.4
M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
Table 6
Extraction in organic and aqueous continuities.
Continuous
dispersion
Feed
BS
Flux
pH
Zn (mg/L)
L/h
L/h
m3/m2/h
Raff
Raff
LS
Organic
110
130
130
150
150
150
55
60
60
70
70
70
33
38
38
44
44
44
2.7
2.8
2.9
2.9
3.1
2.9
11
55
11
47
18
7.2
1910
1880
1800
1880
2520
2240
Aqueous
For a Zn price above US$2/kg, the value of the zinc product covers
the operating costs in addition to solving a serious environmental
problem.
Acknowledgements
The public company EGMASA (Andalusia, Spain) is acknowledged
for their support of the personnel expenses for the present study. The
Spanish Ministry for Science and Innovation is acknowledged for
supporting laboratory expenses at UAB (Project CTQ2009-07432
(subprograma PPQ)).
References
Alamdari, E.K., 2004. Synergistic effect of MEHPA on co-extraction of zinc and cadmium
with DEHPA. Miner. Eng. 17, 89–92.
Barnard, K.R., Shiers, D.W., 2004. Mechanism and rate of butyl phosphinate formation
from reaction of phosphinic acid (Cyanex 272) and tributyl phosphate. Hydrometallurgy 106, 76–83.
Devi, N.B., Nathsarma, K.C., Chakravortty, V., 1997. Extraction and separation of Mn(II)
and Zn (II) from sulfate solutions by sodium salt of Cyanex 272. Hydrometallurgy
45, 169–179.
Ferreira, A.N., Agarwal, S., Machado, R.M., Gameiro, M.L.F., Santos, S.M.M., Reis, M.T.A.,
Ismael, M.R.C., Correia, J.N., Carvalho, J.M.R., 2010. Extraction of copper from acidic
leach solution with Acorga M5640 using a pulsed sieve plate column. Hydrometallurgy 104, 66–75.
Gameiro, M.L.F., Machado, R.M., Ismael, M.R.C., Reis, M.T.A., Carvalho, J.M.R., 2010.
Copper extraction from ammoniacal medium in a pulsed sieve-plate column with
LIX 84-I. J. Hazard. Mater. 183, 165–175.
67
Gottliebsen, K., Grinbaum, B., Chen, D., Stevens, G., 2000. Recovery of sulfuric acid from
copper electrolyte bleeds. Hydrometallurgy 56, 293–307.
Grinbaum, B., 1992. How to fit a simulator to a real liquid-liquid extraction plant. In:
Sekine, T. (Ed.), Solvent Extraction. Elsevier, Amsterdam, pp. 1235–1240.
Grinbaum, B., 1993. The development of liquid–liquid extraction processes using
simulation”. In: Longsdail, D.H., Slater, M.J. (Eds.), Solvent Extraction in the Process
Industries. Elsevier, Amsterdam, pp. 715–722.
Jha, M.K., Kumar, V., Singh, R.J., 2002. Solvent extraction of zinc from chloride solutions.
Solv. Extr. Ion Exch. 20, 389–405.
Lanagan, M.D., Ibana, D.C., 2003. The solvent extraction and stripping of chromium with
Cyanex 272. Miner. Eng. 16, 237–245.
Lupi, C., Pilone, D., 2000. Reductive stripping in vacuum of Fe(III) from D2EHPA.
Hydrometallurgy 57, 201–207.
Mazuelos, A., Romero, R., Palencia, I., Iglesias, N., Carranza, F., 1999. Continuous ferrous
iron biooxidation in flooded packed bed reactors. Miner. Eng. 12, 559–564.
Mazuelos, A., Carranza, F., Romero, R., Iglesias, N., Villalobo, E., 2010a. Operational pH in
packed-bed reactors for ferrous ion bio-oxidation. Hydrometallurgy 104, 186–192.
Mazuelos, A., Iglesias, N., Romero, R., Mejías, M.A., Carranza, F., 2010b. Influence of zinc
on ferrous iron bio-oxidation: biological or physical nature? Biochem. Eng. J. 49,
235–240.
Owusu, G., 1998. Selective extractions of Zn and Cd from Zn–Cd–Co–Ni sulphate
solution using di-2-ethylhexyl phosphoric acid extractant. Hydrometallurgy 47,
205–215.
Rickelton, W.A., Boyle, R.J., 1990. The selective recovery of zinc with new thiophosphinic acids. Solv. Extr. Ion Exch. 8, 783–797.
Ritcey, G.M., 2006. Solvent extraction principles and applications to process metallurgy,
vol.1. G.M. Ritcey & Associates, Ottawa, pp. 336–348.
Ritcey, G.M., Lucas, B.H., 1971. Purification of manganese solutions containing copper
and zinc by liquid-liquid extraction, using bis(2-ethylhexyl)phosphoric acid. Can.
Metall. Quart. 10, 223–228.
Salgado, A.L., Veloso, A.M.O., Pereira, D.D., Gontijo, G.S., Salum, A., Mansur, M.B., 2003.
Recovery of zinc and manganese from spent alkaline batteries by liquid-liquid
extraction with Cyanex 272. J. Power Sources 115, 367–373.
Sayilgan, E., Kukrer, T., Civelekoglu, G., Ferella, F., Akcil, A., Veglio, F., Kitis, M., 2009. A
review of technologies for the recovery of metals from spent alkaline and zinc–
carbon batteries. Hydrometallurgy 97, 158–166.
Sole, K.C., Feather, P.A.M., Cole, M., 2005. Solvent extraction in southern Africa: an
update of some recent hydrometallurgical developments. Hydrometallurgy 78,
52–78.
Tsakiridis, P.E., Oustadakis, P., Katsiapi, A., Agatzini-Leonardou, S., 2010. Hydrometallurgical process for zinc recovery from electric arc furnace dust (EAFD). Part II:
Downstream processing and zinc recovery by electrowinning. J. Hazard. Mater. 179,
8–14.
Yokoyam, T., Yokota, T., Hayashi, S., Izawa, E., 1996. Determination of trace gold in rock
samples by a combination of two-stage solvent extraction and graphite furnace
atomic absorption spectrometry: the problem of iron interference and its solution.
Geochem. J. 30, 175–181.