studiesofpollutedminesoilsandtreatmentof ......summary calcium,copper,aluminumandmanganeseions.i...
TRANSCRIPT
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STUDIES�OF�POLLUTED�MINE�SOILS�AND�TREATMENT�OF�WASTE�WATERS.�APPLICATIONS�OF�SYNCHROTRON�
BASED�TECHNIQUES,�FIELD�PORTABLE�X�RAY�FLUORESCENCE�AND�ADVANCED�OXIDATION�PROCESSES�
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Marta�Ávila�Pérez�
Tesi�doctoral�
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Programa�de�doctorat�en�Química�
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Directors:�Manuel�Valiente�Malmagro,�Gustavo�Pérez�González�
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Departament�de�Química�
Facultat�de�Ciències�
Any�2011�
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Memòria�presentada�per�aspirar�al�Grau�de�Doctor�per�
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Marta�Ávila�Pérez�
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Vist�i�plau,�els�directors�
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Dr.�Manuel�Valiente�Malmagro� � � Dr.�Gustavo�Pérez�González�
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Bellaterra,�10�de�juny�de�2011�
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In�the�beginning�there�was�nothing.�God�said,�'Let�there�be�light!'.�And�there�was�light.��There��was�still�nothing,�but�you�could�see�it�a�whole�lot�better.�
�Ellen�Degeneres�
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AGRAÏMENTS�
L’altre�dia�parlant�amb�alguns�companys�del�grup�sobre�que�a� la� tesi�hi�surten�el�nom�de�l’Elena�o�el�de’n�Gus�com�a�autors�d’algunes�fotos,�la�Pili�va�veure�una�figura�en�la�que�ella�em�va�ajudar�a�col�locar� les� línies�rectes� i�en�Fran�un�dibuix�on�ell�em�va�ajudar�a�triar�els�colors�(sent� daltònic)� i� em� vaig� adonar� que� és� totalment� veritat� pensar� que� en� una� tesi� tothom�hi�col�labora�una�miqueta,� al�menys� en� la�meva.� Pot� ser� en� una� figura,� una� ratlla� vertical,� una�paraula�o�una�idea,�i�sobretot�donant�me�molt�suport�en�tot�moment.�Moltes�gràcies�a�tots!!!��
Evidentment�la�part�d’aquesta�tesi�que�correspon�a�en�Manolo�i�en�Gus�és�molt�més�gran�que�la�de�la�resta�de�les�persones.�Gràcies�Manolo�per�haver�me�donat�la�oportunitat�de�fer�la�tesi�al�grup!�No�vull�desmerèixer�els�altres�grups,�però�des�del�primer�dia�sempre�he�cregut�que�no�podia�haver�anat�a�un�grup�millor� i� en�gran�part� això�és�gràcies�a� tu� i� al� teu�esforç.�Gus,�muchas�gracias�por�estar�siempre�dispuesto�a�ayudarme�y�aguantarme�mis�neuras,�sé�que�no�es�fácil,�muchas�gracias�de�verdad.�Eres�un�tío�genial,�y�un�papá�aun�mejor,�jeje!�
I�ara�toca�a�tota�la�resta�de�la�gent,�els�meus�amics�que�no�són�de�veritat�als�qui�no�estimo�gens�i�sou�poquets�a�més!�Montse�tantes�hores�viscudes,�tantes�històries�juntes,�et�desitjo�tota�la�felicitat�del�món!�A�GTS�hi�ha�qualitat� i� tu�n’ets�una�mostra!�Fran,�el�meu�reietonet,�ets�el�més�maco,�trobaré�a�faltar�les�teves�abraçades�de�les�10:22�i�totes�les�altres.�Pili,�ja�sé�qui�ets!�Ets� una� bona� amiga� i� una� bona� companya,� i� a�més�molt� divertida,� però� no� ho� diré� a� ningú�perquè�no�perdis�la�teva�fama.�Angélica�Angélica,�mi�compañera�de�la�hora�de�comer,�me�vas�a�hacer�mucha�falta,�y�lo�sabes.�Dejo�VIBRA�en�memoria�del�zentinel�para�que�de�vez�en�cuando�Diego� la� ponga� en� el� laboratorio.� Dieguito� todo� (o� casi� todo)� lo� que� dijimos� en� “El� día� de�halagar�a�Diego”�era�verdad,�eres�un�sol!�Muchas�gracias�por�tu�ayuda�con�la�informática�y�en�todo�lo�que�no�es�informática!�Bea,�companya�de�tantes�estades�del�sincrotró,�m’ho�he�passat�molt�bé�amb�tu� i�també�a� les�grans�festes�que�organitzes!�Olgucha,�el�nuevo�gran�fichaje�del�grupo,�gracias�por�venderme�a�“Daviduño”�a�precio�de�saldo�y�no�dejes�que�se�olvide�nunca�la�canción� de� Alfi� “venga� anímate� vamos� a� aprender,� qué� divertido� será� genial� y� no� podrás�parar!”.�Agus,�hem�anat�seguint�aquest�caminet�junts�tota�l’estona�gairebé�des�del�principi�fins�ara�gairebé�el�final,�has�sigut�un�gran�company,�sempre�fent�gala�del�teu�“señorío”,�jeje!�Oriol�Beisbol,�portaré�sempre�amb�orgull�la�samarreta�de�Els�Pétit�PÛt!�La�propera�vegada�que�siguis�alumne�meu�restaré�punts�per�falta�d’assistència!�Elena�Peralta�(mi�otra�amiga�Elena,�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� proclamar�ne� la� independència!� Ara,� no� la� facis� explotar�amb� hidrogen.� Amanda,� que� no� pots� evitar� trencar� cors� allà� on� vagis� (léase� vendedor� de�refrescos�del�Cirque�du�Soleil)�mai�deixis�de�pensar�que�“todo�es�muy�bonito!”�perquè�tens�raó.�Patri,� sempre� et� recordaré� amb� un� somriure.� Berta� que� vens� a� robar�nos� però� amb� un�somriure�sempre�i�dient�nos�“guapos”,�així�dóna�gust�que�et�robin,�jeje!�Julio,�tico,�siempre�tan�amable�y�tan�dispuesto�a�ayudar�y�a�escuchar�(y�a�traer�pasteles!),�Marta�y�Kike�(Kiki,� jijijiji!).�Gràcies�a�la�Maria�Dolors�per�ser�sempre�tan�diligent,�eh,�reina;�a�la�Cristina,�a�la�Maria�Muñoz�i�a�la�Montse�López.�
Tampoc�no�em�vull�oblidar�d’agraïr�l’ajuda�de�la�resta�de�la�gent�amb�qui�he�compartit�tants�moments�i�que�ja�no�volten�per�aquí:�Aleix�tinc�tants�i�tan�bons�records�de�tu�que�ompliria�la�tesi� sencera!�Moltes�gràcies�per� tot�el�que�em�vas�ajudar.�Així� com�també�ho�van� fer� l’Anna�Torrado�a�qui�encara�enyoro,�l’Àngels�amb�qui�hem�compartit�una�amistat�de�molts�anys�i�fins�i�tot�cursos�d’alta�cuina,�l’Anna�Bernaus�que�em�va�introduir�en�el�fabulós�món�del�sincrotró,�el�Johannes�siempre�dispuesto�a�ayudarme,�el�Jordy�Macanás�(Mac)�la�wikipedia�amb�potes�que�sempre�sempre�t’ajuda� i� troba�solucions,� la�Nadia� i� la�Rajaa� (I�miss�youuuuu!!!),�el�Sachin,�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�anticipadament�demano�perdó�per�la�meva�memòria�de�peix...�ups!�Delfín!�La�mascota�de�GTS!�I�allà�al�cel�dels�peixets�també�un�record�per�al�Pezón.�
El�meu�gabinet�de�psicologia�es�mereix�un�raconet�en�aquests�agraïments.�Gràcies�Anabel�per�la�teva�amistat�i�tota�l’ajuda�i�consells�que�sempre�m’has�donat;�y�gracias�Susana,�Tamara�y�Sandra�por�vuestra�ayuda�y�vuestra�sonrisa�siempre�y�por�estar�siempre�dispuestas�a�apuntarse�a� todo!� Y� también� quiero� agradecer� al� resto� de� vuestro� laboratorio� aguantarme� cuando�aparezco:�Anna�y�Sole.�Recordadme�siempre�como�“Esa�chica�qué?”!�
Gracias�a�ti�David�Zamora,�por�ser�el�mister�de�la�selección�de�futbol�femenino�de�la�torre�de�química�y�por�toda�la�ayuda�extradeportiva,�jeje!�Nosotras�Ahí!�El�dream�team�liderat�per�el�míster� David� amb� qui� quasi� vam� aconseguir� arribar� a� la� final� però� el� que� segur� que� vam�aconseguir� va� ser� passar�ho�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,�en�David�i�el�Guga,�per�ser�també�els�meus�amics!�
No� puc� oblidar�me� 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�sea�de�vuestro�agrado.�Y�especialmente�gracias�a�Tu,�mi�angelito,�por�todo�tu�apoyo�siempre,�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�corresponde�a�ti!��
Però�a�qui�vull�enganyar?�Us�estimo�molt�a�tots!�
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Als�meus�pares�i�a�en�David����
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SUMMARY�
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Summary��
Metals� have� been� used� since� prehistoric� times� and� they� play� a� key� role� in� civilization�
development.�Despite�mining�industry�still�represents�an�important�economic�activity�in�many�
countries,�large�amounts�of�solid�and�liquid�wastes�remain�stored�in�controlled�tailings�during�
mining�operations�and�sometimes�in�an�uncontrolled�way,�after�the�mine�closure.�Solid�wastes�
are�produced�from�ore�processing�such�as�crushing,�grinding�and�milling�and�are�disposed�off�in�
surrounding�land.�High�amounts�of�water�are�spent�to�wash�the�ore�and�to�reduce�the�mineral�
to� its�metallic� form.�These�mining�wastewaters�are�generally�dumped� into�ponds�secured�by�
dams.�In�this�sense,�elevated�levels�of�heavy�metals�from�metalliferous�mines�are�found�in�and�
around�the�mines.�Such�metals�represent�a�great�hazard�that�can�restrict�soil�use,�while�tailing�
dam�failures�can�produce�huge�human�and�environmental�damages.��
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.��
In�this�concern,�the�studies�carried�out�are�summarized�as�follows:��
The�characterization�of�four�abandoned�mines�of�Marrakech�region�(Morocco)�by�means�
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�
abandoned�mines�from�Marrakech�region�(Morocco):�Draa�Lasfar,�Kettara,�Sidi�Bou�Othmane�
and� Bir� Nehass.� The� characterization� of� the� heavy� metal� pollution� was� performed� by� Field�
Portable� X�ray� Fluorescence� (FP�XRF)� while� the� spatial� variability� was� determined� by�
Geographic� Information� Systems� (GIS).� A� prediction� of� the� risk� of� each� sampling� point� was�
completed�by�determining�the�mobility�of�anthropogenic�enhanced�heavy�metals�using�single�
leaching�tests.�The�calculation�of�the�Concentration�Enrichment�Ratios�(CER)�revealed�arsenic,�
copper,�lead�and�zinc�as�the�main�pollutants�in�all�mine�areas.�Draa�Lasfar�GIS�contour�maps�of�
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�
mobility�assays�indicate�greater�mobility�of�As�and�Zn�due�to�their�lower�adsorption�process�in�
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�
distribution�is�more�homogeneous�along�the�mine�area.�In�addition,�lead�can�be�considered�the�
main�pollutant� considering� its� high�CER� values.� Regarding� SB�Othmane�mine� area,�GIS�maps�
observe�areas�with�high�contamination,�as�some�samples�have�CER�values�above�200.�Lead�and�
Summary��
zinc�can�be�considered�the�main�pollutants� in�SB�Othmane�mine�area.�Bir�Nehass�mine�area,�
likewise� SB�Othmane,� is� less� contaminated�with� arsenic� and� copper� being� lead� and� zinc� the�
main�pollutants.�In�this�sense,�a�unique�hot�spot�can�be�observed�for�arsenic�and�lead�around�
an�area�corresponding�to�a�residue�deposit�while�several�hot�spots�with�CER=200�can�be�seen�
for�lead�and�zinc,�also�related�to�residue�deposits.�The�mobility�results�point�out�the�greatest�
part�of�samples�to�have�very�low�mobility.�On�the�other�hand,�samples�of�SB�Othmane�and�Bir�
Nehass�are�highly�concentrated�on�Pb�and�Zn�and�present�an�extremely�high�content�on�Pb�and�
Zn�in�the�mobile�phase,�especially�high�for�the�samples�taken�at�the�deposits�of�residues.�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�
work�to�determine�toxicity�of�soils.�Such�technique�involves�the�determination�of�the�chemical�
species�in�which�each�metal�is�present�in�soils.�As�one�of�the�most�toxic�heavy�metals,�mercury�
(Hg)�and�their�related�compounds,�can�be�absorbed�by�living�tissues�in�large�doses,�becoming�a�
great�hazard�due�to�its�ability�to�be�concentrated�and�stored�over�long�periods�of�time.�In�this�
work,� synchrotron�based� X�ray� Absorption� Near� Edge� Structure� (XANES)� has� been� used� to�
determine�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� indicate�a�strong�dependence�of�the�mobility�with�the�concentration�of�chlorides�
and� sulfates.� The� mercury� species� found� in� each� mine� are� related� to� the� efficiency� of� its�
roasting�furnaces.��
The�recovery�of�zinc�from�a�mine�tailing�pond�at�laboratory�and�pilot�plant�scale�to�solve�
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�
amounts�of�wastewater�containing�about�1�g/L�of�Zn�and�significant�amounts�of�ferrous,�ferric,�
Summary��
calcium,�copper,�aluminum�and�manganese�ions.�In�this�sense,�the�recovery�of�zinc�can�provide�
economic�value�to�the�process�while�solving�an�environmental�problem.�A�solvent�extraction�
process�was�considered�as�the�best�methodology�and�in�the�present�PhD�thesis�are�reported�
the�results�for�the�selection�of�the�best�extractant�amongst�DEHPA,�Cyanex�272�and�Ionquest�
290.�As�none�of�the�extractants�were�able�to�extract�Zn�selectively�from�a�solution�containing�
Fe,�a�bio�oxidation�process�followed�by�an�alkaline�precipitation�step�was�performed�prior�to�
the�SX�treatment�in�order�to�obtain�a�solution�without�iron.�The�Fe�removal�as�well�as�the�SX�
process�have�been�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.�Given�that�the�
recycling�of�the�organic�phase�lead�to�a�relative� importance�of�the�extractant�costs,� 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�
recommended�owing�its�lower�volatility�and�flammability.�The�pilot�plant�proved�the�feasibility�
of�the�process,�obtaining�a�zinc�recovery�of�95%�and�leaving�less�than�50�mg/L�in�the�raffinate.�
The�stripping�was�efficient�and�only�a�single�stage�at�O:A=20�was�required�to�achieve�a�transfer�
of�40�g/L.�For�a�Zn�price�above�US$2/kg�the�operating�costs�are�covered�while,�additionally,�a�
serious�environmental�problem�is�solved.�
The�removal�of�organic�compounds�from�wastewater�by�the�Fenton�reaction�using�Fe3+�
loaded�materials.� As� an� example�of� another� remediation� technique,� this� thesis� presents� the�
removal�of�organic�wastewaters�by�three�different�materials�which�have�been�exchanged�with�
Fe�for�their�evaluation�as�heterogeneous�Fenton�catalysts.�The�Fenton�reaction,�consisting�on�
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)�
and�a�clay�(montmorillonite)�loaded�with�Fe.�The�results�indicated�that�high�Fe�content�could�
be� introduced� into� such� materials� with� minimum� time� and� reagents� consumption� and,� in�
addition,� these� Fe�loaded� 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��
only�provided�a�25%�and�85%�COD�removal�respectively.�Column�experiments�using�the�more�
economical�material,� clinoptilolite,�were�performed�obtaining� also� successfully� results� hence�
indicating�the�feasibility�of�these�low�cost�Fe�loaded�materials�as�heterogeneous�catalysts�for�
the�Fenton�reaction.�The�minimal�losses�of�Fe�from�the�materials�avoided�the�necessity�of�red�
mud�removal.��
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,�
several�Fe3+�loaded�materials�with�high�exchange�capability�such�as�zeolite�USY�(USY),�zeolite�Y�
(ZY)�and�a�sponge�(Sp)�have�been�applied� for� the�removal�of�arsenic� from�inorganic�polluted�
wastewaters.� 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,�Fe�loaded�materials�can�be�also�employed�for�the�
removal� of� arsenic.� These� materials� were� characterized� by� FP�XRF� and� Extended� X�ray�
Absorption�Fine�Structure�(EXAFS)�techniques�in�order�to�shed�light�onto�the�different�sorption�
mechanisms� of� arsenic� into� such� materials.� The� sorption� mechanism� reveals� a� strong�
dependence�on�the�specific�surface�area�and�the�available�sites,�thus�as�zeolite�Y�has�specific�
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,�such�as�arsenic,�chromate�or�uranium�oxide�species.�The�characterization�of�the�
adsorption�of� arsenate�onto� these� Fe3+�loaded�materials� revealed�arsenate�bidentate� corner�
sharing�bond�as�the�main�adsorption�process.��
�
�
��
CONTENTS�
�
�
1.�INTRODUCTION ...........................................................................................................................3�
��MINE�SITES�CHARACTERIZATION ......................................................................................................3�
1.1.�MINING�OVERVIEW ..........................................................................................................................3�
1.2.�SOIL�IMPACTS�FROM�MINING ..........................................................................................................4�
1.3.�CHARACTERIZATION�OF�MINE�SITES ................................................................................................7�
1.3.1.�Sampling .....................................................................................................................................7�
1.3.2.�Soil�Physico�chemical�Characterization ......................................................................................8�
1.3.3.�Metal�Analysis ..........................................................................................................................10�
1.4.�SOIL�QUALITY�REGULATIONS..........................................................................................................11�
1.5.�CHEMICAL�SPECIATION�AND�FRACTIONATION�IN�SOILS ...............................................................12�
1.5.1.�Sequential�Extraction�Schemes ................................................................................................14�
1.5.2.�Single�Leaching�Tests ................................................................................................................15�
1.6.�SOIL�RISK�ASSESSMENT�TOOLS.......................................................................................................16�
1.6.2.�Geographic�Information�Systems .............................................................................................16�
1.6.3.�Principal�Component�Analysis�in�Geosciences .........................................................................17�
1.7.�WEAKNESSES�AND�NEEDS�OF�MINING�SITES�CHARACTERIZATION...............................................19�
��REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATER ................................20�
1.8.�SOLVENT�EXTRACTION�FOR�THE�RECOVERY�OF�Zn�FROM�ACIDIC�MINE�WATERS ........................20�
1.8.1.�Zinc�Overview ...........................................................................................................................21�
1.8.2.�The�Solvent�Extraction�Process ................................................................................................22�
1.8.3.�Scaling�Solvent�Extraction�To�a�Pilot�Plant ...............................................................................23�
1.9.�FE�LOADED�MATERIALS�FOR�THE�REMOVAL�OF�ORGANIC�AND�INORGANIC�CONTAMINANTS ..24�
1.9.1.�Zeolites .....................................................................................................................................24�
1.9.2.�Clays..........................................................................................................................................27�
1.9.3.�Sponges ....................................................................................................................................28�
1.10.�THE�FENTON�REACTION ................................................................................................................29�
1.11.�ARSENIC�SORPTION�USING�FE�LOADED�MATERIALS ...................................................................31�
1.11.1.�Arsenic�toxicity .......................................................................................................................31�
1.11.2.�Arsenic�sorbents .....................................................................................................................32�
1.12.�WEAKNESSES�AND�NEEDS�OF�INDUSTRIALLY�CONTAMINATED�WATERS ...................................33�
��ANALYTICAL�TECHNIQUES.........................................................................................................35�
1.13.�X�RAY�FLUORESCENCE ..................................................................................................................35�
1.13.1.�X�Ray�interaction�with�matter ................................................................................................35�
1.13.2.�X�Ray�Fluorescence.................................................................................................................37�
1.13.4.�Field�Portable�XRF�instrumentation .......................................................................................37�
1.14.�SYNCHROTRON�BASED�TECHNIQUES ...........................................................................................39�
1.14.1.�Synchrotron�Light�Sources......................................................................................................39�
1.14.2.�Design�and�Operation�of�a�Synchrotron�Light�Source ............................................................39�
1.14.3.�X�Ray�Absorption�Spectrometry .............................................................................................41�
1.15.�OBJECTIVES ...................................................................................................................................45�
1.16.�REFERENCES ..................................................................................................................................46�
2.�METHODOLOGY...................................................................................................................57�
��MINE�SITES�CHARACTERIZATION..........................................................................................59�
2.1.�STUDIED�MINES�DESCRIPTION .......................................................................................................59�
2.1.1.�Marrakech�Mines:�Draa�Lasfar,�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�(Morocco)..........59�
2.1.2.�European�Mercury�Mining�Districts:�Almadén,�Mieres�and�Idrija ............................................61�
2.1.3.�Aznalcóllar�Tailing�Pond............................................................................................................63�
2.2.�SAMPLING .......................................................................................................................................64�
2.2.1.� Marrakech� Mining� Districts:� Draa� Lasfar,� Kettara,� Sidi�Bou� Othmane� and� Bir� Nehass�
(Morocco) ...........................................................................................................................................64�
2.2.2.�European�Mercury�Mining�Districts:�Almadén,�Asturias�(Spain),�Idrija�(Slovenia) ...................64�
2.3.�CHARACTERIZATION .......................................................................................................................65�
2.3.1.�Physico�chemical�Parameters...................................................................................................65�
2.3.2.�Total�metal�concentration ........................................................................................................65�
2.3.3.�Total�Mercury�Content .............................................................................................................67�
2.3.4.�Mobility�of�the�mine�samples...................................................................................................68�
2.3.5.�XAS�measurements...................................................................................................................69�
2.4.�DATA�TREATMENT ..........................................................................................................................70�
2.4.1.�Concentration�Enrichment�Ratios ............................................................................................70�
2.4.2.�Geographic�Information�Systems .............................................................................................71�
2.4.3.�Statistical�Tools.........................................................................................................................71�
2.4.4.�XAS�Data�Treatment .................................................................................................................72�
��REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATER .........73�
2.5.�ZINC�SOLVENT�EXTRACTION ...........................................................................................................73�
2.5.1.�Laboratory�Experiments ...........................................................................................................73�
2.5.2.�Scaling�the�SX�to�a�Pilot�Plant ...................................................................................................74�
2.6.� Fe�EXCHANGE�MATERIALS� FOR� THE� REMEDIATION�OF� ORGANIC� AND� INORGANIC� POLLUTED�
WATERS..................................................................................................................................................76�
2.6.1.�Fenton�Reaction .......................................................................................................................77�
2.6.2.�Arsenic�removal........................................................................................................................78�
2.7.�REFERENCES ....................................................................................................................................79�
3.�RESULTS�AND�DISCUSSION .......................................................................................85�
��MINE�SITES�CHARACTERIZATION..........................................................................................85�
3.1.�HEAVY�METAL�CONTAMINATION�AND�MOBILITY�AT�THE�DRAA�LASFAR�MINE�AREA .................85�
3.1.1.�Physico�chemical�parameters...................................................................................................85�
3.1.2.�Heavy�metal�concentration�in�the�mine�area...........................................................................86�
3.1.3.�GIS�contour�maps�of�the�main�pollutants.................................................................................85�
3.1.4.�Effect�of�particle�size�and�mobility ...........................................................................................86�
3.2.�CHARACTERIZATION�OF�KETTARA,�SIDI�BOU�OTHMANE�AND�BIR�NEHASS�MINE�AREAS............92�
3.2.1.�Physico�chemical�characterization ...........................................................................................92�
3.2.2.�Heavy�metal�concentration�in�the�mine�area...........................................................................93�
3.2.3.�Application�of�chemometrics ...................................................................................................95�
3.2.4.�GIS�contour�maps�of��the�pollutants.........................................................................................98�
3.3.�XANES�SPECIATION�OF�MERCURY� IN�THREE�MINING�DISTRICTS:�ALMADEN�(SPAIN),�ASTURIAS�
(SPAIN)�AND�IDRIJA�(SLOVENIA) .........................................................................................................104�
3.3.1.�Chemical�analysis�of�the�samples ...........................................................................................104�
3.3.2.�XANES�speciation�and�mobility�results ...................................................................................106�
��REMEDIATION�TECHNOLOGIES.............................................................................................111�
3.4.�EXTRACTANT�AND�SOLVENT�SELECTION�TO�RECOVER�ZINC�FROM�A�MINING�EFFLUENT:�FROM�
LABORATORY�SCALE�TO�PILOT�PLANT.................................................................................................111�
3.4.1.�SX�laboratory�results ..............................................................................................................111�
3.4.2.�SX�pilot�plant�process .............................................................................................................114�
3.5.�FE�LOADED�MATERIALS�FOR�THE�REMEDIATION�OF�ORGANIC�AND�INORGANIC�CONTAMINATED�
WASTE�WATERS ...................................................................................................................................119�
3.5.1.�Fe�loaded�materials�applied�as�Fenton�catalysts ...................................................................120�
3.5.2.�Fe�loaded�materials�applied�to�arsenic�removal ....................................................................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.�
Garcia�Noguero,� Beatriz� Guerrero,� Xavier�Gaona,� Rodrigo� Alvarez,� Gustavo� Perez�Gonzalez,�Manuel�
Valiente,�Pablo�Higueras,�Milena�Horvat�and�Jorge�Loredo.�Journal�of�Synchrotron�Radiation.�(2010).�
Volume:�17,�Issue:�2,�Pages:�179�186.��
ANNEX�III.�EXTRACTANT�AND�SOLVENT�SELECTION�TO�RECOVER�ZINC.�Marta�Avila,�Gustavo�Perez�and�
Manuel�Valiente.�Solvent�Extraction�and�Ion�Exchange�(2011),�29:�384–397.��
ANNEX�IV.�ZINC�RECOVERY�FROM�AN�EFFLUENT�USING�IONQUEST�290:�FROM�LABORATORY�SCALE�TO�
PILOT�PLANT.�M.�Avila,�B.�Grinbaum,�F.�Carranza,�A.�Mazuelos,�R.�Romero,�N.�Iglesias,�J.L.�Lozano,�G.�
Perez,�M.�Valiente.�Hydrometallurgy�(2011),�107:�63�67.��
�
�
�
�
1�
�
1��
INTRODUCTION��MINE�SITES�CHARACTERIZATION ........................................................................................................3�
1.1.�MINING�OVERVIEW ...........................................................................................................................3�1.2.�SOIL�IMPACTS�FROM�MINING ...........................................................................................................4�1.3.�CHARACTERIZATION�OF�MINE�SITES..................................................................................................7�1.4.�SOIL�QUALITY�REGULATIONS...........................................................................................................10�1.5.�CHEMICAL�SPECIATION�AND�FRACTIONATION�IN�SOILS .................................................................12�1.6.�SOIL�RISK�ASSESSMENT�TOOLS........................................................................................................16�1.7.�WEAKNESS�AND�NEEDS�OF�MINING�SITES�CHARACTERIZATION.....................................................19�
��REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATER...................................20�
1.8.�SOLVENT�EXTRACTION�FOR�THE�RECOVERY�OF�Zn�FROM�ACIDIC�MINE�WATERS...........................20�1.9.�FE�LOADED�MATERIALS�FOR�THE�REMOVAL�OF�ORGANIC�AND�INORGANIC�CONTAMINANTS......24�1.10.�THE�FENTON�REACTION.................................................................................................................29�1.11.�ARSENIC�SORPTION�USING�FE�LOADED�MATERIALS .....................................................................31�1.12.�WEAKNESSES�AND�NEEDS�OF�INDUSTRIALLY�CONTAMINATED�WATERS......................................33�
��ANALYTICAL�TECHNIQUES...................................................................................................................35�
1.13.�X�RAY�FLUORESCENCE ...................................................................................................................35�1.14.�SYNCHROTRON�BASED�TECHNIQUES ............................................................................................39�1.15.�OBJECTIVES....................................................................................................................................44�1.16.�REFERENCES...................................................................................................................................45�
2�
�
1.�Introduction�
3�
�This� chapter� is� addressed� to�provide� general� information� related� to� the�work� that�has�
been�performed�in�this� thesis.� In�this�sense,�general�aspects�of�mines,� its�characterization�by�
means�of�different�techniques�and�parameters,�as�well�as�techniques�to�remediate�organic�and�
inorganic� industrial�wastewaters�either�synthetic�or�from�specific�mine�water�are� introduced.�
Thus,� three�main�sections�have�been�distinguished:�Mine�sites�characterization,�Remediation�
techniques�and�Analytical�techniques.��
MINE�SITES�CHARACTERIZATION�
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�
parameters�to�characterize�the�contamination�around�mine�areas�is�also�included.��
1.1.�MINING�OVERVIEW�
Since�prehistory,�mining�has�been�key�to�the�development�of�civilizations.�In�this�sense,�
the�cultural�ages�of�man�are�associated�with�minerals�or�their�derivates�such�as�the�Stone�Age�
(prior�to�4000�BC),�the�Bronze�Age�(4000�to�5000�BC)�or�the�Iron�Age�(1500�BC�to�1780�BC)�[1].�
In�this�regard,�flint�implements�for�agricultural�or�construction�purposes�found�with�the�bones�
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�
believed�to�be�40,000�years�old.�Nonetheless,� it�was�until�Egyptian�times�that�mines�attained�
depths�of�250�m.�During�the�Bronze�and�Iron�Ages�humans�discovered�smelting�and�learned�to�
reduce� ores� into� pure� metals� or� alloys,� which� greatly� improved� their� ability� to� use� these�
metals.�Later�on,�the�Romans�developed�large�scale�mining�methods�such�as�hydraulic�mining�
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,�
1.�Introduction��
4�
Sierra�Morena� and� Almadén� in� Spain;� and� Aljustrel,� Sâo� Domingos,� Valongo,� Jales� and� Três�
Minas�in�Portugal�although�all�the�regions�of�the�Roman�Empire�were�also�exploited�(Table�1.1)�
[2].��
Table�1.1.�Principal�mines�exploited�during�Roman�Empire�and�minerals�extracted�Mine� Minerals�extracted� Uses�during�the�Roman�empire�
Lead� Piping�(aqueducts�plumbing,�gutters�for�villas)�Río�tinto�(Spain)�
Silver� Coins,�weapons�
Almadén�(Spain)� Mercury� Pigment�
Las�Médulas�(Spain),�
Dolaucothi�(Wales)�Gold� Tools,�weapons,�jewellery,�coins�
Aljustrel�(Portugal)� Zinc,�lead� Alloy�copper�into�brass�for�weapons�
Nowadays,�mining�activities�still�represent�an�important�role� in�the�world�development�
and� an� important� economic� activity� in� many� countries.� In� this� sense,� in� 2001� the� mining�
industry�produced�over�6�billion�tons�of�raw�product�valued�at�several�trillion�dollars.�Mineral�
processing� of� these� raw� materials� adds� further� value� as� raw� materials� and� products� are�
created�to�serve�all�aspects�of�industry�and�commerce�worldwide�[3].�
1.2.�SOIL�IMPACTS�FROM�MINING�
Mining�consists�in�the�extraction�of�valuable�minerals�or�other�geological�materials�from�
the�earth,�usually�from�an�ore�body,�vein�or�(coal)�seam,�that�implies�the�removal�of�soil.�Ore�
bodies�are�naturally�occurring�concentrations�of�minerals�with�sufficiently�high�concentrations�
of� metals� as� to� make� them� economically� worthwhile� exploited.� However,� it� has� been�
estimated�that�more�than�70%�of�all�the�material�excavated�in�mining�operations�is�discarded,�
and�high�amounts�of�water�are�spent�on�mineral�processing�(i.e.�washing�the�ore�to�enable�the�
separation�of�valuable�metals�or�minerals�from�their�gangue�or�waste�material,�to�reduce�the�
mineral�to� its�metallic�form�since�most�metals�are�present� in�ores�as�oxides�or�sulfides,�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�
levels�of�heavy�metals�from�metalliferous�mines�are�found�in�and�around�the�mines�due�to�the�
discharge�and�dispersion�of�mine�waste�materials�into�the�ecosystem�resulting�in�large�areas�of�
agricultural� land�contaminated�posing�an�environmental�risk�for�humans�and�ecosystems�and�
thus�restricting�soil�use�[5].�Thus,�the�nature�of�mining�processes�creates�a�potential�negative�
impact�on�the�environment�both�during�the�mining�operations�and�for�years,�after�the�mine�is�
closed� and� many� regions� have� been� contaminated� causing� huge� impact� in� the� soils�
surrounding�mine�areas.��
1.�Introduction�
5�
There�are�around�560,000�abandoned�mines�on�public�and�privately�owned�lands�in�the�
United�States�alone�and�it�was�estimated�that�in�2000�existed�more�than�3,500�tailings�ponds�
with�water� containing� high� amounts� of�metals� [6].� Every� year,� 2� to� 5�major� failures� and� 35�
minor�failures�occurred;�hence�releasing�high�amounts�of�highly�contaminated�waters�into�the�
environment�[7].�To�date�250�cases�of�tailings�dam�failures� in�the�world�have�been�compiled�
producing�huge�human�and�environmental�damages�(Table�1.2)�[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)�are�hazardous�to�human�health�and�
terrestrial�ecosystems.�So�the�determination�of�metals�in�contaminated�soils�should�be�carried�
out� to� obtain� information� about� the� nature,� quantity,� distribution� and� behavior� of�
contaminants�and,�if�necessary,�to�select�the�most�appropriate�use�of�the�site�[11].�Thus,�it�is�a�
foremost�task�to�characterize�heavy�metal�concentration�around�mine�areas�once�the�activity�
has� deceased� to� detect� the� degree� of� contamination� in� order� to� apply� proper�management�
tools.�
In�this�sense,� in�several�developing�countries,�mining�activities�represent�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.� Despite� mining� is� an� important� part� of� the�
industrial� development� in� many� developing� countries� (Philippines,� Morocco,� Peru,� etc.),�
relatively�few�environmental�studies�on�mining�sites�have�been�undertaken�to�determine�the�
heavy�metal�concentration�around�mine�areas�and�their�impact�on�surrounding�soil�and�water�
resources,� where� commonly� no� national� program� for� the� rehabilitation� of� existing� polluted�
sites�is�implemented�[12,�13,�14,�15].��
1.�Introduction��
6�
Table�1.2.�Major�tailing�dam�failures�in�the�last�25�years�Date� Location� Release� Impacts�
2010,� Oct�4th��
Kolontár,�Hungary�
700,000�m3�of�caustic�red�mud�
several� towns� flooded,� 10� people� killed,� approx.�120�people�injured�
2010,�Jun.�25��
Huancavelica,�Peru�
21,420�m3�of�tailings� contamination�of�Escalera�river�and�Opamayo�river�110�km�downstream�
2009,�Aug.�29��
Karamken,�Russia�
?� Eleven�homes�were�carried�away�by�the�mudflow;�at�least�one�person�was�killed�
2009,�May�14��
Huayuan�County,�China�
50,000�m3�of�tailings� A� home� destroyed,� three� people� killed� and� four�people�injured.�
2008,�Sep.�8�
Taoshi,�China� ?� A�mudslide� several� meters� high� buried� a�market,�several�homes�and�a�three�storey�building.�At�least�254�people�were�dead�and�35�injured�
2006,�Nov.�6��
Nchanga,�Zambia�
?� Release� of� highly� acidic� tailings� into� Kafue� river;�drinking�water�supply�of�downstream�communities�shut�down�
2006,�April�30��
near� Miliang,�China�
?� Five�injured�people,�17�residents�missing�and�more�than�130�local�residents�evacuated.�
2004,� Sep�5�
Riverview,�Florida,�USA��
227,000� m3� of� acidic�liquid�
liquid� spilled� into� Archie� Creek� that� leads� to�Hillsborough�Bay�
2003,�Oct.�3�
Cerro� Negro,�Chile�
50,000�tonnes�of�tailings� tailings� flowed� 20� kilometers� downstream� the� La�Ligua�river�
2002,�Aug.� 27� /�Sep.�11�
San� Marcelino,�Philippines�
?� Aug.�27:�some�tailings�spilled�into�Mapanuepe�Lake�and� eventually� into� the� Sto.� Tomas� RiverSep.� 11:� villages� flooded� with� mine� waste;� 250�families�evacuated�
2001,�Jun.�22�
Sebastião� das�Águas� Claras,�Brazil�
?� tailings�wave�traveled�at�least�6�km,�killing�at�least�two�mine�workers,�three�more�workers�are�missing�
2000,�Oct.�18�
Nandan� county,�China�
?� at� least� 15� people� killed,� 100�missing;�more� than�100�houses�destroyed�
2000,�Jan.�30�
Baia� Mare,�Romania�
100,000� m3� of� cyanide�contaminated�liquid�
contamination� of� the� Somes/Szamos� stream,�tributary� of� the� Tisza� River,� killing� tonnes� of� fish�and� poisoning� the� drinking�water� of�more� than� 2�million�people�in�Hungary�
1999,�Apr.�26�
Placer,�Philippines�
700,000� tonnes� of�cyanide�tailings�
17�homes�buried,�51�hectares�of�riceland�swamped�
1998,�Apr.�25�
Aznalcóllar,�Spain�
4�5� million� m3� of� toxic�water�and�slurry�
thousands� of� hectares� of� farmland� covered� with�toxic�slurry�
1997,�Oct.�22�
Pinto� Valley,�USA�
230,000�m3�of�tailings�and�mine�rock�
tailings�flow�covers�16�hectares�
1996,�Aug.�29�
El�Porco,�Bolivia� 400,000�tonnes� 300�km�of�Pilcomayo�river�contaminated�
1996,�Mar.�24�
Marcopper,�Philippines�
1.6�million�m3� Evacuation� of� 1200� residents,� 18� km� of� river�channel�filled�with�tailings,�US$�80�million�damage�
1994,�Oct.�2��
Payne� Creek�Mine,�USA��
6.8�million�m3� 500,000� m3� released� into� Hickey� Branch,� a�tributary�of�Payne�Creek��
1994,�Feb.�22�
Harmony,�Merriespruit,�South�Africa�
600,000�m3� tailings� traveled� 4� km� downstream,� 17� people�killed,�extensive�damage�to�residential�township�
1994,�Feb.�14�
Roxby� Downs,�South�Australia�
5� million� m3� of�contaminated� water� into�subsoil�
?�
1993� Marsa,�Peru� ?� 6�people�killed�1985,� July�19�
Stava,� Trento,�Italy�
200,000�m3� tailings� flow�4.2�km�downstream�at�90�km/h;�268�people�killed,�62�buildings�destroyed�
1.�Introduction�
7�
1.3.�CHARACTERIZATION�OF�MINE�SITES�
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.�In�
this� sense,� sampling� of� potentially� contaminated� soil� from� polluted� areas� is� intended� to�
provide� data� of� several� physico�chemical� parameters� or� metal� content� of� the� soil� for� the�
assessment�of�whether�the�pollution�has�caused�or�may�cause�environmental�problems.��
1.3.1.�SAMPLING�
As� a� previous� step� to� characterize� a� mining� area,� a� sampling� strategy� is� needed.� The�
selection�and�location�of�the�sampling�points�depend�on�the�objectives�of�the�investigation,�the�
preliminary� information� available� and� the� on�site� conditions.� Experiences� (and� theoretical�
considerations)�show�that�in�many�cases�systematic�sampling�on�a�regular�grid�is�both�practical�
and� sufficiently� productive� to� allow� the� creation� of� a� detailed� picture� of� variations� in� soil�
properties.��
A�regular�grid�is�usually�employed�in�environmental�studies,�in�which�the�sampling�area�is�
large�(for�example,�soils�with�different�applications).�When�the�purpose�of�the�sampling�is,�for�
instance,�setting�the�values�of�certain�properties�in�an�homogeneous�area�or�a�first�prospection�
in�an�area�where�contamination�is�suspected,�irregular�grids�in�form�of�X,�W,�S,�etc.�are�usually�
carried�out,� in�which�the�sampling�points�are�also�predefined�(Figure�1.1).�These�samples�are�
usually�mixed�to�form�composite�samples.��
�
Figure�1.1.�Grids�in�form�of�W�and�X�for�systematic�sampling�
Other�similar�approaches�include�simple�regular�grids,�circular�or�clustered�(Figure�2)�in�
order�to�estimate�the�impact�of�a�source�of�pollution�in�the�area�of�study�(with�the�possibility�
of�concentration�gradients)�or�to�estimate�concentration�levels.�The�grids�should�be�designed�
to�study�areas�where�all�points�can�have�a�similar�concentration�of�the�target�analytes,�as�is�the�
case�of�simple�irregulars�grids�(Figure�1.2A)�and�alternatives�that�allow�the�subdivision�of�the�
areas�in�quintets�(Figure�1.2B)�or�can�cover�an�area�assuming�a�localized�source�of�the�target�
analytes,�as�is�the�case�of�circular�grids�(Figure�1.2C).�
1.�Introduction��
8�
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�
certain�number�of�blocks�in�a�regular�grid,�and�take�a�number�of�individual�samples�at�random�
(Figure� 1.2D).� The� samples� are� analyzed� individually� or� as� composite� samples,� allowing� an�
estimation�of�variability�at�the�local�level�(within�each�group)�or�global�(between�groups).�
Figure�1.2.�Systematic�sampling�grids�
The� number� of� sampling� points� can� be� easily� increased� (e.g.,� in� areas� meriting� more�
detailed�investigation),�the�grid�is�easy�marked�by�means�of�GPS�systems�and�sampling�points�
can�be�easily�relocated.�However,�sometimes�other�patterns�are� followed�based�on�a�known�
local� distribution�or�hot� spot�distributions� along�a� line� towards� specific� receptors� allowing� a�
reduction�on�costs�and�resource�consumption.��
1.3.2.�SOIL�PHYSICO�CHEMICAL�CHARACTERIZATION��
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�
assessing�risks�from�contaminated�sites�such�as�electrical�conductivity,�pH,� loss�on�ignition�or�
the�carbonate�content�amongst�many�others�[16].��
Acidity.�Soil�pH�reflects�the�intensity�of�acidity�that�in�turn�influences�soil�conditions�and�
plant� uptake� of� metal� contaminants.� pH� influences� the� solubility� and� activity� of� various�
1.�Introduction�
9�
biologically� important� elements� and� processes.� Depending� on� the� soil:water� ratio� and� the�
composition�and�temperature�of�the�equilibration�solution,�the�resulting�pH�will�vary.�The�pH�
of�surface�soils� (0�100mm)�commonly� range� from�6.0� to�8.0,�and� it� is�useful� to�note� that�pH�
values�around�4.0�or�less�suggest�the�presence�of�sulfides,�while�levels�above�8.5�are�indicative�
of�the�presence�of�significant�quantities�of�exchangeable�Na+.��
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)�
employed�to�determine�salinity�are�1:1,�1:2,�1:2.5�and�1:5�although�the�1:5�ratio�is�preferred�as�
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�
regarding�potential�leaching�and�drainage�problems.�Electrical�conductivity�is�a�gross�measure�
of�dissolved�salts�in�soil�solution,�but�provides�no�information�as�to�which�salts�are�present�and�
in�what�proportion.�For�non�sensitive�plants,�EC�measurements�<�4dS�m�1�are�satisfactory.�Soils�
with� EC� >� 4dS� m�1� are� considered� saline� and� plant� growth� may� be� inhibited.� Electrical�
conductivity� values� increase� with� increasing� temperature� and� must� be� corrected� if� not�
measured�at�25ºC�[18].��
Organic�matter.�The�loss�on�ignition�(LOI)�method�is�a�simple�and�relatively�inexpensive�
method� for� determining� organic� matter� [19].� The� method� is� based� on� differential� thermal�
analysis�of�the�sample�weight�after�heating�at�500�550ºC�to�oxidize�the�organic�matter�to�CO2�
and� SO2.� However,� the� ignition� temperature� and� the� heating� time� influence� the� results� as�
organic�matter�may�not�be�completely�converted�into�CO2�and�SO2�if�temperature�is�too�low�or�
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�
organic�matter�affects�the�chemical�and�physical�properties�of�the�soil�increasing�the�soil�buffer�
capacity,� so� the� presence� of� organic�matter� tends� to� lower� pH� variations.� Furthermore,� the�
retentive�capacity�of�organic�matter�is�greater�than�most�reactive�clays.�
Carbonate�content.�Carbonate�plays�an� important� role� in�soil� chemistry� influencing� the�
pH�of�soils�given�its�carbonate�bicarbonate�buffering�equilibrium�[21].�In�addition,�carbonates�
can�complex�several�cations,�thus�affecting�the�amount�of�exchangeable�cations,�the�presence�
of�easily�soluble�salts,�the�redox�potential�and�the�partial�pressure�of�CO2�in�the�soil�air�[22].�
1.�Introduction��
10�
1.3.3.�METAL�ANALYSIS�
One� of� the� most� critical� properties� of� metals,� which� differentiate� them� from� organic�
pollutants,�is�that�they�are�not�biodegradable�in�the�environment�[23].�As�a�result�metals�tend�
to�persist� in� the�various� reservoirs�of�natural� systems�such�as�water,� soils�and�sediments,�or�
accumulate�in�biological�systems,�leading�to�an�important�hazard�to�environment�and�human�
health.� In�any�case,�a� typical� feature�of� the�weathering�of�mining�waste,�apart� from�possible�
acidic�water�formation,�is�the�release�of�metals�from�the�mineral�matrix�into�the�environment.��
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�
compounds�such�as�PAHs,�PCBs�or�hydrocarbons),�so�usually�the�analysis�of�metals�on�soils�is�
performed�after�digestion�with�a�strong�acid�solution�in�conjunction�with�a�hot�plate,�a�boiling�
device�or�microwave�heating� system.�After�digestion,� the� samples�are�analyzed�by�means�of�
atomic� absorption� spectrometry� (AAS),� inductively� coupled� plasma� optical� emission�
spectroscopy� (ICP�OES)� or� inductively� coupled� plasma� mass� spectroscopy� (ICP�MS).� In� this�
sense,� wet� chemistry� instrument� techniques� for� elemental� analysis� require� destructive� and�
time�consuming� sample� preparation,� often� using� concentrated� acids� or� other� hazardous�
materials.� Moreover,� the� sample� is� destroyed� and� hazardous� waste� streams� are� generated�
during�the�analytical�process�requiring�disposal.�All�these�factors�lead�to�a�relatively�high�cost�
per�sample.�However,�wet�chemistry�instrumental�analysis�techniques�are�still�necessary�when�
lower� elemental� concentrations� are� the� primary�measurement� need.� Thus,� during� the� lasts�
years�X�ray�fluorescence�(XRF)�has�emerged�as�a�valuable�tool�for�the�measurement�of�heavy�
metals� in� the�environment� given� their� reliable� and� rapid�measurement� [24,�25,� 26,� 27].� XRF�
analytical�methodology�is�often�chosen�as�the�most�appropriate�when�there�is�no�historical�site�
information�as�initial�sampling�costs�are�reduced�and�analyses�are�conducted�quickly�and�with�
less�rigorous�sample�preparation.��
1.4.�SOIL�QUALITY�REGULATIONS�
Soil�provides�us�with�food,�biomass�and�raw�materials.�It�serves�as�a�platform�for�human�
activities�and�landscape�and�as�an�archive�of�heritage�and�plays�a�central�role�as�a�habitat�and�
gene� pool.� It� stores,� filters� and� transform�many� substances,� including� water,� nutrients� and�
carbon.� Thus,� soil� contamination� may� have� important� consequences� affecting� ecological�
1.�Introduction�
11�
systems�and�biological� cycling�of�nutrients�or�being�unable� to�act�as� filter�and�buffer.� In� this�
sense,�hydrosphere,�groundwater�resources�and�aquatic�ecosystems�can�be�threatened�[28].�In�
cases� of� severe� contamination� and� in� places� where� risks� to� human� health� and/or� the�
environment�are�observed,�soil�remediation�is�necessary.��
During�the�last�years,�soil�protection�policies�have�been�developed�and�implemented�in�
several� countries� focused� on� different� contaminants,� diverse� land� uses� and� on� varied�
contamination�sources�(as�for�example�mining�and�industrial�activities,�agricultural�practices�or�
oil�spills)�such�as�the�Netherlands�Guide�[29]�and�the�French�Guidelines�values�[30]�(Table�1.3)�
and�at� international� level� in�the�European�strategy� for�soil�protection�framework�[31].�These�
guidelines�define�different�quality�standard�values�based�on�the�total�concentration�of�several�
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�
(natural�park,�agricultural,�residential,�recreational�or�industrial).�
Table�1.3.�Guidelines�national�values�for�heavy�metals�in�soils�for�Netherlands,�France�and�Catalonia�(Spain)�
Metals� Netherlands[29]� France[30]� Spain�(Catalonia)[32]�
�Target�Value�
Intervention�value�
Sensitive�use�
Non�sensitive�Use�
Industrial�use�
Urban�use�
Other�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�
required�and�becomes�mandatory.��
In� this� sense,� it� is� intended� that� guideline� values� could� represent� an� indication� to� an�
assessor�that�soil�concentrations�above�this�level�could�pose�an�unacceptable�risk�to�the�health�
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�
metals�in�related�soils,�these�values�represent�an�estimation�of�the�potential�hazard,�although�
it� can�be� considered�as� the�most�pessimistic� interpretation�as� it� is� considered� that� the� total�
1.�Introduction��
12�
amount�of�metal�in�soil�is�available�to�be�absorbed�by�plants�or�can�be�mobilized.�So,�not�only�
the�total�content�of�heavy�metals�should�be�considered,�but�also�its�mobility�and�bioavailability�
to�determine�the�real�toxicity.�
1.5.�CHEMICAL�SPECIATION�AND�FRACTIONATION�IN�SOILS�
Mobility� and� bioavailability� of� metals� in� the� environment� depends� strongly� on� their�
specific�chemical�forms�or�types�of�binding�rather�than�the�total�element�content�[33,�34].�So�it�
can�be�generally�considered�as�an�indication�of�toxicity�and�consequently�the�chemical�species�
present�in�a�soil�should�be�determined�in�order�to�assess�the�toxic�effects.�However,�nowadays�
this� distinction� is� not� reflected� in� the� legislation,�which� account� for� the� total� content�of� the�
pollutants�rather�than�for�the�available�content�of�the�pollutants.��
In� this� regard,� the� characteristics� of� just� one� species� of� an� element�may� have� such� a�
radical�impact�on�living�systems�(even�at�extremely�low�concentrations)�that�the�total�element�
concentration�becomes�of� little�value� in�determining� the� impact�of� the� trace�element.�Good�
examples�are�mercury�and�tin.�The�inorganic�forms�of�these�elements�are�much�less�toxic�(or�
even�do�not� show�toxic�properties)� than�the�alkylated� forms�which�are�highly� toxic.� In� these�
sense,�it�is�necessary�to�evaluate�and�characterize�the�chemical�forms�of�the�elements�in�order�
to� understand� their� properties,� their� evolution� possibilities,� as�well� as� the� prediction� of� the�
related�environmental� consequences.�Such�characterization� is� carried�out� the�methodologies�
known�by�speciation�analysis.��
At� this� point� it� is� required� to� define� the� term� speciation.� The� IUPAC� has� defined� the�
terminology�on�elemental�chemical�speciation�as�follows�[35]:�
Chemical� species:� Specific� form� of� an� element� defined� as� to� isotopic� composition,�
electronic�or�oxidation�state,�and/or�complex�or�molecular�structure.�
Speciation�analysis:�Analytical�activities�to�identifying�and/or�measuring�the�quantities�of�
one�or�more�individual�chemical�species�in�a�sample.�
Speciation�of�an�element:�Distribution�of�an�element�amongst�defined�chemical�species�
in�a�system.�When�elemental�speciation�is�not�feasible,�fractionation�is�employed.�
Fractionation:�Process�of�classification�of�an�analyte�or�a�group�of�analytes�from�a�certain�
sample� according� to� physical� (e.g.,� size,� solubility)� or� chemical� (e.g.� bonding,� reactivity)�
properties.��
Although�no�generally�accepted�definition�of�the�term�exists,�speciation�can�broadly�be�
defined� as� the� identification� and� quantification� of� the� different,� defined� species,� forms� or�
phases�in�which�an�element�occurs�[36].�The�term�"fractionation"�(also�referred�to�as�indirect�
1.�Introduction�
13�
speciation)�is�frequently�used�interchangeably�with�speciation�but�emphasizes�the�concept�of�
subdividing�a�"total�content".�Also,�the�analytical�preparations�for�separating�metal�species�are�
referred�to�as�"fractionation".�An�overview�of�techniques�used�in�chemical�speciation�analysis�
is�given�in�Table�1.4�[37].�
Table�1.4.�Analytical�methods�applied�for�chemical�speciation�of�metals�
Method� Metal�species�determined�
Electroanalysis�Ion�selective�electrodes� Free�ionic�concentrations�Voltammetry� Free�ions�and�labile�complexes�
Spectroscopy�Spectrophotometry� Specific�forms�Hydride�generation� Inorganic� and� organometallic� species;� different�
oxidation�states�(Sn,�As,�Sb,�Bi,�Se,�Te)�
Synchrotron�X�ray�spectroscopy� Specific�forms��Chromatography�HPLC� Cations,�anions,�metal�complexes,�inorganic�species�GC�or�LC� Organometallic�compounds�of�mercury,�tin�and�lead�Physico�chemical�fractionation�Ion�exchange�resin� Free�ions�and�labile�complexes�UV�irradiation� Organic�complexes�Solvent�extraction� Organic�complexes�
Size�fractionation�Filtration� Dissolved�and�suspended�matter�associated�Centrifugation� Dissolved�and�suspended�matter�associated��Dialysis� Different�charge,�different�molecular�size�Ultrafiltration� Molecular�size�
LIQUID�PHASE�
Gel�filtration�chromatography� Free� forms� and� complexes� of� different� molecular�size�
Single�reagent�leaching� Reagent�soluble�fractions�Sequential�extractions� Geochemical�fractions�Ion�exchange�resins� Labile�fractions�
SOLID�PHASE
Synchrotron�X�ray�spectroscopy� Specific�forms��
Whilst� different� elemental� speciation�methods� are� available� for� aqueous� systems� [38],�
implementations� of� methodologies� for� speciation� studies� in� solids� have� been� less� well�
developed.�The�speciation�studies�involving�soil�and�sediment�analysis�are�often�based�on�the�
use�of�extraction�procedures�(single�or�sequential).��
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�
environmental�policy.� In�this�regard,�single�and�sequential�extraction�schemes�were�designed�
in�the�1980s� in�order�to�assess�the�different�retention/release�of�metals� in�soil�and�sediment�
samples�as�a�result�of�natural�processes�or�anthropogenic�activities�and�can�be�employed�as�a�
valuable�tool�to�assess�the�potential�impact�in�the�environment�of�mining�districts�[39,�40].��
1.�Introduction��
14�
1.5.1.�SEQUENTIAL�EXTRACTION�SCHEMES�
Despite�being�quite� laborious,�sequential�extraction�schemes�(SES)�have�been�the�main�
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�
regarding�the�possible�release�of�a�given�analyte�(metal)�from�a�soil�or�sediment�phase�under�
certain�conditions�of�gradual�lixiviation�power.��
The�applied�strategy�consists�on�the�use�of�reagents�able�to�selectively�dissolve�a�metal�
fraction�bonded�to�certain�soil�materials,�i.e.�water�soluble�compounds,�exchangeable�cations,�
carbonates,� easily� reducible,� oxidizable�phase�and� residual.� These� fractions�may� vary� among�
different� extraction� schemes.� Most� common� reagents� used� include:� no� hydrolysable� salts,�
weak�acids,�reducing�agents,�oxidant�agents�and�strong�acids�[44].��
Several�SES�schemes�have�been�developed� to�evaluate�metal� fractionation� in� soils�and�
sediments�normally�varying�in�the�number�of�extraction�steps�between�3�and�8�and�those�most�
widely�used�are�Tessier�[35,�42]�and�BCR�SES�[45,�46]�(Table�5).�Comparatively,�both�methods�
provide� a� similar� fractionation,� although� the� exchangeable� fraction� of� BCR� resumes�
“exchangeable”�and�“carbonate”�fractions�from�Tessier.�
Table�1.5.�Sequential�extraction�procedures�defined�by�Tessier�and�BCR�SES�applied�to�1g�of�sample�Method� Fraction� Extraction�conditions�
T1:�Exchangeable� 8�mL�1M�MgCl2�pH�7,�25ºC,�1h�T2:�Link�to�carbonates� 8�mL�1M�CH3COONa�+�CH3COOH,�pH�5,�25ºC,�5h�T3:�Link�to�iron�and�manganese�oxides�
20�mL�0.04M�NH2OH�HCl�(25%�v/v�CH3COOH),�96ºC,�6h�
T4:�Link�to�organic�matter�3�mL�0.02M�HNO3�+�2�ml�30%�H2O2�(pH�2),�85ºC,�2h;�3�mL�30%�H2O2�(pH�2),�85ºC,�2h;�5�mL�3.2M�CH3COONH4�
in�20%�HNO3�+�7mL�H2O�25ºC,�30�min�
Tessier�
T5:�Residual�7.5�mL�37%�HCl�+�2.5�mL�65%�HNO3,�25ºC�during�1�
night,�reflux�2h�Water�soluble,�exchangeable�and�link�to�carbonates�
20�mL�0.1M�CH3COOH,�25ºC,�16h�
Link�to�iron�and�manganese�oxides�
20�mL�0.5M�NH2OH�HCl,�pH�2,�25ºC,�16h�BCR�SES�
Link�to�organic�matter�and�sulfides�
5�mL�30%�H2O2,�25ºC,�1h�+�5�mL�30%�H2O2,�85ºC,�1h�+�25�mL�1M�CH3COONH4,�pH�2,�25ºC,�16h�
Nevertheless,�SES�have�several�drawbacks�mainly�related�to�the�excessive�time�required�
(a�traditional�sequential�extraction�requires�at�least�50�hours)�and�the�possible�modification�of�
the�metal� species� during� extraction� procedure� [47].� It� is� worth�mentioning� that� not� all� the�
fractions�obtained�from�applying�SES�are�equally�important�from�the�environmental�risk�point�
of�view.�The�metals�related�to�the�residual�fraction�(obtained�through�extraction�or�digestion�
1.�Introduction�
15�
with� mixtures� of� strong� acids)� are� unlikely� to� be� released� under� weathering� conditions;�
whereas�metals� linked�to� the�soluble�and�exchangeable� fractions,�and�those�related�to�more�
labile�metal�species�are�more�mobile�and�hence�more�available.�Therefore,�in�order�to�assess�
the� environmental� hazard,� efforts� should� be� applied� only� on� the� measurement� of� these�
fractions�opening�the�possibility�of�using�less�laborious�methods�based�on�the�extraction�of�the�
metal�fraction�of�interest�using�a�unique�extracting�reagent�(single�leaching�tests).��
1.5.2.�SINGLE�LEACHING�TESTS�
Single� leaching�tests�are�nonselective�extractions�that�target�groups�of� labile�or�mobile�
phases.�This�approach�can�provide�a�useful�assessment�for�screening�purposes�to�identify�trace�
metal� pollution� with� minimum� time� consumption� [48].� Single� extractants� differ� by� their�
dissolution�power,� including:� i)�mild�unbuffered�extractants�that�extract�the�fraction�of�easily�
exchangeable� elements;� ii)� acidic� extractants� that� release� the� fraction� remobilized� by�
acidification�processes;�and�iii)�complexing�reagents�(Table�1.6).�[49].��
Table�1.6.�Leaching�tests�used�in�soil�analysis�[50]�
Group� Type�and�solution�strength� References�
HNO3�0.43�2.0�M� [51]�Aqua�regia� [52]�HCl�0.1�1�M� [53]�CH3COOH�0.1�M� [54]�
Acid�extraction�
HCl�0.05�M�+�H2SO4�0.0125�M� [55]�EDTA�0.01�0.05�M�at�different�pH� [53]�DTPA�0.005�M�+�TEA�0.1�M�+�CaCl2�0.01�M� [56]�
Chelating�agents�
CH3COOH� 0.02� M+NH4F� 0.015� M� +� HNO3�0.013M�+�EDTA�0.001�M�
[57]�
NH4�acetate,�acetate�acid�buffer�1�M�pH=7� [58]�Buffered�salt�solution�NH4�acetate,�acetate�acid�buffer�1�M�pH=4.8� [53]�CaCl2�0.01��0.1�M� [53]�NaNO3�0.1�M� [58]�NH4NO3�1M� [53]�AlCl3�0.3�M� [59]�
Unbuffered�salt�solution�
BaCl2�0.1�M� [60]�
Leaching� tests� are� focused� on� providing� information� about� the� release� of� specific�
components�under�given�conditions,�or�under�conditions�that�may�approximate�more�closely�
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,� dissolution�precipitation,� reduction–oxidation,� and�
complexation�decomplexation�processes),�and�can�modify�the�concentration�of�metals� in�soil�
solution� [61,56].� The� application� of� these� procedures� to� polluted� or� naturally� contaminated�
soils�is�mainly�focused�to�ascertain�the�potential�availability�and�mobility�of�metals,�in�studies�
1.�Introduction��
16�
on� the� soil�plant� transference� and� metal� migration� in� a� soil� profile� due� to� groundwater�
transport.� Subsequently,� this� information� is� usually� used� for� the� risk� assessment� of� wastes�
when�they�are�deposited�in�a�landfill�or�to�characterize�and�classify�them�in�terms�of�risk�[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,�leaching�tests�such�as�(NH4)2SO4�or�HCl�single�non�selective�extractions�methods,�
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�
leaching�tests�and�the�mobility�provided�by�the�sum�of�different�stages�of�SES�[67].�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.��
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,�SES�are�destructive�techniques.�For�direct�speciation,�synchrotron�based�techniques�
have�arose�as�a�valuable�tool�by�means�of�techniques�such�as�X�ray�Absorption�Spectroscopy�
(XAS)�using�synchrotron�facilities�as�X�rays�radiation�sources.�
1.6.�SOIL�RISK�ASSESSMENT�TOOLS�
Various�tools�can�be�employed�to�determine�the�degree�of�contamination�of�a�specific�
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�
determination�of�the�risk�of�a�contaminated�site�and�thus�help�the�decision�making.�
1.6.2.�GEOGRAPHIC�INFORMATION�SYSTEMS�
Further� characterization� in�environmental� studies�of�polluted� soils� is� achieved� through�
the�determination�of� the�pollutants� spatial� variability� in�a�polluted�area� through�Geographic�
Information�Systems�(GIS)�[68,�69,�70].�From�a�practical�point�of�view,�they�provided�the�first�
reference�values�for�assessing�soil�contamination�at�a�given�potentially�polluted�site.�Assessing�
the� spatial� extent� of� soil� metal� concentration� is� also� a� powerful� tool� in� understanding� and�
monitoring�the�adverse�effects�of�contamination.�Soil�maps�are�used� in�soil�description,� land�
appraisal� (taxation),� and� for� soil� monitoring� sites� to� establish� the� basic� information� on� the�
1.�Introduction�
17�
genesis� and� distribution� of� naturally� occurring� or� man�made� soils,� their� chemical,�
mineralogical,�biological�composition,�and�their�physical�properties�at�selected�positions.��
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].�While�spline�methods�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�[72],�and�no�assumptions�are�required�
for�the�data,�being�this�method�suitable�for�irregular�samplings�[73].�
Combining�Geographic� Information�Systems�(GIS)�with�some�analytical�tools�the�spatial�
variability�in�a�mine�area,�can�be�determined.�Such�combination�let�to�produce�maps�which�are�
helpful�for�a�cost�effective� identification�of�the�sources�and�the�spatial�patterns�of�pollutants�
[71,�72,�74].��
1.6.3.�PRINCIPAL�COMPONENT�ANALYSIS�IN�GEOSCIENCES�
Within�the�environmental�investigations�associated�with�the�impact�of�metals�in�soils,�it�
is� often� necessary� the� determination� of�multiple� parameters,� obtaining�multivariate� data.� A�
first� comparison�between� samples�across� individual�parameters� can�be�performed,� although�
when�the�simultaneous�consideration�of�all�the�parameters�determined�is�carried�out�(known�
as�multivariate�methods� of� analysis),� a� characterization� of� the� combined� effect� of� different�
variables�and�especially�of�the�various�relationships�between�them�can�also�be�obtained.��
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�
retaining�as�much�of�the�original�information�as�possible.��
PCA�works�by�decomposing�the�X�matrix�that�contains�all�the�data�as�the�product�of�two�
smaller�matrices,�which�are�called�the�loading�and�score�matrices:�
X�=�TPT�+�E� � � (Equation�1.1)�
The�loading�matrix�(P)�contains�information�about�the�variables.�It�is�composed�of�a�few�
vectors�(Principal�Components,�PCs)�which�are�(obtained�as)�linear�combinations�of�the�original�
X�variables.� 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�
1.�Introduction��
18�
information�not�contained�in�these�matrices�remains�as�"unexplained�X�variance"�in�a�residual�
matrix�(E)�which�has�exactly�the�same�dimensionality�as�the�original�X�matrix.�
The�PCs,�among�many�others,�have�two�interesting�properties:�
� They� are� extracted� in� decreasing� order� of� importance.� The� first� PC� always� contains�
more�information�than�the�second,�the�second�more�than�the�third�and�so�on...�
� They� are� orthogonal� to� each� other.� There� is� absolutely� no� correlation� between� the�
information�contained�in�different�PCs�[76].
PCA� is�sensitive�to�the�relative�scaling�of�the�original�variables�so�centering�of�the�data�
for�each�attribute�is�previously�required.�The�results�of�a�PCA�are�usually�discussed�in�terms�of�
component�scores�(the�transformed�variable�values�corresponding�to�a�particular�case� in�the�
data)� and� loadings� (the� weight� by� which� each� standardized� original� variable� should� be�
multiplied�to�get�the�component�score)�[77].��
Often,� PCA� can� be� thought� of� as� revealing� the� internal� structure� of� the� data� in� a�way�
which�best�explains�the�variance�of�the�data.�If�a�multivariate�dataset�is�visualized�as�a�set�of�
coordinates� in� a� high�dimensional� data� space� (1� axis� per� variable),� PCA� can� supply� the� user�
with� a� lower�dimensional� 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�
transformed�data�is�reduced.��
So,�by�using�PCA�a�large�number�of�variables�such�as�concentration�of�elements�can�be�
transformed� into� linearly� independent� sources�of� “information”� (referred� to�as� components)�
that�can�be�interpreted�to�provide�insight�into�the�processes�or�interrelationships�that�underlie�
the� data.� Principal� components� analysis� is� commonly� used� in� a� variety� of� geosciences�
disciplines,�such�as�aeolian�applications�to�help�determine�source�regions�of�particulate�matter�
pollution�[78,�79],�to�characterize�particle�size�data,� including�studies�of�soil� fertility� [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�
components�can�help�identify�source�of�contamination�as�well�as�contamination�patterns.��
PCA�[83]�was�also�applied�to�explain�the�underling�structures�of�the�obtained�data,�i.e.�to�
identify� pollutant� sources,� certain� distribution� patterns� and� their� contributions� on� soils�
affected�by�mining�pollution.�
1.�Introduction�
19�
1.7.�WEAKNESS�AND�NEEDS�OF�MINING�SITES�CHARACTERIZATION�
� Weaknesses:�
� 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)�is�not�considered.�
� A�great�deal�of�mines�is�abandoned�every�year�without�concern�for�the�environment.�
This� is� especially� dramatic� in� developing� countries� were� no� legislation� concerning�
contaminated�soils�are�implemented.��
� Needs:�
� The� characterization� of� several� parameters� including� not� only� heavy� metal�
concentration�but�also�its�distribution�around�the�polluted�area,�the�physico�chemical�
parameters,�the�origin�of�the�contamination�and�the�mobility�of�the�heavy�elements�of�
soils�should�be�performed�to�assess�environmental�health�hazards.�
1.�Introduction��
20�
�
REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATER�
The�development�of�viable�ways�of�recycling�industrial�waters�and�their�derivate�sludge�
such�as�mining�effluents� rather�than� its�disposal�as�a�hazardous�waste� in�specially�controlled�
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�
inorganic�contaminated�wastewaters�is�resumed.�
1.8.� SOLVENT� EXTRACTION� FOR� THE� RECOVERY� OF� ZN� FROM� ACIDIC�MINE�WATERS�
Refused�mining� tailings� and�water� containing� rejected�materials� are� generally� pumped�
into�tailing�ponds�to�avoid�their�transportation�by�wind�into�populated�areas�where�the�toxic�
chemicals�could�be�dangerous�to�human�health�as�well�as�to�allow�the�sedimentation�of�solid�
particles[84].�However,�mine�tailing�ponds�are�potentially�hazardous�as� they�can�represent�a�
source�of�acid�drainage�but�especially�due�to�dam�failures�of�tailing�ponds.�When�a�tailing�line�
breaks�or�a�dam�breaches,�high�amounts�of�contaminated�water�and�clays�with�dissolved�metal�
ions�are�released�to�the�environment�causing�serious�damages�and�having�toxic�effects�on�the�
biota� in� the� downstream� water[85].� Some� mining� operations� are� able� to� recycle� relatively�
small�amounts�of�water�for�drilling�and�dust�suppression�by�using�simple�sumps�to�clarify�the�
water,�but�in�most�cases,�the�total�volume�is�pumped�to�the�surface�for�their�treatment�with�
the�other� aqueous�drainage� components� so� as� to�minimize� long�term�environmental� effects�
once�active�mining�has�ceased�[86].��
The�volume�and�characteristics�of�materials� contained� in� tailing�ponds�can�vary�widely�
depending�on�mining�methods�and�the�hydrogeological�characteristics�of�the�region.�Water�pH�
can�vary� from�basic� to�very�acid�depending�on�the�nature�of� the�ore�and� its�host�rock�and� it�
may�contain�high�levels�of�dissolved�metals,�suspended�solids,�some�oils�and�ammonia.�
As�an�example�of�economical�feasible�and�valuable�recovery�of�a�heavy�metal�byproduct,�
the� recovery� of� Zn� from� mine� waters� can� diminish� the� volume� of� hazardous� materials�
contained�in�the�mine�tailing�while�providing�economical�profit.�
1.�Introduction�
21�
1.8.1.�ZINC�OVERVIEW�
Zinc�is�the�23rd�most�abundant�element�in�the�earth's�crust.�Zinc�is�necessary�to�modern�
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�
annually�worldwide.�Nearly�50%�of�the�amount� is�used�as�a�coating�to�protect� iron�and�steel�
from�corrosion� (galvanized�metal).�Approximately� 19%� is�used� to�produce�brass�and�16%�go�
into�the�production�of�zinc�base�alloys�to�supply�the�die�casting�industry.�Significant�amounts�
are�also�employed�for�compounds�such�as�zinc�oxide�and�zinc�sulfate�and�semi�manufactures�
including� roofing,� gutters� and� down�pipes� [5]� (Figure� 1.3a).� Main� application� areas� are:�
construction�(45%)�followed�by�transport�(25%),�consumer�goods�&�electrical�appliances�(23%)�
and�general�engineering�(7%)�(Figure�1.3b).�Zinc�is�also�a�necessary�element�for�proper�growth�
and�development�of�humans,�animals,�and�plants;�it�is�the�second�most�common�trace�metal,�
after�iron,�naturally�found�in�the�human�body.��
47%
19%
16%
7% 7% 4%
Zinc�coated�steels Brass�
Zinc�base�alloys Semi�manufactures
Compounds Others
First�use
��
45%
25%
23%7%
Construction Transport
Consumer�&�Electrical�Goods General�Engineering
End�use
Figure�1.3.�Zinc�demand:�First�use�and�End�use�in�2003�estimate�(Source�:�ILZSG�/�Brook�Hunt�/�Outokumpu�/�CRU�[87])�
The� level�of� zinc� recycling� is� increasing�each�year,�as�a�consequence�of� the�progress� in�
the�technology�of�zinc�production�and�zinc�recycling.�Today,�over�80%�of�the�zinc�available�for�
recycling� is� indeed� recycled.� Although,� at� present,� approximately� 70%� of� the� zinc� produced�
worldwide�is�still�originated�from�mined�ores�[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]�
being�the�latter�the�most�economical�and�practical�process�to�extract�Zn�from�industrial�waters�
[89,�90,�91].�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�
1.�Introduction��
22�
production�costs,�as�well�as�due�to�the�diminishing�production�in�high�grade�ore�reserves�[92,�
93].�
1.8.2.�THE�SOLVENT�EXTRACTION�PROCESS�
Solvent�extraction�involves�the�extraction�of�the�target�element�from�the�initial�aqueous�
solution�by�an�extractant�usually�diluted�in�an�organic�solvent�(organic�phase),� leaving�all�the�
other� constituents� in� the� aqueous� raffinate.� A� subsequent� reextraction/stripping� of� the�
extracted�element�present�in�the�organic�phase�is�usually�carried�out�with�some�acidic�solution�
(stripping� solution)� with� higher� affinity� for� the� target� element� than� the� organic� phase.�
However,�when�undesirable�metals�are�extracted�together�with�the�target�element,�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�
stripping�of�the�target�element�or�the�scrubbing�step�is�not�complete�for�further�reuse�of�the�
organic�solvent�(Figure�1.4).��
Figure�1.4.�Typical�solvent�extraction�steps�
Scrub� and� regeneration� steps� generally� increase� the� cost� of� the� process� due� to� the�
expenditure� in�both�reactants�and�time�so� it� is�preferable�to�use�a�selective�extractant�and�a�
proper�strip�solution�so�as�to�these�steps�can�be�avoided.��
Nowadays,�a�wide�number�of�extractants�are�available�for�use�in�SX�for�the�recovery�of�
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�(2�ethylhexyl)� phosphoric� acid� (DEHPA)� and� bis(2,4,4�trimethylpentyl)� phosphinic�
acid�(Cyanex�272)�(Figure�1.5).�
1.�Introduction�
23�
DEHPA (Di-(2-ethylhexyl) phosphoric acid
CAS No. 298-07-7
CYANEX 272 Bis(2,4,4-trimethylpentyl) phosphinic
acid Cas No. 83411-71-6
POHO
OOP
HO
O
CYANEX 301 Bis(2,4,4-trimethylpentyl) dithiophosphinic
acid CAS No. 107667-02-7
CYANEX 302 Bis(2,4,4-trimethylpentyl) monothiophosphinic acid
CAS No. 132767-86-3
PHS
SP
HO
S
Figure�1.5.�Chemical�structures�of�DEHPA,�Cyanex�272,�Cyanex�301�and�Cyanex�302�
DEHPA�has�been�successfully�used�as�an�extractant�for�many�metal�ions�including�Zn�due�
to�its�great�extraction�capacity�and�low�cost�[96,�97,�98].�It�has�been�used�to�extract�Zn�more�
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�
function�of�pH�to�be�Fe3+>Zn2+>Cu2+>Co2+>Ni2+>Mn2+>Mg2+>Ca2+� [100].� In�a�more�recent�study�
of�the�separation�of�divalent�metal�ions�from�a�synthetic�solution,�the�extraction�of�metal�ions�
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�
acidic�conditions�and�the�temperature�as�main�parameters�to�get�pure�solutions�of�the�target�
metals.��
Cyanex� 272� has� been� used� as� well� as� its� thiosubstituted� derivatives� (Cyanex� 302� and�
Cyanex�301)�in�the�extraction�of�several�metal�ions�[102].�Various�studies�report�the�adequacy�
of�Cyanex�272�to�extract�Fe,�Zn,�Cr,�Cu�and�Ni�from�sulfuric�and/or�sulfate�solutions�[103,�104,�
105].��
1.8.3.�SCALING�SOLVENT�EXTRACTION�TO�A�PILOT�PLANT�
The� development� of� new� solvent� extraction� solutions� at� the� laboratory� level,� require�
from�a�pilot�plant�step�in�order�to�validate�the�concept.�The�design�of�a�pilot�plant�is�based�on�
the�data�obtained�both�in�the�laboratory�and�by�process�modeling.��
Laboratory�experiments�are�performed�to�check�the�feasibility�of�the�solvents�that�seem�
to� be� suitable� by� testing� their� chemical� (liquid�liquid� equilibrium)� and� hydrodynamic� (phase�
1.�Introduction��
24�
separation)�properties.�At� laboratory,� the�distribution�coefficient� (D)�can�be�known�for�every�
solute� so� an� approximation� of� the� number� of� steps� of� the� process� can� be� estimated.� For� a�
multi�component� process,� the� feasibility� of� separation� between� various� species� may� be�
determined�too.�In�addition,�by�means�of�kinetic�experiments�the�residence�time�as�a�function�
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�
pilot�plant:�temperature,�pH,�phase�ratio,�the�number�of�stages�and�optimal�flow�sheet�of�the�
process�[106].�
Thus,�the�pilot�plant�has�to�test�mainly�the�parameters�that�cannot�be�predicted�by�the�
simulation:� preferred� dispersion,� flux,� entrainment,� accumulation� phenomena,� precipitation,�
deterioration�of� the�solvent�and�the�equipment�to�be�used.� In� this�sense,� the�recommended�
equipment�is�tested�in�a�dedicated�pilot�plant,�to�estimate�the�mass�transfer,�entrainment�and�
phase� separation,� and� optionally� for� quick� accumulation� phenomena,� e.g.,� precipitation,�
foaming,�etc.�(this�last�point�can�be�checked�only�by�running�the�pilot�plant�with�real�process�
solutions)�and�to�have�an�experimental�proof�of�the�recommended�flow�sheet.�It�can�be�used�
to�discover�problems�that�could�not�have�been�otherwise�detected.��
The�configuration�and�type�of�equipment�of�the�pilot�plant�should�be�similar�to�the�full�
scale�plant,�and�it�should�be�run�in�the�designated�site,�using�the�real�raw�materials.�Its�main�
purpose� is� the� verification� of� the� results� that� were� obtained� in� the� bench�scale� regarding�
production�rate,�recovery,�product�quality�and�analysis�of�accumulation�phenomena.�
All� these� parameters� are� sufficient� for� a� rough� economical� estimate� of� the� industrial�
plant.�Every�dollar�invested�in�the�pilot�plant�pays�itself�tenfold�in�the�industrial�plant�[107].�
1.9.� FE�LOADED� MATERIALS� FOR� THE� REMOVAL� OF� ORGANIC� AND�
INORGANIC�CONTAMINANTS�
Another� technology� to� be� employed� on� the� recovery� of� inorganic� contaminants� in�
industrial�polluted�effluents�deals�with�the�ion�exchange�processes.�Specifically,�the�Fe�loaded�
materials� that�can�be�employed�not�only�to�selectively�remove�the�pollutant� from�the�target�
effluent�but�also�to�treat�organic�waste.�Through�the�next�section,�the�use�of�several�materials�
as�a�support�for�iron�to�be�used�either�as�heterogeneous�Fenton�catalyst�or�as�arsenic�sorbent�
will�be�described.��
�
1.�Introduction�
25�
1.9.1.�ZEOLITES�
The�word�"zeolite"�comes�from�the�Greek�“zeo”�and�“lithos”�that�means�"boiling�stone"�
because�of�the�observation�that�zeolites�release�water�when�heated.�Zeolites�are�a�large�group�
of� natural� and� synthetic� hydrated� aluminum� silicates� characterized� by� complex� three�
dimensional�structures�with�large,�cage�like�cavities�that�can�accommodate�sodium,�calcium�or�
other�cations�(positively�charged�atoms�or�atomic�clusters);�water�molecules;�and�even�small�
organic�molecules.�These�encaged�ions�and�molecules�can�be�removed�or�exchanged�without�
destroying�the�aluminosilicate�framework�[108].��
The� atomic� structures� of� zeolites� are� based�on� three�dimensional� frameworks� of� silica�
and�alumina�tetrahedra�in�a�tetrahedral�configuration,�where�each�oxygen�atom�is�bonded�to�
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�
variety�of�possible�zeolite�structures� is�due�to�the�large�number�of�ways� in�which�these�units�
can�be�linked�to�form�various�structures�(Figure�1.6).�Each�type�of�zeolite�has�specific�uniform�
pore�size,�for�instance,�3.5�4.5��for�zeolite�LTA,�4.5�6.0��for�ZSM�5�and�6.0�8.0��for�zeolite�X,�
Y�type.�
Figure�1.6.�Framework�topologies�of:�a)�Sodalite;�b)�Zeolite�A/ZK�4;�c)�Zeolites�X/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.�Another�zeolite�such�as�Ferrierite�occurs�in�a�few�large�beds,�thus�offering�a�limited�
range�of�atomic�structures�and�properties.�Each�of� the�seven�also�has�been�synthesized,�and�
those� synthetic� zeolites� have� a� wider� range� of� properties� and� larger� cavities� than� natural�
zeolites.�The�principal�synthetic�(aluminosilicate)�zeolites� in�commercial�use�are�Linde�Type�A�
(LTA),�Linde�Types�X�and�Y�(Al�rich�and�Si�rich),�Silicalite�1�and�ZSM�5,�and�Linde�Type�B�(zeolite�
P).�All�are�aluminosilicates�or�pure�silica�analogues.�
The�aluminosilicate�framework�of�a�zeolite�has�a�negative�charge,�which�is�balanced�by�
the�cations�located�in�the�cage�like�cavities�that�can�participate�in�ion�exchange�processes.�This�
characteristic�yields�some�important�properties�for�zeolites�such�as�less�dense�structures�than�
1.�Introduction��
26�
other�silicates.�In�this�sense,�between�20�and�50�percent�of�the�volume�of�a�zeolites�structure�
are�voids.��
Synthetic� zeolites� were� first� produced� in� the� 1950s� and� nowadays� more� than� 100�
different�zeolites�have�been�made�with�an�annual�production�of�synthetic�zeolites�exceeding�
12,000� tons.� The� International� Zeolite� Association� (IZA)� database� shows� that� the� number� of�
structural�types�of�unique�microporous�frameworks�has�been�growing�rapidly,�from�27�in�1970�
to� 133� in� 2001,� whereas� currently� this� number� has� reached� 180� [109].� In� table� 1.9� are�
presented�some�typical�oxide�formula�of�synthetic�zeolites.�
Table�1.9.�Typical�oxide�formula�of�some�synthetic�zeolites�
Zeolites Typical oxide formula Zeolites�A� Na2O.Al2O3.2SiO2.4,5H2O�Zeolites�N�A� (Na,�(CH3)4N+)2O.�Al2O3.4,8SiO2.7H2O��Zeolites�H� K2O.Al2O3.2SiO2.4H2O�Zeolites�L� (K2Na2)O.Al2O3.6SiO2.5H2O�Zeolites�X� Na2O.Al2O3.2,5SiO2.6H2O�Zeolites�Y� Na2O.Al2O3.4.8SiO2.8,9H2O�Zeolites�P� Na2O.Al2O3.2���5SiO2.5H2O�Zeolites�O� (Na2,K2,�(CH3)4N+2)O.Al2O3.7SiO2.3,5H2O��Zeolites��� (Na,�(CH3)4N+)2O.Al2O3.7SiO2.5H2O��
Zeolites�ZK�4� 0,85Na2O.0,15�((CH3)4N+)2O.�Al2O3.3,3SiO2.6H2O�Zeolites�ZK�5� (R,Na2)O.Al2O3.4�6SiO2.6H2O�
The�uses�of�zeolites�derive�from�their�special�properties:�
i)�Ion�exchange:�Zeolites�can�interact�with�water�to�absorb�or�release�ions.�In�this�sense,�
they�are�used�as�water�softeners,�to�remove�calcium�ions,�which�react�with�soap�to�form�scum.�
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�
ammonium�ions�(NH4+)�from�sewage�and�agricultural�wastewater.�Natural�zeolites�are�also�the�
most� effective� filters� yet� found� for� absorbing� sulfur� dioxide� from�waste� gases.� As� efforts� to�
improve�the�continuous�air�quality,�zeolites�can�be�used�to�help�purify�the�gases�from�power�
plants�that�burn�high�sulfur�coal.��
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�
absorb�or�hold�molecules�that�the�natural�zeolites�do�not.�Some�zeolites�are�used�as�molecular�
sieves�to�remove�water�and�nitrogen�impurities�from�natural�gas.�
1.�Introduction�
27�
iii)�Catalytic�cracking:�Zeolites�can�hold�large�molecules�and�help�them�break�into�smaller�
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).�Zeolites�are�also�used�in�hydrogenating�vegetable�oils�and�in�many�other�industrial�
processes�involving�organic�compounds.��
Although�most�zeolites�used�as�catalysts�are�synthetic�and�made�for�specific�applications,�
a�few�natural�zeolites�have�also�been�employed.�Amongst�the�natural�zeolites,�the�most�used�is�
clinoptilolite� for� being� the� most� common� zeolite� occurring� in� large� quantities.� Moreover,�
taking�profit�of�their�high�exchange�capacity,�several�Fe�loaded�zeolites�have�been�also�widely�
employed.� In� this� regard,� Fe�bearing� zeolites�have�been�applied� to�N2O�decomposition� [110,�
111],�selective�catalytic�reduction�of�NO�with�hydrocarbons�[112,�113,�114]�or�NH3�[115,�116,�
117],�oxidation�of�benzene�to�phenol�[118,�119],�epoxidation�of�propene�[120,�121],�oxidation�
of�volatile�organic�carbons�[122],�decolorisation�by�means�of�Fenton�type�reaction�[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.��
The�crystal�structure�of�clays�consists�mainly�in�tetrahedral�silica�and�octahedral�alumina�
linked�together�to�form�layers�of�tetrahedra�and�octahedra.�These�layers�will�share�the�apical�
oxygen�from�the�tetrahedral�layer�with�the�free�oxygen�of�the�octahedral�layer.�A�layer�packing�
type�1:1�contains�one�tetrahedral�and�one�octahedral� layer,�a� layer�2:1�type�two�tetrahedral�
and�one�octahedral�and�a�layer�2:2�type,�two�layers�of�each�(Figure�1.7).�
Exchangeable�cation
(Al,�Mg,�Fe)O6
(Al,�Si)O42:1�silicate�
layer
2:1�silicate�layer
interlayer
Figure�1.7.�Schematic�structure�of�a�2:1�layer�expandable�clay�
1.�Introduction��
28�
The�octahedral�sites�are�usually�occupied�by�Al3+�or�Mg2+.�When�the� ion� is�Mg2+,�all� the�
holes� are� occupied� and� the� configuration� is� trioctahedral,� but� if� the� ion� is� Al3+,� due� to� their�
higher�charge,�only�2/3�of�the�sites�are�occupied,�resulting�in�a�dioctahedral�structure.�
The�Si4+�and�Al3+�in�the�tetrahedral�and�octahedral�layer�respectively,�may�be�substituted�
by� other� elements� with� an� ionic� radius� suitable� to� fit� into� the� structure� (called� isomorphic�
substitution).�Thus,�Si4+�can�be�replaced�by�Al3+,�and�Al3+�by�Mg2+,�Mn2+,�Ca2+�or�Ni2+�causing�a�
negative�charge�density�that�should�be�compensated�by�cations�in�the�interlaminar�space�that�
can�be�exchangeable�(cation�exchange).��
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,�is�that�due�to�laminar�structure,�force�a�chemical�reaction�occurs�in�a�plane�and�no�
three�dimensional�space,�making�it�much�faster.�
Commercial�clays�are�mainly�dedicated�to�the�manufacture�of�raw�materials�for�building�
materials�accounting�for�90%�of�production�and�only�10%�is�allocated�to�other�industries�such�
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� alumino�silicates� and�
hence,� they� possess� high� cation� exchange� capacity.� However,� they� differ� in� their� crystalline�
structure:�zeolites�have�a�rigid�three�dimensional�crystalline�structure�consisting�of�a�network�
of� interconnected�tunnels�and�cages�whilst�clays�have�a� layered�crystalline�structure�and�are�
subject�to�shrinking�and�swelling�as�water�is�absorbed�and�removed�between�the�layers.��
1.9.3.�SPONGES�
In� the� same� way,� Forager™� sponge� is� a� high� porosity� and� economic� ion�exchange�
material�with�selective�affinity�for�dissolved�heavy�metals� in�both�cationic�and�anionic�states.�
Such�material� is� able� to�promote�high� rates�of� adsorption�and� flexibility�which�enables� their�
compressibility� into�an�extremely�small�volume�to�facilitate�disposal�once�the�capacity�of�the�
material�has�been�exhausted�[125].�Forager�is�an�open�celled�cellulose�sponge�which�contains�
a�water�insoluble�polyamide�chelating�polymer� formed�by� the� reaction�of�polyethyleneimine�
and�nitrilotriacetic�acid.�This�material� is�claimed�to�contain� free�available�ethyleneamine�and�
iminodiacetate�groups�to�interact�with�heavy�metals�ions�by�chelation�and�ion�exchange.�In�this�
sense,� it�has� selective�affinity� for�dissolved�heavy�metals� in�both�cationic�and�anionic� states.�
Forager�sponge�and�other�adsorbent�sponges�have�been�successfully�used�in�the�treatment�of�
heavy�metals�solutions�[126,�127].��
1.�Introduction�
29�
Several�advantages�of�the�sponge�material�were�identified.�The�first�was�its�open�celled�
nature�that�allows�relatively�high�flow�rates;�the�second�was�cost�effectiveness;�and�the�third�
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�
enables�the�Forager™�Sponge�to�bind�toxic�heavy�metals�over�benign�monovalent�and�divalent�
cations� such� as� calcium,�magnesium,� potassium� and� sodium.� In� addition,� prior� studies� have�
shown�that�the�sponge�material� is�effective�over�a�wide�range�of�pH.�The�pH�at� the�site�was�
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� open�celled� 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+.�The�sponge�polymer�also�contains�tertiary�
amine� salt� groups� that� can� bind� anionic� contaminants,� such� as� the� chromate,� arsenic,� and�
uranium�oxide�species.�It�can�be�designed�for�site�specific�needs�to�contain�a�cation�that�forms�
a�highly� insoluble�solid�with�the�anion�of� interest.�Another�advantage�is� its�high�porosity�and�
flexibility�which�allows�its�compressibility�into�an�extremely�small�volume�to�facilitate�disposal.��
1.10.�THE�FENTON�REACTION�
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�
resistance�to�biodegradation�or/and�toxic�effects�on�microbial�processes.�As�a�result,�the�use�of�
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,�
advanced�oxidation�processes�(AOPs)�are�already�been�used�for�the�treatment�of�wastewater�
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�
compounds�that�inhibit�biological�wastewater�treatment�processes�[128]�
Advanced�oxidation�processes�(AOPs)�are�based�on�the�generation�of�the�highly�oxidative�
hydroxyl� radical� which� attacks� non�selectively� all� present� organic� compounds� [129].A� great�
number�of�methods�are�classified�under�the�broad�definition�of�AOPs�(Table�10).�Most�of�them�
1.�Introduction��
30�
use�a�combination�of�strong�oxidizing�agents�(e.g.�H2O2,�O3)�with�catalysts�(e.g.�transition�metal�
ions)�and�irradiation�(e.g.�ultraviolet,�visible).��
Table�1.10.�List�of�main�AOP�processes�
Ozonation�under�alkaline�conditions�(O3/OH�)�
Ozonation�assisted�by�hydrogen�peroxide�(O3/H2O2)�and�(O3/H2O2/OH
�)�Without�external�energy�supply�
Hydrogen�peroxide�and�iron�catalysts�(Fenton�process,�H2O2/Fe
2+)�Ozonation�and�ultraviolet�radiation�(O3/UV)�
Hydrogen�peroxide�and�ultraviolet�radiation�(H2O2/UV)�Ozone,�hydrogen�peroxide�and�ultraviolet�radiation�
(O3/H2O2/UV)�Photo�Fenton�(Fe2+/H2O2/UV)�
Ozonation�assisted�by�ultrasounds�(O3/US)�Hydrdogen�peroxide�assisted�by�ultrasounds�(H2O2/US)�
Electrochemical�oxidation�Anodic�oxidation�
HOMOGENEOUS�PROCESSES�
With�external�energy�supply�
Electro�Fenton�Catalytic�ozonation�(O3/Cat.)�
Photocatalytic�ozonation�(O3/TiO2/UV)�HETEROGENEOUS�
PROCESSES�Heterogeneous�photocatalysis�(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].�The�Fenton�reaction�consists�on�the�generation�of� the�hydroxyl� radical� from�
hydrogen�peroxide�and�Fe(II)�ions�in�mild�conditions�(reactions�1�and�2):��
Fe2+�+�H2O2�� � �� Fe3+�+�OH��+��OH�� k1=10�7�M�1s�1� � (Eq.�1)�
�OH�+�Organic�matter���� Oxidized�Products�� � � � (Eq.�2)�Fe3+�+�H2O2�� � �� FeOOH2+�+�H+�� k3=0.001�0.01�M
�1s�1� � (Eq.�3)�FeOOH2+�� � ��� Fe2+�+��HO2�� � � � � (Eq.�4)�Fe2++�HO2�� � ��� Fe3+�+�HO2
��� � � � � (Eq.�5)�Fe3+�+�HO2�� � ��� Fe2+�+�O2�+�H
+�� � � � � (Eq.�6)�2�HO2�� � � �� H2O2�+�O2�� � � � � (Eq.�7)�
As� iron� is� catalytically� cycled� between� Fe(II)� and� Fe(III)� (reaction� 1� and� 3� to� 6),� the�
hydroxyl�radicals�can�be�generated�also�with�Fe(III),�which�is�called�Fenton�like�reaction�[133].�
The�reaction�using�Fe(III)� is�slower�than�with�Fe(II)�although�the�use�of�Fe(III)� instead�of�Fe(II)�
present�some�advantages�mainly�related�to�the�working�pH�that�can�be�broadened�from�3.0�to�
4.5.� [134]� Other� advantages� concern� with� the� reduction� on� the� reactants� costs� due� to� the�
lower�expenditure�of�Fe(III)�salts�compared�to�Fe(II)�salts.�However,�the�use�of�Fe(II/III)�salts�as�
a�homogeneous�catalyst�has�some�drawbacks�concerning�the�removal�of�Fe�due�to:�
� Chelating� pollutants� as�well� as� some� inorganic� components� of�wastewater� solutions�
(e.g.�phosphate)��
1.�Introduction�
31�
� Loss�of�the�catalyst�due�to� iron�hydroxide�precipitation�which�causes�red�mud�sludge�
that� should� be� removed� from� the� solution,� sometimes� requiring� further� treatment�
thus�increasing�the�cost�of�the�whole�process.��
To�overcome�these�drawbacks,�several�heterogeneous�Fenton�catalysts�bearing�Fe(II/III)�
ions,�clusters�or�oxides�have�been�developed�[135,�136,�137,�138,�139,�140,�141].�In�this�sense,�
several�layered�and�porous�aluminosilicates�such�as�clays�and�zeolites�have�been�proposed�as�a�
support�for�the�catalytic�Fe�due�to�their�high�specific�surface,�high�thermal�stability,�exchange�
capacity�and�homogeneous�distribution�of�active�sites�(142).�With�regard�to�zeolites,�Fe�loaded�
synthetic�commercial�zeolites�such�as�ZSM�5�zeolites�[143,�144,�145]�and�Y�zeolites�[146,�147,�
148]�have�been�widely� reported� in� the� literature� to�provide� similar� catalytic�activities�as� the�
homogeneous�catalysis.�However,�in�addition�to�the�elevated�cost�of�these�synthetic�materials�
and�the�preparation�of�these�Fe�loaded�materials,�long�and�tedious�procedures�are�required.��
1.11.�ARSENIC�SORPTION�USING�FE�LOADED�MATERIALS�
Arsenic� is� a� naturally� occurring� metal� released� into� the� environment� by� natural� and�
anthropogenic�(industrial�and�commercial)�processes.�It�has�received�huge�public�and�scientific�
attention�due�to�environmental�and�public�health�disasters�around�the�world�[149,�150,�151].��
1.11.1.�ARSENIC�TOXICITY�
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,�arsine�gas�have�the�highest�toxicity�of�As�compounds�and�it�is�formed�by�the�reaction�
of�hydrogen�with�arsenic,�during�the�synthesis�of�organic�arsenic�compounds,�and�generated�
accidentally�during� the�smelting�and� refining�of�nonferrous�metals� in�mining�processes.�High�
levels�of�naturally�occurring�arsenic�are�found�in�soil�and�rocks�leading�to�unacceptable�levels�
of�arsenic�in�drinking�water�such�as�in�Bangladesh.��
The�toxicity�of�arsenic�varies�widely�based�on�the�route�of�exposure,�the�form,�the�dose,�
the�duration�of�exposure,�and�the� time�elapsed�since� the�exposure.� Ingestion�and� inhalation�
1.�Introduction��
32�
are�the�primary�routes�of�both�acute�and�chronic�exposures.�Arsine�gas�is�one�of�the�most�toxic�
forms�and�is�readily�absorbed�into�the�body�by�inhalation.��
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�
contact�with�high�concentrations�of�inorganic�arsenic�compounds�may�result�in�skin�irritation,�
redness,� and� swelling� and� high� acute� exposures� may� cause� cholera�like� gastrointestinal�
symptoms�of�vomiting�(often�times�bloody)�and�severe�diarrhoea�(often�bloody).�Ingestion�of�
large�doses�of�inorganic�arsenic�(70�to�180�mg)�may�be�fatal.�
Arsenic�has�been�classified�as�a�known�human�carcinogen�by�multiple�agencies�based�on�
the�increased�prevalence�of� lung�and�skin�cancer�observed�in�human�populations�exposed�to�
arsenic.��
Every�day,�lack�of�access�to�clean�water�and�sanitation�kills�thousands�of�people,�leaving�
others� with� reduced� quality� of� life� and� as� cities� and� slums� grow� at� increasing� rates,� the�
situation�worsens.�Nowadays�clean�water�is�a�scarce�resource�and�arsenic�removal�from�waters�
has�emerged�as�a�major�concern�in�certain�developing�countries.��
1.11.2.�ARSENIC�SORBENTS�
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�
iron�hydroxyoxides�such�as�goethite� (either�natural�or�synthetic)� [153,�154,�155],� ferrihydrite�
[156,�157,�158]�or�hematite�[159,�160]�and�different�Fe�bearing�materials�such�as�Fe(III)�loaded�
zeolites� [161],�aluminosilicates� [162]�or� resins� [163].�To�predict� the� long�term�fate�of�arsenic�
and�design�new�materials�with�improved�capacity�and�efficiency�for�As�sorption,�the�molecular�
understanding�of� the� sorption�of�arsenic�by� iron� (oxy)hydroxides�and�Fe�bearing�materials� is�
required.�Fe�loaded�zeolites�have�been�successfully�employed�for�arsenic�removal�[164].�In�this�
sense,� arsenate� and� arsenite� adsorption� from� water� was� carried� on� by� using� iron� treated�
activated�carbon�and�natural�zeolite,�comparing�their�efficiency�with�the�results�obtained�using�
Faujasite� (13X)� and� Linde� type� A� (5A)� molecular� sieves.� Iron�treated� activated� carbon� and�
chabazite� were� promising� as� low�cost� arsenic� adsorbents� removing� approximately� 60%� of�
arsenate�and�arsenite�and�50%�of�arsenate�and�30%�of�arsenite,� respectively� [165].�Besides,�
aqueous�arsenic�sorption�by�natural�zeolites,�volcanic�stone,�cactaceous�powder�CACMM�and�
clinoptilolite�containing�rocks�with�different�clinoptilolite,�erionite�and�mordenite�percentages�
have�been�also�reported� [166,�167].�Each�zeolite�sample� in� the�0.1–4�mg/L�Fe�concentration�
1.�Introduction�
33�
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�
directly�to�the�iron�content�in�the�acid�washed�zeolite.�Moreover,�the�adsorption�of�As(V)�from�
drinking�water�by�an�aluminum�loaded�Shirasu�zeolite�(Al�SZP1)�was�studied�obtaining�results�
equivalent�to�that�of�activated�alumina.�A�ligand�exchange�mechanism�between�As(V)�ions�and�
surface�hydroxide�groups�on�Al�SZP1�was�presumed� [168].� Furthermore,�an� iron�conditioned�
zeolite� was� prepared� and� used� for� arsenic� removal� from� groundwater� at� pH� 7.8� and�
temperature�145�ºC�[169].�On�the�other�hand,�Forager�Sponge�and�other�adsorbent�sponges�
have�been�successfully�used�in�the�treatment�of�heavy�metal�solutions�[128,�170]�but�scarcely�
employed�for�the�arsenic�removal�after�being�loaded�with�iron�[171].��
Several� researchers� have� investigated� the� structure� of� the� As� adsorbed� onto� such�
materials� using� Extended� X�ray� Absorption� Fine� Structrue� (EXAFS)� and� IR� spectroscopic�
techniques�and�previous�studies�have�shown�that�As(V)�oxyanions�are�strongly�adsorbed�to�the�
surfaces�of�such� iron�oxides�as�goethite,� ferrihydrite,�and�hematite� [172,�173,�174,�175].�The�
assessment�and�characterization�of�these�Fe�bearing�materials�can�shed�light�on�the�sorption�
mechanisms� taking�part�on� these�materials� to�provide�useful�data�concerning� the�As�uptake�
mechanism.� Thus,� modifications� concerning� the� As� chemical� and� electronic� structures�
depending�on�the�type�of�adsorbent�can�be�of�general�interest.��
1.12.� WEAKNESSES� AND� NEEDS� OF� INDUSTRIALLY� CONTAMINATED�
WATERS�
� Weaknesses:
� 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,�these�treatment�plants�are�expensive�and�the�process�costs�are�not�
recovered.��
� The�Fenton�reaction�involves�the�presence�of�iron�salts�as�catalyst�although�two�main�
drawbacks� arise,� the� first� mainly� concerning� the� loss� of� the� catalyst� due� to� iron�
hydroxide�precipitation�and�its�consequent�red�mud�sludge�generation.��
� Fe�loaded� materials� employed� as� Fenton� catalysts� involves� long� and� tedious�
procedures�in�addition�to�the�elevated�cost�of�the�synthetic�materials�used.��
� Several�iron�compounds�have�been�employed�for�the�sorption�of�arsenic.�Its�efficiency�
is�believed�to�be�strongly�influenced�by�its�structure.�
1.�Introduction��
34�
� Needs:�
� The� recovery� of� metals� contained� on� tailing� ponds� by� existing� methodologies� can�
provide�economical�value�to�these�residues�while�solving�an�environmental�problem.�
� New�iron�loaded�based�catalysts�into�different�materials�should�be�tested�to�avoid�the�
removal�of�the�catalyst,�the�generation�of�red�sludge�derived�from�the�precipitation�of�
iron�hydroxyoxides�and�to�improve�the�efficiency.��
� Nobel� Fe�loaded� materials� more� economical� should� be� tested� as� well� as� simpler�
preparation�methodologies�for�the�Fe�loading�of�these�materials.�
� To�predict� the� long�term� fate�of� arsenic� and� to�design�new�materials�with� improved�
capacity�and�efficiency�for�As�sorption,�the�molecular�understanding�of�the�sorption�of�
arsenic�by�iron�hydroxyoxides�and�Fe�bearing�materials�is�required.�
1.�Introduction�
35�
�
ANALYTICAL�TECHNIQUES�
Through� the� following� section,� the�general� aspects�of� the� techniques�employed� in� this�
work�are�overviewed.�In�this�sense,�the�analysis�of�heavy�metals�has�been�carried�out�by�means�
of�hand�held�X�ray�fluorescence�technique�while�synchrotron�based�techniques�such�as�X�ray�
Absorption�Near�Edge�Structure�(XANES)�and�Extended�X�ray�Absorption�Fine�Structure�(EXAFS)�
have�been�the�main�tools�applied�for�the�determination�of�mercury�species�as�well�as�to�the�
characterization�of�the�adsorption�of�arsenic�onto�Fe�loaded�materials.�
1.13.�X�RAY�FLUORESCENCE��
XRF� spectrometry� can� easily� and� quickly� identify� and� quantify� elements� over� a� wide�
dynamic�range,�from�ppm�levels�up�to�virtually�100%�w/w,�without�destroying�the�sample�and�
with� little,� if� any,� sample� preparation.� These� factors� lead� to� a� significant� reduction� in� the�
sample�analytical�cost�compared�to�other�elemental�analysis�techniques.��
1.13.1.�X�RAY�INTERACTION�WITH�MATTER�
Recording� the� image�of�a�given�structure�requires� the�use�of�a�wavelength�equal� to�or�
smaller� than� the� size� of� the� structure.� X�rays� are� actually� electromagnetic� waves� between�
ultraviolet�light�and�gamma�rays�on�the�wavelength�scale.�Their�wavelength�is�comparable�to�
interatomic�distances,�so�it�can�be�used�to�“see”�interatomic�distances�(Figure�1.8).��
Figure�1.8.�Electromagnetic�spectrum�
X�rays�interact�with�atoms�in�essentially�two�ways:�scattering�and�X�ray�absorption�or�the�
photoelectric�effect�(Figure�1.9).�Scattering�causes�the�photon�to�change�its�direction�and�it�can�
be� elastic� (Rayleigh� scattering)� or� inelastic� (Compton� scattering).� In� Rayleigh� scattering� the�
energy� of� the� photon� is� conserved� and� occurs� when� X�ray� photons� interact� with� strongly�
bound�electrons;�whereas�Compton�scattering�occurs�when�X�ray�photons�interact�with�weakly�
1.�Introduction��
36�
bound� electrons� and� the� energy� of� the� photon� is� conserved� after� the� interaction.� Rayleigh�
scattering�forms�the�basis�of�X�ray�diffraction�(Figure�1.10).�
MATERIAL
Incident X�ray beam Transmitted X�rays
Fluorescence
Rayleigh scattering
Compton scattering
Figure�1.9.�Interaction�of�X�rays�with�matter�
(1) Incoming X�ray photon
(2) Oscillating electron
(3) Scattered photon
No�loss of�energy
Nucleus
Electron
RAYLEIGH SCATTERING(Coherent scattering)
(1)(2)
(3) Ef=�E0
Energy E0
(1) Incoming X�ray photon
(2) Energy is partially
transferred to electron
(3) Scattered photon
Loss of�energy
Nucleus
Electron
COMPTON SCATTERING(Incoherent scattering)
(1)(2)
(3) Ef <�E0
Energy E0
Figure�1.10.�Rayleigh�and�Compton�scattering�
On�the�other�hand,�X�ray�absorption�occurs�when�an�atom�acquires�the�energy�of�an�X�
ray� to� excite� electrons� into� higher� energy� electron� orbitals� that� are� unoccupied,� or� into� the�
continuum�where�the�electron�is�no�longer�associated�with�the�atom.�To�fill�the�void�created�in�
the�inner�shell,�an�electron�from�a�higher�energy�shell�drop�down�almost�instantaneously.�The�
excess� energy� resulting� from� this� transition� can� be� released� either� in� the� form� of� an� X�ray�
photon�with�a�wavelength�characteristic�of�the�atom�(fluorescence)�or�to�an�electron�from�an�
outer�shell�that�receives�sufficient�energy�to�leave�the�atom�(Auger�electron�emission)�(Figure�
1.11).�
(1)Incoming X�ray photon
(2) A�K�shell electron is ejected (photoelectron)��
(3) Outer shell electron moves to the inner shell
hole created
(4) Energy excess emitted as�fluorescence
Nucleus
Electron
FLUORESCENCE
(1)
(2)(3)
Ef =�EL � EK
Energy E0
Ejected K�shellelectron
L�shell
K�shell
L�� K�transition
(4)
(1)Incoming X�ray photon
(2) A�K�shell 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
AUGER ELECTRON
(1)
(2)(3)
Energy E0
Ejected K�shellelectron
L�shell
K�shell
L�� K�transition
(4)
(5)
1.�Introduction�
37�
Figure�1.11.�Release�of�energy�process�after�X�ray�absorption�by�matter�
All�elements�emit�X�rays�at� their�own�characteristic�energies.�These�X�rays�are�called�K�
lines�if�they�result�from�an�electron�filling�the�K�shell,�and�L�lines�if�they�result�from�filling�the�
next�electron�shell�out,� the�L�shell.�The�energy�of� the�emitted�fluorescent�X�rays� identify� the�
elements� present� in� the� sample� and,� in� general,� the� intensities� of� the� X�ray� lines� are�
proportional� to� the� concentration� of� the� elements� in� the� sample,� allowing� quantitative�
chemical�analysis�by�X�ray�Fluorescence�(XRF)�spectrometers.�
1.13.2.�X�RAY�FLUORESCENCE�
XRF� is� a� non�destructive,� simultaneous� multi�element� technique� that� covers� a� wide�
dynamic�range�from�100%�down�to�the�μg/g� level�with�typical�relative�precision�approaching�
1%� [176].� This� well�established� analytical� method� has� been� applied� to� environmental,�
geological,�archaeological,�metal�and�alloy�samples.��
In�addition,� in�the�last�40�years�hand�held�XRF�equipments�have�been�emerged�as�very�
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.�With�minimal�sample�preparation�requirements,�XRF�may�provide�quick�qualitative,�
semi�quantitative,�or�even�quantitative�analysis�of� liquids,�powder,�solid�or� thin� film�samples�
[178].� 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�[179,�180].��
1.13.4.�FIELD�PORTABLE�XRF�INSTRUMENTATION�
A� typical� XRF� system� has� three� major� components:� an� excitation� source,� a�
spectrometer/detector� and� a� data� collection/processing� unit.� In� Field�Portable� X�Ray�
Fluorescence�(FP�XRF)�equipments,�the�excitation�source�and�the�detector�device�are�generally�
assembled� in� a� fixed� position� in� order� to� reduce� the� size� and� weight� to� facilitate�
transportation.�The�instrument�device�may�be�very�compact�by�incorporation�of�an�embedded�
microcomputer�or� it�may�be�rendered�more�flexible�by�using�a�standard�notebook�computer�
(Figure�1.12).��
1.�Introduction��
38�
Figure�1.12.�Scheme�of�a�FP�XRF�
Various� excitation� sources� may� be� used� to� irradiate� a� sample� although� the� more�
employed� are� radioisotopes� sources� and� X�ray� tubes.� In� a� radioisotope� source,� the�
characteristic�X�rays�emitted�from�a�sealed�radioisotope�source�such�as�55Fe,�57Co,�109Cd,�241Am�
and�244Cm�are�employed.�However,�the�intensity�of�these�excitation�sources�gradually�falls�as�
the�isotope�decays�and�the�emitted�X�ray�wavelengths�and�intensities�are�not�adjustable.�On�
the�other�hand,�X�ray�tubes�have�increased�sensitivity�and�analytical�range.�X�ray�tube�offer�a�
faster�analytical� time�because�the�X�ray� flux�can�be�higher� than�most� isotope�based�sources.�
They� can� also� be� used� over� a� wider� range� of� excitation� energies,� eliminating� the� need� for�
multiple�isotopic�sources�to�produce�X�rays�over�the�entire�excitations�spectrum.�In�addition,�it�
is�worth�mentioning�that�transportation�of�miniature�X�ray�tubes�involves�less�problems�than�
when�traveling�with�radioisotope�sources.�The�cathode�in�the�miniature�X�ray�tube�is�heated�by�
a� filament,� and� it� then� emits� electrons� that� are� accelerated� by� a� high� electric� field.� The�
accelerated� electrons� hit� the� anode,� which� emits� an� X�ray� continuum� accompanied� by� the�
characteristic�lines�of�anodic�metal.�Depending�upon�the�application,�the�anode�material�may�
be�Cd,�Cu,�Mo,�Rh,�Ag,�W,�Pt�or�Au.� These� sources�are�powered�with�an�external�AC�power�
supply,�or�an�internal�rechargeable�battery�[181].�
There�are�several�detectors�available�such�as�gas�flow�proportional�counters,�scintillation�
counters� and� solid� state� detectors� being� the� latter� the� most� employed� given� their� high�
resolution.� Solid�state� 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)�
(lithium�drifted�silicon),�Si�PIN�(silicon�positive�intrinsic�negative),�CCD�(charge�coupled�device),�
PDA� (photo� diode� array),� PIPS� (passivated� implanted� planar� silicon)� and� SSB� (silicon� surface�
1.�Introduction�
39�
barrier).�The�semiconductor�detectors�typically�require�cryogenic�cooling�to�improve�the�signal�
to�noise�ratio.�Besides,�the�development�of�personal�computers�with�high�speed�and�memory�
has� also� allowed� fundamental� parameter� algorithms� to� be� quickly� performed�using�multiple�
standards,� resulting� in� rapid� and� more� accurate� standardization� and� analyses� for�
multicomponent,�complex�matrices�over�standard�empirical�methods�[182].��
1.14.�SYNCHROTRON�BASED�TECHNIQUES�
1.14.1.�SYNCHROTRON�LIGHT�SOURCES�
The�first�accelerators�(cyclotrons)�were�built�by�particle�physicists�in�the�1930’s�to�study�
collisions�between�high�energy�particles.�In�this�role�they�were�very�successful,�and�the�Large�
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�
experiments�carried�out�using�synchrotron�light�were�performed�at�Cornell�(USA)�in�1956�and�
over�the�years,�the�number�of�experiments�increased,�all�using�machines�built�for�high�energy�
particle�physics.�This�changed�in�1980�when�the�UK�built�the�world’s�first�synchrotron�specially�
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,�
environmental�sciences�and�many�more.��
1.14.2.�DESIGN�AND�OPERATION�OF�A�SYNCHROTRON�LIGHT�SOURCE��
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�
traveling�near�the�speed�of� light� into�a�storage�ring.�The�particles�within�the�storage�ring�are�
forced�to�change� its� trajectory�by�bending�magnets� so� that� they� travel� in�a�closed� loop.�This�
causes�X�rays�with�a�broad�spectrum�of�energies�(white�light)�to�be�emitted�tangential�to�the�
storage� ring.� Therefore,� a� synchrotron� storage� ring� is� an� N�sided� polygon,� where� N� is� the�
number�of�bends.��
1.�Introduction��
40�
Experimental station
X-raybeam line
Electronbeam
LINACLINACBooster ring
Electronbeam
Electrongun
Insertiondevice
Bendingmagnet
Storage ring
Figure�1.13.�Synchrotron�schematic�overview�
Wigglers�and�undulators�are�two�types�of�specialized�insertion�devices�that�are�placed�in�
the�straight�sections�of�the�storage�ring.�A�wiggler�consists�of�several�closely�spaced�bending�
magnets� that� increase� the� intensity� of� the� X�ray� pulse.� An� undulator� oscillates� the� charged�
particles�using�carefully�spaced�magnets�such�that�the�interference�between�their�poles�affects�
the�emitted�X�ray�spectrum.�This�interference�is�additive�at�particular�wavelengths,�producing�
an�intense�X�ray�beam�at�a�wavelength�that�can�be�selected�by�varying�the�gap�between�the�
poles�of�the�magnets�(Figure�1.14).�Beamlines�are�placed�tangential�to�the�storage�ring�to�use�
the�X�rays�emitted�by�bending�the�charged�particles�[183].��
Wiggler
Undulator
Free electron laser
Bending magnet
Electron beam X-ray radiation Magnetic structures
Wiggler
Undulator
Free electron laser
Bending magnet
Electron beam X-ray radiation Magnetic structures
Figure�1.14.�Bending�magnets�and�insertion�devices�
1.�Introduction�
41�
Synchrotron�light�presents�very�special�characteristics:�
� High� intensity� or� flux� (photons� per� second)� over� a� continuous�wavelength� spectrum�
from� microwaves� to� hard� X�rays� and� gamma� radiation.� In� contrast� to� laser� light,�
synchrotron�radiation�is�non�monochromatic.
