ContentslistsavailableatSciVerseScienceDirect
Resources,
Conservation
and
Recycling
j o u r n al ho me p ag e : w w w . e l s e v i e r . c o m / l o c a t e / r e s c o n r e c
Greywater
production
in
airports:
Qualitative
and
quantitative
assessment
Eduardo
de
Aguiar
do
Couto
∗,
Maria
Lúcia
Calijuri,
Paula
Peixoto
Assemany,
Aníbal
da
Fonseca
Santiago,
Isabella
de
Castro
Carvalho
FederalUniversityofVic¸osa(UniversidadeFederaldeVic¸osa/UFV),DepartamentofCivilEngineering,EnvironmentalEngineeringGroup–nPA,CampusVic¸osa,36570-000Vic¸osa, MG,Brazil
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received16October2012
Receivedinrevisedform14February2013 Accepted11May2013
Keywords: Greywater Reuse
Airportenvironments Rationalwateruse
a
b
s
t
r
a
c
t
Airportcomplexesaregreatwaterconsumerswheretheadoptionofreusepracticesadaptedtotheir particularcharacteristicsmayrepresentsignificantsavingsoffinancialandenvironmentalresources. Greywaterreuseisanimportantalternativeforreducingpotablewaterconsumptioninairports.The objectiveofthisstudywastoassessthequalityofgreywaterproducedinairportenvironmentsandthe reusepotentialofsucheffluent.Thisstudywasdevelopedinamid-sizeairportinBrazil,wherea qualita-tiveassessmentofgreywaterproducedbydifferentactivitieswasperformed.Theresultswereanalyzed usingdescriptiveandmultivariatestatistics.Greywaterproductionintheadministrativebuildingswas estimatedbytheapplicationofquestionnairesandinterviewingemployees,andcomparedtothe non-potabledemandinthesebuildings.Theresultsshowedthatthequalityofthegreywaterproducedinthe airportissimilartothatproducedinresidencesandcanbeeasilytreatedforreusepurposes.In quanti-tativeterms,greywaterreusecanmeetthenon-potabledemandandprovidegreatsavingsofwaterand financialresources,inadditiontopricelessenvironmentalbenefits.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Greatairportcomplexesworldwideconsumewaterinthesame proportionofsmallandmid-sizecities.TheAtlantaInternational Airport(Hartsfield-Jackson)presented,in2009,water consump-tionequaltothatofacitywithapopulationof13,000people(HAIA, 2009).TheLondon-HeathrowAirportconsumed1,852,000m3 in 2010,whichequalstheconsumptionofacityof25,000people,and presentedpassengertrafficof66millionpeople(LHA,2010).The NaritaInternationalAirport,inJapan,withover33million pas-sengersin2010,presentedinthesameyearwaterconsumption equivalenttothatofacityof24,000people(NIAC,2010).
Greatpartofthewaternecessarytomaintainairport infrastruc-tureandoperationroutine isdestined tonon-potableactivities suchasaircoolingsystems,landscapeirrigation,washingof air-craft,vehiclesandrunways,firecontroltesting,andtoiletflushing. Consideringthisscenario,airportsarepotentialenvironmentsfor implementingprocessesandtechnologiesaimedattherationaluse ofwatersuchasgreywaterreuse.
Greywaterconsistsofeffluentfromlavatories,showers, bath-tubs,kitchensinksandwashingmachines(OttosonandStenstrom, 2003;Liuetal.,2010;Nazeretal.,2010;HernándezLealetal.,2011;
∗ Correspondingauthor.Tel.:+553138993098;fax:+553138993093. E-mailaddresses:eduardo.acouto@hotmail.com(E.A.Couto),
calijuri@gmail.com(M.L.Calijuri),paulaassemany@hotmail.com(P.P.Assemany), anibalsantiago@gmail.com(A.d.F.Santiago),isakpi@yahoo.com.br(I.d.C.Carvalho).
Hurlimann,2011).Garlandetal.(2004)andLamineetal.(2007) statethatthemainfactorswhichinterfereingreywaterqualityare thesupplywaterquality,thematerialusedinthedistribution sys-temandtheactivitiesperformedinacertainbuildingorresidence. AccordingtoZhangetal.(2010)andMouradetal.(2011) greywa-terisasuitablealternativewatersourcefornon-potableuses,such astoiletflushing,laundryandgardenirrigation.
Regardingthequantitativeaspects,thevolumeof greywater produced in residences and commercial or industrial activities varies accordingto local characteristicssuch asthe number of employees,frequencyofcleaning,existenceofadininghall,among others. These characteristics also vary according to the region, habitsandpurchasingpowerofthepopulation.Accordingto Al-Hamaiedeh and Bino (2010),50–80% of thevolume of effluent producedinahouseisduetogreywater.
Theobjectiveofthisstudywastoassessandcharacterizethe qualitativeandquantitativeaspectsofgreywaterproducedinan airportcomplexinordertoinferaboutitsreusepotential.
2. Methodology
2.1. Studyarea
ThestudywascarriedoutintheTancredoNevesInternational Airport(TNIA)locatedbetween19◦39and19◦37Sand43◦59and 43◦57W,inConfins,stateofMinasGerais,Brazil.
0921-3449/$–seefrontmatter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.resconrec.2013.05.004
According to the Brazilian Airport Infrastructure Enterprise (Infraero),theTNIAcomplexhasanareaof15km2andthe capac-itytotransportover 10millionpassengerseveryyear.In2011, theTNIAtransportedover9.50millionpassengersandconsumed 259,470m3 ofwater,whichis31%higherthanthevolume con-sumedin2010andmakesit thefourthintherankingofwater consumptionamongallBrazilianairports(Infraero–personal com-munication).
Thecurrentcontextofhighwaterconsumptionandexpansion oftheairportcapacityjustifiesthedevelopmentofthisstudysince thegreywaterreusepotentialcanbeconsideredintheplanningof waterresourcesmanagementpracticesintheairportcomplex. 2.2. Greywaterqualitativecharacterization
In order to assess the quality of greywater from different sources,samplesfrombathroomsinks,kitchensinksandshowers locatedinseveralareasoftheTNIAwereindividuallyanalyzed.
Thesamplingpointswherewaterwascollectedfrombathroom sinks(Points1,2,3and4)werechoseninordertorepresentthe characteristicsoftheTNIAbuildingsincludingthosewithafixed number ofemployees,those withfloating populationand with differenthierarchylevels.Withrespecttothekitchensinks,two samplingpointswereselected:onesinkusedforwashingfruitand vegetables(Point5)andoneforwashingdishesandkitchenware (Point6).Samplesfromshowers(Point7)werecollectedinthe lockerroomusedbytheemployeesofthemaintenancecompany outsourcedbyInfraero.
ThesamplingcampaignswerecarriedoutfromSeptember2010 toMay2011.Plasticsiphonswereadaptedtotheexistinghydraulic facilitiesinordertodiverttheeffluentto50Lrecipients.Aftereach samplingcampaign,theeffluentwashomogenizedandlaboratory sampleswerecollectedandconservedat4◦C.
Theanalyseswereperformedaccordingtorecommendationsof theStandardMethodsfortheExaminationofWaterand Waste-water–21stedition2005(APHA,2005),andthenumberofthe procedureisindicatedinparenthesis.Thephysical,chemicaland microbiologicalvariablesanalyzedwerepH(4500-H+B), turbid-ity (2130B), temperature (2550B), total suspended solids (TSS) (2540D),totalsolids(TS)(2540B),totalalkalinity(Alk)(2320B), total hardness (2340C), electrical conductivity (EC) (2510A), biochemical oxygen demand (BOD) (5220B), chemical oxygen demand(COD)(5220D),dissolvedoxygen(DO)(4500-0G),nitrate (NO3−)(4500-NO3E),ammonianitrogen(NH4+)(4500-NH3C),total kjeldahl nitrogen (TKN) (4500-Norg C), total phosphorus (TP) (4500-PA),oilsand grease(5520A)and Escherichiacoli(E.coli) (Colilert®).
2.2.1. Statisticalanalyses
Descriptive statistics (mean and standard deviation) were obtainedusingthesoftwareMicrosoft® Excel2010.Inaddition, twomultivariatestatisticalanalyseswereused:Principal Compo-nentsAnalysis(PCA)andClusterAnalysis.PCAisusedtotransform anoriginaldatasetofvariablesfromamultidimensionalspaceinto amoreconciseequivalentset(Omo-Iraboretal.,2008).This tech-niqueconsistsoftransformingtheoriginalvariablesintoothers, non-correlated,named principalcomponents,whichcorrespond tolinearcombinationsoftheoriginalvariables (Sarbuand Pop, 2005).Thisanalysiswasusedwiththeobjectiveofassessingthe importanceofeachofthevariablesstudiedinthedynamicsofthe greywaterqualityproducedintheairportcomplex.
Theclusteranalysiswasusedtodetectsimilaritiesamongthe samplingpoints,separatingthemintogroupsaccordingtothese similarcharacteristics.Thistechnique revealsthebehavior of a datasetwithoutmakinganypriorassumptionsin orderto clas-sifyobjectsofthestudiedsystemintocategoriesorgroups,based
ontheirsimilarities(Pandaetal.,2006).Theclusteranalysiswas performedinordertoassesssimilarities/patternsinthe character-isticsofgreywaterinthesamplingpointsandthusacknowledge theaspects related totheproduction ofthis effluentin airport environments.
These analyseswere performedusing the softwareR©, ver-sion 2.10.1, developed by the R Foundation for Statistical (R Developmentcoreteam,2009).Themultivariatestatistical proce-duresusedthepackages“FactoMineR”forPCAand“Cluster”for clusteranalysis.
2.3. Quantificationofgreywaterproductionandnon-potable demand
Greywaterproduction and non-potable demandat theTNIA wereestimatedfromtheamountofwaterconsumedinthe build-ings locatedin theairport. The methodology based on studies carried outbyProenc¸aand Ghisi(2005) andGhisi and Ferreira (2007),ledtoanestimateofthevolumeconsumedineach build-ingbyeachoftheactivitiesperformed.Contributionsfromkitchen sinks,showers,bathroomsinksandcleaningactivitieswereadded toobtainthetotalamountofwaterproducedineachbuilding.In ordertoestimatethenon-potabledemand,thevolumesconsumed byactivitiessuchastoiletflushing,cleaningandirrigationwere added.
Thesurvey wascarried outin eight buildings: Internal Rev-enueService(IRS),CargoTerminal(CT),FireSection(FS),Airspace Control(AC),Fuel Area(FA), EquipmentShelter(ES),RA Group restaurant(RA)andtheInfraeroMaintenanceBuilding(MB).
Theestimateofwaterconsumptionineachbuildingwas per-formedby identifyingthe consumptionhabits oftheusers and assessingthespecificflowforeachsanitaryfixture.Consumption habitswereinvestigatedbyrequestingemployeestofillout ques-tionnairesandbyinterviewingthoseresponsibleforcleaningthe buildings. The questions of both questionnairesand interviews wererelatedtofrequency,formanddurationofsanitaryfixtures utilizationandcleaningactivities.