� High�brightness,�thousands�of�million�fold�higher�than�conventional�X�ray�sources.�
� Linearly� polarized� light,� the� light� oscillates� only� within� certain� planes.� The� light� is�
emitted� in� very� short� (picoseconds)� pulses�with� a� periodic� structure� (microseconds),�
therefore�showing�a�high�potential�for�studies�of�transient�phenomena.�
� Light� source� remaining� stable� along� the� time.�Depending�on� the� facility,� each�bunch�
refill�shows�a�lifetime�between�4�and�24�hours.��
Despite� the� strong� potential� shown� by� synchrotron�based� techniques� and� the�
spectacular�increase�of�their�possible�uses,�these�techniques�present�as�well�some�drawbacks:�
� Poor�detection�limits�
� Limited�number�of�synchrotron�facilities�
� Complex�data�treatment�
1.14.3.�X�RAY�ABSORPTION�SPECTROMETRY�
The�general� aspects�of�X�Ray�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� X�rays�
[186,�187,�188,�189].�Also,�a�number�of�reviews�papers�and�book�sections�describe�techniques�
and�applications�of�XAS� in�geochemistry�and�soil� science� [190,�191,�192,�193,�194,�195].�The�
principles�of�XAS�and�data�analysis�have�been�also�widely�described�[196,�197,�198]�while�more�
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�
abovementioned�reviews�for�more�detailed�information.��
As� aforementioned,� when� the� X�rays� interact� with�matter� the� radiation� can� be� either�
scattered�by�the�electrons�or�absorbed�and�excite�the�electrons�(Figure�1.9).�When�the�energy�
of�the�incident�photons�is�sufficient�enough,�a�core�electron�of�the�absorbing�atom�is�excited�to�
a�continuum�state�(i.e.�producing�a�photoelectron)�causing�a�wave�that�is�backscattered�by�the�
neighboring�atoms�producing�the�characteristic�features�of�a�transmission�XAS�spectra.�To�fill�
the� created� vacancy,� an� electron� from� a� higher� shell� drops� emitting� fluorescence� of�
characteristic� wavelength.� Several� detection� setups� have� been� developed� for� XAS� studies,�
1.�Introduction��
42�
depending� on� the� nature� of� the� absorber� and� the� matrix� type.� In� this� sense,� the� most�
commonly�used�involve�the�measurement�of�either�the�transmission�of�X�rays�or�the�emitted�
fluorescence.��
The� absorption� spectrum� of� an� element� in� the� vicinity� of� an� absorption� edge� can� be�
divided�in�four�main�regions:�Pre�edge,�Edge�(or�white�line),�XANES�and�EXAFS.�(Figure�1.15).�
�Figure�1.15.�Typical�X�ray�absorption�spectrum.��
1)�Pre�edge:�E�~2�50�eV�below�the�main�absorption�edge.�In�this�region�there�is�no�significant�
absorption� phenomena,� only� localized� electronic� transitions� to� unfilled� (or� partially� filled)�
atomic�levels�(e.g.,�s��p,�or�p�d).��
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�
close�energy�or�continuum�states.�A�sudden�rise�of�absorption�is�observed.��
3)� XANES:� E� from� ~2� to� 50� eV� above� the� edge.� Low�energy� photoelectrons� are� strongly�
scattered�and�multiple�scattering�dominates�[203].�The�resulting�features�are�intense,�and�can�
be� interpreted� in� terms� of� multiple� scattering� from� atoms� in� the� first� coordination� shells�
around�the�absorber,�yielding� information�about� interatomic�distances�and�angles� (Figure�9).�
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�
combination�of�reference�spectra�able�to�appropriately�reproduce�the�unknown�spectrum.�In�
these�terms,�XANES�has�been�widely�employed�as�a�speciation�technique.��
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.�
1.�Introduction�
43�
When�a�photoelectron�interacts�with�its�neighboring�atoms,�it�will�be�scattered�(Figure�16).�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�
backscattering�photoelectron�waves�produce�the�EXAFS�oscillations�(Figure�16).�EXAFS�region�
can� be� analyzed� to� obtain� information� about� the� distance� between� the� absorber� and� the�
neighboring�atoms,�extending�out�to�several�shells�of�ligands.��
Backscatter photoelectron
Photoelectron
Oscillation
Atom with neighbor
E�
Isolated atom
Single�scatteringTwo�legs Multiple scattering
Three�legsPhotoelectron
Backscatterer
Figure�1.16.�Origin�of�fine�structure�of�EXAFS�
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�
backscatterer�distance,�and�the�amplitude�of�the�oscillations�is�directly�related�to�the�number�
of�backscattering�ligands.��
Regardless�of�the�complexity�of�the�sample,�the�XAS�signal�comes�from�all�of�the�atoms�
of�a�single�element�as�selected�by�the�X�ray�energy.�The�structural�information�obtained�from�
XAS� is� useful� for� identifying� the� chemical� speciation� of� an� element,� including� mineral,�
noncrystalline�solid�or�adsorbed�phases.��
In�this�regard,�XAS�techniques�have�been�shown�to�provide�reliable� information�on�the�
speciation�of�several�elements�being�especially�interesting�the�case�of�mercury,�the�toxicity�of�
which� strongly� depends� on� its� speciation.� In� this� sense,� several� studies� dealt� with� mercury�
speciation�without�requiring�sample�pretreatment�[204,�205,�206,�207].�Moreover,�among�XAS�
techniques,� both� EXAFS� (extended� X�ray� absorption� fine� structure)� and� XANES� (X�ray�
absorption� near�edge)� 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].�
1.�Introduction��
44�
1.15.�OBJECTIVES�
This�PhD�thesis�has�been�focused�on�two�main�objectives�both�regarding�the�application�
to�environmental�problems� concerning:� i)� contaminated� soils� surrounding�mine�areas�and� ii)�
industrial�contaminated�waters.��
In�this�sense,�more�specific�goals�of�the�present�thesis�are;�
� The� application� of� different� analytical� techniques� such� as� Field�Portable� X�Ray�
Fluorescence� and� X�Ray� Absorption� Spectroscopy� to� the� study� of� highly� impacted�
environments�focused�on:�
� the� characterization� of� soils� surrounding� four� different� mine� areas� from�
Marrakech.� Study� of� heavy� metal� distribution� throughout� abandoned� mine�
areas�and�assessment�of�heavy�metal�mobility.��
� the� study� of� mercury� speciation� through� synchrotron� techniques� and�
estimation�of� its�mobility� from� soil� samples� from� three�of� the�main�mercury�
mine�areas�in�Europe.�
� 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�
environmental�problem.��
� To�assess�the�feasibility�of�novel�Fe�loaded�materials�
� as� catalysts� to� degrade� different� organic� pollutants� by� means� of� Fenton�
reaction.��
� as� arsenic� sorbents.� In� this� regard,� the� know�how� gained� on� synchrotron�
techniques� can� be� applied� to� shed� light� onto� the� sorption� mechanisms� of�
arsenic�onto�these�materials.�
1.�Introduction�
45�
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1.�Introduction�
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1.�Introduction��
54�
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1.�Introduction�
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56�
57�
�
2��
METHODOLOGY��MINE�SITES�CHARACTERIZATION ......................................................................................................59�
2.1.�STUDIED�MINES�DESCRIPTION.........................................................................................................59�2.2.�SAMPLING........................................................................................................................................64�2.3.�CHARACTERIZATION ........................................................................................................................65�2.4.�DATA�TREATMENT...........................................................................................................................70�
��REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATERS.................................73�
2.5.�ZINC�SOLVENT�EXTRACTION ............................................................................................................73�2.6.� Fe�LOADED� MATERIALS� FOR� THE� REMEDIATION� OF� ORGANIC� AND� INORGANIC� POLLUTED�WATERS ..................................................................................................................................................76�2.7.REFERENCES .....................................................................................................................................79�
58�
�
2.�Methodology�
59�
�Following�the�pattern�outlined� in�the� Introduction�chapter,�the�Methodology� is�divided�
into�two�main�parts:�I)�Mine�sites�characterization�and�II)�Remediation�technologies�applied�to�
aquatic�sources�containing�organic�and�inorganic�pollutants.��
MINE�SITES�CHARACTERIZATION�
In�this�section�the�methodologies�to�characterize�different�mine�areas�from�Morocco�and�
the�speciation�of�mercury�in�three�European�mines�are�described.��
2.1.�STUDIED�MINES�DESCRIPTION�
The� characterization� of� different� abandoned� mines� located� in� Marrakech� region�
(Morocco)�has�been�accomplished�by�determining�physical�and�chemical�parameters�of�mine�
area� soils,� whilst� soils� from� three� well� characterized� European� mercury� mines� have� been�
studied�to�determine�its�mercury�speciation�to�assess�its�toxicity.��
2.1.1.�MARRAKECH�MINES:�DRAA�LASFAR,�KETTARA,�SIDI�BOU�OTHMANE�AND�BIR�NEHASS�
(MOROCCO)�
The�studied�mines�are�located�35�km�northwest�of�Marrakesh�in�the�core�of�the�central�
Jebilet�mountains�(Figure�2.1).�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�
[1].�Central�Jebilet�mineralized�body�consists�of�major�and�minor�lenses�of�massive�pyrrhotite�
(Fe2+0.95S),� with� small� amounts� of� sphalerite� (Zn0.95Fe2+0.05S),� galena� (PbS),� chalcopyrite�
(CuFe2+S2),�pyrite�(Fe2+S2),�arsenopyrite�(Fe
3+AsS)�and�glaucodot�(Co0.75Fe2+0.25AsS)�[2].��
The�Draa�Lasfar�mine�is�located�a�few�hundred�meters�from�the�Tensift�River,�close�to�a�
rural�community�of�about�5790�ha,�which�65%�are�occupied�by�farmland.�Draa�Lasfar�consists�
on�a�deposit�of�pyrite�mineral�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� 60�Mt� of� products� in� the� first� two� years� (1979�1980)� [1].� Industrial�
activity�stopped�in�March�1981,�although�it�restarted�in�1999�due�to�its�great�resource�of�poly�
2.�Methodology��
60�
metallic�components�(As,�Cd,�Cu,�Fe,�Pb,�Zn).�During�its�exploitation,�tailings�were�discharge�all�
around�the�mine�area�posing�a�risk�for�the�environment.�
�
Draa LasfarDraa Lasfar
Figure�2.1.�Location�of�Draa�Lasfar,�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�mines�
The�Kettara�mine�produced�more�than�5.2�Mt�of�pyrrhotite�from�1964�to�1981,�although�
it�was�closed�in�1982�due�to�difficulties�during�the�production�of�the�pyrrhotite�and�its�use�in�
the�roasting�unit�[3].�During�the�exploitation,�more�than�3�Mt�of�mine�waste�were�stockpiled�
over�an�area�of�16�ha�without�concern�for�environmental�issues�(Figure�2.2).��
Figure�2.2.�Pictures�of�Kettara�mine�site�(Photo:�Gustavo�Pérez)�
The�Bir�Nehass�zinc�mine�and�the�Sidi�Bou�Othmane�(SB�Othmane)�mine�consist�on�old�
graphite�mines�characterized�by�an�intense�metamorphism�with�irregular�masses�of�grenatites�
and� marble.� SB�Othmane� mine� is� located� close� to� a� rural� district� and� surrounded� by�
agricultural� lands� with� estimated� reserves� of� 0.02� Mt� of� graphite� ore� deposit� (30�50%� 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�
reserves�were�evaluated�to�be�0.25�Mt�containing�20%�of�the�mineral�enriched�in�Pb�(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�
closed�at�the�end�of�the�20th�century.�
2.�Methodology�
61�
2.1.2.�EUROPEAN�MERCURY�MINING�DISTRICTS:�ALMADÉN,�MIERES�AND�IDRIJA��
The�location�of�the�three�mining�districts�is�depicted�in�Figure�2.3.�
Main mercury mines
Sampling sites
Main mercury mines
Sampling sites
Figure�2.3.�Metallurgical�sites�of�the�three�mercury�mining�districts,�Almadén,�Asturias�and�Idrija�
The�Almadén�mining�district�is�located�in�Ciudad�Real,�Spain�(Figure�2.3)�and�occupies�30�
km2.� 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�
population�is�7000�inhabitants).�Other�traditional�activities�are�agriculture�and�sheep�farming.�
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�
temperatures�of�up�to�800�ºC.�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�[7].�
� El�Entredicho�open�pit�mine El�Entredicho�dump
Figure�2.4.�Pictures�of�Almadén�mine�sites�(Photo:�José�Mª�Esbrí)�
2.�Methodology��
62�
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�
world�surpassed�only�by�the�Almadén�mine.�After�500�years�of�mining�activity�producing�a�total�
of�about�105,000�tons�of�Hg,�from�more�than�3�106�m3�of�ore�and�gangue,�the�mine�of�Idrija�
closed�in�1995�[8].�Taking�into�account�losses�during�mining�and�inefficient�smelting,�the�total�
volume�of�mined�Hg�is�estimated�to�be�at�least�140,000�tons�(9,�10).�Idrija�mining�district�is,�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�
Idrija�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�a�result�of�weathering�of�carbonate�
and�clastic�rock�in�the�Idrija�catchment�[11].�Metallurgical�processing�was�similar�to�Almadén�
during� the� last�century,�using�Pacific� furnaces�able� to�reach�up�to�800�ºC.�Due�to�the�mining�
and�ore�processing�operations,�Idrija�and�its�surroundings�have�been�polluted�with�Hg.�
Idrija�mine� Miéres�mine�
Figure�2.5.�Pictures�of�Idrija�and�Asturias�mine�(Photo:�José�María�Esbrí)
On� the� other� hand,� La� Peña�El� 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�
annual�production�ca.�517�tones�of�kg� [12].Mercury� is�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� [13].� To�
summarize,�La�Peña�El�Terronal�mine�has�a�more�complex�mineralogy�than�Almadén�and�Idrija,�
with�high�amounts�of�arsenic�in�its�paragenesis,�In�addition,�their�rotary�furnaces�achieve�lower�
calcinations�temperatures�(over�600�ºC)�than�the�other�mining�districts�[14].��
2.�Methodology�
63�
The� total�mercury� concentration� in� soils� and� sediments�of� the� three�mining�districts� is�
well�documented�[15,�16,�17,�18,�19],�although�only�a�few�studies�dealt�with�inorganic�mercury�
speciation�[20,�21,�22,�23,�24].��
2.1.3.�AZNALCÓLLAR�TAILING�POND��
Aznalcóllar�mine� is� located� in� a�pyrite�rich� 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�
are�stockpiled�in�a�tailings�pond.�The�tailings�reservoir�in�Aznalcóllar�is�situated�near�the�Agrio�
River,�a�small�tributary�of�the�Guadiamar�River.�The�waters�used�in�the�mining�operations�are�
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�
although�it�was�enlarged�several�times�using�tailing�materials.�At�the�time�of�the�tailings�dam�
failure�accident,�the�dam�was�approximately�25�m�high�[25].�
Figure�2.6.�Aznalcóllar�mine�location�
Figure�2.7.�Aznalcollar�tailing�ponds�(Photo:�Baruch�Grinbaum)
�
2.�Methodology��
64�
2.2.�SAMPLING��
Different� sampling� strategies� were� undertaken� for� the� mines� of� Marrakech� and� the�
European�mines�depending�on�the�specific�purpose�of�each�study.��
2.2.1.�MARRAKECH�MINING�DISTRICTS:�DRAA�LASFAR,�KETTARA,�SIDI�BOU�OTHMANE�AND�BIR�NEHASS�
(MOROCCO)�
Samples� were� taken� every� 50�meters� from� the�mining� area� towards� specific� receptor�
media�(river�creeks,�hills,�villages,� farms,�etc).�After�removing�the�first� layer�of�surface�soil� (2�
cm),�samples�were�taken�from�the�upper�20�cm�within�an�area�of�100�cm2�per�sample.�Residue�
samples�were� taken� on� the� stockpiled� dykes,� piles� or� ponds�where� tailings�were� deposited.�
Additionally,�3�representative�background�samples�were�collected�at�1km�from�the�mining�site,�
far�enough� to�avoid�disturbance� from�mining�operations.�After�air�drying�during�48h�at�30C,�
samples�were� sieved�below�2mm�through�a� stainless� steel� sieve� to� remove� large�debris� and�
stored�in�plastic�bottles�at�room�temperature.�
2.2.2.�EUROPEAN�MERCURY�MINING�DISTRICTS:�ALMADÉN,�ASTURIAS�(SPAIN),�IDRIJA�(SLOVENIA)�
Samples� of� soils,�mine� tailings,� calcines� and� riparian� soils� from� the�Almadén� site�were�
taken�at�a�depth�of�0–20�cm�and�stored�in�polyethylene�bags.�Samples�of�suspended�particles�
were�collected� from�the�water�column�and� let� to� sediment� in�a�clean� room.�All� the�samples�
were�air�dried�to�prevent�mercury�losses,�homogenized,�milled�and�sieved�below�2�mm.��
Soil�samples�from�Idrija�were�taken�using�a�stainless�steel�auger�at�a�depth�of�0–10�cm�
and� stored� in� polyethylene� bottles.� Suspended� river� sediment� was� sampled� during� a� flood�
event�of�the�Idrijca�river�by�means�of�a�net�drift�sampler�and,�after�removal�of�gravel,�stones�
and� plant� residues,� river� bed� and� suspended� sediments,� samples� were� dried� at� 30C� during�
three�days�until�constant�weight�in�the�dark,�homogenized�in�an�agate�mortar�and�separated�in�
two�grain�size�fractions:�fraction�below�0.063�mm�and�fraction�0.063–2�mm.��
Samples�from�Mieres�were�collected�in�La�Peña�El�Terronal�mine�site,�near�the�town�of�
Mieres.� The� sampling� included� samples� from� 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�below�0.1�mm.��
�
�
2.�Methodology�
65�
2.3.�CHARACTERIZATION��
The�characterization�of�the�Marrakech�mine�soils�have�been�performed�by�determining�
its�physico�chemical�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,�in�turn,�were�also�applied�to�the�characterization�of�arsenic�sorption�onto�Fe�exchanged�
materials.��
2.3.1.�PHYSICO�CHEMICAL�PARAMETERS�
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�
standard�methodologies�[26].��
Thus,�pH�measurements�were�done�in�a�soil�suspension�(2g/5�ml�of�distilled�water�stirred�
vigorously)� after� 2� h� of� deposition� using� a� pH�meter�WTW�Multiline� P4� Universal� pH�meter�
cabled�Sen�Tix�92T�pH�electrode�(Germany).��
The�EC�was�determined�in�a�soil�saturated�paste�(1g�soil/5�ml�of�distilled�water)�with�a�
conductimeter� WTW� Multiline� P4� Universal� Standard� Conductivity� Cell� TetraCon®� 325�
(Germany)�once�corrected�to�the�working�temperature�(20�C).��
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,�after�filtering,�calcium�was�measured�using�a�JENWAY�PFP7�flame�photometer.��
2.3.2.�TOTAL�METAL�CONCENTRATION�
For� the� determination� of� the� total�metal� concentration,� aliquots� of� each� sample�were�
encapsulated�in�ten�milliliter�polyethylene�sample�cups�(Chemplex,�FL,�USA)�and�sealed�using�
pre�cut�Mylar®�circles�film�prior�to�their�analysis�with�a�FP�XRF�equipment�Alpha�6500R,�Innov�
X�Systems�(USA).�The�sample�thickness�in�the�cup�should�be�at�lest�1.2�cm�so�as�the�X�rays�can�
penetrate�the�sample.��
This�equipment�is�a�tube�type�energy�dispersive�instrument�with�a�tungsten�cathode�and�
a�silver�anode�that�can�generate�X�rays� in� the�energy�range�10�to�40�keV�and�10�50�μA.�The�
instrument� is� provided�with� a� circular� probe�window� (1.54� cm2� area)� and� employs� a� Si�PiN�
diodes�detector�with�an�energy�resolution�of�230eV�at�the�full�width�at�half�maximum�intensity�
of�the�manganese�(Mn)�K�X�ray�line.��
2.�Methodology��
66�
The�standardization�consists�in�the�collection�of�a�spectrum�of�a�known�spectrum�(Alloy�
316)�and�the�comparison�of�a�variety�of�parameters�to�values�stored�when�the�instrument�was�
calibrated�at� the�factory.�This�procedure�takes�about�1�minute�and�should�be�done�any�time�
the�hardware�is�initiated�or�restarted�and�must�be�repeated�if�the�instrument�is�operating�for�
more�than�4�hours.�
The�analyzing�time�for�each�sample�was�set�to�120s�for�the�heavy�elements�and�90s�for�
the�light�elements.�This�time�period�is�established�as�the�best�trade�off�between�accuracy�and�
speed�of�analysis.�For�accuracy,�an�instrument�blank�and�a�calibration�verification�check�(NIST�
2710)�was�checked�each�working�day�before�and�after�analyses�are�conducted�and�once�per�
every�twenty�samples�following�EPA�Method�6200�[27].��
Figure�2.8.�Innov�X�FP�XRF�model�ALPHA�6500�and�stand�for�laboratory�measurements�(Photo:�
Elena�Peralta)�
An�instrument�blank�is�used�to�verify�that�no�contamination�exists�in�the�spectrometer�or�
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.�An�instrument�blank�should�also�be�analyzed�whenever�contamination�is�suspected�
by�the�analyst.�
A�calibration�verification�check�sample� is�used�to�check�the�accuracy�of�the� instrument�
and�to�assess�the�stability�and�consistency�of�the�analysis�for�the�target�analytes.��
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�
contains�contaminants�at�concentrations�near�the�action�levels.�If�a�site�specific�sample�is�not�
available,�then�an�NIST�or�other�reference�material�that�contains�the�analytes�of� interest�can�
be�used�to�verify�the�accuracy�of�the�instrument.�To�verify�the�calibration,�the�measured�value�
for� each� target� analyte� should� be� within� ±20%� of� the� true� value.� In� this� sense,� NIST� 2710�
2.�Methodology�
67�
(Montana� soil)� standard� reference� sample� was� employed� as� calibration� verification� check,�
providing�results�within�specified�tolerances�(Table�2.1).��
Table�2.1.�NIST�2710�Certified�values�Element� Mass�fraction�(%)� � Element� Mass�fraction�(%)�
Aluminum� 6.44�±�.0.08� � Antimony� 38.4�±�3�Calcium� 1.25�±�0.03� � Arsenic� 626�±�38�Iron� 3.38�±�0.10� � Barium� 707�±�51�
Magnesium� 0.0853�±�0.042� � Cadmium� 21.8�±�0.2�Phosphorus� 1.01�±�0.04� � Copper� 2950�±�130�Potassium� 2.11�±�0.11� � Lead� 5532�±�80�Silicon� 28.97�±�0.18� � Mercury� 32.6�±�1.8�Sodium� 1.14�±�0.06� � Nickel� 14.3�±�1.0�Sulfur� 0.240�±�0.006� � Silver� 35.3�±�1.5�
Titanium� 0.283�±�0.010� � Vanadium� 76.6�±�2.3�� � � Zinc� 6952�±�91�
2.3.3.�TOTAL�MERCURY�CONTENT�
The� detection� of� mercury� at� trace� levels� is� a� complex� analytical� task� because� of� its��
specific�physical�and�chemical�properties.�Many�techniques�exist�for�mercury�determination�in�
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� (CV�AAS)� [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�
light�polarization�(ZAAS�HFM)�with�a�Lumex�RA�915+�analyzer�[35].��
In� this�mercury� analyzer,� the�mercury� contained� in� the� sample� is� atomized� by� a� glow�
discharge�mercury� lamp�placed� in�a�permanent�magnetic� field.�This�magnetic� field� �splits�the�
254�nm� 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�
frequency�of�50�kHz�and�thus�triggers�the�line�components�in�turn,�the�radiation�then�passes�
through�a�multi�path�cell,�whose�equivalent�optical�length�is�about�10�m.�Being�equipped�with�
narrow�band�high� reflectivity�mirrors,� the� cell� isolates� solely� the�254�nm� 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�
upon�detecting�the�radiation�by�a�photodetector�and�subsequent�analog�digital�conversion�of�
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�
2.�Methodology��
68�
concentration� and� is� independent� of� the� presence� of� dust,� aerosols,� and� other� foreign�
contaminants�in�the�analytical�cell.�
The�detection�limit�of�this�technique�for�soils�and�sediments�samples�is�0.5�mg�Hg�kg-1.�
For�accuracy,�a�certified�reference�material�(CRM�025)�was�simultaneously�analyzed.�
Figure�2.9.�Lumex�RA�915+�analyzer�for�mercury�determinations�
2.3.4.�MOBILITY�OF�THE�MINE�SAMPLES�
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�
suspension�was�centrifuged�10�min�at�3500�rpm�and�the�supernatant�was�filtered�using�0.22�
μm�Millipore�Millex�GS� filters� (Ireland).� The� extracts�were� analyzed� by�means� of� Inductively�
Coupled� Plasma�Optical� Emission� Spectroscopy� (ICP�OES)� using� an� equipment�
ThermoElemental�Intrepid�II�XLS�(USA)�(Figure�2.10).��
Agitation (1h) Sample + HCl (0,5M) (1:20)
Centrifuge 10min 4000rpm
Filtration of the extract
ICP-OES
�Figure�2.10.�Single�extraction�procedure�scheme�
The�ICP�OES�analytical�technique�allows�multielemental�analysis�of�metals� in�soils,�with�
an�excellent�performance�and�a�wide�analytic�range.�In�this�technique,�a�plasma�(ionized�gas,�
electrically�neutral)�is�used�to�excite�the�atoms�of�the�sample�so�that�when�relaxed�they�emit�
electromagnetic� radiation� at� wavelengths� characteristic� of� each� element� (in� the� region� of�
corresponding�UV�visible�spectrum)�with�an�intensity�proportional�to�its�concentration.��
The� plasma,� maintained� by� the� interaction� between� RF� frequency� and� ionized� argon,�
reaches�temperatures�up�to�10000�K.�The�sample�is�introduced�through�a�peristaltic�pump�into�
2.�Methodology�
69�
the� instrument� through� a� nebulizer� using� a� flow� of� argon,� that� disperses� the� liquid� into�
droplets�that�are�carried�to�a�cyclonic�chamber.�At�the�cyclonic�chamber�the�larger�droplets�are�
separated�from�the�smaller�drops,�which�are�moved�towards�the�plasma�by�a�flow�of�argon.��
In�table�2.2.�can�be�found�the�characteristic�wavelengths�used�for�the�measurement�of�
the�target�elements.��
Table�2.2.�Characteristic�wavelengths�of�elements�measured,�limit�of�detection�and�linearity�Element� Wavelength�(nm)� Limit�of�detection�(μg/L)� Linearity�(μg/L)�
As� 193.759� 1� 1�50�Cu� 324.754� 0.5� 0.5�50�Pb� 220.353� 1� 1�50�Zn� 213.856� 0.5� 0.5�50�
2.3.5.�XAS�MEASUREMENTS�
All� solid� samples� from� the� European�mercury�mines�were� prepared�mixing� an� aliquot�
with�polyethylene�(IR�quality),�homogenized�with�a�vortex�for�2�min,�pressed�as�a�pellet�with�5�
ton�cm�2�of�pressure�using�an�IR�press�and�sealed�between�Kapton™�tape.��
Its� XANES� measurements� were� performed� at� the� HASYLAB� synchrotron� facility�
(Germany)� at� A1� bending�magnet� beamline.� 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� 7�pixel� Ge� fluorescence� detector.� The�
absorption�of�mercury�was�recorded�at�its�LIII�energy�(12284�eV)�(Figure�2.11).��
A)� B)� Figure�2.11.�A)�Sample�holder�containing�6�pellets�of�the�samples.�B)�Schematic�XAFS�set�up�
References�for�XANES�fingerprint�adjustments�included�the�following�minerals�and�pure�
compounds:� HgCl2,� HgSO4,� HgO,� CH3HgCl,� Hg2Cl2� (calomel),� HgS� red� (cinnabar),� HgS� black�
(metacinnabar),� Hg2NCl0.5(SO4)0.3(MoO4)0.1(CO3)0.1�H2O� (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�studied�areas�[16,�17,�18,�19,�20,�
21],�as�well�as�the�possible�weathering�and�anthropogenic�processes�taking�place�in�each�site.�
2.�Methodology��
70�
On�the�other�hand,�the� local�environment�of�arsenic�adsorbed�onto�different�materials�
was� investigated� by� both� X�ray� absorption� near�edge� structure� (XANES)� and� extended� X�ray�
absorption�fine�structure�(EXAFS)�spectroscopies.�Samples�were�prepared�following�the�same�
methodology�as�described�for�mine�soil�samples.�Arsenic�spectra�were�collected�at�its�K�edge�
energy� (11867�eV)�at� beamline�C�of�DORIS� III�HASYLAB� facilities.� This�beamline� is� essentially�
equal�to�A1�beamline�(Figure�2.11).�The�monochromator�consisted�in�a�Si�(111)�double�crystal�
and� the�detection�was�measured�either�by�adsorption�and� fluorescence�using�a�7�pixel� Si(Li)�
detector�over�the�energy�range�11700�12700�eV.�The�monochromator�was�calibrated�using�the�
LIII�edge�of�a�gold�foil�(11919�eV).�As�a�standard�for�the�EXAFS�fitting,�Na2HAsO4�7H2O�(Panreac)�
was�selected.�Thus,�by�the�comparison�of�the�empirical�results�with�the�theoretical�results�for�
Na2HAsO4�7H2O�an�estimation�of�the�goodness�of�the�selected�paths�can�be�obtained.��
2.4.�DATA�TREATMENT�
Collected�data�with�the�different�experimental�techniques�have�been�treated�to�extract�
appropriate� information� to� characterize� target� samples� and� corresponding� processes� in� the�
present�studies.�The�following�tools�were�used�to�this�purpose:�
2.4.1.�CONCENTRATION�ENRICHMENT�RATIOS��
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�
enrichment�factors,�were�used�in�the�studies�concerning�the�characterization�of�mine�area�soils�
from�Morocco�and�are�used�to�identify�and�quantify�the�extent�of�human�interference�in�soils�
that,�by�extension,�are�also�an�indicator�of�soil�contamination.��
CER�were�initially�developed�to�speculate�on�the�origin�of�elements�in�the�atmosphere,�
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�
target�pollutant�with�a�background�element.�The�formula�to�calculate�CER�can�be�generalized�
as:��
sample sample
background background
[El] /[X]CER=
[El] /[X]� � (Equation�2.1)
where�“El”� is� the�element�under�consideration,�“X”� is� the�chosen�reference�element�and�the�
subscripts�“sample”�or�“background”�indicate�which�medium�the�concentration�refers�to�[42].�
The�reference�element�“X”�should�be�little�affected�by�weathering�processes�and�should�show�
2.�Methodology�
71�
little�variability�of�occurrence.�In�this�sense,�the�most�common�reference�elements�employed�
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]�and�calcium�[48].�In�this�work,�Zr�was�selected�as�
lithogenic�element�due�to�homogeneity�of�Zr�concentration�in�all�samples�and�background.�
The� interpretation� of� CERs� can� be� employed� to� determine� the� anthropogenic�
contribution�[49]�(Table�2.3).�
Table�2.3.�Anthropogenic�contribution�at�different�CER�values�CER� Anthropogenic�contribution�
<2� Minimal�or�nule�2�5� Moderate�5�20� Significant�20�40� Strong�>40� Extreme�
2.4.2.�GEOGRAPHIC�INFORMATION�SYSTEMS�
Used� in�the�studies�concerning�the�characterization�of� the�mine�sites� from�Marrakech.�
Contour� maps� of� CER� values� of� target� elements� were� done� by� GIS� representation� using�
Miramon�v6.4�Complete�Geographical�Information�System�and�Remote�Sensing�software�[50]�
choosing�IDW�interpolator�as�the�most�suitable�due�to�the�irregular�sampling�realized�on�the�
mine�sites.��
2.4.3.�STATISTICAL�TOOLS�
Box�plots�graphs�of�Kettara,�Bir�Nehass�and�Sidi�Bou�Othmane�were�obtained�using�SPSS�
Statistics�17.0�software.�PCA/APCS�was�realized�by�using�Excel�add�in�XLStat�Data�Analysis�and�
Statistical� Software� [51].� Samples� were� scaled� by� using� the� standard� normal� variate� (SNV)�
algorithm�and�Bartlett�sphericity�test�was�checked�in�order�to�confirm�that�the�variables�were�
uncorrelated.�Kaiser�criterion�was�used�to�select�principal�components,�and�only�factors�with�
eigenvalues�greater�than�1�were�considered.��
2.4.4.�XAS�DATA�TREATMENT��
XANES�spectra�of�samples�from�European�mercury�mines�were�processed�using�SixPACK�
data�analysis�software�package�[52,�53,�54,�55].�Spectra�processing�included�energy�correction,�
signal� normalization� and� background� correction.� After� data� correction� and� normalization,�
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�
2.�Methodology��
72�
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� determined� to� be� present� in� the� corresponding� sample.� Finally,� a� linear� least�squares�
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�
combination�procedure�[59]�and�is�defined�as:��
2 2
1
1reduced � ( )-
Nobs fiti iiN P� �
�� � � (Equation�2.2)
where�obsi� is�the�ordinate�of�the�XANES�spectrum�measured�from�the�sample�at�the�ith�energy�
point,�fiti� is�the�ordinate�of�the�fitted�XANES�spectrum,�N�is�the�number�of�data�points�in�the�
fitted� XANES� energy� range� and� P� is� the� number� of� fitted� components.� 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.�
EXAFS�data�treatment�was�performed�with�VIPER�software�[60].�In�VIPER,�the�extracted�
EXAFS�signal�or���function�was�converted�to�frequency�(k)�space,�weighted�by�k2,�and�Fourier�
transformed� to� produce� the� R�space� EXAFS� pair�correlation� function,� which� is� similar� to� a�
radial� distribution� function.� The� program� includes� an� option� to� iterate� the� post�edge�
background� to� obtain� peaks� with� good� resolution� and� to� minimize� spurious� peaks� at� small�
radial� distances.� After� back�transforming� 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� (FeAsO4�4H2O).� The� Debye�Waller�
factor�(�)�for�the�unknown�samples�were�constrained�to��=0.003�for�the�first�shell,�and��=0.008�
for�the�second�shell.��
2.�Methodology�
73�
�
REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATERS�
Several�remediation�technologies�have�been�studied�in�this�thesis�to�their�application�on�
the�treatment�of�industrial�waters�containing�different�types�of�pollutants.�In�this�concern,�two�
different�techniques�have�been�studied:��
1)�Conventional�methodologies�such�as�solvent�extraction�has�been�employed�to�the�recycling�
of� zinc� from� a� mine� tailing� pond� to� provide� an� economical� benefit� while� diminishing� the�
volume�of�hazardous�materials�contained�in�the�mine�tailing�at�laboratory�and�pilot�plant�scale.��
2)�Fe�exchanged�materials�have�been�employed�as�catalysts�for�the�degradation�of�persistent�
organic�pollutants�(POPs)�by�means�of�Fenton�processes�as�well�as�to�the�sorption�of�inorganic�
contaminants.��
2.5.�ZINC�SOLVENT�EXTRACTION��
2.5.1.�LABORATORY�EXPERIMENTS�
In�order�to�get�a�Zn�sulphate�rich�liquor�to�be�used�later�in�electrowinning�process,�the�
performance� of� a� newer� commercial� extractant,� Ionquest� 290� (Bis(2,4,4�trimethylpentyl)�
phosphinic�acid),�is�compared�with�the�results�of�more�conventional�extractants�DEHPA�(Di�2�
(ethylhexyl)� phosphoric� acid)� and�Cyanex�272� (Bis(2,4,4�trimethylpentyl)� phosphinic� acid)� for�
the�solvent�extraction�of�a�Zn�rich�mine�effluent.��
In�the�related�tailing�mine�samples,�Fe�was�removed�from�the�mine�water�prior�to�the�SX�
treatment�by�means�of�a�biooxidation�process�using�Thiobacillus�ferrooxidans�and�a�selective�
alkaline�precipitation�step�[61,�62]�to�obtain�a�pregnant�leach�solution�(PLS)�without�iron,�since�
there�are�no�reagents�commercially�available�capable�to�extract�Zn�selectively�from�a�solution�
containing�Fe.��
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.� Ionquest� 290� has� the� same� active� ingredient� as� Cyanex� 272� but� has� a� lower�
content�of�inactive�impurities,�the�phosphine�oxide�impurity�is�<5%�in�Ionquest�290�but�around�
15%�in�Cyanex�272�[63].��
Two� type� of� kerosene�with� different� flash� point�were� also� studied� as� solvents� for� the�
extractants.� Commercial� grade� extra�pure� aliphatic� kerosene� Ketrul� D80� and� Ketrul� D100�
2.�Methodology��
74�
(Batch�ref.�20062016�and�20061560,�respectively)�were�kindly�supplied�by�Total�Fluides�France.�
Ketrul� D80� and� Ketrul� D100,� have� a� flash� point� of� 72� ºC� and� 100ºC� or� superior� (ISO� 2719),�
respectively.�It�must�be�pointed�out�that�the�higher�the�flash�point�the�lesser�the�flammability�
of�the�kerosene,�and,�therefore�the�higher�the�security�of�the�solvent�extraction�process.��
Sulfuric� Acid� 95�98%�was� purchased� from� J.T.� Baker,� Phillipsburg,� NJ,� USA� and� it� was�
used�to�strip�the�zinc�from�the�organic�enriched�phase.�All�the�reagents�were�used�as�received�
without�any�further�purification.�Stoppered�glass�tubes�of�50�mL�were�used�for�the�two�phases�
contact�and�the�agitation�took�place�in�a�rotating�rack.��
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�during�5,�10,�20,�
30,�40�or�60�min.�The�organic�phase�loaded�with�the�target�metal/s�(OP)�was�stripped�with�5�
mL�of�H2SO4�2.0�M�during�3h�to�ensure�complete�stripping.�DEHPA�concentration�was�higher�
due�to�efficiency�related�to�extraction�yield�and�extractant�cost.�
To�determine�selectivity,� isotherms�varying�the�A:O�ratio�from�0.1�to�10�were�realized.�
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�during�15�min�and�thereafter�OP�was�stripped�
with�5�mL�H2SO4�2.0�M.�No�centrifugation�of�the�dual�phase�system�was�required�because�of�
the�clear�phase�separation�obtained.�Selectivity�of�the�solvents�towards�Zn�was�determined�by�
the� recovery�of�each�metal� (equation�2.3)� and�by� the�amount�of�metal� remaining� in� the�OP�
(Equation�2.5)�which�is�calculated�by�the�different�amounts�of�metal�in�the�raffinate�(Equation�
2.4)�and�in�the�OP.�
strip
PLS
Zn%Recovery= ×100Zn
� � � �
(Equation�2.3)
raffinate
PLS
Zn%Remaining R= ×100Zn
� � � �
(Equation�2.4)
%Remaining OP = 100 - %Recovery - %Remaining R (Equation�2.5)
Major� elements� present� in� the� PLS,� in� the� strip� liquor� and� in� the� raffinate� were�
determined�by�ICP�OES�Thermo�Iris�Intrepid�II�XLS�(USA).�
2.5.2.�SCALING�THE�SX�TO�A�PILOT�PLANT�
The�bio�oxidation�reactor�consisted�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�to�pH�and�Eh�control�and�a�50mm�pipe�to�let�the�solution�overflow.�The�
effluent� circulated� through�a� tank�where�pH�was� initially�adjusted�and,�after�pH�adjustment,�
2.�Methodology�
75�
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� lime�solution�from�a�separated�tank.�
After�precipitation�and�sedimentation�of� iron�compounds,� the�supernatant�was�directly�used�
as�feed�solution�to�the�solvent�extraction�stage�(Figure�2.12).�
The� pilot� plant� process�was� undergone�with� Ionquest� 290.� Ionquest� 290� (Purity>95%)�
was� supplied� by� Rhodia� UK� Ltd.� and� commercial� grade� extra�pure� 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�
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.�
Figure�2.12.�Bio�oxidation�and�SX�flow�sheet�at�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�[64,�65,�66].�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� [67].�An�80�mm�diameter�
BPC,�7m�high�(equivalent�to�3�theoretical�mixer�settler�stages)�was�chosen�for�the�SX�process�
2.�Methodology��
76�
and�a�40�mm�diameter�BPC�6�meter�high�for�the�stripping.�The�complete�piping�of�the�plant�is�
shown�in�Figure�2.12.�
EXTRACTIONCOLUMN
EXTRACTIONCOLUMN
Figure�2.13.�Section�of�the�columns�layout�at�Aznalcollar�pilot�plant�(Photo:�Baruch�Grinbaum)�
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�hours�a�day�
on�average,�a�total�of�120�hours.�The�average�flow�rate�of�the�aqueous�feed�was�150�L/h,�so,�
about�18�m3�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� pilot�
plant� 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.�
2.6.� FE�EXCHANGE� MATERIALS� FOR� THE� REMEDIATION� OF� ORGANIC�AND�INORGANIC�POLLUTED�WATERS�
Several�materials�have�been�studied�as�Fe�supports�to�be�used�either�as�Fenton�catalysts�
or�arsenic�sorbents.�These�materials�included,�USY�zeolite�(Zeolyst�International).�In�this�case,�
conditions� for� Fe3+� immobilization� have� been�modified�with� respect� to� the� ones� present� on�
Neamtu�et�al.,[68],�i.e.�3�cycles�of�6�hours�of�Fe�exchanging�with�an�excess�of�Fe(NO3)3�1M�at�
80C.� These� modifications� resulted� on� a� faster� Fe�exchanging� preparation� and� diminish� the�
employed� Fe(III)� solution� concentration.� In� addition,� the� whole� process� was� done� at� room�
2.�Methodology�
77�
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� K�10� (namely�
MMT;� Aldrich)� were� conditioned� at� pH=4� to� be� afterwards� loaded� with� Fe(III)� using� the�
indicated�conditions�optimized�for�USY.��
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�milli�Q� 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� Field�Portable� X�Ray� Fluorescence� (FP�XRF)� equipment� Innov�X� Systems,�
model�Alpha�6500R.�
2.6.1.�FENTON�REACTION�
The� commercial� azo� dye� Acid� Red� 14� (Chromotrope� FB� 50%,� C.I.� 14720,� Aldrich)� was�
selected�as�a�model�dye,�since�it�is�a�common�textile�and�leather�industry�dye,�also�employed�
for�dying�nylon,�wool�and�silk�[69,�70,�71].��
The�decolorisation�tests�were�performed�adding�0.25g�of�catalyst�and�8.75x10-3�mmols�
of�hydrogen�peroxide� (H2O2�35%,�Fluka)� to�100�ml�of�AR14�0.05�mM�at�75ºC.�Decolorisation�
and�mineralization�of�AR14�was�measured�at��=516�nm�and���=324�nm,�respectively,�using�an�
Unicam�UV/Vis�Spectrometer�UV2�(Unicam)�at�regular�time�intervals�so�as�to�establish�kinetics�
correlations.�Finally,�the�analysis�of�the�degradation�products�in�the�final�solution�was�done�by�
GC�MS�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�
performed�in�homogeneous�catalysis�mode�using�the�same�amount�of�Fe�loaded�in�the�USY.�
In�addition,� acetic� acid� (CH3COOH�96%,�Panreac)� and�phenol� (C6H5OH�99.5%,�Panreac)�
were�selected�as�model�compounds�due�to�their�refractoriness�to�degradation�by�conventional�
oxidation�methods�[73].�The�reaction�was�performed�over�100�ml�solution�of�either�acetic�acid�
0.5%�or�phenol�0.5%�at�75C�during�1h,�using�0.25�g�of�each�Fe�loaded�supported�material�as�a�
catalyst�and�8.75x10�3�mmols�of�H2O2.�To�avoid� interferences�by�H2O2� in�COD�measurements,�
0.2g�of�MnO2�were�added�to�remove�residual�H2O2�[74].�COD�was�measured�before�and�after�
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�
analyzed�by�means�of�an�ICP�OES�Thermo�Iris�Intrepid�II�XLS�(USA).��
The� performance� of� the� Fe�loading� process� as� well� as� the� Fenton� reaction� was� also�
tested�in�continuous�column�process�by�using�1cm�diameter�glass�columns�filled�with�the�Fe�
2.�Methodology��
78�
exchanged�materials.� The�Fe�loading�process�was�also�done� in� countercurrent�at�2ml/min�at�
the�same�conditions�of�the�batch�process.�Washing�of�the�materials�was�done�circulating�milli�
Q�water�during�3h�and,�after�drying�in�an�oven�at�60C�overnight,�the�amount�of�Fe�loaded�into�
each�material�was�measured�by�FP�XRF.�The�Fenton� reaction� in�column�was�performed�over�
100�ml�of�AR14�0.05�mM,�100�ml�of�acetic�acid�0.5%�and�100�ml�of�phenol�0.5%�.�
2.6.2.�ARSENIC�REMOVAL�
Each�Fe(III)�bearing�material�was�slowly�added� into�a� solution�containing�1000�ppm�of�
Na2HAsO4�7H2O�(Panreac)�at�pH=4.�The�solution�was�agitated�in�a�rotating�rack�during�4h�at�30�
rpm.� The� suspension�was� filtered,� and� the� solids�were� thoroughly�washed� three� times�with�
distilled�water�and�dried�in�an�oven�under�air�at�60ºC�overnight.�Pellets�of�each�sample�were�
done�using�an�IR�press�and�sealed�between�Kapton™�tape.�The�amount�of�Fe�and�As�contained�
in�the�pellets�of�each�material�was�finally�determined�by�FP�XRF.��
2.�Methodology�
79�
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[74]�Pardieck,�D.;�Bouwer,�E.�J.;�Stone,�A.�T.�(1992).�Hydrogen�peroxide�use�to�increase�oxidant�capacity�for�in�situ�bioremediation�of�contaminated�soils�and�aquifers.�J.�Contam.�Hydrol.�9:�221�242.�
[75]�APHA�AWWA�WPCF�(1995).�Standard�Methods�for�the�Examination�of�Water�and�Wastewater.�19th�ed.,�American�Public�Health�Association�USA.�
�
83�
�
3��
RESULTS�AND�DISCUSSION��MINE�SITES�CHARACTERIZATION ......................................................................................................85�
3.1.�HEAVY�METAL�CONTAMINATION�AND�MOBILITY�AT�THE�DRAA�LASFAR�MINE�AREA.....................85�3.2.�CHARACTERIZATION�OF�KETTARA,�SIDI�BOU�OTHMANE�AND�BIR�NEHASS�MINE�AREAS ...............92�3.3.� XANES� SPECIATION� OF�MERCURY� IN� THREE� MINING� DISTRICTS:� ALMADEN� (SPAIN),� ASTURIAS�(SPAIN)�AND�IDRIJA�(SLOVENIA) ...........................................................................................................104�
��REMEDIATION�TECHNIQUES�OF�INDUSTRIAL�CONTAMINATED�WATERS...............................111�
3.4.� EXTRACTANT� AND� SOLVENT� SELECTION� TO� RECOVER� ZINC� FROM�A�MINING� EFFLUENT:� FROM�LABORATORY�SCALE�TO�PILOT�PLANT ..................................................................................................111�3.5.� Fe�LOADED� MATERIALS� FOR� THE� REMEDIATION� OF� ORGANIC� AND� INORGANIC� POLLUTED�WATERS ................................................................................................................................................119�3.6.�REFERENCES...................................................................................................................................128�
84�
�
�
3.�Results�and�discussion�
85�
�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�
inorganic�pollutants,�i.e.,�the�basic�and�scale�up�process�for�the�valorization�on�an�acidic�mine�
water� residue.� Accordingly� to� this� content,� results� include� two� main� sections:� Mine� sites�
Characterization�and�Remediation�Technologies.�
MINE�SITES�CHARACTERIZATION�
The�physico�chemical�parameters�and�the�concentration�of�heavy�metals�of� four�mines�
located�in�Marrakech�region�(Morocco)�were�evaluated�to�characterize�the�area�and�its�level�of�
contamination,�as�well�as� its� toxicological� risk�derived�from�target�heavy�metals�mobility.�On�
the� other� hand,� the� results� focused� in� the� speciation� of�mercury� samples� from� three�main�
European�mercury�mines�are�also�outlined�in�the�following�section.��
3.1.� HEAVY� METAL� CONTAMINATION� AND� MOBILITY� AT� THE� DRAA� LASFAR� MINE�
AREA�(SEE�ANNEX�I)��
The�main�results�of�a�first�insight�into�the�Draa�Lasfar�mine�(Marrakech)�are�outlined�in�
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�
pollutants�by�employing�single�leaching�tests�to�predict�the�risk�of�their�mobility.�
3.1.1.�PHYSICO�CHEMICAL�PARAMETERS�
The�results�obtained�for�the�soil�pH,�electrical�conductivity�(EC),�loss�on�ignition�(LOI)�and�
CaCO3�content�measurements� corresponding� to�Draa�Lasfar�mine�area�are� resumed� in�Table�
3.1.��
It� is�revealed�that,� in�general,�all�sampled�points�showed�neutral�to�alkaline�pH�ranging�
from�7�to�9,�similar� to�pH�of�background�samples�with�the�exception�of�a�very�acidic�sample�
corresponding�to�sample�#48�with�pH�3.5�just�beside�the�mine�site,�mainly�related�to�deposits�
3.�Results�and�discussion��
�86�
of�sulfidic�residues,�which�by�oxidation�and�formation�of�sulfuric�acid,�can�cause�such�decrease�
of�the�pH.��
Table�3.1.�Summary�of�physico�chemical�parameters�for�Draa�Lasfar�samples�� � pH� CE�(μS/cm)� LOI�(g/kg)� CaCO3�(mg/g)�
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�
Mine�area�samples�
(81�samples)�st.�dev.� 0.7� 2170� 14� 32�
Mean�� 8.5� 136.3� 30.7� 24.9�Background�samples��
(4�samples)�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�
(Table�3.1)�which,�in�general,�are�in�accordance�with�previous�studies�performed�on�Morocco�
soils�[1].�Likewise�pH,�the�sample�with�higher�EC�value�(sample�#31)�is�located�just�beside�the�
mine� site� and� corresponds� to� the� sample�with� the�higher� amounts�of�metals� in� the� area.� In�
addition,�the�values�obtained�for�the�mine�area�samples�are�significantly�higher�than�those�of�
the�background�samples,�thus�indicating�high�amounts�of�labile�ions�close�to�the�mine�site�as�
consequence�of�mining�activities.��
LOI�values�for�mine�area�and�background�samples�(Table�3.1)�values�are�similar�and�quite�
homogeneous�(around�30�g/kg)�although�some�specific�points�have�LOI�reaching�76�g/kg�due�
to�some�close�localized�agricultural�activities.��
The�observed�carbonate�content�ranged�from�10�to�210�mg.g�1�(Table�3.1)�although�the�
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�
related�hydroxyoxides�precipitation,�the�principal�retention�mechanism�for�heavy�metals�[2].��
3.1.2.�HEAVY�METAL�CONCENTRATION�IN�THE�MINE�AREA�
Regarding�the�metal�concentration�measured�by�FP�XRF�and�its�corresponding�calculated�
CER� values,� elements� have�been� classified� as� either� pollutants� (when� the� samples� have�CER�
values�above�5)�or�lithogenic�elements�for�CER�values�below�5.�In�this�sense,�As,�Cu,�Pb�and�Zn�
can� be� classified� as� pollutants� whereas� the� rest� of� elements� measured� are� considered�
lithogenic�components�of�the�soil�(Table�3.2).��
�
3.�Results�and�discussion�
87�
Table�3.2.�Minimum,�maximum�and�mean�concentration�and�CER�values�for�the�measured�elements�in�mine�area�and�background�samples�
� � �Mine�area�samples�
Background�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�
Pollutants�
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�
Lithogen
ic�com
pone
nts�
Zr� Conc.� 112� 335�(#67)� 215±50� 210±17�Conc�is�given�in�mg/kg.�In�parenthesis�is�given�the�sample�with�maximum�concentration.��
3.1.3.�GIS�CONTOUR�MAPS�OF�THE�MAIN�POLLUTANTS�
Although�arsenic�can�not�be�considered�a�metal�it�will�be�referred�to�as�belonging�to�the�
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�
indicates�that�remediation�is�mandatory�for�this�specific�area.�At�increasing�distances�from�the�
mine�site,�arsenic�concentration�decreases�to�values�similar�to�background�samples,�except�for�
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�
river�creek�and�its�arsenic�concentration�is�higher�than�neighboring�samples.�It�is�probably�due�
to� a� waste� deposit� when� mining� was� active.� Given� the� proximity� of� this� area� to� the� creek�
waters,�it�is�foremost�to�monitor�this�area.��
Copper�CER�distribution�map�along�the�mining�area�(Figure�3.2)�followed�a�trend�similar�
to�the�one�expressed�by�arsenic,�being�the�samples�close�to�the�mine�site�the�ones�with�higher�
3.�Results�and�discussion��
�88�
copper�CER�values.�Likewise�arsenic,�sample�#21�(51�ppm,�CER=1.9)�located�at�the�other�side�of�
the�river�basin,�has�high�copper�concentration�despite�being�far�from�the�mine�area.�This�can�
be�explained�by�the�fact�that�minor�amounts�of�Cu�are�found�typically�adsorbed�in�arsenopyrite�
(FeAsS)�ores.��
The�lead�distribution�around�the�mine�(Figure�3.3)�showed�four�hot�spots�located�around�
samples�#31� (180�ppm,�CER=13.0),�#45� (770�ppm,�CER=45.9),�#48� (2310�ppm,�CER=130)�and�
#58�(420�ppm,�CER=30).�It�is�also�noteworthy�to�highlight�sample�#21�(62�ppm,�CER=4.6)�given�
its�high�CER�values�and�proximity�to�creek�waters.��
CER�distribution�map�for�Zn�(Figure�3.4)�followed�the�same�trend�as�the�one�depicted�by�
Pb�with�4�hot� spots� located�at� samples�#20� (630�ppm,�CER=8.5),�#45� (1110�ppm,�CER=13.6),�
#48�(30�ppm,�CER=10.8)�and�#58�(930�ppm,�CER=10.8).��
Thus,� taking� into� account� the� GIS� maps� obtained� by� using� CER� values� for� the� main�
pollutants�of�the�Draa�Lasfar�mine�area,�it�can�be�stated�that�the�most�polluted�sites�are�found�
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�
mainly�attributed�to�weathering�effects�and�the�topography�of�the�terrain�that�facilitates�the�
disposal� of�mine� residues� towards�descendent� areas� such�as� the� river� creek� and� reduce� the�
deposition�on�elevated�areas�such�as�hills.��
Other� measured� elements� showed� CER� values� close� to� background� samples,� hence�
considered�as�lithogenic�components�of�the�soil.�This�group�of�elements�is�formed�by�Ba,�Fe,�K,�
Rb,�Sr,�Ti�and�Zr�(Table�3.2).�In�this�sense,�the�mean�concentration�of�Ba,�Fe,�K,�Mn,�Rb,�Sr,�Ti�
and� Zr� is� similar� to� background� samples� and� therefore� its� correspondent� CER� values� range�
between�0�and�2,�excepting�sample�#48.�Such�samples�contain�high�Fe�content�that,�given�the�
high� As� content� can� be� related� to� an� arsenopyrite� (FeAsS)� deposit.� Thus,� no� anthropogenic�
enhancement�of�these�elements�is�observed.��
Finally,�some�specific�samples�present�extremely�high�concentration�on�some�elements.�
High� sulfur� concentrations�were� found� in� samples� #19� (18400� ppm),� #31� (14500� ppm),� #33�
(15500�ppm),�#45�(36800�ppm),�#48�(113700�ppm),�#58�(5300�ppm),�#59�(14800�ppm)�and�#70�
(32400�ppm)�also�related�to�arsenopyrite�deposits�thus�supporting�the�arsenopyrite�nature�of�
the�mineral�ores�extracted.��
�
3.�Results�and�discussion�
89�
Background�samples
As
Mine
area
Scale�1:25000
CER=0
CER=6
CER=3
Mine
area
Scale�1:25000 Background�samples
Cu
CER=0
CER=6
CER=3
Figure� 3.1.� GIS� contour� map� of� arsenic�distribution�around�the�mine�area.�
Figure� 3.2.� GIS� contour� map� of� copper�distribution�around�the�mine�area.�
Mine
area
Scale�1:25000 Background�samples
Pb
CER=0
CER=20
CER=10
Scale�1:25000
Background�samples
Zn
CER=0
CER=20
CER=10
Mine
area
�
Figure�3.3.�GIS�contour�map�of�lead�distribution�around�the�mine�area.�
Figure� 3.4.� GIS� contour� map� of� zinc�distribution�around�the�mine�area.�
3.1.4.�EFFECT�OF�PARTICLE�SIZE�AND�MOBILITY�
Samples�with�high�CER�values�and/or�with�spatial�significance�were�selected�to�study�the�
effect�of�particle�size�and�the�mobility�of�pollutants.�Thus,�samples�#20,�#31,�#46,�#48,�#58�and�
#70�were�selected.�Related�results�are�collected�in�Table�3.3.��
The�results�obtained� for� target� fractions�show�a�generalized� increase�on�As,�Pb�and�Zn�
concentrations�after�milling�the�samples�below�100�μm.�In�this�sense,�samples�#20,�#31,�#46,�
#48�and�#58�had�an�enrichment�on�As,�Pb�and�Zn�when�milled.�Therefore,�it�can�be�stated�that,�
3.�Results�and�discussion��
�90�
in�general,�these�elements�are�part�of�the�particle�core.�On�the�other�hand,�a�decrease�of�the�
concentration�on�copper�as�the�soil�is�milled�is�observed�thus�indicating�copper�is�adsorbed�at�
the� surface� of� the� soil� particles� instead� of� forming� part� of� the� mineral� ore� revealing� an�
anthropogenic�input�of�copper.��
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),�with�high�EC�(EC=2151�μS/cm)�and�relatively�high�carbonate�content�([CaCO3]=58.4�
mg/g).� In�these�conditions,�mobility� is�not�specially� favored�although�given�the�relatively� low�
LOI�value�(LOI=39.3�g/kg)�it�can�be�supposed�that�this�factor�enables�the�availability�of�cations�
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�
organic�matter�(based�on�LOI�determinations).��
It�is�also�important�to�highlight�sample�#48,�which�accounts�for�being�the�most�polluted�
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�#48�has�also�a�high�organic�matter�content�as�indicates�its�LOI�value�(LOI=56.0�mg/g),�
which� benefits� the� adsorption� of� soil� labile� ions,� thus� explaining� the� relatively� low�mobility�
observed.�That�leads�to�lower�its�environmental�risk�when�considering�only�total�concentration�
values.� In� this� sense,� soil� organic� matter� is� considered� one� of� the� primary� immobilizing�
processes�for�trace�and�toxic�pollutants�[4].�
In�this�sense,�GIS�contour�maps�of�pollutants�using�CER�data�have�been�a�valuable�tool�to�
characterize� pollutants� distribution� around� the� mine� area� and� to� determine� sources� of�
contamination.��
3.�Results�and�discussion�
91�
�
Table�3.3.�Particle�size�effects�and�mobility�assays.�Concentration�of�pollutants�at�fraction�<�2�mm�and�fraction�<100�μm�and�amount�of�pollutants�mobile�
SAMPLE� � As� Cu� Pb� Zn�2mm�(mg/kg)� 125� 80� 55� 628�100μm�(mg/kg)� 167� 72� 66� 713�#20�Mobility�(mg/L)� <0.5�� <0.5� <0.5�� <0.5�2mm�(mg/kg)� 72� 51� 62� 144�100μm�(mg/kg)� 67� 48� 61� 150�#21�Mobility�(mg/L)� <0.5� <0.5� <0.5� <0.5�2mm�(mg/kg)� 203� 43� 180� 481�100μm�(mg/kg)� 268� 77� 313� 734�#31�Mobility�(mg/L)� 49� 2� 6� 18�2mm�(mg/kg)� 125� 60� 375� 774�100μm�(mg/kg)� 172� 59� 477� 933�#46�Mobility�(mg/L)� 54� 1� 17� 23�2mm�(mg/kg)� 3,108� 144� 2,309� 631�100μm�(mg/kg)� 3,569� 167� 2,614� 704�#48�Mobility�(mg/L)� 5� 1� <0.5� 4�2mm�(mg/kg)� 113� 71� 425� 925�100μm�(mg/kg)� 149� 77� 537� 1,087�#58�Mobility�(mg/L)� <0.5� <0.5� <0.5� <0.5�2mm�(mg/kg)� 15� 33� 24� 97�100μm�(mg/kg)� 15� 50� 20� 91�#70�Mobility�(mg/L)� 29� <0.5� <0.5� <0.5�
�
3.�Results�and�discussion��
�92�
3.2.�CHARACTERIZATION�OF�KETTARA,�SIDI�BOU�OTHMANE�AND�BIR�NEHASS�MINE�
AREAS�
Following�the�methodology�employed�for�the�characterization�of�Draa�Lasfar�mine�area,�
three�additional�abandoned�mines�of� the�area�at�sites�of:�Kettara,�Sidi�Bou�Othmane�and�Bir�
Nehass�were�characterized�for�their�potential�pollutant�impact.��
3.2.1.�PHYSICO�CHEMICAL�CHARACTERIZATION�
The�results�obtained�for�the�measured�physico�chemical�parameters�are�summarized�in�
Table�3.4.�Samples�have�been�distinguished�between�residues�samples,�the�samples�taken�at�
specific�points�were�residues�were�stored�and�mine�area�samples,�sampled�at�regular�distances�
from�the�mine�site.��
Table�3.4.�Summary�of�pH,�conductivity�and�carbonate�content�for�the�samples�taken�at�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�
� � �pH� EC�(μS/cm)� LOI�(mg/g)� CaCO3�(mg/g)�
Min� 2.0�(S10)� 0.1�(S37)� 6�(KS15)� 2.0�(S2,�S9)�Max� 8.2�(S46)� 4,900�(S10)� 71�(KS41)� 167�(S48)�
Mine�Area�(58�samples)�
Mean� 6.3±1.9� 680±900� 34±20� 37±60�Min� 2.0�(R2)� 2,686�(R1)� 5�(KR1)� 1.25�(R3)�Max� 2.4�(R3)� 7,295�(R7)� 11�(KR6)� 7.5�(R4)�
Residues�(7�samples)�
Mean� 2.2±0.2� 3,618±1700� 7.6±2� 4±3�Min� 7.8� 153� 41� 217�Max� 8.5� 100� 53� 275�
Kettara�
Background�(3�samples)�
Mean� 8.1±0.4� 126±30� 48±7� 240±30�
Min� 7.1�(S1)� 226�(S30)� 37.5�(S2)� 3.7�(S20)�Max� 8.1�(S21)� 823�(S16)� 93.7�(S1)� 439�(S12)�
Mine�Area�(30�samples)�
Mean� 7.8±0.2� 349±130� 63.4±13� 690±1100�Min� 3.2�(R20)� 159�(R17)� 23�(R17)� 7.5�(R17)�Max� 8.4�(R9)� 5,469�(R12)� 50�(R12)� 299.6�(R16)�
Residues�(17�samples)�
Mean� 7.3±1.1� 1,898±1200� 35±8� 78±70�Min� 6.8� 324� 30.5� 2�Max� 8.0� 670� 35.3� 10�
SB�Othman
e�
Background�(3�samples)�
Mean� 7.6±0.8� 447±200� 33±2� 7±5�
Min� 8.0�(S20)� 58�(S32)� 23.8�(S31)� 3�(S31)�Max� 8.4�(S11)� 2,005�(S19)� 46.5�(S8)� 165�(S1)�
Mine�Area�(33�samples)�
Mean� 7.7±0.5� 250±400� 31±5� 24±40�Min� 1.8�(R1)� 2,119�(R3)� 18.4�(R1)� 5.0�(R1)�Max� 3.5�(R3)� 9,011(R1)� 35.4�(R2)� 24�(R3)�
Residues�(4�samples)�
Mean� 2.9±0.7� 4,135±3000� 29±7� 13±8�Min� 7.4� 87� 29.9� 3�Max� 7.7� 94� 36.7� 4�
Bir�Neh
ass�
Background�(3�samples)�
Mean� 7.5±3� 91±4� 33±4� 3.7±0.3�
3.�Results�and�discussion�
93�
The�mean�pH�value�obtained�for�Kettara�mine�area�samples�is�slightly�acidic�as�a�result�of�
the�oxidation�of�pyrite�that�releases�sulfuric�acid�and�lowers�the�pH.�In�this�sense,�23�out�of�58�
samples�have�pH�below�7�and�the�pH�is�comprised�between�2�and�4�in�13�samples.�For�the�rest�
of� the� samples� the� pH� ranged� from� 7� to� 9,� which� is� a� normal� pH� also� observed� for� the�
background�data.�On�the�other�hand,�samples�taken�at�the�residues�deposits,�are�very�acidic,�
all�of�them�in�the�range�of�pH�of�2�2.4.�These�low�pH�values�are�also�related�to�the�oxidation�of�
high�contents�of�sulfur.�Unlike�Kettara,�Bir�Nehass�and�Sidi�Bou�Othmane�mine�area�samples�
have�neutral�to�alkaline�pH�for�the�majority�of�the�samples,�being�these�values�are�similar�to�
the�background�samples.�Regarding�the�sampling�corresponding�to�the�residues,�pH�at�Kettara�
and� Bir� Nehass� residues� were� strongly� acidic� (pH� from� 2� to� 3.5)� related� to� pyrite� deposits�
whereas�all�Sidi�Bou�Othmane�residues�(except�one�sampling�point)�were�quite�basic�with�a�pH�
similar�to�background�samples.��
In� this� sense,� the�most� acidic� samples� have�also� the�higher� EC� values.� This� correlation�
between�low�pH�and�high�EC�value�can�be�explained�by�the�presence�of�high�amounts�of�sulfur�
ions� that� causes� an� increase� of� the� EC� and� by� oxidation� lowers� the� pH� by� acid� sulfuric�
formation.�In�this�regard,�Kettara�mine�area�samples�had�higher�EC�than�the�mine�area�samples�
of�SB�Othmane�and�Bir�Nehass,�and�moreover,�the�EC�found�for�the�residues�were�much�higher�
than�mine�area�samples.���
LOI�values�are�similar�for�mine�area,�residues�and�background�samples�for�all�the�studied�
mines�(around�30�g/kg)�although�some�specific�points�of�Sb�Othmane�mine�areas�samples�had�
higher�LOI�values�mainly�related�to�some�close�localized�agricultural�activities.��
Regarding�the�carbonate�content�it�can�be�stated�that�soils�with�a�pH�of�7.5�and�higher�
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�
carbonates�increase�the�retention�of�heavy�metals�in�soils.�
3.2.2.�HEAVY�METAL�CONCENTRATION�IN�THE�MINE�AREA�
Taking� into�account� the�CER�values�of� the�metals�measured� for�all� three�mines�and�by�
comparing�samples,� residues�and�backgrounds�values,� it� is�observed�As,�Cu,�Pb�and�Zn�to�be�
the�main�pollutants�of� the� studied�mine�areas� since�most�of� the� samples�exceed�CER�values�
above�5,�thus�indicating�the�significant�anthropogenic�contribution�(Table�3.5).��
3.�Results�and�discussion��
�94�
Table�3.5.�Summary�of�As,�Cu,�Pb,�Zn�and�Zr�concentration�and�CER�values�for�the�samples�and�residues�of�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�
� � � � As� Cu� Pb� Zn�Min� 10� 27� 17� 49�Max� 237� 1362� 486� 243�Conc.�Mean� 34±40� 256±300� 76±100� 106±40�Min� 0.5� 0.35� 0.7� 0.7�Max� 18.8� 27.6� 34� 4.2�
Mine�Area�(58�samples)� CER�
Mean� 2±3� 4±6� 4±6� 1.4±0.6�Min� 28� 364� 105� 93�Max� 104� 2113� 349� 337�Conc.�Mean� 64±40� 1,287±500� 233±100� 197±100�Min� 3.6� 16.5� 14� 2.6�Max� 7.8� 74� 36� 10.0�
Residues�(7�samples)�
CER�Mean� 6±2� 38±17� 23±7� 5±3�Min� 11� 44� 12� 49�Max� 24� 52� 15� 76�
Kettara�
Background�(3�samples)�
Conc.�Mean� 16±7� 48±4� 14±2� 60±14�Min� 10� 25� 23� 101�Max� 112� 46� 6706� 36267�Conc.�Mean� 27±30� 31±8� 1,467±2000� 5,018±9000�Min� 0.6� 0.9� 0.8� 0.6�Max� 11� 1.7� 391� 414�
Mine�area�(30�samples)�
CER�Mean� 2±3� 1.1±0.1� 62±100� 43±90�Min� 6� 109� 76� 85�Max� 203� 36140� 13044� 57380�Conc.�Mean� 98±60� 10,653±16000� 4,112±4000� 19,238±1500�Min� 4.6� 6.7� 2.7� 0.5�Max� 26� 1405� 786� 638�
Residues�(17�samples)�
CER�Mean� 14±6� 380±20� 249±200� 242±180�Min� 13� 22� 30� 144�Max� 17� 29� 37� 167�
Sidi�Bou
�Othman
e�
Background�(3�samples)�
Conc.�Mean� 15±2� 27±8� 32±4� 156±12�Min� 8� 24� 19� 86�Max� 113� 46� 1495� 29732�Conc.�Mean� 23±20� 29±6� 148±300� 1,744±6000�Min� 0.5� 0.9� 1.1� 1.2�Max� 11� 2.1� 94� 366�
Mine�area�(33�samples)�
CER�Mean� 2±2� 1.2±0.4� 8±20� 22±70�Min� 92� 52� 1776� 8521�Max� 760� 310� 29559� 23309�Conc.�Mean� 282±300� 149±140� 9,423±13000� 15,075±6000�Min� 11� 3.3� 164� 141�Max� 70� 14.8� 2010� 350�
Residues�(4�samples)�
CER�Mean� 29±30� 8±6� 803±1000� 273±80�Min� 9� 22� 19� 68�Max� 16� 29� 23� 99�
Bir�Neh
ass�
Background�(3�samples)�
Conc.�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�
mine�affects�the�level�of�contamination.�In�this�sense,�Kettara�mine�site�was�less�exploited�and�
3.�Results�and�discussion�
95�
hence�less�polluted�than�SB�Othmane�and�Bir�Nehass�mine�sites�both�exploited�within�a�period�
of�approximately�30�years�and�with�exploitation�outputs�of�115�and�90�tons�per�day.��
3.2.3.�APPLICATION�OF�CHEMOMETRICS�
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,� box�plot� figures� of� the�data� for� the�mine� area� samples� and� the� residues� of� each�
mine�can�provide�a�more�understanding�representation�of�the�obtained�data.��
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).�Concerning�box�plot�figures�for�mine�area�and�residues�for�SB�Othmane�(Figure�3.6)�
and�Bir�Nehass� (Figure�3.7)� it� can�be�observed� that�CER�values�are�much�higher� for� residues�
than�for�mine�area�samples�especially�regarding�Pb�and�Zn.�Given�the�similarities�between�box�
plot� figures� for� both� SB�Othmane� and� Bir� Nehass� it� can� be� stated� that� both� mines� are�
mineralogically�comparable.�In�this�sense,�box�plot�figures�of�CER�values�highlight�differences�
between�mine� area� and� residues� samples� as�well� as� to� graphically� determine� the� degree� of�
contamination�regarding�a�certain�element.�
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�
three�mines�(Figure�3.8,�3.9,�and�3.10)�it�can�be�distinguished�in�each�mine:�mine�area�samples�
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�
representation�of�each�mine,�it�can�be�observed�that�many�of�the�samples�with�high�CER�values�
(and�hence�with�high�concentration�on�pollutants)�can�be�clearly�distinguished�from�the�bulk�of�
the�mine� area� samples� (S8� to� S10� in� Kettara,� S2� in� SB�Othmane� and� S8,� S19� and� S20� in� Bir�
Nehass).�In�this�regard,�it�can�be�concluded�that�PCA�is�able�to�point�out�differences�between�
mine�area�samples�and�residues�and�even�to�distinguish�the�most�polluted�from�samples�less�
polluted�as�well�as�to�indicate�the�parameters�that�affect�the�distinction�between�samples.�In�
general� it�can�be�stated�that�for�the�three�mines�studied,�PC1�is�mainly�assigned�to�the�main�
pollutants�(As,�Cu,�Pb�and�Zn)�while�lithoghenic�elements�such�as�Ba,�K,�Ca,�Rb,�Mn�and�Sr�or�
the�physico�chemical� variables� charge� the� rest�of� PC.�On� the�other�hand,� PCA� is� not� able� to�
3.�Results�and�discussion��
�96�
establish� patterns� for� the� three� mines� since� loading� distribution� is� different� for� the� three�
mines.��
Figure�3.5.�Box�plot�distribution�of�CER�values�for�As,�Cu,�Pb�and�Zn�for�Kettara�mine�area�(M.A.)�
and�residue�samples.�CER=5�(dotted�line),�CER=20�(lines)�and�CER=40�(straight�line)
� �Figure�3.6.�Box�plot�distribution�of�CER�values�for�As,�Cu,�Pb�and�Zn�for�SB�Othmane�mine�area�(M.A.)�and�residue�samples.�CER=5�(dotted�line),�CER=20�(lines)�and�CER=40�(straight�line)
� �Figure�3.7.�Box�plot�distribution�of�CER�values�for�As,�Cu,�Pb�and�Zn�for�Bir�Nehass�mine�area�(M.A.)�
and�residue�samples.�CER=5�(dotted�line),�CER=20�(lines)�and�CER=40�(straight�line)
3.�Results�and�discussion�
97�
KS1KS2KS3KS4KS5KS6
KS7 KS8
KS9 KS10
KS11KS12 KS13KS14KS15
KS16
KS17
KS18KS19KS20KS21KS22KS23KS24
KS25KS26KS27KS28
KS29KS30KS31KS32
KS33
KS34
KS35KS36
KS37KS38
KS39
KS40
KS41KS42KS43
KS44
KS45
KS46KS47KS48
KS49
KS50KS51
KS52KS53
KS55KS56KS58
KR1KR2
SR3KR4
KR5
KR6
KR7
As
BaCa Cu
Fe
K
Mn
PbRbSr
Ti
ZnpH
CECaCO3
�15
�10
�5
0
5
10
15
�10 �5 0 5 10 15 20 25 30
PC2�(15.39�%
)
PC1(36.36�%)
Biplot�(PC1�and�PC2:�51.75�%)
�
Figure�3.8.�Kettara�biplot�representation�of�loadings�and�scores�of�PC1�and�PC2��
SB1SB2
SB3
SB4
SB5SB6SB7SB8SB9
SB10
SB11SB12
SB13SB14SB16SB17
SB18
SB19SB20SB21
SB22SB23
SB24SB25SB26SB27
SR1R4B2
RS3
R4B6
R4B7SR8
R4B9R4B10R4B12
SR13
R4B14R4B17
R4B18
R4B19SR20
As
Ba
Ca
Cu
Fe
K
Mn
Pb
Rb
Sr
TiZn pH
CE
CaCO3
�20
�15
�10
�5
0
5
10
15
20
�20 �15 �10 �5 0 5 10 15 20 25 30
PC2�(20.69�%
)
PC1�(38.97�%)
Biplot�(PC1�and�PC2:�59.66�%)
�
Figure�3.9.�SB�Othmane�biplot�representation�of�loadings�and�scores�of�PC1�and�PC2��
BN1
BN2BN3
BN4BN5
BN6BN7
BS8
BN9BN10BN11
BN12BN13BN14BN15
BN16
BN17BN18
BS19
BS20BN21BN22BN23
BN24
BN25BN26BN27BN28BN29BN30BN31
BN32
BN33 BR1
BR3 BR4BR5AsBa
Ca
CuFe
K Mn
Pb
Rb
Sr
TiZn
pH
CECaCO3
�20
�15
�10
�5
0
5
10
15
20
�20 �15 �10 �5 0 5 10 15 20 25 30 35
PC2�(19.36�%
)
PC1�(35.78�%)
Biplot�(PC1�and�PC2:�55.14�%)
�
Figure�3.10.�Bir�Nehass�biplot�representation�of�loadings�and�scores�of�PC1�and�PC2��
3.�Results�and�discussion��
�98�
3.2.4.�GIS�CONTOUR�MAPS�OF�THE�POLLUTANTS�
With�separate�sample�values�and�in�order�to�delineate�the�distribution�of�target�metals�
around�the�mine�area�a�GIS�representation�of�corresponding�CER�values�has�been�carried�out.�
The� obtained� information� can� be� applied� on� the� management� of� the� target� areas,� i.e.,�
monitoring,�isolation�or�pollution�removal.�
Kettara�GIS� contour�maps� (Figures�3.11� to�3.14)� show� two�well� separated�areas:� a�big�
area�at� the�center�of� the�mine�area�and�a�small�area�at� the�north.�Both�areas�are� located�at�
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�
each�element�it�can�be�stated�that�the�distribution�of�pollutants� is�similar�for�arsenic,�copper�
and�lead,�whilst�zinc�distribution�is�more�homogeneous�along�the�mine�area.�In�addition,�lead�
can�be�considered�the�main�pollutant�regarding�its�high�CER�values.��
Regarding�SB�Othmane�mine�area�GIS�maps�it�can�be�pointed�out�that�the�mine�area�is�
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�SB�Othmane�present� two�hot�
spots�located�at�deposits�of�residues�while�Pb�and�Zn�GIS�maps�indicate�a�more�risky�situation�
with�CER�values�above�200�(Figures�3.17�and�3.18).�The�distribution�of�contaminants�is�affected�
mainly�by�the�location�of�the�deposits�of�residues.��
With� respect� to�Bir�Nehass�mine� area� it� can�be� stated� that,� likewise� SB�Othmane,� the�
mine� is� less� contaminated� with� arsenic� (Figure� 3.19)� and� copper� (Figure� 3.20)� being� lead�
(Figure�3.21)�and�zinc�(Figure�3.22)�the�main�pollutants.�In�this�sense,�a�unique�hot�spot�can�be�
observed�for�arsenic�and�lead�around�a�sample�located�at�a�residue�deposit�while�several�hot�
spots�with�CER=200�can�be�seen�for�lead�and�zinc,�related�to�specific�residue�deposits.�The�area�
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�
potential�risk�can�be�obtained�from�the�results�of�metals�mobility,�shown�in�Table�3.6,�by�using�
the�single�extraction�procedure.��
The�mobility�results�let�to�point�out�that�all�the�mine�area�samples�taken�at�Kettara�mine�
area�have�very�low�mobility,�with�the�exception�of�the�residues�(more�than�30�mg�Cu/L�on�the�
mobile�fraction).�These�samples�have�an�acidic�pH�(around�2)�and�very�low�organic�matter�(LOI�
~10),�in�such�situation,�mobility�of�heavy�metals�is�favored.��
3.�Results�and�discussion�
99�
On� the� other� hand,� SB�Othmane� 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�
phase�for�mine�area�samples�and�even�higher�for�the�residues,�thus�representing�a�threaten�for�
the�environment.�The�physic�chemical�characteristics�of�these�samples�(pH�around�7,�and�high�
content�on�organic�matter)�do�not�favor�mobility�of�the�elements�as�can�be�observed�for�the�
low�levels�of�arsenic�and�copper�found�in�the�mobile�phase.�However,�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.� In� this�sense,� the�residues�
sampling�points�which� are� associated�with�waste�disposal� sites�are�even�more� concentrated�
than�mine�area�samples�and,�given�the�high�amount�of�metals,�the�immobilization�processes�in�
soils� to� retain�metals� are�also�overwhelmed.� In� this� regard,� the� level�of�metals� found� in� the�
mobile� phase� for� SB�Othmane� 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.��
3.�Results�and�discussion��
�100�
Table�3.6.�Total�concentration�(in�mg/Kg)�and�mobility�(in�mg/L)�for�As,�Cu,�Pb�and�Zn�of�the�most�contaminated�samples�of�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�mine�areas�
� � � � 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�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�
Mine�area�
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�
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�
�
Residues�
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�Total� <15� <25� 3,723±50� 13,555±160�
S11�Mobile� 1.4±0.1� 1.5±0.1� 280±3� 719±50�Total� <15� <25� 6,706±87� 22,641±300�
Mine�area�
S30�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�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�
Sidi�Bou�Othmane�
Residues�
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�Total� 113±13� <25� 1,500±30� 13,140±160�
S20�Mobile� <0.5� <0.5� 40±3� 369±70�Total� 36±5� <25� 259±8� 1,960±30�
Mine�area�
S21�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�
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�
Bir�Nehass�
Residues�
R5�Mobile� <0.5� 3.0±0.1� 55.7±1.4� 639±7�
3.�Results�and�discussion�
101�
Scale 1:25000
Arsenic
CER=0
CER=20
CER=10
Scale 1:25000
Arsenic
CER=0
CER=20
CER=10
Copper
Scale 1:25000
CER=0
CER=80
CER=40
Copper
Scale 1:25000
CER=0
CER=80
CER=40
Figure�3.11.�Kettara�mine�area�GIS�contour�map�of�arsenic.
Figure�3.12.�Kettara�mine�area�GIS�contour�map�of�copper
Scale 1:25000
Lead
CER=0
CER=40
CER=20
Scale 1:25000
Lead
CER=0
CER=40
CER=20
Scale 1:25000
Zinc
CER=0
CER=20
CER=10
Scale 1:25000
Zinc
CER=0
CER=20
CER=10
Figure�3.13.�Kettara�mine�area�GIS�contour�map�of�lead.
Figure�3.14.�Kettara�mine�area�GIS�contour�map�of�zinc.