Theflowsofkitchensinksandexternaltapsusedforirrigation wereobtainedusingthedirectflow measurement method.The meanvalueamongalltapstestedwasusedtoestimatethetotal vol-umeofwaterconsumed.Fortoiletsequippedwithsanitaryflushing valves,theflowof1.7Ls−1wasadopted,whichisthemaximum flowrecommendedbyNBR5626(ABNT,1998).Theflowadopted forshowerswas0.34Ls−1,assuggestedbyaresearchdevelopedby theUniversityofSãoPauloandthesanitationcompanyofthestate ofSãoPaulo–Sabesp,forshowersbetween15and20mwc,which istheregularwaterpressure formostshowersin thebuildings (SABESP,1996).
Afterobtainingresponsestothequestionnairesandthe spe-cificflowsfor thesanitaryfixtures,thewaterconsumption was calculatedforeachactivityineachbuilding.Inordertoestimate consumptionintaps,showers,toiletsandurinals,Eqs.(1)–(3)were used.
C1=N1TQ1 (taps and showers) (1)
C2=N2Q2 (toilets and urinals) (2)
C3=(C1+C2)·D (3)
whereC1:waterconsumptionperuseroftaps/showers(liters/day); C2:waterconsumptionperuseroftoilets/urinals(liters/day);C3: monthly water consumption for each employee (liters/month); N1:frequencyofutilization(numberoftimes/day);N2:frequency of utilization (number of flushes/day); C1+C2: total daily con-sumptionforeachemployee(liters/day);Q1:sanitaryfixtureflow
Table1
QualitativecharacterizationresultsforgreywateratTNIA.
Bathroomsinks Kitchensinks Showers Point1 Point2 Point3 Point4 Point5 Point6 Point7 Physicalvariables
Turbidity(NTU) 16.4±5.5 10.7±5.1 11.7±6.9 24.7±9.6 64.1±79.9 123.7±158.7 26.1±26.9 TSS(mgL−1) 105.2±55.5 40.5±37.3 40.5±12.7 67.2±14.6 155.6±143.1 208.2±67.6 99.9±43.9
TS(mgL−1) 309.2±107.4 226.2±121.0 262.0±109.9 266.4±73.4 578.9±355.5 763.1±200.2 404.1±90.9
Chemicalvariables(nutrients)
TKN(mgL−1) 6.9±10.1 4.5±3.9 7.2±9.6 8.5±2.8 12.4±18.3 9.3±10.3 32.5±12.7
N-NH4+(mgL−1) 0.8±0.8 0.9±1.1 0.6±0.8 4.2±3.7 0.3±0.4 0.5±0.9 26.6±14.1
NO3−(mgL−1) 5.8±6.9 3.0±3.1 5.3±3.5 7.8±5.0 6.6±8.2 11.0±7.5 5.5±6.1
TP(mgL−1) 1.4±0.7 0.8±0.5 0.7±0.3 0.8±0.5 3.2±3.1 8.4±3.5 2.4±1.2
Chemicalvariables(organiccompounds)
BOD5(mgL−1) 79.7±35.6 49.6±20.5 45.7±19.8 95.2±19.6 549.4±507.6 613.0±243.3 82.5±90.7
COD(mgL−1) 123.5±48.1 68.4±73.4 97.6±36.5 228.3±51.2 558.9±528.8 912.2±169.3 159.0±131.6
Bathroomsinks Kitchensinks Showers
Point1 Point2 Point3 Point4 Point5 Point6 Point7 Otherchemicalvariables
pH 7.5±0.4 7.4±0.3 7.4±0.4 7.4±0.5 6.2±1.4 6.6±0.8 7.5±0.3 EC(Scm−1) 468.8±63.5 424.1±39.9 424.2±48.9 489.4±15.4 635.6±542.7 499.9±122.2 855.9±263.2
Alk(mgCaCO3L−1) 210.2±58.4 202.4±36.4 195.5±46.2 216.4±49.3 200.0±147.1 208.9±42.7 302.7±53.2
Totalhardness(mgCaCO3L−1) 176.7±30.0 160.7±11.4 208.0±45.3 - 185.3±29.1 154.7±7.6 171.0±4.2
Chloride(mgL−1) 33.1±27.7 18.9±6.4 21.0±13.8 19.6±8.8 96.0±183.8 33.9±11.4 74.0±38.2
Oilsandgrease(mgL−1) 21.1±27.1 2.3±5.2 3.1±6.8 17.3±14.3 54.9±65.8 201.8±127.1 34.3±9.3 Microbiologicalvariables
E.colia(MPN/100mL) 3.17×100 2.57×100 2.43×100 1.62×101 2.00×102 6.71×102 5.92×102 aGeometricmeanvalues.
(liters/second);Q2:sanitaryfixtureflow(liters/flush);T:duration ofeachutilization(seconds/timeofuse);D:numberofworkdays oftheemployeeinamonth(days/month).
ThusEq.(3)representsthesumoftheresultsobtainedforEqs. (1)and(2)andprovidestheresultformonthlyconsumption.The volumeofwaterusedforcleaningwasestimatedusing10L recip-ients,sinceitistheusualcleaningprocedureadoptedformostof thebuildingsstudied.Thevolumeconsumedinotheractivitieswas calculatedaccordingtothetypeofusebymultiplyingfrequency anddurationofeachactivity(asobtainedfromthequestionnaires andinterviews)bythevolumeconsumedeachtimetheactivityis performed.
3. Resultsanddiscussion
3.1. Qualitativecharacterizationofgreywaterproducedinthe TNIA
Theresultsobtainedbythequalitativecharacterizationofthe greywaterproducedintheTNIAareshowninTable1.The discus-sionoftheresultswasperformedconsideringgroupsofvariables inordertofacilitatecomprehension.
3.1.1. Physicalvariables(turbidity,totalsuspendedsolidsand totalsolids)
TSconcentrations,evidencedbyturbidityandTSSvalues,were higherforkitcheneffluent(points5and6).Inthesepoints,TSS aremostly duetoresidual foodremovedduringtheprocess of sanitationor whencleaning kitchenware.Eriksson et al.(2002) assessedthequalityofhouseholdgreywatercollectedseparately fromsewageeffluent,andfoundhigherTSconcentrationsforthe kitcheneffluent.Suchresultwasexplainedbythediversityofthe particlesfound.Forbathroomsinksandshowers,wherethemain usesarehandwashing,mouthcleaningandbathing,TSScanbe originatedfromhair,tissuefiberandsandparticles,whichexplains thelower TSS concentrationsfound in suchpoints.In greywa-tertreatmentandreuseprojects,theimportanceoftheTSSlevel isrelatedtothepossibilityofobstructionofpipesthatconduct theeffluenttothetreatmentplant.Althoughsolidsconcentration
ingreywaterisusuallylowerthanthatfor sewageeffluent,the problemsregardingpipeobstructioncannotbeneglected(Eriksson etal.,2002).Besides,TSScannegativelyinterfereineffluent dis-infection,sincelargeparticlesshieldpathogenicorganismsfrom inactivation(Winwardetal.,2008).
Anothercharacteristic observed regarding the physical vari-ableswasthatdifferentresultswerefoundforthesamesampling point,asshownbythehighstandarddeviationvalue.Althougha standardizedsamplingschedulehasbeenfollowed,theactivities performedineachsamplingpointonthedifferentsamplingdates werediverse,andconsequentlyledtotheproductionofeffluent withdistinctcharacteristics.Forinstance,thetypeoffoodthatis preparedortheactivityperformedbytheemployeebeforetaking ashowerarefactorswhichcancontributetothepresenceofsolids intheeffluent.
Donneretal.(2010)gatheredinformationfrommanystudies andpresentedTSSvaluesrangingfrom7to207mgL−1for bath-roomsinkeffluent,andfrom235to720mgL−1 forkitchensink effluent.HoldenandWard(1999)studiedgreywaterfrom bath-roomsinksandfoundturbidityvaluesrangingfrom12to100NTU. GilboaandFriedler(2008)foundturbidityvaluesbetween15and 240NTUforbathtub,showerandbathroomsinkeffluent.These studiesalsoobtainedahighstandarddeviationforphysical vari-ables in theiranalysis, corroboratingtheresults ofthe present studyandreinforcingtheinformationthatlocalandpersonalhabits directlyinfluencethequalityoftheeffluentproduced.
3.1.2. Nutrients(NTK,N-NH4+,NO3−andTP)
The TKN averageconcentrations for bathroom sinksamples rangedfrom4.48to8.48mgL−1,whereasforkitchensinksthese valueswerebetween9.28and12.45mgL-1.AsforN-NH
4+,
con-centrationsrangedfrom0.57 to4.18mgL−1 forbathroomsinks and0.26to0.49mgL−1forkitchensinks.Thedifferencebetween TKNandN-NH4+ concentrationsisexplainedbythepresenceof organicnitrogen,mostlyduetofoodresidue.Suchvaluesarelower thanthoseusuallyfoundinuntreatedsewageeffluent,wherethe mainnitrogensourceisurine.AccordingtoOtterpohl(2002),only 3%ofthenitrogenfoundindomesticsewage isdueto greywa-ter,whereas urinecontributes with87%and feceswith10%.In
greywater,themainsourceofnitrogenisfoodresidue.However,for point7(whereshowersampleswerecollected),TKNandN-NH4+ valueswerehigherthanthosefoundinotherpoints,reaching32.51 and26.65mgL−1,respectively.Thiscanbeexplainedbythehabit someusersmighthaveofurinatingduringtheshower.
Themeannitratevalues rangedfrom2.96to7.84mgL−1 for bathroomsinks,from6.65to10.98mgL−1 forkitchensinks,and 5.54mgL−1fortheshowereffluent.Prathaparetal.(2005)studied greywaterproducedinresidencesinOmanandfoundnitratevalues of28.7mgL−1forshowereffluentand10.2mgL−1 forbathroom sinks.Erikssonetal.(2009)statethatnitrate,aswellasN-NH4+, arenotusedintoiletriesandcleaningproducts.Thustheprobable sourcesareurineorevenhumanskincells.
TheTPmeanconcentrationsrangedfrom0.71to8.43mgL−1, andthelowervalueswerefoundforbathroomsinkswhereasthe higherconcentrationswereobservedinthekitcheneffluent.The mainsourcesofphosphorusingreywaterarehygieneand clean-ingproducts.Even thoughsuchproductsare usedatall ofthe samplingpointsassessedinthisstudy,theiruseismore signifi-cantinkitchensinks,duetothemorefrequentuseofdetergent forwashingkitchenware,whichexplainswhyhigherTP concen-trationswereobservedatsuchpoints.HernándezLealetal.(2011), inaresearchdevelopedinHollandwithgreywaterfromresidences whichincludedusessuchaslaundryandfoodpreparation,found meanTPconcentrationsof7.2mgL−1.
Ingeneral,greywaterhaslowernutrientconcentrationswhen compared to domestic sewage, especially nitrogen,because its mainsourceisurine,whereasthemainsourcesofphosphorusare cleaningproducts.Nevertheless,thevaluesobtainedforgreywater producedintheTNIAarewithintherangesfoundforseveralother authorsintheliterature.
Thelownutrientconcentrationsingreywatercaninterferein thetreatmentsystemadopted.Al-Jayyousi(2003)statesthatthe lownutrientconcentrationinthiseffluentcanlimittheefficiency ofabiologicaltreatmentsystem.HernándezLealetal.(2011)state thatthenutrientdeficitcancompromisethedevelopmentofan aerobictreatment,whereasananaerobicprocesswouldnotsuffer suchimpact.