Arsenic
CER=0
CER=20
CER=10
Scale 1:15000
Arsenic
CER=0
CER=20
CER=10
Scale 1:15000
Figure�3.15.�Sidi�Bou�Othmane�mine�area�GIS�contour�map�of�arsenic
3.�Results�and�discussion��
�102�
Copper
CER=0
CER=20
CER=10
Scale 1:15000
Copper
CER=0
CER=20
CER=10
CER=0
CER=20
CER=10
Scale 1:15000
Figure�3.16.�Sidi�Bou�Othmane�mine�area�GIS�contour�map�of�copper
Lead
CER=0
CER=400
CER=200
Scale 1:15000
Lead
CER=0
CER=400
CER=200
Scale 1:15000
Figure�3.17.�Sidi�Bou�Othmane�mine�area�GIS�contour�map�of�lead
Zinc
CER=0
CER=200
CER=100
Scale 1:15000
Zinc
CER=0
CER=200
CER=100
CER=0
CER=200
CER=100
Scale 1:15000
Figure�3.18.�Sidi�Bou�Othmane�mine�area�GIS�contour�map�of�zinc
3.�Results�and�discussion�
103�
Arsenic
CER=0
CER=80
CER=40
Scale 1:5000Arsenic
CER=0
CER=80
CER=40
CER=0
CER=80
CER=40
Scale 1:5000
Copper
CER=0
CER=20
CER=10
Scale 1:5000Copper
CER=0
CER=20
CER=10
CER=0
CER=20
CER=10
Scale 1:5000
Figure�3.19.�Bir�Nehass�mine�area�GIS�contour�map�of�arsenic
Figure�3.20.�Bir�Nehass�mine�area�GIS�contour�map�of�copper
LeadScale 1:5000
CER=0
CER=200
CER=100
LeadScale 1:5000
CER=0
CER=200
CER=100
ZincScale 1:5000
CER=0
CER=200
CER=100
ZincScale 1:5000
CER=0
CER=200
CER=100
Figure� 3.21.� Bir� Nehass� mine� area� GIS� contour�map�of�lead
Figure�3.22.�Bir�Nehass�mine�area�GIS�contour�map�of�zinc
3.�Results�and�discussion��
�104�
3.3.� XANES� SPECIATION� OF� MERCURY� IN� THREE� MINING� DISTRICTS:� ALMADÉN,�ASTURIAS�(SPAIN),�IDRIA�(SLOVENIA)�(ANNEX�2)�
Mercury�is�one�of�the�most�toxic�elements�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�causing�chronic�or�acute�damages�[5].�The�toxicity�of�heavy�
metals�is�mainly�controlled�by�the�dose�and�its�chemical�speciation.�Hence,�the�assessment�of�
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� co�workers� 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�
extraction�protocol�for�the�determination�of�Hg�mobility�to�determine�toxicity�of�the�samples.��
3.3.1.�CHEMICAL�ANALYSIS�OF�THE�SAMPLES�
A� list� of� samples� from� the� metallurgical� plants� and� drainage� network� of� the� three�
districts,�their�corresponding�acronyms�and�a�short�description�of�the�sampling�site�is�provided�
in�Table�3.7.�Their�location�on�the�mine�is�depicted�in�Figure�3.23.�
Figure�3.23.�Sampling�locations,�mines�and�metallurgical�sites�of�the�three�mercury�mining�districts:�
Almadén,�Asturias�and�Idria.��
3.�Results�and�discussion�
105�
Table�3.7.�Samples�collected�at�the�three�mining�districts�Location� Id� Sampling�area� Material�
ALMADEN�SITE�
Almadén� HR� Huerta�del�Rey� Soils�from�an�old�metallurgical�plant�of�the�17th�century�
� CH� Main�dump�of�Almadén�mine� Dump�material,�sediments�and�riparian�soils�� AZG� Azogado�river�stream� Riparian�soils�and�stream�sediments�Almadenejos� ALM� Decommissioned�metallurgical�plant� Soils�from�the�metallurgical�plant�Valdeazogues�river� RD� Downstream�of�El�Entredicho�pit� Suspended�particles�San�Quintín�� SQ� Decommissioned�Pb�Zn�Ag�mine� Mine�wastes�and�soils�from�and�old�flotation�
plant�used�to�treat�cinnabar�
ASTURIAS�SITE�
Mine�tailings� TRRmn� Mine�and�metallurgical�plant� Dumps�in�the�vicinity�of�rotary�furnaces�Calcines� TRRc� Mine�and�metallurgical�plant� Calcination�waste�Soil� TRRs� Metallurgical�plant� Soil�from�an�abandoned�chimney�channel�Forest�Soils� TRRfs� El�Terronal�mine� Forest�soils�from�the�mining�area�
IDRIA�SITE�
Soils� S1�S3� Vicinity�of�the�metallurgical�plant�� Soils�� S2� Pront�Hill� Meadow�soils��� S4� Confluence�of�the�Idrijca�and�Baca�
rivers�Alluvial�soil�samples�collected�along�the�Idrijca�river�40�km�downstream�from�the�mine�
� S5�S6�� Confluence�of�Idrijca�and�Baca�rivers� Soils�from�a�deep�profile�at�50�cm�depth�(S5)�and�100�cm�(S6)�
Sediments� RS� Idrijca�river,�35�km�downstream�from�the�mine�before�Baca�river�inflow�
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)�
� SS� Idrijca�river,�35�km�downstream�from�the�mine�before�Baca�river�inflow�
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)�
High�mercury�concentrations�in�soil�samples�from�metallurgical�sites�were�found�at�the�
Almadén� district� (Table� 3.8)� that� can� be� mainly� attributed� to� the� inefficient� metallurgical�
techniques�used�in�the�old�plants�of�Almadenejos�and�Huerta�del�Rey�[8].�In�these�plants,�the�
roasting� temperatures�were� below�500� ºC.� Also� high�mercury� concentrations�were� found� in�
sediments�and�riparian�soils�from�Valdeazogues�river�(RD)�and�especially�from�Azogado�stream�
(AZG)�(2,816�mg�Hg�g�1).�These�results�coincide�with�previous�studies�undertaken�at�the�same�
sampling� site� [9].� Other� heavy� metals� are� also� found� in� Almaden� site� samples� although� in�
minor�concentrations,�and�especially�high�amounts�of�lead�and�zinc�were�found�in�samples�of�
San�Quintín�area�(SQ).��
Likewise�Almaden,�the�total�mercury�content�of�soil�and�dump�samples�of�Asturias�mine�
(Table� 3.9)� show� the� high�mercury� content� (the� highest� from� the� three�mines� studied)�with�
27,350� mg� Hg� g�1� in� dump� samples� (TRRmn�116)� and� 18,000� mg� Hg� g�1� in� soils� from� the�
chimney�channel,�with�also�high�amounts�of�arsenic�(from�735�mg�As�g�1�to�187,218�mg�As�g�1).��
On�the�other�hand,�Idria�samples�(Table�3.10)�revealed�minor�amounts�of�the�metals�analyzed�
compared�to�Almaden�and�Asturias,�being�the�samples�near�the�former�smelting�facilities�the�
most�polluted�caused�by�the�settling�down�of�Hg�enriched�particles�in�the�immediate�vicinity�of�
3.�Results�and�discussion��
�106�
the�smokestack�of�the�smelter.�It�is�important�to�highlight�the�high�Hg�concentration�observed�
in� Idria� sediments� (RS)� and� in� alluvial� soils� (S4)� 40� km�downstream� from� the�mine� probably�
linked�to�mercury�bearing�rocks,�wastes�from�combustion�processes�or�contaminated�river�bed�
sediments.�These� inputs� to� the�aquatic�environment� remain� in� the�area�even�a�decade�after�
the�ending�of�mining�operations.�
Table� 3.8.� Almaden� average� metal� content�(given�in�μg�g�1)�
� Table� 3.9.� Asturias� average� metal� content�(given�in�μg�g�1)�
� SAMPLE� Hg� As� Pb�� Zn�TRRmn�115� 1,470� 39338� <10� <15�TRRmn�116� 27,350 11,7553� <10� <15�TRRs�118� 3,280� 735� <10� <15�TRRs�121� 18,000 12,133� <10� <15�TRRmn�122� 5,785� 4,2300� <10� <15�TRRfs�3� 1,570� 1,6826� 107� 173�TRRfs�4� 1,080� 1,120� 53� 137�TRRc�5� 34� 187,218� <10� <15�TRRc�55� 54� 25,876� <10� <15��
� Table�3.10.�Idria�average�metal�content�(given�in�μg�g�1)�
SAMPLE� Hg� As� Pb�� Zn�CH�127� 989� <15� <10� 112�HR�108� 976� <15� 214� 96�HR�109� 404� <15� 111� 104�HR�110� 200� <15� 130� 185�RD�124� 105� <15� <10� <10�CH�125� 1,800� <15� <10� 112�AZG�105� 2,816� 23� 139� 233�CH�128� 450� <15� 102� 185�ALM�101� 2,720� <15� 74� 153�ALM�102� 2,629� <15� 102� 193�CH�126� 2,230� <15� <10� 365�SQ�111� 902� <15� 15,837 6,877SQ�112� 1,730� <15� 2,154� 1,221SQ�113� 1,935� <15� 19,049 7,134SQ�114� 390� �� �� ���
� SAMPLE� Hg� As� Pb�� Zn�S�1� 333� 21� <10� 112�S�2� 47� 26� <10� 102�S�4� 76� <15� <10� 64�S�5� 175� <15� 47� 145�S�6� 144� <15� 73� 496�RS�1� 6,540� <15� 302� 270�RS�2� 1920� <15� 14� <15�SS–1� 96� <15� <10� 449�SS�1� 11� <15� <10� 24�S�3� 95� 27� 46� 130��
3.3.2.�XANES�SPECIATION�AND�MOBILITY�RESULTS�
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�
separately�for�each�mining�district.�An�example�of�fitting�for�a�selected�sample�of�each�mine�is�
given�in�Figure�3.25.��
PCA� results� for� Almaden� district� indicated� that� five� components� are� required� to�
reconstruct�each�of�the�experimental�spectra�(cinnabar,�Cb�(red�HgS);�metacinnabar,�Mc�(black�
HgS);�HgCl2;�calomel�(Hg2Cl2)�and�schuetteite,�Sc�(Hg3(SO4)O2))�with�above�95%�of�confidence.�
The�most� common�species� found� in�almost�all� samples�were�mercury� sulfides� (cinnabar�and�
metacinnabar)� but� also� non�sulfide� phases� like� schuetteite,� calomel� (Hg2Cl2)� and� mercury�
chloride�(HgCl2)�which�were�found�in�soil�and�sediment�samples.��
3.�Results�and�discussion�
107�
Figure�3.24.�XANES�spectra�of�selected�Hg�pure�compounds�and�samples�from Almaden,�Idria�and�Asturias�mining�districts�(all�spectra�are�deliberately�stacked�to�show�differences).�Each�spectrum�
corresponds�to�the�mean�value�of�five�replicates.�
Figure�3.25.�XANES�spectra�of�selected�samples�from�the�three�mining�districts�with�reconstructed�
spectra�shown�as�dashed�lines.�
3.�Results�and�discussion��
�108�
Table�3.11.�Main�mercury�species�(in�%)�and�mobile�mercury�(in�mg�L�1�and�%).�Abbreviations:�Cb:�cinnabar;�Mc:�metacinnabar;�Sc:�schuetteite;�Co:�corderoite�
�Sample� Cb� Mc� Sc� Co� HgO� HgSO4� Hg2Cl2� HgCl2�
Red.�Chi�Sq.(10�3)�
Mobility��mg�L�1�(%)�
CH�127� 62� 0� 0� 0� 0� 0� 38� 0� 0.4� 1.4±0.3�HR�108� 37� 23� 0� 0� 0� 0� 40� 0� 0.6� 0.6±0.2�HR�109� 33� 24� 0� 0� 0� 0� 43� 0� 0.7� 0.2±0.1�HR�110� 41� 22� 0� 0� 0� 0� 37� 0� 0.6� <0.2�RD�124� 0� 0� 94� 0� 0� 0� 0� 6� 0.5� <0.2�CH�125� 7� 0� 83� 0� 0� 0� 0� 10� 0.4� <0.2�AZG�105� 0� 0� 80� 0� 0� 0� 20� 0� 0.3� <0.2�CH�128� 24� 22� 0� 0� 0� 0� 35� 19� 0.4� <0.2�ALM�101� 38� 39� 23� 0� 0� 0� 0� 0� 0.3� 10.8±0.3�ALM�102� 39� 31� 0� 0� 0� 0� 30� 0� 0.7� 21.3±0.5�CH�126� 33� 32� 35� 0� 0� 0� 0� 0� 0.3� <0.2�SQ�111� 54� 0� 17� 0� 0� 0� 29� 0� 0.2� 0.6±0.1�SQ�112� 51� 0� 21� 0� 0� 0� 28� 0� 0.2� 3.7±0.2�SQ�113� 59� 0� 17� 0� 0� 0� 24� 0� 0.2� <0.2�
ALM
ADEN
�
SQ�114� 47� 0� 20� 0� 0� 0� 33� 0� 0.3� <0.2�TRRmn�115� 29� 24� 0� 0� 0� 0� 0� 47� 1� 0.4±0.1�TRRmn�116� 28� 22� 0� 0� 0� 0� 0� 50� 0.9� 73±2�TRRs�118� 28� 22� 0� 0� 0� 0� 0� 50� 0.8� 20.1±1.3�TRRs�121� 29� 22� 0� 0� 0� 0� 0� 49� 0.7� 56.5±2�TRRmn�122� 30� 24� 0� 0� 0� 0� 0� 46� 0.7� 43.6±2�TRRfs�3� 44� 28� 0� 0� 10� 18� 0� 0� 3� 0.7±0.2�TRRfs�4� 50� 36� 0� 14� 0� 0� 0� 0� 3� <0.2�TRRc�5� 52� 30� 0� 18� 0� 0� 0� 0� 8� <0.2�
ASTURIAS�
TRRc�55� 57� 43� 0� 0� 0� 0� 0� 0� 7� <0.2�S�1� 44� 0� 32� 0� 0� 24� 0� 0� 6� <0.2�S�2� 55� 0� 0� 0� 0� 45� 0� 0� 2� 0.2±0.1�S�4� 85� 15� 0� 0� 0� 0� 0� 0� 4� <0.2�S�5� 90� 0� 0� 0� 10� 0� 0� 0� 4� <0.2�S�6� 58� 0� 0� 0� 0� 42� 0� 0� 5� <0.2�RS�1� 57� 0� 0� 0� 0� 43� 0� 0� 2� <0.2�RS�2� 100� 0� 0� 0� 0� 0� 0� 0� 3� <0.2�SS�1� 90� 0� 0� 0� 0� 10� 0� 0� 4� <0.2�SS�1� 55� 0� 0� 0� 0� 45� 0� 0� 9� <0.2�
IDRIA�
S�3� 66� 0� 26� 0� 8� 0� 0� 0� 07� 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�
polymorph�of�cinnabar�that�occurs�when�the�roasting�process�of�mercury�ores�is�not�complete�
or�it�is�done�in�the�presence�of�impurities�[10],�is�associated�to�the�historical�use�of�the�site,�as�
this� site�was� used� to� perform� flotation� tests� and�no� furnaces�were� employed.�On� the�other�
hand,�metacinnabar�has�been�identified�in�soil�samples�from�Almadenejos�(ALM)�(31–39%)�and�
Huerta�del�Rey�(HR)�(~23%),�locations�with�historical�metallurgical�activity.�
3.�Results�and�discussion�
109�
Other�non�sulfide�phases�like�mercurous�chloride�(24–43%)�have�also�been�identified�at�
San�Quintín�and�Huerta�del�Rey,�attributable�to�the�process�of�soil�formation.�High�amounts�of�
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�
sunlight�exposed�side�of�the�rock�surface,�and�it�is�frequently�found�near�old�furnaces�and�ore�
dumps�[11].�Relatively�more�soluble�phases�have�been�identified�in�soil�and�sediment�samples�
from�Valdeazogues�River�(100%)�and�Azogado�stream�(100%)�(Hg2Cl2,�HgCl2�and�Hg3(SO4)O2)�as�
a� result�of�weathering�processes� caused�by� 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)�(21.3�mg�L�1;�Table�3.11)�related�to�the�presence�of�Hg2Cl2.��
In�Asturias�mining�district,�all�samples�from�the�decommissioned�mine�and�metallurgical�
facility�showed�high�mercury�contents� in�soils� (TRRfs),�dump�materials� (TRRmn)�and�chimney�
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�
samples�is�higher�than�in�Almadén�area�since�in�Asturias�the�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�and�thus�the�mobility�of�
mercury� in� this� district� is� higher� than� in� Almadén.� In� qualitative� terms,� the�mobile�mercury�
determined�is�correlated�with�the�presence�of�HgCl2�(except�for�TRRmn�115),�a�mobile�phase�
of� mercury� and,� in� a� lesser� account,� to� the� presence� of� metacinnabar� resulting� from� the�
incomplete�combustion.��
At� the� Idria� mining� district,� cinnabar� is� the� most� common� form� of� mercury� in� soil,�
sediments�and�suspended�particles,�while�metacinnabar� is�also� found� in�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�resulting�
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� L�1,� see� Table� 3.11)� is� in�
agreement� with� former� studies� on� the� area� [12]� that� described� low� water�soluble�mercury�
species�in�sediments�and�suspended�particles.��
Considering�the�three�districts,�the�main�processes�affecting�mercury�speciation�are�ore�
composition,�mining�history�and�roasting�process.�The�type�of�metallurgical�processing�arises�
3.�Results�and�discussion��
�110�
as�one�of� the�most� important� factors� in�defining�mercury�availability.� In� this� sense,�mercury�
mobility�is�higher�in�Asturias�district�owing�to�its�roasting�treatment�was�less�efficient�than�in�
Almaden�or�Idria�(lower�roasting�temperatures�and�poorer�recovering�rates)�that�increases�the�
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�
mobility�values�were�found�in�Idria�district�related�to�its�efficient�metallurgical�process�(similar�
to�Almadén�area),� together�with� the�appropriate�management�of�calcines� that�were�used� to�
refill�old�galleries�as�well�as�the�shorter�mining�history�of�this�district.�
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�behavior.�
3.�Results�and�discussion�
111�
�
REMEDIATION�TECHNOLOGIES�
This� section� includes� results� of� treatment� processes� for� industrial� water� from� two�
different� sectors:� mining� activities� and� textile� industry.� These� results� constitute� specific�
examples�of�innovation�in�water�treatment�process�for�both�inorganic�and�organic�pollutants.�
Thus,�results�of�laboratory�and�pilot�plant�scale�for�recycling�of�water�from�a�mine�tailing�
pond�are� reported�here.�On� the�other�hand,� the� results�achieved�by�Fe�exchanged�materials�
for� the�degradation�of�persistent�organic�pollutants� (POPs)�by�means�of�Fenton�processes�as�
well�as�to�the�sorption�of�inorganic�contaminants�are�also�summarized.��
3.4.� EXTRACTANT� AND� SOLVENT� SELECTION� TO� RECOVER� ZINC� FROM� A� MINING�
EFFLUENT:�FROM�LABORATORY�SCALE�TO�PILOT�PLANT�
In�a�tailing�pond�from�an�abandon�mine�is�stored�a�huge�stream�of�effluent,�estimated�to�
be�10,000�m3/day�and�containing�about�1�g/L�of�Zn�and�significant�amounts�of�ferrous,�ferric,�
calcium,�copper,�aluminum�and�manganese�ions.�In�this�sense,�to�prevent�dam�breaches�from�
the�tailing�pond�that�can�cause�huge�hazards�to�humans�and�the�environment�it�is�required�to�
reduce�the�amount�of�wastewater�contained�in�the�mine�tailing�pond.�In�addition,�the�recovery�
of�zinc�can�provide�economic�value�to�the�process�while�solving�an�environmental�problem.�
3.4.1.�SX�LABORATORY�RESULTS��
To� accomplish� for� a� valuable� Zn� recovery,� separation� of� Zn� from� Fe� and� Ca� must� be�
obtained�since�any�further�use�of�the�Zn�liquor�product,�i.e.,�Electro�wining�(EW)�process,�will�
require�of�such�conditions.�The�recovery�of�Zn�was� investigated�to�select� the�extractant�with�
higher� efficiency� and� selectivity� between� DEHPA,� Cyanex� 272� or� Ionquest� 290.� Additionally,�
two�types�of�kerosene�were�also�evaluated.��
Since�there�are�no�reagents�commercially�available�able�to�extract�Zn�selectively�from�a�
solution�containing�Fe,�Fe�was�removed�from�the�mine�water�prior� to�the�SX�treatment�by�a�
bio�oxidation�process�followed�by�an�alkaline�precipitation�step�[13,�14]�to�obtain�a�pregnant�
leach�solution�(PLS)�without�iron.��
After� the� precipitation� step,� Fe� was� completely� removed� and� also� the� amount� of� Al�
decreased�drastically�and�Cu�dropped�by�half�(from�45.0�mg/L�to�21.7�mg/L).�The�process�did�
not�alter�the�content�of�Zn,�so�the�whole�process�of�zinc�recovery�does�not�lose�effectiveness�
3.�Results�and�discussion��
�112�
due�to�the� iron�removal�step.�The�concentration�of�the�other�metals�remained�similar�to�the�
initial�(Table�3.12).�
Table�3.12.�Solution�composition,�before�and�after�bio�oxidation�treatment�� Concentration�(mg/L)�
Element� Initial�solution� After�bio�oxidation After�precipitation�
[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�
Regarding�the�selectivity�experiments�performed�with�the�three�extractants�studied,�the�
obtained�results�(Figure�3.26,�3.28�and�3.30)�depicted�a�rational�reduction�of�the�recovery�of�
zinc� as� the� A:O� phase� ratio� increases� due� to� a� saturation� of� the� extractant.� In� this� sense,�
Cyanex�272�and�Ionquest�290,�used�in�a�lesser�concentration�than�DEHPA�(5%�vs�40%�(v/v)�for�
DEHPA),�present�a�plateau�at�a�A:O�>�1�which�suggests�that�the�extractant�is�saturated,�whilst�
zinc�recovery�for�DEHPA�is�still�diminishing.�
The�recovery�of�metals�achieved�by�DEHPA�was�Zn>�Ca>�Mn�>�Al�>�Cu,�and�at�A:O=1�the�
recovery� of� Zn� was� around� 75%,� but� also� other� metal� impurities� were� also� recovered,�
especially�Ca�and�Mn�(60%�and�30%�recovered,�respectively)�pointing�out�that�DEHPA�is�poorly�
selective�towards�Zn�extraction�(Figure�3.26).�In�addition,�around�80%�of�the�Al�remained�in�the�
organic�phase�(OP)�after�the�stripping�step�(Figure�3.27)�limiting�the�reuse�of�the�extractant.��
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�
Cyanex�272�can�be�reused�several�cycles�with�practically�no�regeneration.��
The�recovery�of�metals�for�Ionquest�290�was�Zn>>Al>Cu~Mn~Ca�(Figure�3.30).�The�trend�
is�similar�to�Cyanex�272�since�the�recovery�of�Zinc�at�A:O=1�is�around�40%�and�other�impurities�
are�practically�not�recovered.�In�addition,�less�than�5%�of�the�elements�analyzed�remain�in�the�
organic�phase�after�the�stripping�step�(Figure�3.31).�Thus,�contrary�to�DEHPA,�Cyanex�272�and�
Ionquest�290�selectively�extract�Zn�from�a�solution�containing�high�amounts�of�Ca�and�other�
metals�without�fouling�of�the�OP.��
3.�Results�and�discussion�
113�
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(a)�Recovery�DEHPA
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�D80 Al�D100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(b)�Remaining�OP�DEHPA
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�D80 Al�D100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
Figure�3.26.�%Recovery�at�different�A:O�ratios�
for�DEHPA�40%�(v/v)�Figure�3.27.�%Remaining�OP�at�different�A:O�
ratios�for�DEHPA�40%�(v/v)
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(a)�Recovery�Cyanex�272
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�KD80 Al�KD100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(b)�Remaining�OP�Cyanex
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�KD80 Al�KD100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
Figure�3.28.�%Recovery�at�different�A:O�ratios�for�Cyanex�272�5%�(v/v)
Figure�3.29.�%Remaining�OP�at�different�A:O�ratios�for�Cyanex�272�5%�(v/v)
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(a)�Recovery�IONQUEST
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�D80 Al�D100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio�A/O
(b)�Remaining�OP�Ionquest
Zn�KD80 Zn�KD100Ca�KD80 Ca�KD100Al�D80 Al�D100Mn�KD80 Mn�KD100Cu�KD80 Cu�KD100
Figure�3.30.�%Recovery�at�different�A/O�ratios�for�Ionquest�290�5%�(v/v)
Figure�3.31.�%Remaining�OP�at�different�A:O�ratios�for�Cyanex�272�5%�(v/v)
The� differences� observed� on� the� recovery� trends� between� DEHPA� and� the� other� two�
extractants�can�be�associated�to�their�chemical�nature,�given�that�phosphoric�extractants�(as�
DEHPA)�have�higher�affinity�for�calcium�than�phosphinic�extractants�(such�as�Cyanex�272�and�
Ionquest� 290).� The� small� differences� observed� between� Cyanex� 272� and� Ionquest� 290� are�
3.�Results�and�discussion��
�114�
explained� by� both� the� different� phosphinic� acid� concentration� (Ionquest� 290� is� 5�10%�more�
concentrated�than�Cyanex�272)�and�also�owed�different�product�impurities�in�each�extractant.�
As�a�summary,�DEHPA�reported�poor�selectivity�towards�zinc�due�to�the�coextraction�of�
manganese�and�calcium� (that� resulted� in�a�gypsum�precipitate� in� the� stripping� solution)�and�
high�amounts�of�aluminum�remained�in�the�organic�phase�after�the�stripping�step�reducing�its�
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� 5�10%� 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�
equally�feasible�for�the�recovery�of�zinc.�However,�from�an�engineering�point�of�view,�the�use�
of�Ketrul�D100�is�recommended�due�to�its�lower�flammability�compared�to�Ketrul�D80,�and�for�
that�reason�it�was�selected�as�dissolvent.�
3.4.2.�SX�PILOT�PLANT�PROCESS��
To�fulfill�the�requirements,�the�pilot�plant�may�produce�an�economically�effective�output�
and� the�overall�process� should�be�environmentally� friendly.� The�pilot�plant�process� layout� is�
depicted�in�Figure�3.32.�
SOLVENT�EXTRACTION
SOLVENT�STRIPPING
ELECTROWINNING
Pregnant�Leach�Solution
Loaded�Solvent
Weak�Electrolyte
Strong�Electrolyte
Barren�Solvent
Zinc
Raffinate
�Figure�3.32.�Inputs�and�outputs�at�the�pilot�plant�
To� satisfy� the� environmental� requirements� at� least� 95%� of� the� Zn�must� be� recovered�
from�the�effluent,�whereas�to�produce�the�economic�effective�output�the�zinc�contained�in�the�
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�
should�provide�a�final�product�stream�of�90�g/L�Zn�in�the�stripping�step�(strong�electrolyte)�by�
using�a�weak�electrolyte�with�50�g/L.��
3.�Results�and�discussion�
115�
Previously�to�the�pilot�plant�operations,�computer�simulation�was�performed�to�estimate�
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�
design�tool�than�McCabe�Thiele�diagrams�for�pulsed�columns�[15,�16,�17].�The�results�obtained�
in�the�simulation,�collected�in�Table�3.13,�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� 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.�<10�mg/L�Zn�in�the�raffinate.�The�concentration�of�
Zn� in� the� loaded�solvent� should�be� in� the� range�of�2.2�2.8�g/L,� that� is�around�70�85%�of� the�
total�theoretical�loading�of�3.3�g�Zn/L�for�Ionquest�290�5%�(v/v),�which�is�quite�reasonable.�In�
order�to�get�a�final�solution�of�90�g/L�Zn,�the�Zn�transfer�from�the�organic�phase�to�the�strip�
phase�should�be�of�40�g/L;�to�achieve�that�value�the�stripping�should�be�run�at�a�phase�ratio�of�
O:A=20,�so�only�one�equilibrium�stage�is�required�for�the�stripping.�
Table�3.13.�Recovery�of�Zn�depending�on�the�plant�configuration�using�5%�Ionquest�290�No.�Stages� Phase�ratio�O:A� Zn�in�raff.�(mg/L)� %�Recovery��
0.50� 51� 94.7�2�
0.60� 24� 97.6�
0.35� 75� 92.1�0.40� 30� 96.8�0.45� 11� 98.9�
3�
0.5� 4� 99.6�
The�maximum� loading� obtained� experimentally� at� limiting� conditions� (by� contacting� 3�
times�the�solvent�with�corresponding�fresh�portions�of�the�effluent�at�phase�ratio�O:A=0.1)�was�
2.9�g�Zn/L.�Since�this�result�was�similar�to�the�obtained�after�a�single�contact,�it�revealed�that�
the�limiting�conditions�could�be�achieved�by�a�single�contact.�
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�
extracted�and�only�a�small�amount�of�Ca�was�extracted.�Such�fact�is�also�confirmed�by�the�high�
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� co�extraction,� pH�
should�be�around�3�as�indicated�by�the�isotherms�given�in�the�online�User�Manual,�page�5�from�
Cytec�Corporation�for�Cyanex�272�and�considering�the�same�composition�of�both�Cyanex�272�
and� Ionquest� 290.� (http://www.cytec.com/specialty�chemicals/PDFs/CYANEX%20272.pdf,�
accessed�26th�December�2010).�
3.�Results�and�discussion��
�116�
Table�3.14.�Extraction�experiments�without�pH�correction,�22°C�
Aqueous�(mg/L)� Organic�(mg/L)� D�values�&�Separation�factors�Phase�ratio�O:A�
Final�pH Zn� Mn Ca� Zn 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� 1�104�
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� 270 203� 601 76� 0.1 8 0.3� 22.5� 610�
When� adjusting� to� pH=3� (Table� 3.15)� higher� amounts� of� zinc� were� extracted� and� the�
distribution�coefficient�of�zinc�(DZn)�was�higher�than�without�pH�adjustment.�The�extraction�of�
Mn�and�Ca�still�remained�quite�low�at�pH=3�as�is�also�indicated�by�the�high�separation�factors�
obtained.�Therefore,�given�that�higher�distribution�coefficient� for�zinc� is�obtained�at� this�pH,�
the�pilot�plant�effluent�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,�with�and�average�consumption�of�1.62�kg�
Na2CO3�per�kg�of�zinc�treated.��
Table�3.15.�Extraction�experiments�at�pH�3,�22°C�Aqueous�(mg/L)� Organic�(mg/L)� D�values�&�Separation�factors�Phase�ratio�
O:A� Zn� Mn Ca Zn Mn� Ca� DZn DZn/DCa� DZn/DMn�
PLS�(pH�5.0)� 963 213 583 �
0.1 784� 231� 531� 2878� 0 76 3.7 26.4 9�104
0.3� 343� 233� 509� 2073� 0.4� 72� 6.0� 42.9� 3�103�
0.5 182� 211� 536� 1700� 0.5� 40� 9.3 133.0� 5�103�
1� 49� 213� 547� 875� 1.5� 38� 17.9 199.9 3�103�
2� 22� 194� 534� 502� 3.4� 43� 22.8 285.0 1�103
3� 14� 127� 532� 297� 2.7� 46� 21.2� 235.6 1�103
5 4� 181� 584 192� 3� 42� 48� 685.7 2�103
10 1 183� 579 90� 1 48� 90� 1125 2�104
Shake�out� stripping� experiments�were� carried�out� at�O:A=10,�by� contacting�200�mL�of�
loaded�solvent�(LS)�containing�1.95�g/L�Zn�with�20�mL�of�H2SO4�200�g/L�(weak�electrolyte,�WE)�
containing�different�zinc�concentrations�ranging�from�40�to�90�g/L�(Table�3.16).�To�achieve�the�
required� transfer,� the� concentration�of� Zn� should� increase�by�~20�g/L,�which�was� consistent�
with�the�results�shown�in�Table�3.16.�In�all�cases,�only�5�17�mg/L�of�Zn�remained�in�the�barren�
3.�Results�and�discussion�
117�
solvent� (BS),� so� almost� all� zinc� was� recovered.� Therefore,� one� stage� of� stripping� is� enough�
regardless�the�concentration�of�Zn�in�the�stripping�solution.�
Table�3.16.�Stripping�experiments�at�phase�ratio�O:A=10,�22ºC�Aqueous�In� Aqueous�out�
Zn(g/L)� H2SO4�(g/L)� Zn�(g/L)�
40� 200� 58.8�50� 194� 68.2�60� 188� 77.8�70� 176� 91.0�80� 200� 102.8�90� 200� 115.4�
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�Zn/L,�producing�an�SE�containing�90�g/L�Zn,�
i.e.�a�zinc�transfer�of�40�g/L,�as�it�was�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.� 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�
operated�at�a�flux�of�40�m3/m2/h�(35�l/h�solvent).�The�pulsing�of�the�columns�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.�
Table�3.17.�Extraction�in�organic�and�aqueous�dispersion�continuities�Zn�(mg/L)�
� Feed�(L/h)� BS�(L/h)� Flux�(m3/m2/h)�pH�Raff.� Raff.� LS�
110 55� 33� 2.7� 11� 1,910�130� 60� 38� 2.8� 55� 1,880�
Organic�continuous�dispersion�
130� 60� 38� 2.9� 11� 1,800�150� 70� 44� 2.9� � 1,880�150� 70� 44� 3.1� � 2,520�
Aqueous�continuous�dispersion�
150� 70� 44� 2.9� � 2,240�
Three� tests� with� both� organic� continuous� and� aqueous� continuous� dispersion� (Table�
3.17)�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,�facilitating�the�comparison�between�both�dispersions�results.�Every�test�took�5�hours,�
long�enough�to�reach�steady�state�and�the�phase�ratio�was�kept�at�A:O=2.1�during�all�the�test�
work.� The� results� were� similar� for� both� dispersions.� The� concentration� of� Zn� in� the� LS� was�
around�2,000�mg/L�and�in�the�raffinate�below�50�mg/L,�indicating�than�more�than�95%�of�the�
Zn�was�recovered.�Thus,�the�extraction�process�operated�successfully�with�both�aqueous�and�
3.�Results�and�discussion��
�118�
organic� continuous� dispersions� 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.�Using�an�aqueous�continuous�dispersion�the�danger�of�fire�due�
to�kerosene�ignition�is�also�diminished.�
The�stripping�of�the�LS�(containing�around�2�g/L�Zn)�achieved�a�SE�with�30�40�g/L�Zn�(a�
zinc�transfer�of�30�40�g/L)�whilst�less�than�50�mg/L�Zn�in�the�raffinate�at�a�flux�of�45�m3/m2/h�
using� an� aqueous� continuous� dispersion� at� O:A=20.� The� stripping� column� worked� well� and�
supplied�the�required�BS�to�the�extraction.�Given�that�the� laboratory�tests�proved�that�there�
was� no� need� for� an� extra� column,� 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.�
As�a�summary,�given�that�the�recycling�of�the�organic�phase�lead�to�a�relative�importance�
of� the� extractant� costs,� Ionquest� 290� was� selected� as� the� most� suitable� extractant� for� the�
target�stream�due�to�its�higher�selectivity�and�loading�capacity�towards�Zn�extraction.�Ionquest�
290�avoids�the�necessity�of�scrubbing�the�gypsum�precipitate�in�the�strip�liquor�as�well�as�the�
regeneration�of�the�solvent�after�high�amounts�of�aluminum�are�not�stripped�if�compared�with�
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�
recommended�owing�its�lower�volatility�and�flammability.�The�pilot�plant�proved�the�feasibility�
of�the�process,�obtaining�a�zinc�recovery�of�95%�and�leaving�less�than�50�mg/L�in�the�raffinate.�
The�stripping�was�efficient�and�only�a�single�stage�at�O:A=20�was�required�to�achieve�a�transfer�
of�40�g/L.�For�a�Zn�price�above�US$2/kg�the�operating�costs�are�covered�while,�additionally,�a�
serious�environmental�problem�is�solved.�
3.�Results�and�discussion�
119�
3.5.� FE�LOADED� MATERIALS� FOR� THE� REMEDIATION� OF� ORGANIC� AND� INORGANIC�
CONTAMINATED�WASTE�WATERS�
Here,� are� summarized� the� results� obtained�by�using� Fe�loaded�materials� to� remediate�
organic� and� inorganic� wastewaters.� In� this� sense,� organic� pollutants� such� as� dyes� and�
persistent�organic�pollutants�were�degraded�by�following�Fenton�treatment�using�as�a�catalyst�
Fe�loaded� materials.� Such� Fe�loaded� 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�
obtained�for�loading�the�USY�zeolite�with�Fe(III)�are�presented�in�Figure�3.33�and�indicated�a�Fe�
loading�increase�with�time.�One�hour�was�selected�as�appropriate�loading�time.��
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1h� 3h 6h 3*6h 3�days
[Fe3
+ ]�(W
t.%)�
Time�of�Fe3+�exchange
0
0.5
1
1.5
2
2.5
3
0.01M 0.05M 0.1M 0.2M 0.5M
[Fe3
+ ]�(W
t.%)�
Concentration�of�the�Fe�loading�solution
Figure�3.33.�Fe�concentration�on�USY�zeolite�at�1h,�3h,�6h,�3�days�and�6�cycles�of�3h�of�Fe�exchange�(error�bars�correspond�to�the�standard�deviation�on�the�determination)�
Figure�3.34.�Fe�concentration�of�USY�zeolite�between�0.01�to�0.5M�initial�Fe(NO3)3�
concentration�(error�bars�correspond�to�the�standard�deviation�on�the�determination)�
A�decrease�on�the�Fe�content�is�observed�when�the�concentration�of�the�loading�solution�
is�increased�(see�Figure�3.34)�being�explained�by�the�formation�of�polynuclear�Fe�complexes�at�
high� Fe� concentration,� so� less� Fe� is� available� for� the� exchange�with� USY� [19].� On� the� other�
hand,�at�a�very�low�Fe�concentration�(0.01M),�not�enough�Fe�is�available�to�occupy�all�the�sites,�
thus�being�the�maximum�loading�at�a�concentration�of�Fe(NO3)3�0.05M,�hence�being�selected�
as� the�most�appropriate� loading�concentration.�Under� this�conditions,�at� room�temperature,�
the�amount�of�Fe�introduced�into�the�USY�zeolite�was�2.7±0.2�wt.%.�According�to�Neamtu�et�al.�
[20]�1.69wt.%�of�Fe�can�be� introduced�when�exchanging�USY�three�times�during�6h�using�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�
exchange�properties�such�as�zeolite�Y,�clinoptilolite,�montmorillonite�and�Forager�sponge.��
�
3.�Results�and�discussion��
�120�
3.5.1.�FE�LOADED�MATERIALS�APPLIED�AS�FENTON�CATALYSTS�
Two� different� low� cost� materials� such� the� natural� zeolite� clinoptilolite� and� the� clay�
montmorillonite� K�10� (MMT)� along� with� the� commercial� synthetic� zeolite� USY� were� Fe�
exchanged�for�their�evaluation�as�Fenton�catalysts.�The�amount�of�Fe�on�each�material�before�
and�after�loading�is�shown�in�Figure�3.35.�From�the�obtained�results�it�can�be�pointed�out�that�
before�the�exchanging�process�no�Fe�was�detected�in�the�USY�whilst�MMT�structure�contained�
2.1±0.1� wt.%� and� clinoptilolite� 1.0±0.1� wt.%� of� Fe.� The� initial� Fe� content� of� MMT� and�
clinoptilolite�is�structural�and�due�to�their�natural�origin�related�to�soils�usually�rich�in�Fe.�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�
with�Fe�was�2.1�wt.%,�so�only�1.0�wt.%�Fe�was�introduced�onto�the�clinoptilolite.�These�values�
are�strongly�related�to�the�surface�area�of�each�material,�as�USY�has�bigger�surface�area�than�
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�
demonstrated�to�be�suitable�for�loading�Fe�low�cost�materials�such�as�natural�zeolites�and�clays�
with�different�surface�areas.��
00.51
1.52
2.53
3.54
4.55
USY MMT Clinoptilolite
[Fe3
+ ]�(W
t.%)�
After�loading
Before�loading
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
200 250 300 350 400 450 500 550 600
Abs
orba
nce
Wavelength (nm)
�=218nm
�=324nm
�=516nm
Figure�3.35.�Fe�content�of�the�materials�before�and�after�the�loading�with�Fe(NO3)3�
Figure�3.36.�UV�Vis�spectra�of�AR14�0.05�mM�
To�evaluate�the�feasibility�of�such�Fe�loaded�materials�as�heterogeneous�catalysts�on�the�
degradation�of�organic�compounds,�the�decolorisation�of�a�model�dye�has�been�employed.�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�
attributed�to�the�absorbance�of�the�naphthalene�groups,�the�peak�at�324�nm�corresponds�to�
the� electron� conjugation� of� naphthalene� rings� with� the� �N=N�� group,� whereas� the� peak� at�
516nm�is�related�to�the�high�conjugated�structure�of�the�whole�dye�molecule�that�confers�its�
characteristic�color�to�the�dye�[21].��
3.�Results�and�discussion�
121�
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
Time�(min)
%Decolorization��=516nm
Hom.�Cat.Fe�USYFe�MMTFe�Clinoptilolite
�
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
Time�(min)
%Degradation��=324nm
Hom.�Cat.Fe�USYFe�MMTFe�Clinoptilolite
Figure�3.37.�Kinetics�of�AR14�decolorisation�(�=516�nm)�for�Fe�USY,�Fe�MMT,�Fe�
Clinoptilolite�and�homogeneous�catalysis�(Hom.�Cat.)�
Figure�3.38.�Kinetics�of�AR14�mineralization�(�=324�nm)�for�Fe�USY,�Fe�MMT,�Fe�
Clinoptilolite�and�homogeneous�catalysis�(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�
mineralization�of�the�dye�was�followed�through�the�characteristic�wavelength�related�to�bond�
breaking�(�=324�nm).�From�the�obtained�results�(Figure�3.37)�it�can�be�stated�that�all�Fe�loaded�
materials� are� able� to� discolor� AR14� 0.05mM� in� less� than� 60� min.� Moreover,� according� to�
literature�[22,�23]�the�Fenton�reaction�follows�a�pseudo�first�order�equation�[ln(C/Co)=�Ka�t],�so�
the�linear�region�of�the�kinetic�experiments�can�be�used�to�obtain�the�apparent�first�order�rate�
constant�(Ka)�for�all�the�Fe�loaded�materials�studied.�Thus,�taking�into�account�the�Ka�values�for�
the� Fe�loaded� materials� together� with� the� homogeneous� catalysis� (Table� 3.18),� it� can� be�
pointed�out�that�the�catalytic�activities�followed�the�order�Homogeneous�catalysis>Fe�USY>Fe�
MMT>Fe�Clinoptilolite.��
Table�3.18.�Apparent�first�order�rate�constant�(Ka)�for�the�exchanged�materials�Catalyst� Ka�(min�1)� R2� [Fe3+]solution�(mg/L)�
Homogeneous�catalysis� 0.368±0.02� 0.995� 17.6±0.5�
Fe�USY� 0.311±0.009� 0.998� <0.2�
Fe�MMT� 0.143±0.003� 0.998� 0.5±0.2�
Fe�Clinoptilolite� 0.041±0.002� 0.994� 1.4±0.2�
In� this� regard,� the� reaction� kinetics� for� Fe�USY� is� comparable� to� the� homogeneous�
catalysis,� whilst� Fe�MMT� and� Fe�clinoptilolite� showed� slower� kinetics.� These� results� can� be�
associated�with�the�amount�of�Fe�loaded�after�the�Fe�exchanging�(not�to�the�total�Fe�content),�
that�indicates�that�the�structural�Fe�present�in�MMT�and�clinoptilolite�is�not�accessible�to�act�as�
catalyst�of� the�Fenton� reaction.�Moreover,�negligible�amounts�of�Fe�were� released� from�the�
Fe�loaded�materials,�as�small�amounts�of�Fe�were�found�in�solution�after�the�reaction,�so�the�
reaction�mainly�occurs�due�to�the�Fe�linked�to�the�support�(Table�3.18).�
3.�Results�and�discussion��
�122�
From�the�values�obtained�at�324�nm�(Figure�3.38),�mostly�related�to�the�degradation�of�
naphthalene,�and�thus�to�the�mineralization�of�the�dye,�it�can�be�observed�that�the�maximum�
degradation�achieved�was�95%,�thus�the�mineralization�of�AR14�was�not�complete,�even�after�
90�min�of�reaction.� In�this�regard,�the�analysis�by�GC�MS�of�the�solution,�after�being�treated,�
revealed� only� oxalic� acid� and� malonic� acid� as� degradation� products.� These� compounds� are�
refractory�to�oxidation�by�Fenton�processes�as�it�has�been�demonstrated�that�low�chain�acids�
are�difficult�to�degrade�by�the�radical�hydroxyl�[24].�It�is�also�noteworthy�that�Fe�USY�is�able�to�
achieve�similar�kinetics�to�the�homogeneous�catalysis�process�(maximum�mineralization�at�20�
min� for� both)� whereas� Fe�MMT� and� Fe�clinoptilolite� lasted� 25� and� 50� min� respectively� to�
achieve�maximum�mineralization.��
To� complement� the� feasibility� of� the� Fe�loaded� materials� studied� to� degrade� organic�
pollutants,�the�degradation�of�two�refractory�organic�compounds,�acetic�acid�and�phenol,�was�
also�evaluated.�Such�degradation�was�followed�by�measuring�chemical�oxygen�demand�(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� Fe�loaded� catalysts� together� with� the� results� obtained� for� the�
homogeneous�catalysis�and�the�amount�of�Fe�in�solution�are�given�in�Table�3.19.��
Table�3.19.�Acetic�acid�and�phenol�removal�by�Fe�supported�materials�and�homogeneous�catalysis�� Acetic�COD�removal� Phenol�COD�removal� [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�Homogeneous�catalysis� 25±4%� 85±3%� 18±2�
Mineralization�of� acetic� acid� is�partly� achieved� for� all� heterogeneous� catalysts� and� the�
homogeneous�catalysis,�with�a�COD�diminution�of�20�40%.�Comparing�the�values�obtained�for�
each� of� the� Fe�exchanged� materials� it� can� be� observed� a� higher� performance� over� the�
homogeneous�catalysis.�All� the�Fe�loaded�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�Fe�exchanged�catalysts�were�
higher� than� for� the� homogeneous� catalysis.� Among� the� supported� catalysts,� Fe�USY� and� Fe�
MMT�achieved�higher�COD�removal�than�clinoptilolite�mainly�due�to�their�higher�Fe�content.�
These�values�are�similar� to�those�reported�before�[25],�where�20%�and�96%�of�COD�removal�
were�achieved�for�acetic�acid�and�phenol,�respectively,�indicating�that�the�process�is�also�viable�
for� the� removal� of� persistent� organic� pollutants.� Moreover,� iron� hydroxyoxides� were� not�
3.�Results�and�discussion�
123�
formed,� so� there�was�no�need� to� remove� the� red� sludge� caused�by� iron�hydroxyoxides� as� it�
happened�when�using�the�homogeneous�catalysis.�
Finally,�column�tests�applying�similar�conditions�to�those�employed�in�batch�experiments�
were�performed.�Given�the�small�particle�size�of�the�materials�used�in�batch�experiments,�the�
column�blocked�avoiding�the�circulation�of�the�solution.�However,�given�the�natural�origin�of�
the� clinoptilolite� zeolite,� it� was� able� to� be� grind� and� milled� to� obtain� different� grain� sizes�
allowing�its�use�in�column.�As,�the�clinoptilolite�at�grain�size�0.2�2mm�did�not�block�the�column,�
it�was�employed�for�column�experiments.�In�this�sense,�three�columns�were�filled�with�4.6g�of�
clinoptilolite�grain�size�0.2�2mm�and�loaded�with�iron�by�circulating�100�ml�of�Fe(NO3)3�0.05M�
at� room� temperature� at� 2mL/min� in� countercurrent.� The� amount� of� Fe� introduced� into� the�
clinoptilolite�was�1.0±0.1%�wt,�equal�to�the�obtained�for�the�batch�process�using�clinoptilolite�
fine�grain�size.��
Discoloring�of�100�mL�AR14�0.05�mM�was�done�over�one�of�the�columns�obtaining�total�
discoloring� of� the� dye� in� less� than� 15� minutes� and� achieving� kinetics� of� discoloring�
(Ka=0.365±0.02,�R2=0.993)�comparable�to�the�homogeneous�catalysis�and�the�Fe�USY�material.�
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�
processes�the�contact�of�solution�and�catalyst�is�enhanced.�Moreover,�when�the�reaction�was�
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�
materials�studied�and�the�homogeneous�catalysis.�This�fact�demonstrates�the�feasibility�of�Fe�
loaded�clinoptilolite�as�heterogeneous�Fenton�catalysts�also�in�column.