3.1.3. Organicmatter(BOD5andCOD)
Althoughgreywater doesnot receive any contributionfrom toileteffluent,theorganicmatterconcentrationsfoundin some sampling points were similar or even higher than the values expectedfordomestic sewage.For kitchensinkeffluent(points 5and6),themeanBOD5 concentrations rangedfrom569.40to 613.00mgL−1,andthemeanvaluesforCODrangedfrom558.86 to912.19mgL−1.Inthesepoints,organicmatterisoriginatedfrom foodresidue,oilsandgrease,detergentandothercleaning prod-ucts.Erikssonetal.(2002)statethatoneofthemaincontributions forCODinsewageeffluentistheuseofcleaningproductssuchas detergents.Giventhewideuseofsuchproductsinkitchensinks, thisexplainsthehighCODconcentrationsfoundingreywater.
In bathroom sinks and showers, the mean concentrations rangedfrom45.68to95.18mgL−1 for BOD5 andfrom68.39to 228.32mgL−1forCOD.Themainsourcesinthesepointsarebody residue,hairandsoap.Theresultsshowsomeimportant distinc-tionsintheconstituentsofgreywaterfromdifferentsourceswith respecttoorganicmatter.AccordingtoErikssonetal.(2009),such distinctioncanbeattributedtotheamountandtypeofproduct whichaccountsforBOD5 andCODconcentrations ingreywater. Thesimpleactofwashinghandsortakingashowerwilldifferently influenceorganicmattercompositionifcomparedtotheeffluent fromkitchensthatpresenthighconcentrationsoffoodresidueand detergents.
HernándezLealetal.(2007)obtaineda meanBOD5 valueof 215mgL−1forgreywaterproducedinresidences,andameanCOD
concentration of 425mgL−1, which are higher than the values foundintheTNIAforbathroomsinkeffluentandlowerthanthose forkitchensinks. Shafranet al.(2005),inastudyperformedin Israel,obtainedlowerconcentrations:62and200mgL−1forBOD5 andCOD,respectively.
GreywaterproducedintheTNIApresentedgood biodegradabil-itycharacteristics,withaCOD/BODratiorangingfrom1.02to2.4 dependingonthesamplingpoints,andcanbeefficientlytreatedby abiologicalsystem.Lamineetal.(2007)andLietal.(2009)obtained similarresultswithCOD/BODratiosof1.05and1.50,respectively. However,dependingonthesupplywatersourceandend-usesin acertainlocation,greywatercanpresent distinctcharacteristics regarding biodegradability.AccordingtoAl-Jayyousi(2003),the COD/BODratioingreywatercanreachupto4/1,whichis consid-eredaneffluentwithpoorlybiodegradableorganicmatter.Thisis likelytohappenwhengreatpartoftheCODcomesfrom chemi-calssuchascleaningproductsanddetergents,aspointedoutby Winwardetal.(2008),whofoundaCOD/BODratioof4.3/1ina studyperformedinEngland.
Inadditiontoinformationregardingeffluentbiodegradability, BODandCODconcentrationsalsoindicatetheriskofdissolved oxy-gendepletion.Thisisarelevantissueincaseswhenitisnecessary tostoreeffluentpriortoitstreatment.Dissolvedoxygen consump-tionandanaerobicconditionscancausetheproductionofsulfide bysulfatereductionandtheconsequentemissionofunpleasant odors.
3.1.4. Otherchemicalqualityvariables
OtherchemicalvariablessuchaspH,EC,totalalkalinity,total hardness,chloridesandoilsandgreaseweremeasured.
ThepHvaluesingreywaterdependonthepHofthesupply waterandcanbeinfluencedbythewaterend-use.Thesampling pointspresentedvaluesnear7.0andsmallvariationsamongthe samples,accordingtothelowstandarddeviationvalues.
Waterqualityvariablesrelatedtodissolvedsolids,suchastotal alkalinity,hardnessandEC,presentedhighconcentrations.These valuescanbeexplainedbythenaturalcharacteristicsofthe sup-plywaterintheTNIA.Theairport issituatedinakarsticregion where theinteractionbetweensoiland groundwaterisintense and thedissolution ofrocksduringwater percolationgivesit a highcontentofdissolvedsolids,resultinginhighvaluesofEC,total alkalinityandhardness.ThemeanECvaluesrangedfrom424.13 to635.65Scm−1,andtotalalkalinityfrom195.55mgCaCO3L−1 to302.73mgCaCO3L−1.Forbothvariables,significantdifferences amongthesamplingpointswerenotobserved.
Withrespecttototalhardness,thereisnoevidencethatthis variablecancausesanitaryproblems,however,itsmonitoringis important due to its association with scaling processes in hot water pipes,boilersand heaters.Themean values rangedfrom 154.67mgCaCO3L−1to208.00mgCaCO3L−1,thustheTNIAsupply watercanbeconsideredhard(hardness>50mgCaCO3L−1).
Themean chloridevalues forbathroomsinkeffluentranged from18.85to33.06mgL−1.For thepointswherekitchen efflu-ent wascollected, the mean concentrations rangedfrom 33.92 to 95.98mgL−1. For the shower effluent, the mean value was 73.97mgL−1.Thepresenceofchlorideingreywaterismostlydue tothedissolutionofsaltssuchassodiumchloride,whichjustifies thehighervaluesfoundinthekitcheneffluent.
Kitchensinksalsopresentedthehighestconcentrationsforoils andgreaseamongallthesamplingpoints,withmeanvalues reach-ing54.92mgL−1forpoint5and201.82mgL−1forpoint6.Inthis typeofeffluent,oilsandgreaseareoriginatedduringthe prepa-rationoffood,andevenfromfoodresiduesmixedtotheeffluent. Lowervalueswereobservedforbathroomsinksandfortheshower, between2.32and34.27mgL−1.Thesourcesforoilsandgreasein
Table2
Coefficientsforeachvariableinthethreefirstprincipalcomponents.
Variables PC1 PC2 PC3 pH −0.63 −0.01 0.61 Turb 0.67 0.08 0.33 EC 0.35 0.89 −0.04 Alk 0.13 0.85 0.21 COD 0.95 −0.10 0.18 BOD 0.86 −0.22 0.06 TS 0.80 −0.26 −0.22 TSS 0.74 −0.19 −0.29 Chloride 0.40 0.73 −0.09 TKN 0.25 0.79 −0.22 NO3− 0.50 0.02 0.53 NH4+ −0.07 0.68 −0.12 TP 0.78 −0.03 0.21 E.coli 0.35 −0.20 −0.81 Oilsandgrease 0.78 −0.26 0.33 %ofthevarianceexplained 37.48 22.73 12.41 %Accumulated 37.48 60.21 72.62
thesepointsareprobablyresiduesfromthehumanbody,which explainswhylowerconcentrationswerefoundwhencomparedto thosefromkitchensinks.
3.1.5. Escherichiacoli
Althoughgreywaterdoesnotpresentcontributionfromtoilet effluent,whichisthemainsourceformostpathogens,thepresence ofE.coliwasdetectedinthesamples.
Forbathroomsinks,washinghandsafterusingthetoilet rep-resentsthemainsourceofsuchorganisms.Forkitchensinks,the sourceofE.coliistheprocessofwashingfoodwhichmaypresent suchorganismsonitssurface.
TheeffluentfrombathroomsinkspresentedmaximumE.coli concentrationof101NMP/100mL,whereaskitchensinksreached
valuesof104NMP/100mL.Thesevaluesapproachthosefoundin
CanberraAirport,Australia,of103NMP/100mL(CanberraAirport,
2009).
Otherresearchersalsofoundsignificantconcentrationsof indi-cator organisms in greywater. Halalshehet al. (2008) analyzed greywaterproduced in smallruralvillagesin Jordan andfound E.coli valuesof upto105 NMP/100mL.Ottosonand Stenstrom (2003),inastudycarriedoutinStockholm,Sweden,obtainedE.coli valuesofupto106NMP/100mL.Thepresenceofindicator orga-nismsindifferentconcentrationsingreywaterallowsustoaffirm thatpopulationhabitscandirectlyinterfereinsuchvariable.
The results obtained in this research show that greywater produced in the TNIA meets reuse recommendations for some activitieswithrespecttocontaminationindicatororganisms,for instancethe dripirrigationof ornamentalplants (WHO, 2006), whichisanactivityperformedattheTNIA.However,itisimportant tohighlightthatthepresenceofE.coli,eveninlowconcentrations whencomparedtothedomesticsewage,indicatestheneedofa disinfectionstepinthegreywatertreatmentsysteminorderto minimizemicrobiologicalcontaminationrisks.
3.2. Multivariatestatistics 3.2.1. Principalcomponentanalysis
Theprincipalcomponentanalysis(PCA)wascarriedoutinorder toassessthebehaviorofwaterqualityvariablesatthedifferent samplingpoints(sources)andthusinferabouttheimportanceof eachofthemintheproductiondynamicsofthiseffluentintheTNIA. Theresultsallowedustoreducethe15initialvariablesto3 prin-cipalcomponentswhichaccountedfor72.62%ofthetotalvariance inthedata.Table2presentsthecoefficientsforeachvariableinthe first3principalcomponents.Theboldfacevaluesarethehighest coefficientsobtained. P oint1 P oint2 P oint3 P oint4 P oint7 P oint5 P oint6 50 100 150 200 250 300 350 Agglomerative Coefficient = 0.53 x Height
Fig.1. Dendrogramresultingfromtheclusteranalysis.
Principalcomponent1explained37.48%ofthetotalvariance, andthevariableswhichmostpositivelycontributedtoitwereBOD andCOD.
Thiscomponentshowstheimportanceofcertainactivitiesinthe compositionofgreywater.Themainsourcesofthevariableswith highcoefficientsarefoodresidueandbodyresiduefrombathing andwashinghands.Althoughallgreywatersourcesevaluatedin this study may contributeat some level to suchvariables, the descriptivestatisticalresultsshowthatthemeanconcentrations inthekitcheneffluentwerealwayshigherthanthosefortheother points.Thusitispossibletostatethatprincipalcomponent1 rep-resentstherelevanceofthekitchensinkeffluentinthequalityof thegreywaterproducedintheTNIA.Ifagreywatertreatmentand reusesystemistobeimplemented,suchfactneedstobetakeninto consideration.Thetreatmentsystemproposedshouldbeableto receivetheorganicmatterloadandotherpollutantsfromkitchen effluentinordertomeeteffluentreuserecommendations.
Principal component 2 explained 22.73% of the total vari-anceandpresentedstrongcontributionbythevariableselectrical conductivityandtotalalkalinity.Thiscomponentshowsthe impor-tanceofthequalityofthesupplywateringreywatercharacteristics. TheTNIAissuppliedbygroundwaterandissituatedinakarstic area.Theseareenvironmentswherethewaterresourcespresent highlevels of ionsin solution fromthe dissolution of carbon-aterocks.Theresultsevidencedacloserelationshipbetweensoil componentsandgroundwater,aswellastheimportanceofthis relationshipinthequalityofthegreywaterproducedintheairport. Principalcomponent3explained12.41%ofthetotalvariance and presented strong contribution by the variable E. coli. This resultrepresentstheinfluenceoffecalcontaminationonthe qual-ityofgreywaterproducedintheTNIA.Thisstatementallowsus toinferthatadisinfectionprocessisnecessaryinorderto mini-mizecontaminationrisksincaseofeffluentreuse.Nevertheless, itisimportanttohighlightthatE.coliconcentrationsin greywa-terwerelowerthanthoseusuallyfoundindomesticeffluentand, dependingontheintendedusefortreatedgreywater,E.colivalues presentedinTable1canbesatisfactory.