3.�Results�and�discussion��
�124�
3.5.2.�FE�LOADED�MATERIALS�APPLIED�TO�ARSENIC�REMOVAL�
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,� Fe�loaded�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� (Ultra�Stable� Steamed� Y� zeolite),� zeolite� Y� (ZY)� and�
Forager�sponge�(Sp)�have�been�tested�as�arsenic�sorbents.�In�this�regard,�the�characterization�
of� these�materials�by�FP�XRF�and�XAFS�techniques�can�shed� light�onto�the�different�sorption�
mechanisms�of�arsenic�into�such�materials.��
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.�In�this�sense,�under�the�same�conditions,�ZY�achieved�greater�Fe�content�than�USY�or�Sp�
(Table�3.20).�As�it�was�concluded�in�the�previous�section,�the�loading�of�Fe�into�the�materials�is�
strongly�related�to�its�specific�surface�area,�thus�as�zeolite�Y�has�a�specific�surface�area�higher�
than� zeolite� USY,� its� Fe� loading� was� superior� (Specific� surface� area:� USY=730�m2/g;� ZY=900�
m2/g).�Although�specific�surface�area�plays�an�important�role�on�the�loading�capacity�of�Fe�on�
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=� 10�15�
m2/g�according�to�producer)�the�high�content�of� functional�groups�allows�higher�Fe� loadings.�
After�the�arsenic�sorption�process,�it�can�be�observed�that�for�both�studied�zeolites,�a�relation�
between� the� As� sorbed� and� the� content� of� iron� can� be� depicted� (equal� As:Fe� ratio).�
Nevertheless,�Forager�sponge,�has�an�As:Fe�ratio�higher�than�for�the�zeolites�mainly�owed�to�
tertiary�amine�salt�groups�contained�in�the�sponge�that�can�bind�anionic�contaminants,�such�as�
arsenic,�chromate�or�uranium�oxide�species.��
Table�3.20.�Fe�and�As�content�of�the�USY,�ZY�and�sponge�Material� [Fe]�(mg/Kg)� [As]�(mg/Kg)� %Arsenic�Adsorption� As:Fe�ratio�USY3�As� 46,000±50� 16,560±30� 41±3� 0.4�ZY3�As� 88,930±70� 36,140±40� 90±5� 0.4�Sp3�As� 46,730±50� 29,640±40� 74±4� 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� (FeAsO4�2H2O)� were� used� to� determine� bond� lengths� and� coordination� numbers�
regarding� to� the�presence�of� Fe�As� bonds� in� its� structure.� The� goodness� of� these�paths�was�
validated� by� the� calculation� of� bond� lengths� and� coordination� number� for� rösslerite�
(MgHAsO4�7H2O)�and�ferrihydrite� (Fe2O3�0.5H2O),�the�standards�measured�at�the�synchrotron�
3.�Results�and�discussion�
125�
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�
coordination�shells�containing�2�Oxygen�atoms�each�at�1.66��and�1.70�,�respectively,�while�
the�experimental�results�obtained�were�1�coordination�shell�containing�3.6±0.3�Oxygen�atoms�
at�1.70�.�Given�that�the�distances�and�the�coordination�number�are�very�similar,�it�can�be�said�
that�the�paths�are�correct�and�can�be�used�to�fit�the�spectra�of�the�unknown�samples.�
Table�3.20.�Theoretical�and�fit�values�for�rösslerite�� Theoretical�values� Fit�results�
� R�()� CN� R()� CN� ��(10�3��2)� E0�(eV)�
As�O� 1.66� 2�As�O� 1.70� 2�
1.70±0.01� 3.6±0.3� 3.7±1� 1.4±1.7�
�
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
0 1 2 3 4 5 6 7 8
FT(X
(k)·k
2)
r, A
�
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2 4 6 8 10 12
x(k)
·k2
k, A
�
Figure�3.38.�EXAFS�spectra�for�Rosslerite.�a)�Fourier–transformed�spectra�(k2�weighted)�and�b)�As�K�edge�spectra.�(min�k=3.89;�max�k=11.54;�min�R=0.57;�max�R=2.04)�
The�distances�and�coordination�shells�obtained�for�each�of�the�Fe�loaded�materials�using�
the�rösslerite�paths�are�given� in�Table�3.21.�The�spectra�and� fit� spectra� for�arsenic�adsorbed�
onto�USY3�are�given�in�Figure�3.39,�for�arsenic�adsorbed�onto�ZY3�in�Figure�3.40�and�for�arsenic�
adsorbed�onto�Fe�loaded�sponge�in�Figure�3.41.�
Table�3.21.�Fit�results�for�Sp3,�USY3�and�ZY3�first�and�second�coordination�shells�
Material�Coordination�
shell�R(�)� CN� ��(10�3���2)� E0�(eV)�
1st�=�As�O� 1.69±0.02� 4.2±0.2� 3� 2±2�Sp3�As(R=14.6%)�
2nd�=�As�Fe� 3.23±0.07� 2.4±0.9� 8� �3±7�1st�=�As�O� 1.69±0.02� 4.7±0.2� 3� 2±2�
USY3�As(R=12.0%)�2nd�=�As�Fe� 3.19±0.05� 3.3±0.8� 8� �8±5�1st�=�As�O� 1.69±0.02� 4.4±0.2� 3� 3±2�
ZY3�As(R=12.3%)�2nd�=�As�Fe� 3.21±0.05� 3.4±0.8� 8� �5±5�
The� first� coordination� shell� around� As� (As�O)� is� at� similar� distance� and� coordination�
numbers�are�almost�equal�for�all�the�materials�(Sp3,�USY3�and�ZY3).�The�main�differences�are�
observed� for� the� second�coordination� shell� (As�Fe),�which� is�at� the� same�distance� for�all� the�
Rosslerite, As(V) standard R2=8.7%
Rosslerite, As(V) standard
3.�Results�and�discussion��
�126�
materials�although�the�coordination�number�is�slightly�higher�for�both�zeolites�(USY3�and�ZY3)�
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�
number�is�decreased.�
-4
-3
-2
-1
0
1
2
3
4
0 1 2 3 4 5 6 7 8
FT(X
(k)·k
2)
r, A
�
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2 4 6 8 10 12
x(k)
·k2
k, A
�Figure�3.39.�USY3�As.�a)�Fourier�transformed�spectra�(k2�weighted)�and�b)�As�K�edge�spectra.��The�model�fits�are�shown�as�grey�line.�(min�k=3.89;�max�k=11.54;�min�R=0.57;�max�R=3.07.�
Constraints:��1=0.003��2;��2=0.08��2)�
-4
-3
-2
-1
0
1
2
3
4
0 1 2 3 4 5 6 7 8
FT(X
(k)·k
2)
r, A
�
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2 4 6 8 10 12
x(k)
·k2
k, A
�Figure�3.40.�ZY3�As�a)�Fourier�transformed�spectra�(k2�weighted)and�b)�As�K�edge�spectra.��The�model�fits�are�shown�as�grey�line.�(min�k=3.89;�max�k=11.54;�min�R=0.57;�max�R=3.07.�
Constraints:��1=0.003��2;��2=0.08��2)�
-4
-3
-2
-1
0
1
2
3
4
0 1 2 3 4 5 6 7 8
FT(X
(k)·k
2)
r, A
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2 4 6 8 10 12
x(k)
·k2
k, A Figure�3.41.�Sp3�As�a)�Fourier�transformed�spectra�(k2�weighted)�and�b)�As�K�edge�spectra.�The�model�fits�are�shown�as�grey�line.�(min�k=3.89;�max�k=11.54;�min�R=0.57;�max�R=3.07.�
Constraints:��1=0.003��2;��2=0.08��2)�
USY3-AsR2=8.7%
USY3-As
ZY3-As R2=8.7%
ZY3-As
Sp3-As
2
Sp3-As
3.�Results�and�discussion�
127�
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� corner�sharing� between� AsO4� tetrahedra� and� two� FeO6�
octahedra� (namely� 2C).� Furthermore� monodentate� complexes� from� corner�sharing� between�
AsO4�tetrahedra�and�FeO6�octahedra�(namely�1V)�were�also�inferred�[26].�Several�other�studies�
proposed�also�bidentate�edge�sharing�between�AsO4�tetrahedra�and�a�free�edge�of�the�same�
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� Fe�loaded�materials� studied�
and� the� bond� length� for� each� type� of� coordination,� it� can� be� inferred� that� the� arsenate� is�
complexed�with�the�Fe�of�the�Fe�loaded�materials�as�a�bidentate�corner�sharing�bond.��
�
Figure�3.42.�Possible�surface�complexes�on�iron�oxide�hydroxides�
Table�3.22.�Interatomic�distances�according�the�type�of�complex�Name�of�complex� Type�of�complex� Bond�sharing� R�As�Fe�(Å)�
1V� Monodentate�complex� Corner�sharing� 3.6�2C� Bidentate�complex� Corner�sharing� 3.26�2E� Bidentate�complex� Edge�sharing� 2.8�
3.�Results�and�discussion��
�128�
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[22]� Gumy,� D.;� Fernandez�Ibañez,� P.;� Malato,� S.;� Pulgarin,� C.;� Enea,� O.;� Kiwi,� J.� Supported� Fe/C� and�Fe/Nafion/C� catalysts� for� the� photo�Fenton� degradation� of� Orange� II� under� solar� irradiation,� Catal.�Today�101:�375�382.�[23]�Lei,�J.;�Liu,�C.;�Li,�F.;�Li,�X.;�Zhou,�S.;�Liu,�T.;�Gu,�M.;�Wu,�Q.�(2006).�Photodegradation�of�orange�I�in�the�heterogeneous�iron�oxide�oxalate�complex�system�under�UVA�irradiation,�J.�Hazrad.�Mat.�137:�1016�1024.�[24]�Franch,�M.I.;�Ayllón,�J.A.;�Peral,�J.;�Doménech,�X.�(2004).�Fe(III)�photocatalyzed�degradation�of�low�chain�carboxylic�acids�implications�of�the�iron�salt.�Applied�Catalysis�B:�Environmental�50:�89�99.�[25]� Hermosilla,� D;� Cortijo,� M.;� Huang,� C.P.� (2009).� The� role� of� iron� on� the� degradation� and�mineralization�of�organic�compounds�using�conventional�Fenton�and�photo�Fenton�processes.�Chemical�Engineering�Journal�155:�637�646.�[26]�Waychunas,�G.A.;�Rea,�B.A.;�Fuller,�C.C.;�Davis,�J.A.�(1993).�Surface�chemistry�of�ferrihydrite:�Part�1.�EXAFS� studies� of� the� geometry� of� coprecipitated� and� absorbed� absorbed� arsenate.� Geochim.�Cosmochim.�Acta�57:�2251�2269.�[27]�Fendorf,�S.;�Eick,�M.J.;�Grossl,�P.;�Sparks,�D.L.�(1997).�Arsenate�and�chromate�retention�mechanisms�on�goethite:�I.�Surface�structure.�Environ.�Sci.�Technol.�31:�315��320.�[28]�Fuller,�C.C.;�Davis,�J.A.;�Waychunas,�G.A.�(1993).�Surface�chemistry�of�ferrihydrite,�Part�2,�Kinetics�of�arsenate�adsorption�and�coprecipitation:�Geochim.�Cosmochim.�Acta�57:�2271�2282.�
�
130�
�
131�
�
4��
CONCLUSIONS�
�
132�
�
4.�Conclusions�
133�
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�
contaminated�waters.�The�following�are�the�most�important�conclusions�to�be�drawn�from�the�
results�obtained�in�the�different�works�contained�herein.��
� Field�Portable�X�Ray�Fluorescence�(FP�XRF)�spectrometry�has�been�an�effective�tool�to�
characterize�soil�samples�from�four�Moroccan�mine�sites.�In�this�sense,�the�application�
of�Geographic� Information�Systems�(GIS)� let� to�produce�maps�revealing� the�pollution�
trends� in� these� areas.� Likewise,� X�Ray� Absorption� Spectroscopy� (XAS)� has� been�
successfully� applied� to� determine� the�mercury� speciation� in� soil� samples� from� three�
main�European�mercury�mines.��
� The�pilot�plant� study� to� recover� zinc� from�a�mine� tailing�pond�has�been� carried�out,�
obtaining�proper�results�for�a�Zn�price�quotation�above�US$2/kg.�The�value�of�the�zinc�
product�covers�the�whole�treatment�while�an�environmental�problem�is�solved.��
� Various� Fe�loaded� materials� have� been� tested� as� Fenton� catalysts� and� arsenic�
sorbents.�The�results�obtained�when�used�as�Fenton�catalysts�were�comparable�to�the�
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�Fe�loaded�
materials�let�to�characterize�the�sorption�of�arsenic�on�these�Fe�loaded�materials.��
More� specific� conclusions� driven� from� the� obtained� results� for� each� of� the� studies�
performed�in�this�thesis�are�summarized�as:�
Heavy�Metal�Contamination�and�Mobility�at�the�Draa�Lasfar�mine�area:�
� Regarding�CER�values�calculated�using�the�FP�XRF�results,�arsenic,�copper,�lead�and�zinc�
can�be�distinguished�as�the�main�pollutants�of�the�mine�area�whilst�Ba,�Fe,�K,�Rb,�Sr,�Ti�
can�be�considered�lithogenic�components.��
� The�most�polluted�sites�are�found�beside�the�mine�site�towards�the�river�creek�whilst�
samples�closed�to�Koudiyat�hill�reported�values�similar�to�background��
� GIS�contour�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.��
4.�Conclusions��
134�
� The� leading� factor� regarding� mobility� of� the� samples� at� Draa� Lasfar� mine� area� is�
concentration�of�metals�and�organic�matter�(based�on�LOI�determinations).�However,�
given�the�low�metal�content�on�the�mobile�phase,�it�may�be�considered�lower�risk�than�
expected�when�taking�into�account�only�total�concentration�values.��
Characterization�of�Kettara,�Sidi�Bou�Othmane�and�Bir�Nehass�mines:�
� Likewise�Draaa�Lasfar,�As,�Cu,�Pb�and�Zn�are�the�main�pollutants�at�the�three�mine�sites�
regarding�its�CER�values.��
� The�level�of�contamination�of�each�mine�is�strongly�dependent�on�the�exploitation�time�
since�the�annual�extraction�for�each�mine�was�similar.��
� Samples�taken�at�residues�deposits�are�highly�polluted�compared�to�samples�taken�at�
the�mine�area.�These�results�were�corroborated�by�box�plot�representations�and�PCA.��
� 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�
metals�exceed�the�capacity�of�the�soil�to�retain�them.�
XANES� speciation� of� mercury� in� three� mining� districts:� Almadén,� Asturias� (Spain),� Idria�
(Slovenia):�
� This�work� represents� the� first� inter�regional� study� of�mercury� speciation� of� the� two�
main� European� Hg�mining� districts� (Almaden� and� Idria),� and� a� polymetallic� district�
located�in�Asturias.�
� XANES� revealed� that� rather� insoluble� mercury� compounds� (cinnabar,� metacinnabar,�
schuetteite,� corderoite)� 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.��
� It� can� be� established� from� the� results� from� the� three� districts,� that� the� presence� of�
mercury�chlorides�or�sulfates�can�be�related�to�mobile�mercury.��
� 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,�HgCl2�phases�responsible�for�the�mobility�of�mercury.��
4.�Conclusions�
135�
� 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�behavior.�
Extractant�and�solvent�selection�to�recover�zinc�from�a�mining�effluent:�from�laboratory�scale�to�
pilot�plant:�
� Opposed�to�DEHPA,�Cyanex�272�and�Ionquest�290�provide�high�zinc�selectivity�towards�
calcium�and�negligible�amounts�of�metals�are�found�in�the�organic�phase�avoiding�the�
regeneration�of�the�organic�phase�step.��
� Ionquest�290� is� considered� the�best�extractant�amongst� the� three� studied�due� to� its�
higher�selectivity�compared�to�DEHPA�and�a�higher�Zinc�recovery�(5–10%)�than�Cyanex�
272.��
� Amongst� the� studied� solvents� Ketrul� D80� and� Ketrul� D100,� the� latter� is� the�
recommended�due�to�its�lower�volatility�and�flammability.��
� The�pilot�plant�has�proven�the� feasibility�of� the�process�as� the�zinc� recovery� is�up�to�
95%�and�less�than�50�mg/L�are�left�in�the�raffinate.�The�stripping�is�efficient�and�only�a�
single�stage�at�O:A=20�is�required�to�achieve�a�transfer�of�40�g/L.��
Fe�loaded�materials�applied�as�Fenton�catalysts:�
� An� enhanced� methodology� using� mild� conditions� to� achieve� high� Fe�loadings� into�
zeolite�USY�is�presented�in�this�thesis.�In�this�sense,�1h�of�contact�with�zeolite�USY�and�
a� 0.05M�Fe(NO3)3� solution� at� room� temperature� reached�higher� loadings� than�when�
increased�contact�times�and�concentration�solutions�were�employed.�
� Other� low�cost�materials�with�exchange�properties� such�as�montmorillonite� clay�and�
natural� zeolite� clinoptilolite� have� also� been� loaded�with� the� enhanced�methodology�
applied�to�zeolite�USY�obtaining�also�high�Fe�loadings.��
� The�treatment�of�a�synthetic�dye�solution�(Acid�Red�14)�by�Fenton�reaction�using�the�
aforementioned�Fe�loaded�materials�achieved�total�decolorization.��
� Moreover,�using�such�Fe�loaded�materials�as�Fenton�catalyst,�the�removal�of�COD�from�
solutions� containing� acetic� acid� and�phenol� is� ca.� 30%�and�90%,� respectively,� results�
that�are�even�higher�than�those��obtained�when�using�homogeneous�catalysis.�
4.�Conclusions��
136�
� The� process� in� column� was� also� tested� for� the� clinoptilolite� grain� size� 0.2�2mm�
obtaining�degradation�kinetics�and�COD�removal�from�a�solution�containing�acetic�acid�
and�phenol�similar�to�the�homogeneous�catalysis��
� No�significant�amounts�of�Fe�are�stripped�from�the�materials�at�the�employed�reaction�
conditions.�
Fe�loaded�materials�applied�to�arsenic�removal:�
� 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�
greater�Fe�content�than�zeolite�USY�or�the�Forager�sponge.�
� A�relation�between�the�adsorbed�arsenic�and�the�content�of�iron�can�be�observed�for�
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,�in�addition�to�the�arsenic�already�linked�to�Fe.��
� EXAFS�spectra� inferred�that� the�arsenate� is�complexed�with�Fe�as�a�bidentate�corner�
sharing�complex.��
With� this� thesis,� the� line�of�work�of�our� investigation�group�concerning�environmental�
problems�and�the�use�of�novel�techniques�to�its�characterization�is�broadened�by:�
� The�use�of�Geographic�Information�Systems�to�determine�spatial�variability�of�samples.�
� The�application�FP�XRF�and�EXAFS�techniques�to�the�characterization�of�soils�and�solid�
materials.�
� The�employment�of�Fe�loaded�materials�as�Fenton�catalysts.�
�
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�
�
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 Centre GTS. Chemistry Department, Universitat Autonoma de Barcelona, 08193 Spain. 5
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 Geology Department. Universitat Autonoma de Barcelona, 08193 Spain
*Corresponding author phone: +34-935812903; fax: +34-935811985; e-mail: [email protected]. 10
The present study represents a first insight into the Draa Sfar mine (Marrakech) to assess the possible diffusion of heavy metalsand 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 15
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 orewhile Cu behaviour corresponded to an anthropogenic origin. Additionally, mobility assays employing single leaching tests 20
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 The presence of heavy metals in soils originates 25
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 30
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 35
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 40
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 45
Fluorescence (FP-XRF) equipments have been applied given their reliable and rapid heavy metal measurement which
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 50
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 55
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 60
[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 65
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 70
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)
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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 5
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 10
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 15
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 20
(iv) the assessment of the pollutants mobility employing single leaching tests.
ExperimentalSite description
Draa Sfar mine is located a few hundred meters from the 25
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 30
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 35
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 40
due to its great resource of poly-metallic components (As, Cd, Cu, Fe, Pb, Zn).
Figure 1. Location of Draa Sfar mine.45
Table 1. Anthropogenic contribution at different CER values
CER Anthropogenic contribution <2 Minimal or nule 2-5 Moderate
5-20 Significant 20-40 Strong >40 Extreme
Sampling description
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 50
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 55
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 60
a 2 mm stainless steel sieve and stored in plastic bottles for their transportation to the laboratory.
Sample analysis and data treatment
The physical characterization consisted in the determination of the soil pH, the electrical conductivity (EC), the loss on 65
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 70
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 75
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 80
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 85
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. 90
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). 95
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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 5
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 10
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 15
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 20
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.
25
30
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 35
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 40
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 45
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]. 50
Results and discussion Soil properties
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. 55
(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 60
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 65
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 70
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 75
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 80
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 85
carbonate salts as a consequence of precipitation, the principal retention mechanism of heavy metals [35].
Heavy metals content
From the obtained results employing FP-XRF and the corresponding CER values, elements can be classified into 90
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. 95
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 100
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 105
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. 110
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.
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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 5
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 10
ppm, CER=45.9), D48 (2310 ppm, CER=130) and D58 (420
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 15
(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 20
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
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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 5
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 10
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), 15
D33 (15500 ppm), D45 (36800 ppm), D48 (113700 ppm), D58 (5300 ppm), D59 (14800 ppm) and D70 (32400 ppm).
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, 20
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 25
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 30
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
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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 5
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
adsorbed at the surface of the soil particles thus indicating and 10
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 15
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.
Mine area samples Background samples Min Max Mean Min Max Mean
Conc. 221,5 541 (D17) 389±70 404 530 449±60 Ba CER 0,4 1,4 0,9±0,3 0,9 1,2 1,0±0,1 Conc. 19,632 121,652 (D48) 32,721±10,000 30,769 38,106 35,555±3,000 Fe CER 0,4 4,8 0,9±0,5 0,9 1,1 1,0±0,1 Conc. 6,071 32,976 (D81) 23,327±5,000 26,770 30,263 38,244±1,500 K CER 0,3 1,4 0,8±0,3 0,9 1,1 1,0±0,1 Conc. 290 1,119 (D21) 607±150 631 737 699±50 Mn CER 0,4 2,0 0,9±0,3 0,9 1,0 1,0±0,1 Conc. 47 106 (D10) 76±13 73,4 86,4 81±6 Rb CER 0,5 1,7 1,0±0,3 0,9 1,1 1,0±0,1 Conc. 86 322 (D26) 144±40 119 140 131±9 Sr CER 0,5 3,1 1,1±0,5 0,9 1,0 1,0±0,1 Conc. 2,802 5,860 (D17) 4,460±700 4,431 5,741 5,229±600 Ti CER 0,4 1,3 0,9±0,2 0,9 1,1 1,0±0,1
Zr Conc. 112 335 (D67) 215±50 199 235 210±17 Conc is given in mg/kg. Max concentration is in parenthesis.
20
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 25
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. 30
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 35
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. 40
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.
45
Table 3. Results for < 2 mm and <100 μm particle size and fraction of
metal mobile.50
As Cu Pb Zn 100µm (mg/kg) 125 80 55 628 2mm (mg/kg) 167 72 66 713 D20
Mobility (mg/L) BDL BDL BDL BDL 100µm (mg/kg) 72 51 62 144 2mm (mg/kg) 67 48 61 150 D21
Mobility (mg/L) BDL BDL BDL BDL 100µm (mg/kg) 203 43 180 481 2mm (mg/kg) 268 77 313 734 D31
Mobility (mg/L) 49 2 6 18 100µm (mg/kg) 125 60 375 774 2mm (mg/kg) 172 59 477 933 D46
Mobility (mg/L) 54 1 17 23 100µm (mg/kg) 3,108 144 2,309 631 2mm (mg/kg) 3,569 167 2,614 704 D48
Mobility (mg/L) 5 1 BDL 4 100µm (mg/kg) 113 71 425 925 2mm (mg/kg) 149 77 537 1,087 D58
Mobility (mg/L) BDL BDL BDL BDL 100µm (mg/kg) 15 33 24 97 2mm (mg/kg) 15 50 20 91 D70
Mobility (mg/L) 29 BDL BDL BDL
55
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Conclusions 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 5
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 10
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 15
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 20
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.
25
Acknowledgements 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 30
suelos contaminados” and the pole of competences on Water and Environment (Morocco).
Notes†Electronic Supplementary Information (ESI) available.
References 35
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[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 40
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. 45
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. 50
[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 55
a contaminated soils: effect of redox potential and pH. Water, Air Soil Pollut. 1996; 90: 543-556.
ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT
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 LOI
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
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.
Sample As(mg/kg) CER Cu
(mg/kg) CER Pb(mg/kg) CER Zn
(mg/kg) CER Zr (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 As(mg/kg) CER Cu
(mg/kg) CER Pb(mg/kg) CER Zn
(mg/kg) CER Zr (mg/kg)
50 13.2 0.8 24.7 0.6 27.3 1.3 89.0 0.9 253.6 51 BDL - 29.7 0.7 28.9 1.4 78.0 0.8 245.6 52 18.8 1.2 28.4 0.7 30.1 1.4 86.5 0.8 254.8 53 15.4 0.8 24.8 0.5 20.5 0.8 75.8 0.6 315.2 54 12.6 0.7 24.0 0.5 23.4 1.0 80.9 0.7 273.4 55 17.5 1.2 27.9 0.7 25.4 1.3 83.4 0.9 237.5 56 11.7 0.8 BDL - 19.8 1.1 80.0 0.9 226.2 57 9.2 0.6 28.2 0.7 19.4 0.9 60.1 0.6 251.6 58 113.5 8.5 70.9 2.0 424.6 24.1 925.2 10.8 214.4 59 14.3 1.1 32.0 0.9 15.5 0.9 68.2 0.8 209.0 60 22.7 2.2 39.2 1.4 12.6 0.9 91.2 1.4 168.1 61 16.7 1.4 29.4 0.9 17.7 1.1 80.3 1.0 196.2 62 14.7 1.3 25.5 0.8 20.3 1.3 88.0 1.2 184.3 63 12.1 1.0 37.9 1.1 18.8 1.1 101.0 1.2 203.6 64 BDL - 24.4 0.7 18.1 1.0 76.4 0.9 221.8 65 10.1 0.7 28.4 0.7 15.3 0.8 68.4 0.7 237.2 66 8.9 0.5 30.3 0.7 13.5 0.6 58.8 0.6 261.0 67 17.5 0.8 17.3 0.3 40.2 1.5 143.6 1.1 334.5 68 10.6 0.6 22.4 0.5 8.4 0.4 46.2 0.4 273.5 69 14.2 1.0 20.3 0.5 61.2 3.2 254.0 2.7 231.2 70 15.2 1.5 33.0 1.2 23.9 1.8 96.9 1.5 164.5 71 17.9 1.7 36.2 1.3 14.4 1.1 86.6 1.3 167.1 72 15.4 1.5 31.8 1.2 20.3 1.5 91.2 1.4 162.8 73 14.1 1.2 23.4 0.8 14.3 0.9 74.7 1.0 186.6 74 13.2 1.1 37.1 1.2 18.9 1.2 92.1 1.2 187.9 75 18.6 1.4 37.3 1.1 17.4 1.0 88.8 1.1 208.7 76 13.8 1.2 30.6 1.0 13.4 0.9 87.5 1.1 190.9 77 12.5 1.1 38.4 1.2 16.5 1.1 82.2 1.1 185.9 78 17.5 1.4 23.3 0.7 28.5 1.7 110.0 1.3 207.7 79 14.9 1.2 26.3 0.8 34.8 2.2 101.9 1.3 192.6 80 18.8 1.4 37.9 1.1 29.6 1.7 121.7 1.4 215.6 81 19.9 1.8 33.4 1.1 31.8 2.2 102.5 1.5 174.7 82 11.3 0.9 26.7 0.8 15.5 0.9 73.8 0.9 200.6 83 9.4 0.8 31.5 1.0 16.8 1.0 87.7 1.1 198.8 84 18.9 1.3 34.0 0.9 20.6 1.1 84.9 0.9 235.3 85 12.7 1.0 44.7 1.3 16.6 1.0 81.6 1.0 204.3
ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT
Table S3. Concentration and CER values of lithogenic components of the mine area soils.
Sample [Ba] CER [Ca] CER [Fe] CER [K] CER [Mn] CER [Rb] CER [Sr] CER [Ti] CER1 258 0.4 27289 0.7 20967 0.4 16935 0.4 366 0.4 60.4 0.5 101 0.5 3396 0.4 2 281 0.6 22533 0.9 21316 0.6 15710 0.6 331 0.5 54.3 0.7 111 0.8 3069 0.6 3 221 0.4 19581 0.6 21932 0.5 16496 0.4 359 0.4 58.6 0.6 102 0.6 3533 0.5 4 362 0.8 22154 0.9 26064 0.7 20143 0.7 589 0.9 64.3 0.8 114 0.9 3677 0.7 5 357 0.5 30911 0.8 27227 0.5 21029 0.5 473 0.4 66.5 0.6 114 0.6 4441 0.6 6 357 0.6 28711 0.8 28338 0.6 21479 0.6 509 0.5 70.2 0.6 121 0.7 4563 0.6 7 269 0.5 29235 0.9 24544 0.5 17130 0.5 417 0.5 61.6 0.6 119 0.7 4080 0.6 8 391 0.6 34868 1.0 28881 0.6 21204 0.5 531 0.5 68.7 0.6 140 0.8 4446 0.6 9 309 0.5 30801 0.8 30383 0.6 22375 0.5 536 0.5 73.6 0.6 135 0.7 4730 0.6
10 524 1.3 28820 1.3 45017 1.4 31405 1.3 953 1.5 106.4 1.5 143 1.2 5731 1.2 11 351 0.7 26997 1.0 31924 0.8 24433 0.8 681 0.9 78.6 0.9 142 1.0 5275 0.9 12 390 0.9 32758 1.4 30029 0.9 25208 1.0 552 0.8 76.3 1.0 157 1.3 4855 1.0 13 285 0.5 19025 0.5 22565 0.5 20010 0.5 379 0.4 66.7 0.6 86 0.5 3589 0.5 14 351 0.6 27108 0.8 28183 0.6 22776 0.6 523 0.6 69.0 0.6 119 0.7 4606 0.7 15 395 0.6 29798 0.8 33504 0.7 26127 0.7 622 0.6 82.2 0.7 127 0.7 4865 0.7 16 374 0.8 26120 0.9 34044 0.9 25434 0.8 646 0.8 87.0 1.0 128 0.9 4799 0.8 17 541 1.4 30486 1.3 42132 1.3 30390 1.2 873 1.4 100.9 1.4 137 1.2 5860 1.3 18 425 1.3 22717 1.1 30945 1.1 25338 1.2 560 1.0 71.0 1.2 126 1.3 5105 1.3 19 314 0.5 29023 0.8 27468 0.5 20836 0.5 477 0.5 67.1 0.6 122 0.7 4933 0.7 20 332 0.8 26896 1.2 31008 1.0 24356 1.0 816 1.3 82.4 1.2 163 1.4 4714 1.0 21 504 1.4 32042 1.6 48007 1.7 26530 1.2 1119 2.0 102.8 1.6 168 1.6 5277 1.3 22 268 1.1 23227 1.7 19632 1.0 18323 1.2 290 0.8 56.3 1.3 114 1.6 2802 1.0 23 277 0.5 26142 0.8 26535 0.6 18592 0.5 547 0.6 65.4 0.7 119 0.7 4482 0.7 24 268 0.6 24012 1.0 22180 0.7 17151 0.7 436 0.7 56.3 0.8 148 1.2 3251 0.7 25 282 0.8 26180 1.3 22429 0.8 17945 0.8 424 0.8 54.5 0.9 102 1.0 3351 0.8 26 254 0.7 152754 7.4 21726 0.8 6071 0.3 327 0.6 54.6 0.9 322 3.1 2913 0.7 27 398 1.0 80860 3.4 31861 1.0 20968 0.8 553 0.9 74.0 1.0 250 2.1 3787 0.8 28 489 1.3 57198 2.7 37649 1.3 28488 1.2 577 1.0 96.0 1.5 201 1.9 4614 1.1 29 260 0.7 26920 1.3 24892 0.9 19394 0.9 533 1.0 63.3 1.0 156 1.5 3712 0.9 30 358 1.0 24843 1.2 25017 0.9 18161 0.8 525 1.0 57.9 0.9 213 2.1 3382 0.8 31 400 1.1 31011 1.5 28026 1.0 15913 0.7 542 0.9 62.5 1.0 184 1.7 3211 0.8 32 447 0.8 26868 0.8 28776 0.6 21636 0.6 506 0.5 65.8 0.6 125 0.7 4979 0.7 33 471 1.3 35369 1.6 36671 1.2 27816 1.2 824 1.4 89.4 1.3 181 1.6 4770 1.1 34 417 1.0 35113 1.5 35246 1.1 26579 1.0 601 0.9 80.8 1.1 167 1.4 4543 0.9 35 444 1.2 30086 1.4 36326 1.2 28645 1.2 751 1.3 91.8 1.4 139 1.3 4839 1.1 36 478 1.2 31344 1.4 38300 1.2 29693 1.2 795 1.3 97.3 1.4 149 1.3 4924 1.1 37 331 0.9 38313 1.7 34242 1.1 26694 1.1 655 1.1 84.9 1.3 141 1.3 5110 1.2 38 419 1.2 31113 1.5 35236 1.2 30085 1.4 640 1.2 87.8 1.4 146 1.4 5001 1.2 39 460 1.4 47652 2.5 43267 1.6 29429 1.4 897 1.7 98.4 1.7 166 1.7 4876 1.3 40 464 1.1 28833 1.1 37955 1.1 27871 1.0 739 1.1 89.3 1.2 142 1.1 4950 1.0 41 403 1.0 50142 2.1 35597 1.1 24743 1.0 656 1.0 84.6 1.2 193 1.6 4939 1.0 42 391 1.0 45098 1.9 30991 0.9 23222 0.9 671 1.0 75.4 1.0 171 1.4 4097 0.9 43 483 1.1 43702 1.7 37225 1.0 28315 1.0 840 1.2 87.7 1.1 172 1.3 4557 0.9 44 501 1.2 71367 2.9 31805 0.9 19598 0.7 586 0.9 77.6 1.0 224 1.8 3458 0.7 45 370 0.8 45868 1.8 50814 1.4 16688 0.6 882 1.3 70.0 0.9 187 1.4 4497 0.9 46 456 1.0 41987 1.5 36602 1.0 22309 0.7 768 1.0 78.8 0.9 172 1.2 4588 0.8 47 303 0.6 30427 1.1 23885 0.6 17281 0.6 447 0.6 58.7 0.7 133 0.9 3688 0.7 48 336 1.1 24281 1.3 121652 4.8 BDL 564 1.1 47.0 0.8 132 1.4 3471 0.9 49 382 0.8 18375 0.7 30573 0.8 24754 0.9 488 0.7 67.8 0.8 99 0.7 4809 0.9
ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT
50 478 0.9 9961 0.3 36278 0.8 25687 0.7 549 0.6 79.5 0.8 93 0.6 4715 0.7 51 375 0.7 21304 0.7 30194 0.7 23103 0.7 524 0.6 67.4 0.7 103 0.7 4870 0.8 52 376 0.7 25823 0.8 30123 0.7 21614 0.6 503 0.6 67.5 0.7 108 0.7 5259 0.8 53 431 0.6 30841 0.8 29506 0.5 20669 0.5 502 0.5 67.2 0.6 108 0.5 4919 0.6 54 386 0.7 30322 0.9 29334 0.6 20068 0.5 470 0.5 66.4 0.6 117 0.7 5190 0.8 55 323 0.6 54544 1.8 27301 0.7 16463 0.5 465 0.6 65.0 0.7 138 0.9 5222 0.9 56 430 0.9 31668 1.1 29771 0.8 23137 0.7 495 0.6 70.9 0.8 108 0.8 4377 0.8 57 439 0.8 21703 0.7 30631 0.7 22118 0.6 493 0.6 70.1 0.7 92 0.6 4685 0.7 58 423 0.9 21702 0.8 40552 1.1 24804 0.8 658 0.9 74.9 0.9 103 0.8 4722 0.9 59 398 0.9 34736 1.3 31449 0.9 22958 0.8 692 1.0 80.7 1.0 229 1.7 4863 0.9 60 473 1.3 28184 1.3 38171 1.3 31414 1.4 660 1.2 96.5 1.5 154 1.4 4929 1.2 61 405 1.0 33259 1.3 34537 1.0 27695 1.0 651 1.0 82.8 1.1 148 1.2 5285 1.1 62 411 1.0 34232 1.5 33698 1.0 26177 1.0 753 1.2 80.8 1.1 148 1.3 4546 1.0 63 450 1.0 27892 1.1 35943 1.0 27599 1.0 702 1.0 85.4 1.1 142 1.1 4933 1.0 64 458 1.0 34855 1.2 30434 0.8 24809 0.8 547 0.7 72.2 0.8 139 1.0 4288 0.8 65 430 0.8 39220 1.3 27891 0.7 23153 0.7 525 0.6 71.6 0.8 147 1.0 3964 0.7 66 434 0.8 19555 0.6 28723 0.6 28953 0.8 562 0.6 76.3 0.8 113 0.7 3673 0.6 67 418 0.6 17613 0.4 29975 0.5 23603 0.5 496 0.4 70.4 0.5 101 0.5 4757 0.6 68 382 0.6 25783 0.7 28280 0.6 22584 0.6 497 0.5 66.7 0.6 120 0.7 4560 0.7 69 330 0.7 19160 0.7 24598 0.6 19955 0.6 439 0.6 66.5 0.7 107 0.7 3465 0.6 70 453 1.3 73693 3.6 34977 1.2 25748 1.1 839 1.5 98.5 1.6 179 1.7 3948 1.0 71 375 1.0 78690 3.7 32739 1.1 20540 0.9 747 1.3 80.9 1.3 190 1.8 3823 0.9 72 473 1.3 61498 3.0 38035 1.3 26637 1.2 689 1.2 92.0 1.5 179 1.7 4257 1.0 73 351 0.9 46338 2.0 28371 0.9 20922 0.8 611 1.0 70.4 1.0 156 1.3 3630 0.8 74 391 1.0 80724 3.4 32023 1.0 21234 0.8 802 1.3 78.2 1.1 171 1.4 4722 1.0 75 346 0.8 29240 1.1 30191 0.8 23764 0.8 615 0.9 77.8 1.0 138 1.0 4660 0.9 76 454 1.1 27167 1.1 33211 1.0 27808 1.1 675 1.0 80.9 1.1 133 1.1 5070 1.1 77 447 1.1 27857 1.2 37287 1.1 27383 1.1 781 1.2 88.7 1.2 143 1.2 5152 1.1 78 343 0.8 46893 1.8 31315 0.9 24632 0.9 628 0.9 74.3 0.9 158 1.2 4283 0.8 79 365 0.9 27641 1.1 32822 1.0 27028 1.0 664 1.0 83.1 1.1 142 1.2 5042 1.0 80 456 1.0 20194 0.7 36070 1.0 29217 1.0 708 1.0 90.3 1.1 128 0.9 4662 0.9 81 463 1.2 19429 0.9 37833 1.2 32976 1.4 688 1.2 92.1 1.4 121 1.1 5018 1.1 82 423 1.0 22577 0.9 30769 0.9 26770 1.0 631 0.9 73.4 0.9 119 0.9 4431 0.9 83 404 0.9 25740 1.0 35658 1.0 28047 1.0 697 1.0 78.8 1.0 130 1.0 5143 1.0 84 440 0.9 28436 1.0 37686 0.9 27896 0.9 730 0.9 86.4 1.0 140 0.9 5741 1.0 85 530 1.2 24581 1.0 38106 1.1 30263 1.1 737 1.1 84.9 1.1 134 1.0 5599 1.1
�
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.�Garcia�Noguero,�Beatriz�Guerrero,�Xavier�Gaona,�
Rodrigo�Alvarez,�Gustavo�Perez�Gonzalez,�Manuel�Valiente,�Pablo�Higueras,�Milena�Horvat�and�Jorge�Loredo.�
Journal�of�Synchrotron�Radiation��Journal�of�Synchrotron�Radiation.�(2010)�17,�2:�179�186.�
�
soil and geosciences
J. Synchrotron Rad. (2010). 17, 179–186 doi:10.1107/S0909049510001925 179
Journal of
SynchrotronRadiation
ISSN 0909-0495
Received 22 June 2009
Accepted 15 January 2010
# 2010 International Union of Crystallography
Printed in Singapore – all rights reserved
XANES speciation of mercury in threemining districts – Almaden, Asturias (Spain),Idria (Slovenia)
Jose Maria Esbrı,a* Anna Bernaus,b Marta Avila,b David Kocman,c
Eva M. Garcıa-Noguero,a Beatriz Guerrero,b Xavier Gaona,b Rodrigo Alvarez,d
Gustavo Perez-Gonzalez,b Manuel Valiente,b Pablo Higueras,a Milena Horvatc and
Jorge Loredod
aDepartamento de Ingenierıa Geologica y Minera, Escuela Universitaria Politecnica de Almaden,
Universidad de Castilla-La Mancha, 13400 Almaden (Ciudad Real), Spain, bGrup de Tecniques de
Separacio en Quımica (GTS), Departament de Quımica, Universitat Autonoma de Barcelona,
08193 Bellaterra (Barcelona), Spain, cDepartment of Environmental Sciences, Jozef Stefan Institute,
Ljubljana SI-1001, Slovenia, and dDepartamento de Explotacion y Prospeccion de Minas,
Universidad de Oviedo, Oviedo 33004, Spain. E-mail: [email protected]
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: Almaden
(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.