3.2.2. Clusteranalysis
TheresultoftheclusteranalysisisshowninFig.1,wherethree distinctgroupscanbeobserved.
Table3
Waterconsumptionestimatedforeachactivityineachbuilding(m3).
IRS FC MB AC FA ES CT RA Toilets 34.6 23.3 31.0 50.2 23.3 71.3 53.9 31.5 Taps 26.2 42.5 42.4 78.5 53.5 122.9 63.6 26.1 Urinals 1.3 3.7 2.2 1.7 7.2 13.9 13.7 3.5 Showers 49.5 131.3 28.6 141.0 101.2 0.0 0.0 54.5 Cleaning 8.7 9.7 2.5 20 1.5 3.7 54.1 2.5 Irrigation 101.9 15.3 69 18.1 18.1 – 280 9.1 Otheractivities 2.7 2.6 – – 7.6 – – 9.1 Total 224.9 228.2 175.8 309.5 212.4 211.8 465.3 136.1 Sampleerror(%) 12.9 8.5 11 12.1 15 – 13.9 6.7
GroupIis consistsof points1, 2, 3and 4, where bathroom sinkeffluentwascollected.Theeffluentproducedinthesepoints presented lowervalues for mostof thevariables analyzed.The exceptionsareelectricalconductivity,totalalkalinityandhardness, whicharenotstronglyinfluencedbythetypeofactivityandthus presentedvaluessimilartothoseobservedinsupplywater.
GroupIIisformedbypoint7alone,whereshowereffluentwas collected.Thispointischaracterizedbyhighvalues ofTKNand N-NH4+.AccordingtoOtterpohl(2002),sincegreywaterdo not
receivetoileteffluentcontribution,theypresentlower concentra-tionsofseveralfractionsofnitrogen,andaccountforonly3%ofthe nitrogenconcentrationindomesticsewage.Thuswecanassume suchhighTKNandN-NH4+valuesareduetothehabitofurinating duringtheshower.
GroupIIIiscomposedbypoints5and6,whichrepresentkitchen sinkeffluent.Thesepointsarecharacterizedbyhigh concentra-tionsofsolidsandorganicmatter(BOD5andCOD),andpresentthe highestvaluesformanyothervariablessuchasnitrate,total phos-phorus,chloridesandoilsandgrease,whencomparedtotheother samplingpoints.Suchcharacteristicswerereflectedonthe separa-tionofthesepointsintoaseparate,whichallowsustoinferabout therelevanceofkitcheneffluentonthequalityofthegreywater producedintheTNIA.Thedifferenceamongthestudied greywa-tersourceswassignificant,andsincekitcheneffluentpresented thehighestconcentrationsformanyoftheanalyzedvariables,it is necessarytoassess thepossibility, or thenecessity,of sepa-ratinggreywaterfromdifferentsources inordertomake reuse projectstechnicallyandeconomicallyfeasible.Such information canbereinforcedbyNolde(1999)andOtterpohl(2001),whichdo notclassifykitcheneffluentasgreywaterduetothehighlevelsof organicmatter.
3.3. Greywaterproductionandnon-potablewaterdemand
Theanalysisoftheresponsestoquestionnairesappliedtothe employeesofthestudiedbuildingsandtheinterviewscarriedout withthoseresponsibleforcleaningactivitiesallowedustoidentify themainwaterend-usesandthosewhicharethegreatestwater consumers.Throughthisdiscretizedsurvey,itwaspossibleto esti-matethegreywaterproduction,aswellasthenon-potabledemand inthebuildingsattheTNIA.
Table3presentsthemonthlywaterconsumption(m3)ofthe activitiesperformedineachbuildingconsideredinthestudy,as wellasthesampleerrorassumedforeachbuilding.Thesample errorshouldbethelowestpossible;however,forbuildingswith smallpopulations,iflowsampleerrorswereused,samplestended tobethetotalpopulationsize.Thusweadoptedthemaximum sampleerroras15%accordingtothenumberofquestionnaires appliedtoeachbuilding.Thisvalueiswithintherecommendations byBarbetta(2003),whosuggestsamaximumerrorof20%.
Fromconsumptionvolumesestimatedforshowers,taps, clean-ingandotheractivities,thetotalestimatedvolumeofgreywater
produced ineach buildingwasobtained. Toiletandurinal con-sumptions, as well as water used for irrigation and cleaning activities, were considered to estimate the non-potable water demandforeachbuildingintheTNIA.Fig.2presentstheamount ofgreywaterproducedandthenon-potabledemand.
Fig.2showsthatintheFC,AC,FA,ESandRA,greywater produc-tionishigherthanthenon-potabledemand.AttheIRS,MBandCT buildings,thedemandovercomesproduction.
Acommoncharacteristicamongthethree highestgreywater producersistheelevatedshowerconsumption.AttheFC,ACand FA,thewaterusedinshowersrepresented57.5,45.5and47.6% ofthetotalvolumeconsumed,respectively.Thuseventhoughthe showersarenotusedatallbuildingsintheTNIA,thisisan impor-tantend-useforgreywaterproduction.