Keywords: mercury speciation; XANES; Almaden; Idria; Asturias; bioavailability.
1. Introduction
Assessing the distribution and mobilization of heavy metals in
the environment as a result of natural processes or anthro-
pogenic 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 (Forstner, 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;
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 techni-
ques 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 tech-
niques have been shown to provide reliable information on
the speciation of mercury without requiring sample pre-
treatment (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 wet-
chemistry 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
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 environ-
mental 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 Almaden 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,
Almaden 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 para-
genesis. It is important to highlight that Almaden 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 produc-
tion (Hernandez et al., 1999; Saupe, 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 Almaden 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 (Garcıa 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 Almaden, 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
appearing in the paragenesis are metacinnabar, pyrite,
marcasite, dolomite, calcite, kaolinite, epsomite and melan-
terite.
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
soil and geosciences
180 Jose Maria Esbrı et al. � XANES speciation of mercury J. Synchrotron Rad. (2010). 17, 179–186
Figure 1Sampling locations, mines and metallurgical sites of the three mercurymining districts, Almaden, Asturias and Idria. Abbreviations: ALM:Almadenejos decommissioned metallurgical plant; RD: Valdeazoguesriver downstream; El Entredicho: decommissioned open pit; AZG:Azogado stream; CH: dump of Almaden mine; HR: Huerta del Rey;SQ: San Quintın (real location: 50 km to the east of Almaden); TRR:El Terronal mine. (See Table 1 for more details.)
remnants of carbonate and clastic rock weathering products in
the Idrijca catchment (Kanduc 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 & Gutierrez, 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 Almaden were taken at a depth of 0–20 cm, stored
in polyethylene bags and sieved at the Almaden 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, homo-
genized and ground before analysis.
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 poly-
propylene containers.
The samples from Asturias area were collected in the La
Pena-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 poly-
ethylene 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 cm�2 of pressure.
2.3. Chemical characterization
Total mercury content of all solid samples was determined
by Zeeman atomic absorption spectrometry using high-
frequency 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
soil and geosciences
J. Synchrotron Rad. (2010). 17, 179–186 Jose Maria Esbrı et al. � XANES speciation of mercury 181
Table 1Samples collected at the three mining districts.
Location ID Sampling area Material
Almaden siteAlmaden HR Huerta del Rey metallurgical precinct Soils from old metallurgical plant of the 17th century
CH Main dump of Almaden mine Dump material, sediments and riparian soilsAZG Azogado stream Riparian soils and stream sediments
Almadenejos ALM Decommissioned metallurgical plant Soils from the metallurgical precinctValdeazogues river RD Downstream of El Entredicho pit Suspended particlesSan Quintın SQ Decommissioned Pb–Zn–Ag mine Mine wastes and soils from an old flotation plant tested for
cinnabar treatment
Idria siteSoils S1–S3 Vicinity of the metallurgical plant Soils
S2 Pront Hill Meadow soilsS4 Idrijca merges with the river Baca Alluvial soil samples collected along the river Idrijca 40 km
downstream from the mineS5–S6 Alluvial plain confluence of Idrijca and Baca rivers Soils from a deep profile at depth 50 cm (S5) and 100 cm (S6)
Sediments RS Idrijca river, 35 km downstream from the mine beforeBaca river inflow
River bed sediments of a composite sample taken within adistance of 50 m with grain size <0.063 mm (RS1) and0.063–2 mm (RS2)
SS Idrijca river, 35 km downstream from the mine beforeBaca river inflow
Suspended river sediments of a composite sample takenwithin a distance of 50 m with grain size <0.063 mm (SS1)and 0.063–2 mm (SS2)
Asturias siteMine tailings TRRmn Mine and metallurgical plant Dumps in the vicinity of rotary furnacesCalcines TRRc Mine and metallurgical plant Calcination wasteSoil TRRs Metallurgical plant Soil from an abandoned chimney channelForest soils TRRfs El Terronal mine Forest soils from the mining area
samples is 0.5 mg Hg kg�1. 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 measure-
ments 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), Hg2NCl0.5(SO4)0.3(MoO4)0.1(CO3)0.1�H2O
(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; Kanduc 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 1
N � P
XN
i¼1
�obsi � �fit
i
� �2; 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.
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 MHCl 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 (Ther-
moElemental ICP-OES, model Intrepid II XLS, Franklyn,
MA, USA).
3. Results and discussion
Total mercury content (Table 2) in the Almaden district shows
high Hg concentrations in soil samples from metallurgical
sites, which can be mainly attributed to the inefficient metal-
lurgical 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 g�1). 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 Quintın 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 envir-
onment 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 g�1 in dump samples (TRRmn-
116) and 18000 mg g�1 in soils from the chimney channel, with
high amounts of arsenic content (from 735 mg g�1 to
187218 mg g�1).
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
soil and geosciences
182 Jose Maria Esbrı et al. � XANES speciation of mercury J. Synchrotron Rad. (2010). 17, 179–186
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 Almaden 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 Alma-
denejos area and Huerta del Rey (Almaden area). Non-sulfide
phases like schuetteite [Hg3(SO4)O2], calomel (Hg2Cl2) and
mercuric chloride (HgCl2) are present in different ratios in soil
and sediment samples.
XANES analyses in the samples from San Quintın area (see
Table 3) have shown the absence of metacinnabar but high
amounts of cinnabar (47–59%) and minor amounts of rela-
tively more soluble species like calomel (24–33%) and
schuetteite (17–21%) which can be attributed to weathering
processes. The absence of metacinnabar, a metastable poly-
morph 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 metallur-
gical activity.
Other non-sulfide phases like mercurous chloride (24–43%)
have also been identified at San Quintın and Huerta del Rey,
and can be attributed to the process of soil formation. High
soil and geosciences
J. Synchrotron Rad. (2010). 17, 179–186 Jose Maria Esbrı et al. � XANES speciation of mercury 183
Table 2Average heavy metals content in samples from the three mining districts(in mg g�1).
Mercury was analyzed by ZAAS-HFM and As, Zn and Pb by XRF. BDL: databelow detection limits. � = grain size.
Sample Material Hg As Pb Zn
AlmadenCH-127 Dump 989 BDL BDL 112HR-108 Soil 976 BDL 214 96HR-109 Soil 404 BDL 111 104HR-110 Soil 200 BDL 130 185RD-124 Suspended
particles105 BDL BDL BDL
CH-125 Sediment 1800 BDL BDL 112AZG-105 Riparian soils 2816 23 139 233CH-128 Riparian soils 450 BDL 102 185ALM-101 Soil 2720 BDL 74 153ALM-102 Soil 2629 BDL 102 193CH-126 Soil 2230 BDL BDL 365SQ-111 Dump 902 BDL 15837 6877SQ-112 Dump 1730 BDL 2154 1221SQ-113 Soil 1935 BDL 19049 7134SQ-114 Soil 390
AsturiasTRRmn-115 Dump 1470 39338 BDL BDLTRRmn-116 Dump 27350 117553 BDL BDLTRRs-118 Chimney soil 3280 735 BDL BDLTRRs-121 Chimney soil 18000 12133 BDL BDLTRRmn-122 Dump 5785 42300 BDL BDLTRRfs-3 Soil 1570 16826 107 173TRRfs-4 Soil 1080 1120 53 137TRRc-5 Calcined 34 187218 BDL BDLTRRc-55 Calcined 54 25876 BDL BDL
IdriaS-1 Soil 333 21 BDL 112S-2 Meadow Soil 47 26 BDL 102S-4 Alluvial soil 76 BDL BDL 64S-5 Soil
(50 cm depth)175 BDL 47 145
S-6 Soil(100 cm depth)
144 BDL 73 496
RS-1 Sediment� < 63 mm
6540 BDL 302 270
RS-2 Sediment� < 2 mm
1920 BDL 14 BDL
SS-1 Suspendedparticles� < 63 mm
96 BDL BDL 449
SS-2 Suspendedparticles� <2 mm
11 BDL BDL 24
S-3 Soil 95 27 46 130Hg ore Ore
Figure 2XANES spectra of selected Hg pure compounds and samples fromAlmaden, Idria and Asturias mining districts (all spectra are deliberatelydisplaced to show differences). Each spectrum corresponds to the meanvalue of five replicates.
amounts of schuetteite have been identified in ore stockpile in
San Quintın 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 Valdeazo-
gues 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 L�1; 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,
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 L�1, 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, corder-
oite (Co), HgCl2, HgO, HgSO4]. All samples from the
decommissioned mine and metallurgical facility show high
mercury contents in soils (TRRfs), dump materials (TRRmn)
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184 Jose Maria Esbrı et al. � XANES speciation of mercury J. Synchrotron Rad. (2010). 17, 179–186
Table 3Main mercury species (in %) and mobile mercury (in mg L�1 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 L�1) (%)
AlmadenCH-127 62 0 0 0 0 0 38 0 0.0004 1.4 (3.2)HR-108 37 23 0 0 0 0 40 0 0.0006 0.6 (1.2)HR-109 33 24 0 0 0 0 43 0 0.0007 0.2 (1)HR-110 41 22 0 0 0 0 37 0 0.0006 BDLRD-124 0 0 94 0 0 0 0 6 0.0006 BDLCH-125 7 0 83 0 0 0 0 10 0.0004 BDLAZG-105 0 0 80 0 0 0 20 0 0.0003 BDLCH-128 24 22 0 0 0 0 35 19 0.0004 BDLALM-101 38 39 23 0 0 0 0 0 0.0003 10.8 (7.9)ALM-102 39 31 0 0 0 0 30 0 0.0007 21.3 (16.2)CH-126 33 32 35 0 0 0 0 0 0.0003 BDLSQ-111 54 0 17 0 0 0 29 0 0.0002 0.6 (1.3)SQ-112 51 0 21 0 0 0 28 0 0.0002 3.7 (4.3)SQ-113 59 0 17 0 0 0 24 0 0.0002 BDLSQ-114 47 0 20 0 0 0 33 0 0.0003 BDL
AsturiasTRRmn-115 29 24 0 0 0 0 0 47 0.001 0.4 (0.5)TRRmn-116 28 22 0 0 0 0 0 50 0.0009 73.3 (5.4)TRRs-118 28 22 0 0 0 0 0 50 0.0008 20.1 (12.3)TRRs-121 29 22 0 0 0 0 0 49 0.0007 56.5 (6.3)TRRmn-122 30 24 0 0 0 0 0 46 0.0007 43.6 (15.1)TRRfs-3 44 28 0 0 10 18 0 0 0.003 0.7 (0.9)TRRfs-4 50 36 0 14 0 0 0 0 0.003 0.1 (0.2)TRRc-5 52 30 0 18 0 0 0 0 0.008 BDLTRRc-55 57 43 0 0 0 0 0 0 0.007 BDL
IdriaS-1 44 0 32 0 0 24 0 0 0.006 BDLS-2 55 0 0 0 0 45 0 0 0.002 0.2 (8.5)S-4 85 15 0 0 0 0 0 0 0.004 BDLS-5 90 0 0 0 10 0 0 0 0.004 BDLS-6 58 0 0 0 0 42 0 0 0.005 BDLRS-1 57 0 0 0 0 43 0 0 0.002 BDLRS-2 100 0 0 0 0 0 0 0 0.003 BDLSS-1 90 0 0 0 0 10 0 0 0.004 BDLSS-2 55 0 0 0 0 45 0 0 0.009 BDLS-3 66 0 26 0 8 0 0 0 0.007 0.3 (6.3)Hg ore 100 0 0 0 0 0 0 0
† Determined according to the method of Perez et al. (2008).
and chimney soils (TRRs) (Table 2), and a predominance of
sulfides species (50–100%) with significant presence of meta-
cinnabar in all samples (Table 3). Ratios between cinnabar and
metacinnabar in these samples are lower than in Almaden
area, where metallurgical activity was not the predominant
activity. In this mining site, metallurgy was less efficient than in
Idria and Almaden 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 Almaden. 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 Almaden
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 Almaden area), together with
the appropriate management of calcines used for refilling old
galleries and the shorter mining history of the district.
4. Conclusions
This work represents the first inter-regional study of mercury
speciation of the two main European Hg-mining districts
(Almaden 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.
Synchrotron experiments at HASYLAB were financially
supported by the European Community, Research Infra-
structure Action under the FP6 ‘Structuring the European
Research Area’ Programme (through the Integrated Infra-
structure Initiative ‘Integrating Activity on Synchrotron and
Free Electron Laser Science’). Financial contribution from the
projects PPQ2003-01902, CTQ2005-09430-C05 and CTM2006-
13091-C02-02/TECNO funded by the Spanish Ministry of
Science and Innovation is also acknowledged.
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�
III�
EXTRACTANT�AND�SOLVENT�SELECTION�TO�RECOVER�ZINC
Marta�Avila,�Gustavo�Perez�and�Manuel�Valiente�
Solvent�Extraction�and�Ion�Exchange�(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
Extractant and Solvent Selection to Recover Zincfrom 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 extrac-
tants. 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 signifi-
cantly. 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 dam-
aging both environmentally and economically. In a currently abandoned
mine in Andalusia, in the south of Spain, a huge stream of effluent con-
taining 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: [email protected]
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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 cast-
ing 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 economi-
cal 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, electro-
chemical recovery, membrane separation, and solvent extraction (SX).[2]
In this regard, SX has been widely proposed as some of the most econom-
ical 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 re-
extraction/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 strip-
ping 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 func-
tion 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 met-
als. 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 [(C8H17)2P(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 precipi-
tation 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 pur-
chased 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 sul-
furic 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 con-
tent 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 H2SO4 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 H2SO4
2.0 M. DEHPA concentration was higher due to efficiency related to the
extraction yield and the extractant cost. No centrifugation of the dual-
phase system was required because of the clear-phase separation obtained.
Selectivity of the solvents towards Zn was determined by the correspond-
ing 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 consider-
ing 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)).
%Recovery =(
Znstrip
ZnPLS
)×100 (1)
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388 M. Avila et al.
%Remaining R =(
Znraffinate
ZnPLS
)×100 (2)
%Remaining OP = 100 − %Recovery − %Remaining R (3)
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 H2SO4
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 met-
als 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 pro-
cess. 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 differ-
ences 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
[Zn] [Ca] [Cu] [Fe] [Al] [Mn]
pH (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)
4.3±0.1 881 ± 50 580 ± 20 45 ± 7 1.2 ± 0.9 210 ± 10 195 ± 10
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Zinc Recovery from a Mining Effluent 389
0
20
40
60
80
100
0 5 10 15 20 25 30 35 40 45 50 55 60
% R
ecov
ery
Time (min)
Extractant kinetics
DEHPA 40% KD80 DEHPA 40% KD100CYANEX 272 5% KD80 CYANEX 272 5% KD100IONQUEST 5% KD80 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 indi-
cating 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 dimin-
ishing 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 affin-
ity for calcium than phosphinic extractants. In addition, the differences in
trends between Cyanex 272 and Ionquest 290 are relatively very small and
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Zinc Recovery from a Mining Effluent 391
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(a) Recovery DEHPA
Zn KD80 Zn KD100Ca KD80 Ca KD100Al D80 Al D100Mn KD80 Mn KD100Cu KD80 Cu KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(b) Remaining OP DEHPA
Zn KD80 Zn KD100Ca KD80 Ca KD100Al D80 Al D100Mn KD80 Mn KD100Cu KD80 Cu KD100
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.
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392 M. Avila et al.
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(a) Recovery Cyanex 272
Zn KD80 Zn KD100Ca KD80 Ca KD100Al KD80 Al KD100Mn KD80 Mn KD100Cu KD80 Cu KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(b) Remaining OP Cyanex
Zn KD80 Zn KD100Ca KD80 Ca KD100Al KD80 Al KD100Mn KD80 Mn KD100Cu KD80 Cu KD100
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
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Zinc Recovery from a Mining Effluent 393
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(a) Recovery IONQUEST
Zn KD80 Zn KD100Ca KD80 Ca KD100Al D80 Al D100Mn KD80 Mn KD100Cu KD80 Cu KD100
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
(b) Remaining OP Ionquest
Zn KD80 Zn KD100Ca KD80 Ca KD100Al D80 Al D100Mn KD80 Mn KD100Cu KD80 Cu KD100
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 pro-
cess. 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
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394 M. Avila et al.
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 fea-
sible for this application. Thus, from an engineering point of view, the use
of Ketrul D100 is recommended due to their lower flammability.
Effect of the Extractant Concentration
Because Cyanex 272 and Ionquest 290 are five to seven times more expen-
sive 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 recov-
ery 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.
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10
%
Ratio A/O
Effect of Ionquest 290 concentration
Zn 5% KD80 Zn 5% KD100Zn 10% KD80 Zn 10% KD100Zn 20% KD80 Zn 20% KD100DEHPA 40% KD80 DEHPA 40% KD100
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).
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Zinc Recovery from a Mining Effluent 395
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, avoid-
ing 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 extrac-
tion, 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 sup-
porting the laboratory expenses (Project CTQ2009-07432 (Subprograma
PPQ)).
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�
IV�
ZINC�RECOVERY�FROM�AN�EFFLUENT�USING�IONQUEST�290:�FROM�
LABORATORY�SCALE�TO�PILOT�PLANT
M.�Avila,�B.�Grinbaum,�F.�Carranza,�A.�Mazuelos,�R.�Romero,�N.�Iglesias,�J.L.�Lozano,�G.�Perez,�M.�Valiente.��
Hydrometallurgy�(2011),�107:�63�67�
�
Zinc recovery from an effluent using Ionquest 290: From laboratory scale topilot 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, Spainb Bateman Litwin N.V. POB 15, Yokneam 20692, Israelc Universidad de Sevilla, Depto. de Ingeniería Química, 41092 Sevilla, Spaind EGMASA, Isla de la Cartuja, 41092 Sevilla, Spain
a b s t r a c ta r t i c l e i n f o
Article history:Received 28 September 2010Received in revised form 17 January 2011Accepted 17 January 2011Available online 17 February 2011
Keywords:Mine tailing pondZinc extractionFe bioxidation removalBateman Pulsed ColumnIonquest 290
A stream of effluent from a mine tailings pond, containing zinc, ferrous ions and other metals, requiredtreatment to prevent pollution and recover valuable metals. A solvent extraction (SX) process using Ionquest290 as extractant was developed to recover the Zn from the effluent. Ferrous ions were bio-oxidized andremoved by selective alkaline precipitation prior to the zinc extraction. The Fe removal as well as the SXprocess were developed successfully at laboratory scale and verified in a pilot plant on-site, using twoBateman Pulsed Columns for the extraction and stripping of Zn. The results were satisfactory obtaining above95% recovery of the Zn.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
An abandoned mine in Andalusia, Spain, has a huge stream ofeffluent, estimated to be 10,000 m3/day, flowing into a tailings pond.Since the effluent contains about 1 g/L of Zn and significant amountsof ferrous, ferric, calcium, copper, aluminum and manganese ions,their removal is required in order to prevent pollution of a naturereserve downstream from the area. The recycling of such effluentsrather than simple neutralisation and disposal as a hazardous wastecan provide an economical benefit, while diminishing the volume ofhazardous materials contained in the mine tailing. Zinc metal has ahigh economical value and recycling can add economical value tothose residues.
Conventional methods for separation of pure Zn include precipitation,ion exchange, adsorption, electrochemical recovery, membrane separa-tion and solvent extraction (SX) (Sayilgan et al., 2009) with SX being themost economical and practical process to extract Zn from industrialwaters (Devi et al., 1997; Jha et al., 2002; Salgado et al., 2003). In recentyears SX has become essential to the hydrometallurgical industry due to agrowing demand for high purity metals, rigid environmental regulations,the need for lower production costs, as well as due to the diminishingproduction in high-grade ore reserves (Alamdari, 2004; Owusu, 1998). In
this sense, the organophosphorus acids, i.e., DEHPA (Ritcey and Lucas,1971) and Cyanex272 (Lanagan and Ibana, 2003) and their thiosub-stituted 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 organophosphorusextractant, Ionquest 290, has been employed for the selectiverecovery 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 ofinactive impurities, the phosphine oxide impurity is b5% in Ionquest290 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 (Lupiand Pilone, 2000; Yokoyam et al., 1996) so it needs to be removedprior to SX process. For this purpose, a process based on the bio-oxidation of Fe2+, using specific bacteria, followed by selectivealkaline precipitation of Fe3+, was required (Mazuelos et al., 1999,2010a).
EGMASA, the regional environmental government company inAndalusia (Spain), suggested the recovery of Zn from the mentionedeffluent by combining solvent extraction (SX) and electrowinning(EW) process. Therefore, this process should be environmentallyfriendly and also to produce an economically effective output. At least95% of the Zn must be recovered from the effluents in order to satisfythe environmental requirements and the SX plant should provide afinal product stream of 90 g/L Zn in the stripping step (strong
Hydrometallurgy 107 (2011) 63–67
⁎ Corresponding author. Tel.: +34 935812903; fax: +34 935811985.E-mail address: [email protected] (M. Valiente).
0304-386X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.hydromet.2011.01.009
Contents lists available at ScienceDirect
Hydrometallurgy
j ourna l homepage: www.e lsev ie r.com/ locate /hydromet
electrolyte) by using a spent electrolyte with 50 g/L Zn, to fulfill theoperating conditions of the EW plant. The whole bio-oxidation and Feprecipitation, as well as the SX process were developed successfully atlaboratory scale and afterwards verified in a pilot plant on-site.
2. Materials and methods
2.1. Iron removal
Laboratory testswere aimedatdetermining theoperatingconditionsfor the pilot plant. To reach the target concentration of b5 ppm it isnecessary to completely convert ferrous to ferric ions in the bio-oxidation stage.
Bio-oxidation laboratory tests were performed in a methacrylatecolumn packed with siliceous stone particles and inoculated with amixed culture of Acidithiobacillus ferrooxidans, Leptospirillum ferrooxidansand some heterotrophic bacteria including Acidiphillium organovorum,facilis and cryptum (Mazuelos et al., 2010b) The inoculum was obtainedfrom the Riotinto Mine acid mine drainage waters. The culture wasroutinely maintained on a modified Silverman and Lungren 9 K nutrientmedium at pH 1.25 (adjusted with H2SO4) in the University of Sevillelaboratories. The effluent solution was fed into the bottom of the columnand overflowedwhile air was supplied under pressure (0.5 bar) from thebottom. Precipitation tests were carried out in stirred reactors with pH-controlled addition of alkaline reagent. The pH and pumping ratesdetermined the length of tests, avoiding a rapid increase in pH thatwould lead to undesirable co-precipitation phenomena. Ferrous concen-tration 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 stainlesssteel column divided to three zones: a 30 cm deep bottom spacewhere air and solution were fed in, a siliceous stone packed bedcontaining the inoculum supported by a stainless steel screen and anair space at the top where pH and Eh control was made, a 50 mm pipeformed the solution overflow. The effluent circulated through a tankwhere pHwas initially adjusted and, after pH adjustment, the solutionwas transferred to the bioreactor where bio-oxidation of ferrous ionstook place, to be finally transferred to a precipitation tank fed by a
lime solution from a separated tank. The bioreactor operatedcontinuously with a residence time of 60 min and connected to thepilot plant in order to bio-oxidize a total volume of 56 m3 of minewater. After precipitation and sedimentation of iron compounds, thesupernatant was directly used as feed solution to the solventextraction stage (Fig. 1).
2.2. Solvent extraction
For the laboratory investigation of the solvent extraction process, asolution of 5% Ionquest 290 dissolved in kerosene was used. Ionquest290 (PurityN95%) was supplied by Rhodia UK Ltd. and commercialgrade extra-pure aliphatic kerosene Ketrul D100 (bp 100 °C) by TotalFluides France. All reagents were used as received without furtherpurification. A solution of Na2CO3 was used for pH adjustment duringthe solvent extraction experiments. For the stripping step, the loadedsolvent was contacted with 2 M H2SO4 at a phase ratio O:A=10, theinitially expected phase ratio in the plant to achieve the required zinctransfer in the EW plant of 20 g/L. In practice, it was found that therequired transfer in the EW plant was 40 g/L Zn, consequently, thephase ratio was modified to O:A=20. No laboratory tests wereundertaken at this phase ratio, but directly applied in the pilot plant.
Two Bateman Pulsed Columns (BPC) were required for the SX andstripping processes at the pilot plant due to their demonstratedfeasibility in several SX plants (Ferreira et al., 2010; Gameiro et al.,2010; Sole et al., 2005). BPC are large diameter vertical pipes filledalternately with disk and doughnut shaped baffles to promote contactbetween the organic and aqueous phases through the column. Adecanter at each end of the column allows the liquids to coalesce andbe decanted separately. When the solvent phase is continuous, theinterface between the phases is in the lower decanter and when theaqueous phase is continuous, it is in the upper decanter. The columnsare pulsed by blowing air at the required amplitude and frequency ofthe pulses (Ritcey, 2006).
An 80 mm diameter BPC, 7 m high (equivalent to 3 theoreticalmixer-settler stages) was chosen for the SX process and a 40 mmdiameter BPC 6 meter high for the stripping. The piping of the plant isshown in Fig. 1.
BIO-OXIDATIONREACTOR
Area of distribution ofair and liquid
Diffusers
pH ADJUSTMENT TANK
Feed
Air
PRECIPITATION TANK
LIME TANK
Loadedsolvent
(LS)
EXTRACTION COLUMN
disk
doughnut
Raffinate
Soda
Lowerdecanter
Upperdecanter
ORGANIC SOLVENT
TANK
Strongelectrolyte (SE)
Weakelectrolyte
(WE)
Barren solvent (BS)
STRIPPING COLUMN
Bed containingthe inoculum
Fig. 1. Bio-oxidation and SX flowsheet at the pilot plant.
64 M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
All flows were fed through metering pumps and the flow rates ofall inlets and aqueous outlets were measured by rotameters. The pilotwas run for 12 working days, 10 h a day on average, i.e. a total of120 h. The average flow rate of the aqueous feed was 150 L/h, so,about 18m3 of tailing solution after Fe precipitation were treated. Thetotal volume of the solvent was 300 L and it had 5% Ionquest 290dissolved in kerosene (20% aromatic and 80% aliphatic); the weakelectrolyte (WE, strip solution) consisted of 190 g/L H2SO4 with 50 g/LZn2+. A solution of 50–100 g/L Na2CO3 was prepared periodically in a60 L barrel and used to adjust the pH.
The concentration of Zn was determined using a Perkin Elmer3110 AAS at the mine laboratory. The Zn in the raffinate and SE wasdetermined directly, while the Zn in the barren and loaded solvent (BSand LS) solution were determined after stripping using H2SO4.
3. Results and discussion
3.1. Iron removal
The representative composition of the major components in theeffluents was Zn 1000±100 mg/L, Fe 500±50 mg/L (36% ferric), Ca600±50 mg/L, Mn 200±20 mg/L and Cu 50±5 mg/L. The pH of theeffluent was always around 3.0.
After the bio-oxidation process, laboratory scale precipitationexperiments indicated that a final pH of 4.5 was reached after 65 minand Fe precipitation was almost complete, with the remainingconcentration below 0.5 ppm. The initial Zn concentration waspractically unaffected by this process (Fig. 2). Lime consumptionwas 4.2 g CaO per kg of solution.
Similar results were obtained in the continuous bio-oxidationprocess at the pilot plant (Fig. 3) achieving total ferrous oxidation atall times and flow rates tested. Table 1 shows the effect of the bio-oxidation – precipitation stage. Iron precipitation took 60 min in thepilot plant. During this time, the lime addition to reach pH 3.5 took45 min followed by intermittent dosing for the next 15 min until pH4.7 was achieved. After the precipitation step Fe was completelyremoved, the amount of Al decreased drastically, Cu dropped by half(from 45.0 ppm to 21.7 ppm) while the concentration of the otherelements measured, including Zn, remained similar to the initial.Precipitation and sedimentation stages accurately reproduced labo-ratory results, producing 56 m3 of iron-free solution with practicallyall the initial zinc.
3.2. Computer simulation
Computer simulations were performed to estimate the requiredpilot plant inputs and outputs by using CurveExpert 1.3 to calculatethe distribution coefficients, D, and the number of stages). Experiencehas shown that computer simulation is a more flexible design toolthan 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 todecrease 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 theraffinate. The concentration of Zn in the loaded solvent should be inthe range of 2.2–2.8 g/L extractant, around 70–85% of the theoreticaltotal loading of 3.3 g/L, which is quite reasonable. In order to get a finalsolution of 90 g/L Zn in the SE, i.e. a Zn transfer of 40 g/L, the strippingshould be run at a phase ratio of O:A=20, and only one equilibriumstage 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 phaseratio, while a lower concentration would increase the flow rate of thesolvent and, accordingly, the size of the stripping unit. The maximumloading that was obtained experimentally at limiting conditions, i.e.,by contacting 3 times the solvent with corresponding fresh portions ofeffluent at phase ratio O:A=0.1, was 2.9 g Zn/L Ionquest 290. Sincethis result is similar to the obtained after a single contact, it revealsthat the limiting conditions can be achieved by a single contact.
Tests to determine pH control were done at laboratory. From theresults shown in Table 3, it can be seen that without pH control, theextraction was quite selective. Thus, no Mn, Cu and Al were extractedand only a small amount of Ca was extracted. In addition, separationfactors were high enough to support the indicated selectivity.However, the distribution ratio of Zn (DZn) at natural pH range waslow, 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 2.1as the phase ratio O:A increased, while the suitable pH for Znextraction by Ionquest 290 is above 2.5 (Tsakiridis et al., 2010).Furthermore, to avoid Ca co-extraction, pH should be around 3 asindicated by the isotherms in the Cyanex 272 online User Manual p. 5
time (min)
[Zn]
(pp
m)
pH; [
Fe]
(pp
m)
[Zn] pH [Fe]
900920940960980
100010201040106010801100
00,511,522,533,544,55
0 10 20 30 40 50 60 70 80
Fig. 2. Evolution of [Zn], [Fe] and pH in laboratory precipitation tests.
Time (h)[Fe(II)]in [Fe(II)]out [Zn]out [Zn]in Flow rate
0
200
400
600
800
1000
1200
0
50
100
150
200
250
300
350
400
0 20 40 60 80 100 120 140 160
[Fe(
II)],
[Zn]
(pp
m)
Flo
w r
ate
(L/h
)
Fig. 3. Bio-oxidation process at the pilot plant.
Table 1Solution composition, before and after treatment.
Initial solution After bio-oxidation After precipitation
[Fe2+] (ppm) 254 0 0[Fe3+] (ppm) 446 690 0.2[Zn] (ppm) 1020 1020 1010[Al] (ppm) 292 250 20[Mn] (ppm) 265 260 200[Cu] (ppm) 45 45 21.7[Ca] (ppm) 600 600 600[Pb] (ppm) 1.6 1.6 1.6pH 3.0 1.93 4.78
65M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
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 thanwithout pH adjustment. The extraction of Mn and Ca remains quitelow as indicated by the high separation factors. Therefore, the pH atthe pilot plant should be maintained around pH 3. In practice, the pHadjustment was achieved by direct neutralization of both the acidicraffinate and the organic solvent (by pre-equilibration with aqueoussolution) using Na2CO3. The consumption of Na2CO3 was 1.62 kg/kgZn.
Shake out stripping experiments were carried out by contacting200 mL of loaded solvent (LS) containing 1.95 g/L Zn with 20 mL ofaqueous phase containing 200 g/L H2SO4 and weak electrolyte (WE)containing zinc concentrations of 40 to 90 g/L at 22 °C. The processwas carried out at O:A=10, i.e. the concentration of Zn in the strongelectrolyte (SE) should increase by ~20 g/L with respect to the WEsolution, whichwas consistent with the results shown in Table 5. In allcases, 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 enoughfor the Zn recovery regardless of the concentration of Zn in thestripping solution.
Additional laboratory tests carried out at the mine site during thepilot plant experiments at phase ratio O:A=20, revealed that theloaded solvent from the pilot plant was efficiently stripped in onecontact, i.e. one stage, by the strip solution used in the pilot plantexperiments, using a weak electrolyte with ~50 g/L Zn producing anSE containing 90 g/L Zn, i.e. a zinc transfer of 40 g/L, as required for theEW plant.
Preliminary hydraulic tests at the pilot plant showed that theavailable flux is above 30 m3/m2/h in both columns. The stripping wasrun mainly in order to produce BS and was not optimised. It wasoperated at a flux of 40 m3/m2/h (35 L/h solvent), the pulsing had anamplitude of 15 mm and a frequency of 1 Hz. The flow rate of the WEthrough the pump was 5–7 L/h. The average value of Zn in the BS wasabout 20 mg/L Zn.
Three tests with organic continuous dispersion and with aqueouscontinuous dispersion were undertaken to determine the preferreddispersion. During both organic continuous and aqueous continuousruns, the temperature rose from 25 °C in the morning to 34 °C in theevening, which facilitated the comparison between both dispersions.
Every test took 5 h, long enough to reach steady state and the phaseratio was kept at A:O=2.1 during all the testwork.
As seen from Table 6, similar results were obtained with bothdispersions. The Zn concentration in the LS was around 2000 mg/Land Zn in the raffinate was far below 50 mg/L, indicating than N95% ofthe Zn is recovered. Hence, the extraction process was found tooperate successfully with both aqueous and organic continuousdispersions at column fluxes of about 40 m3/m2/h, at 23–34 °C. Asthe available flux and recovery with both dispersions were similar, itis preferable to use the aqueous continuous dispersion as there is alower expenditure on solvent.With aqueous continuous the danger offire due to kerosene ignition is also significantly diminished.
The stripping of the LS containing around 2 g/L Zn, achieved a SEwith 30–40 g/L Zn above the WE, i.e. a zinc transfer of 30–40 g/Lwhilst achieving b50 mg/L Zn in the BS at a flux of 45 m3/m2/h withaqueous continuous at O:A phase ratio of 20.
While the column worked well and supplied the required BS to theextraction, laboratory tests proved that there was no need for an extracolumn, and one stage of mixer-settler was sufficient to obtain therequired zinc transfer of 40 g/L with barren solvent containing ~50 mg/LZn.
4. Conclusions
The results, as demonstrated by the pilot plant, proved the processfeasibility with 95% Zn recovery from the effluent. The pre-treatmentstage bio-oxidation achieved complete oxidation of ferrous in thebioreactor, subsequent lime precipitation resulted in b1 mg/L Feremaining in the solution whilst not affecting the zinc tenor. The zincextraction stage was successfully carried out in a 80 mmdiameter 7 mhigh 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 continuousdispersions, at a temperature above 25 °C. The stripping was efficientwith only a single stage at O:A phase ratio of 20 required to achieve a40 g/L Zn transfer into the electrolyte. The Na2CO3 consumption was1.7 kg/m3 effluent (1.7 kg/kg Zn).
Table 2Recovery 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 51 94.70.60 24 97.6
3 0.35 75 92.10.40 30 96.80.45 11 98.90.5 4 99.6
Table 3Extraction experiments with 5% Ionquest 290, no pH correction, 22 °C.
Phase ratio FinalpH
Aqueous (mg/L) Organic (mg/L) D values &separation factors
O:A Zn Mn Ca Zn Mn Ca DZn DZn/DCa DZn/DMn
PLS 5.0 962 206 7630.1 2.58 792 208 618 1595 0.03 8 2.0 154.5 1 104
0.3 2.50 692 208 613 910 0.2 12 1.3 66.4 13500.5 2.31 621 205 605 668 0.1 15 1.1 44.4 22601 2.18 536 206 624 444 0.1 13 0.8 38.4 16002 2.16 467 207 613 273 0.03 9 0.6 40.9 42003 2.32 402 202 597 178 0.3 10 0.4 23.9 2705 2.25 342 203 599 132 0.1 6 0.4 39.9 80010 2.10 270 203 601 76 0.1 8 0.3 22.5 610
Table 4Extraction 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) 963 213 5830.1 784 231 531 2878 0 76 3.7 26.4 9·104
0.3 343 233 509 2073 0.4 72 6.0 42.9 3·103
0.5 182 211 536 1700 0.5 40 9.3 133.0 5·103
1 49 213 547 875 1.5 38 17.9 199.9 3·103
2 22 194 534 502 3.4 43 22.8 285.0 1 103
3 14 127 532 297 2.7 46 21.2 235,6 1 103
5 4 181 584 192 3 42 48 685,7 2 103
10 1 183 579 90 1 48 90 1125 2·104
Table 5Stripping 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)
40 200 58.850 194 68.260 188 77.870 176 91.080 200 102.890 200 115.4
66 M. Avila et al. / Hydrometallurgy 107 (2011) 63–67
For a Zn price above US$2/kg, the value of the zinc product coversthe operating costs in addition to solving a serious environmentalproblem.
Acknowledgements
The public company EGMASA (Andalusia, Spain) is acknowledgedfor their support of the personnel expenses for the present study. TheSpanish Ministry for Science and Innovation is acknowledged forsupporting laboratory expenses at UAB (Project CTQ2009-07432(subprograma PPQ)).
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Table 6Extraction in organic and aqueous continuities.
Continuousdispersion
Feed BS Flux pH Zn (mg/L)
L/h L/h m3/m2/h Raff Raff LS
Organic 110 55 33 2.7 11 1910130 60 38 2.8 55 1880130 60 38 2.9 11 1800
Aqueous 150 70 44 2.9 47 1880150 70 44 3.1 18 2520150 70 44 2.9 7.2 2240
67M. Avila et al. / Hydrometallurgy 107 (2011) 63–67