Another aspect was the high consumption by taps. Many employeesaffirmedthattheyusebathroomtaps(sinks)forface rinsingand sometimeswashing fruitsforsnacksorafterlunch. But the main activities are hand washing and teeth brushing. Theestimateddurationforteethbrushingvariedfrom1to2min with the tap open, at a frequency from 1 to 4 times a day. For hand washing, the frequency varied from 1 to15 times a day.
Thebuildings which presented highernon-potable demands wereCT and IRS.The commoncharacteristicobserved in these buildingswasthehighvolumeofwaterconsumedforirrigation ofthesurroundingareas.Fromthetotalvolumeconsumedinthe IRS(224.9m3/month),45.3%isusedforirrigation,whereasforthe CT,fromthetotalof465.3m3consumedeverymonth,60.1%are destinedtoirrigation.Toiletflushingwasthenon-potableactivity withthesecondhighestconsumption.Differentfromirrigation, toi-letflushingisacommonuseforallbuildingsstudied.Fig.3aandb presentsrespectively,thecontribution(percentage)ofeach activ-itytothetotalvolumeofgreywaterproduced,andthenon-potable waterdemand. 0 50 100 150 200 250 300 350 400 450 IRS FS MB AC FA ES CT RA Greywater Producon Non - potable demand
Volume
(m
3)
Fig.2.Greywaterproductionandnon-potablewaterdemandforeachbuildingin theTNIA.
Fig.3.(a)Percentageofeachactivityingreywaterproduction.(b)Percentageofeachactivityinthenon-potablewaterdemand.
The results showed that considering all studied buildings, greywaterproduction (1,086.5m3/month) washigher than the non-potablewaterdemand(1,002.3m3/month).Thusthebalance betweenthevolumeofwaterconsumedinactivitieswhichcan beperformedwithnon-potablewater,and thevolumeof grey-waterproducedispositive.Inotherwords,thetotalnon-potable demandcanbemetbygreywater.Althoughthreebuildings pre-sentedhighernon-potabledemandsthantheamountofgreywater produced,suchdemandcanbesuppliedbygreywaterproducedin otherbuildings.Thepositivebalancebetweengreywater produc-tionandnon-potabledemandemphasizesthegreatpotentialfor reuseofsucheffluentinairports.MoreiraNetoetal.(2012)present activitieswhich canbesuppliedbygreywatersuchasthetests forfirecontrol,aircoolingsystems,washingofvehicles,aircraft, besidesrunwaysandpavedareas.
Greywaterreuseinairportscanprovidegreatwater savings, alongwithfinancialbenefitsandimportantpositiveenvironmental impacts.FortheTNIA,specifically,wherethemainsupplysourceis groundwater,reducingthevolumeofwatercollectedfromaquifers cancontributetomaintainingthe stability ofthis environmen-talsystem.AccordingtoCalijurietal.(2012),thekarsticaquifers arehighlyvulnerable topollutionand extremely susceptibleto anthropicactions.Fromtheresultsobtainedinthisstudy, grey-waterproductionintheTNIAissuitableanddoesnotrepresentan obstacletoreuseofsucheffluentintheairport.
4. Conclusions
Thequalitativeanalysisshowedthatgreywatercanpresent dis-tinctcharacteristicsdependingonitssource.Suchdifferenceswere enhancedbytheclusteranalysisresults,whichseparatedinto dis-tinctgroupsthesamplingpointswheregreywaterwasproduced bydifferentactivities.
Theprincipalcomponentanalysisshowedtheimportanceofthe kitcheneffluentandthequalityofthesupplywaterforthefinal qualityofgreywater,andalsohighlightedtheneedfordisinfection ofsucheffluentpriortoreuse.
Thekitcheneffluent,becauseofhighlevelsoforganicmatter, mayrequireamoresophisticatedtreatmentandaccountforhigh coststothereusesystem.Thusitisnecessarytoassessthe concen-trationofsuchvariableandthevolumeofothertypesofeffluent beforedecidingtotreatkitchensinkeffluentseparatelyfrom efflu-entfromothersources.
Theimportanceofsupplywaterwashighlightedbythe obser-vationofhighlevelsoftotalalkalinityandelectricalconductivity
in greywater, which showed that regional characteristics that influencethequalityofsupplywatermustbeassessedinthe devel-opmentofaprojectforgreywaterreuse.
Thenecessityofadisinfectionstepmustbeassessed,sinceeven thoughthemeanvaluesforE.coliwerewithintheconcentrations requiredformanynon-potableuses,themaximumvaluesfound reached104MPN/100mL.Thusadisinfectionprocesswould min-imizemicrobiologicalrisksandincreasesafetyfortheusers.
Withrespecttoquantitativeterms,importantcharacteristics were identified, such as the activities with greatest greywater production(showersandsinks)andthosewhichpresentthe high-estwaterdemand(irrigationandtoiletflushing).Inaddition,the quantificationof theamountof greywaterproduced in the air-portemphasizedthegreatpotentialforwatersavingsbymeansof reusingeffluent.Thepositivebalancebetweengreywater produc-tionandwaterdemandcanprovidefinancialsavingsandpriceless environmentalbenefits.
Finally,qualitativeandquantitativeaspectsaddressedinthis studyprovedthatthere arenosignificantlimitationsfor imple-mentinggreywaterreusesystemsin airports,oncethis effluent canbetreated bylow-costsystemsandtheamountof greywa-terproducedmeetsthedemandofactivitieswhichcanbesupplied byreusewater.Theresultspresentedherecorroboratethe adop-tionofsuchpracticeformanynon-potableusesbymanyairports worldwide,resultinginfinancialsavingsandenvironmentalgains.
Acknowledgements
Theauthorsacknowledgethefinancialassistanceprovidedby theResearchandProjectsFinancing,FINEPandtheFoundationfor ResearchSupportofMinasGerais,FAPEMIG.
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